diff --git a/README.md b/README.md
index 14a22798c41e1e3b1d60144a8b2c692ee4deafb1..3dc18d8b3d8b8775a868cded2fd54928d3fb6f99 100644
--- a/README.md
+++ b/README.md
@@ -1,13 +1,73 @@
 ---
-title: ReCEP
-emoji: 🏃
-colorFrom: pink
-colorTo: red
+title: B-cell Epitope Prediction Server
+emoji: 🧬
+colorFrom: blue
+colorTo: purple
 sdk: gradio
-sdk_version: 5.36.2
+sdk_version: 4.44.1
 app_file: app.py
 pinned: false
 license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🧬 B-cell Epitope Prediction Server
+
+A web-based interface for B-cell epitope prediction using the ReCEP model.
+
+## 📋 How to Use
+
+### 1. Input Protein Structure
+
+Choose one of two input methods:
+
+#### Option A: PDB ID
+- Enter a 4-character PDB ID (e.g., "5I9Q")
+- Specify the chain ID (e.g., "A")
+
+#### Option B: Upload PDB File
+- Upload a PDB structure file (.pdb or .ent format)
+- Optionally specify a custom PDB ID
+- Specify the chain ID
+
+### 2. Configure Prediction Parameters
+
+#### Basic Parameters:
+- **Chain ID**: Target protein chain (default: A)
+
+#### Advanced Parameters (Optional):
+- **Radius**: Spherical region radius in Ångstroms (default: 19.0)
+- **Top-k Regions**: Number of top regions to analyze (default: 7)
+- **Encoder**: Protein encoder type (ESM-C only now)
+- **Device Configuration**: CPU or GPU processing (CPU Only now)
+- **Threshold**: Custom prediction threshold (leave empty for auto, required)
+
+### 3. View Results
+
+The application provides:
+
+#### Prediction Summary
+- Protein information (PDB ID, chain, length, sequence)
+- Prediction statistics (epitope count, coverage rate, etc.)
+- Top-k region centers
+- Predicted epitope residues
+- Binding region residues
+
+#### Download Options
+- **JSON Results**: Complete prediction data with metadata
+- **CSV Results**: Residue-level predictions for analysis
+- **3D Visualization**: Interactive HTML file with 3Dmol.js viewer
+
+### 4. 3D Visualization
+
+The downloadable HTML file includes:
+- **Display Modes**: 
+  - Predicted Epitopes: Highlight predicted epitope residues
+  - Probability Gradient: Color residues by prediction confidence
+- **Representation Styles**: Cartoon, Surface, Stick, Sphere
+- **Interactive Controls**: Rotate, zoom, pan, reset view, save image
+
+## 📜 License
+
+This project is licensed under the MIT License - see the LICENSE file for details.
+
+**Note**: This is a research tool for B-cell epitope prediction. Results should be validated through experimental methods for clinical or commercial applications.
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..62d5ae2418b8e0497dc3ee84e63c99a36a7e2a49
--- /dev/null
+++ b/app.py
@@ -0,0 +1,1188 @@
+import gradio as gr
+import os
+import json
+import tempfile
+import traceback
+import numpy as np
+import pandas as pd
+from pathlib import Path
+from typing import Optional, Tuple, Dict, Any
+import torch
+import time
+import io
+import base64
+import zipfile
+from datetime import datetime
+
+# Dynamic installation of PyTorch Geometric dependencies
+def install_torch_geometric_deps():
+    """Install PyTorch Geometric dependencies at runtime to avoid compilation issues during Hugging Face Spaces build"""
+    import subprocess
+    import sys
+    
+    # Check if torch-scatter is already installed
+    try:
+        import torch_scatter
+        print("✅ torch-scatter already installed")
+        return True
+    except ImportError:
+        print("🔄 Installing torch-scatter and related packages...")
+        
+        # Get PyTorch version and CUDA info
+        torch_version = torch.__version__
+        torch_version_str = '+'.join(torch_version.split('+')[:1])  # Remove CUDA info
+        
+        # Use PyTorch Geometric official recommended installation method
+        try:
+            # For CPU version, use official CPU wheel
+            pip_cmd = [
+                sys.executable, "-m", "pip", "install", 
+                "torch-scatter", "torch-sparse", "torch-cluster", "torch-spline-conv",
+                "-f", f"https://data.pyg.org/whl/torch-{torch_version_str}+cpu.html",
+                "--no-cache-dir"
+            ]
+            
+            print(f"Running: {' '.join(pip_cmd)}")
+            result = subprocess.run(pip_cmd, capture_output=True, text=True, timeout=300)
+            
+            if result.returncode == 0:
+                print("✅ Successfully installed torch-scatter and related packages")
+                return True
+            else:
+                print(f"❌ Failed to install packages: {result.stderr}")
+                # Try simplified installation method
+                try:
+                    simple_cmd = [sys.executable, "-m", "pip", "install", "torch-scatter", "--no-cache-dir"]
+                    result = subprocess.run(simple_cmd, capture_output=True, text=True, timeout=300)
+                    if result.returncode == 0:
+                        print("✅ Successfully installed torch-scatter with simple method")
+                        return True
+                    else:
+                        print(f"❌ Simple install also failed: {result.stderr}")
+                        return False
+                except Exception as e:
+                    print(f"❌ Exception during simple install: {e}")
+                    return False
+                    
+        except subprocess.TimeoutExpired:
+            print("❌ Installation timeout - packages may not be available")
+            return False
+        except Exception as e:
+            print(f"❌ Exception during installation: {e}")
+            return False
+
+# Try to install PyTorch Geometric dependencies
+deps_installed = install_torch_geometric_deps()
+
+if not deps_installed:
+    print("⚠️ Warning: PyTorch Geometric dependencies not installed. Some features may not work.")
+    print("The application will try to continue with limited functionality.")
+
+# Set up paths and imports for different deployment environments
+import sys
+BASE_DIR = Path(__file__).parent
+
+# Smart import handling for different environments
+def setup_imports():
+    """Smart import setup for different deployment environments"""
+    global AntigenChain, PROJECT_BASE_DIR
+    
+    # Method 1: Try importing from src directory (local development)
+    if (BASE_DIR / "src").exists():
+        sys.path.insert(0, str(BASE_DIR))
+        try:
+            from src.bce.antigen.antigen import AntigenChain
+            from src.bce.utils.constants import BASE_DIR as PROJECT_BASE_DIR
+            print("✅ Successfully imported from src/ directory")
+            return True
+        except ImportError as e:
+            print(f"❌ Failed to import from src/: {e}")
+    
+    # Method 2: Try adding src to path and direct import (Hugging Face Spaces)
+    src_path = BASE_DIR / "src"
+    if src_path.exists():
+        sys.path.insert(0, str(src_path))
+        try:
+            from bce.antigen.antigen import AntigenChain
+            from bce.utils.constants import BASE_DIR as PROJECT_BASE_DIR
+            print("✅ Successfully imported from src/ added to path")
+            return True
+        except ImportError as e:
+            print(f"❌ Failed to import with src/ in path: {e}")
+    
+    # Method 3: Try direct import (if package is installed)
+    try:
+        from bce.antigen.antigen import AntigenChain
+        from bce.utils.constants import BASE_DIR as PROJECT_BASE_DIR
+        print("✅ Successfully imported from installed package")
+        return True
+    except ImportError as e:
+        print(f"❌ Failed to import from installed package: {e}")
+    
+    # If all methods fail, use default settings
+    print("⚠️ All import methods failed, using fallback settings")
+    PROJECT_BASE_DIR = BASE_DIR
+    return False
+
+# Execute import setup
+import_success = setup_imports()
+
+if not import_success:
+    print("❌ Critical: Could not import BCE modules. Please check the file structure.")
+    print("Expected structure:")
+    print("- src/bce/antigen/antigen.py")
+    print("- src/bce/utils/constants.py")
+    print("- src/bce/model/ReCEP.py")
+    print("- src/bce/data/utils.py")
+    sys.exit(1)
+
+# Configuration
+DEFAULT_MODEL_PATH = os.getenv("BCE_MODEL_PATH", str(PROJECT_BASE_DIR / "models" / "ReCEP" / "20250626_110438" / "best_mcc_model.bin"))
+ESM_TOKEN = os.getenv("ESM_TOKEN", "1mzAo8l1uxaU8UfVcGgV7B")
+
+# PDB data directory
+PDB_DATA_DIR = PROJECT_BASE_DIR / "data" / "pdb"
+PDB_DATA_DIR.mkdir(parents=True, exist_ok=True)
+
+def validate_pdb_id(pdb_id: str) -> bool:
+    """Validate PDB ID format"""
+    if not pdb_id or len(pdb_id) != 4:
+        return False
+    return pdb_id.isalnum()
+
+def validate_chain_id(chain_id: str) -> bool:
+    """Validate chain ID format"""
+    if not chain_id or len(chain_id) != 1:
+        return False
+    return chain_id.isalnum()
+
+def create_pdb_visualization_html(pdb_data: str, predicted_epitopes: list, 
+                                 predictions: dict, protein_id: str, top_k_regions: list = None) -> str:
+    """Create HTML with 3Dmol.js visualization compatible with Gradio - enhanced version with more features"""
+    
+    # Prepare data for JavaScript
+    epitope_residues = predicted_epitopes
+    
+    # Process top_k_regions for visualization
+    processed_regions = []
+    if top_k_regions:
+        for i, region in enumerate(top_k_regions):
+            if isinstance(region, dict):
+                processed_regions.append({
+                    'center_idx': region.get('center_idx', 0),
+                    'center_residue': region.get('center_residue', region.get('center_idx', 0)),
+                    'covered_residues': region.get('covered_residues', region.get('covered_indices', [])),
+                    'radius': 19.0,  # Default radius
+                    'predicted_value': region.get('graph_pred', 0.0)
+                })
+    
+    # Create a unique ID for this visualization to avoid conflicts
+    import uuid
+    viewer_id = f"viewer_{uuid.uuid4().hex[:8]}"
+    
+    html_content = f"""
+    <div style="width: 100%; height: 600px; border: 1px solid #ddd; border-radius: 8px; overflow: hidden;">
+        <div style="padding: 10px; background: #f8f9fa; border-bottom: 1px solid #ddd;">
+            <h3 style="margin: 0 0 10px 0; color: #333;">3D Structure Visualization - {protein_id}</h3>
+            <div style="display: flex; gap: 15px; align-items: center; flex-wrap: wrap;">
+                <div>
+                    <label style="font-weight: bold; margin-right: 5px;">Display Mode:</label>
+                    <select id="vizMode_{viewer_id}" onchange="updateVisualization_{viewer_id}()" style="padding: 4px;">
+                        <option value="prediction">Predicted Epitopes</option>
+                        <option value="probability">Probability Gradient</option>
+                        <option value="regions">Top-k Regions</option>
+                    </select>
+                </div>
+                <div>
+                    <label style="font-weight: bold; margin-right: 5px;">Style:</label>
+                    <select id="vizStyle_{viewer_id}" onchange="updateVisualization_{viewer_id}()" style="padding: 4px;">
+                        <option value="cartoon">Cartoon</option>
+                        <option value="surface">Surface</option>
+                        <option value="stick">Stick</option>
+                        <option value="sphere">Sphere</option>
+                    </select>
+                </div>
+                <div>
+                    <label style="font-weight: bold; margin-right: 5px;">
+                        <input type="checkbox" id="showSpheres_{viewer_id}" onchange="updateVisualization_{viewer_id}()" style="margin-right: 3px;"> Show Spheres
+                    </label>
+                </div>
+                <div>
+                    <label style="font-weight: bold; margin-right: 5px;">Sphere Display:</label>
+                    <select id="sphereCount_{viewer_id}" onchange="handleSphereCountChange_{viewer_id}()" style="padding: 4px;">
+                        <option value="1">Top 1</option>
+                        <option value="2">Top 2</option>
+                        <option value="3">Top 3</option>
+                        <option value="4">Top 4</option>
+                        <option value="5" selected>Top 5</option>
+                        <option value="6">Top 6</option>
+                        <option value="7">Top 7</option>
+                        <option value="all">All Spheres</option>
+                        <option value="custom">Custom Selection</option>
+                    </select>
+                </div>
+                <div id="customSphereSelection_{viewer_id}" style="display: none; margin-top: 10px; padding: 10px; background: #f9f9f9; border-radius: 5px; max-height: 120px; overflow-y: auto;">
+                    <label style="font-weight: bold; margin-bottom: 5px; display: block;">Select Spheres to Display:</label>
+                    <div id="sphereCheckboxes_{viewer_id}" style="display: flex; flex-wrap: wrap; gap: 8px; max-height: 80px; overflow-y: auto;">
+                        <!-- Checkboxes will be dynamically generated -->
+                    </div>
+                </div>
+                <div>
+                    <button onclick="resetView_{viewer_id}()" style="padding: 4px 8px; margin-right: 5px;">Reset View</button>
+                    <button onclick="saveImage_{viewer_id}()" style="padding: 4px 8px;">Save Image</button>
+                </div>
+            </div>
+        </div>
+        <div id="{viewer_id}" style="width: 100%; height: 520px; min-height: 400px; position: relative; background: #f0f0f0;">
+            <div style="position: absolute; top: 50%; left: 50%; transform: translate(-50%, -50%); text-align: center;">
+                <p id="status_{viewer_id}" style="color: #666;">Loading 3Dmol.js...</p>
+            </div>
+        </div>
+    </div>
+    
+    <script src="https://unpkg.com/3dmol@2.0.4/build/3Dmol-min.js"></script>
+    <script>
+        // Global variables for this viewer instance
+        window.viewer_{viewer_id} = null;
+        window.pdbData_{viewer_id} = `{pdb_data}`;
+        window.predictedEpitopes_{viewer_id} = {json.dumps(epitope_residues)};
+        window.predictions_{viewer_id} = {json.dumps(predictions)};
+        window.topKRegions_{viewer_id} = {json.dumps(processed_regions)};
+        
+        // Wait for 3Dmol to be available with timeout
+        function wait3Dmol_{viewer_id}(attempts = 0) {{
+            if (typeof $3Dmol !== 'undefined') {{
+                console.log('3Dmol.js loaded successfully for {viewer_id}');
+                document.getElementById('status_{viewer_id}').textContent = 'Initializing 3D viewer...';
+                setTimeout(() => initializeViewer_{viewer_id}(), 100);
+            }} else if (attempts < 50) {{ // 5 second timeout
+                console.log(`Waiting for 3Dmol.js... attempt ${{attempts + 1}}`);
+                setTimeout(() => wait3Dmol_{viewer_id}(attempts + 1), 100);
+            }} else {{
+                console.error('Failed to load 3Dmol.js after 5 seconds');
+                document.getElementById('status_{viewer_id}').textContent = 'Failed to load 3Dmol.js. Please refresh the page.';
+                document.getElementById('status_{viewer_id}').style.color = 'red';
+            }}
+        }}
+        
+        function initializeViewer_{viewer_id}() {{
+            try {{
+                const element = document.getElementById('{viewer_id}');
+                if (!element) {{
+                    console.error('Viewer element not found: {viewer_id}');
+                    return;
+                }}
+                
+                document.getElementById('status_{viewer_id}').textContent = 'Creating viewer...';
+                
+                window.viewer_{viewer_id} = $3Dmol.createViewer(element, {{
+                    defaultcolors: $3Dmol.rasmolElementColors
+                }});
+                
+                document.getElementById('status_{viewer_id}').textContent = 'Loading structure...';
+                
+                window.viewer_{viewer_id}.addModel(window.pdbData_{viewer_id}, 'pdb');
+                
+                // Hide status message
+                const statusEl = document.getElementById('status_{viewer_id}');
+                if (statusEl) statusEl.style.display = 'none';
+                
+                updateVisualization_{viewer_id}();
+                
+                // Initialize sphere checkboxes if data is available
+                if (window.topKRegions_{viewer_id} && window.topKRegions_{viewer_id}.length > 0) {{
+                    generateSphereCheckboxes_{viewer_id}();
+                }}
+                
+                console.log('3D viewer initialized successfully for {viewer_id}');
+            }} catch (error) {{
+                console.error('Error initializing 3D viewer:', error);
+                const statusEl = document.getElementById('status_{viewer_id}');
+                if (statusEl) {{
+                    statusEl.textContent = 'Error loading 3D viewer: ' + error.message;
+                    statusEl.style.color = 'red';
+                }}
+            }}
+        }}
+        
+        function updateVisualization_{viewer_id}() {{
+            if (!window.viewer_{viewer_id}) return;
+            
+            try {{
+                const mode = document.getElementById('vizMode_{viewer_id}').value;
+                const style = document.getElementById('vizStyle_{viewer_id}').value;
+                const showSpheres = document.getElementById('showSpheres_{viewer_id}').checked;
+                
+                // Clear everything
+                window.viewer_{viewer_id}.removeAllShapes();
+                window.viewer_{viewer_id}.removeAllSurfaces();
+                window.viewer_{viewer_id}.setStyle({{}}, {{}});
+                
+                // Base style
+                const baseStyle = {{}};
+                if (style === 'surface') {{
+                    baseStyle['cartoon'] = {{ hidden: true }};
+                }} else {{
+                    baseStyle[style] = {{ color: '#e6e6f7' }};
+                }}
+                window.viewer_{viewer_id}.setStyle({{}}, baseStyle);
+                
+                if (mode === 'prediction') {{
+                    // Highlight predicted epitopes
+                    if (window.predictedEpitopes_{viewer_id}.length > 0 && style !== 'surface') {{
+                        const epitopeStyle = {{}};
+                        epitopeStyle[style] = {{ color: '#9C6ADE' }};
+                        window.viewer_{viewer_id}.setStyle({{ resi: window.predictedEpitopes_{viewer_id} }}, epitopeStyle);
+                    }}
+                    
+                    // Add surface for epitopes if surface mode
+                    if (style === 'surface') {{
+                        window.viewer_{viewer_id}.addSurface($3Dmol.SurfaceType.VDW, {{
+                            opacity: 0.8,
+                            color: '#e6e6f7'
+                        }});
+                        
+                        if (window.predictedEpitopes_{viewer_id}.length > 0) {{
+                            window.viewer_{viewer_id}.addSurface($3Dmol.SurfaceType.VDW, {{
+                                opacity: 1.0,
+                                color: '#9C6ADE'
+                            }}, {{ resi: window.predictedEpitopes_{viewer_id} }});
+                        }}
+                    }}
+                }} else if (mode === 'probability') {{
+                    // Color by probability scores
+                    if (window.predictions_{viewer_id} && Object.keys(window.predictions_{viewer_id}).length > 0) {{
+                        const allProbs = Object.values(window.predictions_{viewer_id}).filter(p => p !== undefined);
+                        const minProb = Math.min(...allProbs);
+                        const maxProb = Math.max(...allProbs);
+                        
+                        Object.entries(window.predictions_{viewer_id}).forEach(([resnum, score]) => {{
+                            const normalizedProb = maxProb > minProb ? (score - minProb) / (maxProb - minProb) : 0.5;
+                            const color = interpolateColor('#E6F3FF', '#DC143C', normalizedProb);
+                            const probStyle = {{}};
+                            if (style !== 'surface') {{
+                                probStyle[style] = {{ color: color }};
+                                window.viewer_{viewer_id}.setStyle({{ resi: parseInt(resnum) }}, probStyle);
+                            }}
+                        }});
+                        
+                        if (style === 'surface') {{
+                            window.viewer_{viewer_id}.addSurface($3Dmol.SurfaceType.VDW, {{
+                                opacity: 0.8,
+                                color: '#e6e6f7'
+                            }});
+                            
+                            Object.entries(window.predictions_{viewer_id}).forEach(([resnum, score]) => {{
+                                const normalizedProb = maxProb > minProb ? (score - minProb) / (maxProb - minProb) : 0.5;
+                                const color = interpolateColor('#E6F3FF', '#DC143C', normalizedProb);
+                                window.viewer_{viewer_id}.addSurface($3Dmol.SurfaceType.VDW, {{
+                                    opacity: 1.0,
+                                    color: color
+                                }}, {{ resi: parseInt(resnum) }});
+                            }});
+                        }}
+                    }}
+                }} else if (mode === 'regions') {{
+                    // Color top-k regions
+                    const colors = ['#FF6B6B', '#96CEB4', '#4ECDC4', '#45B7D1', '#FFEAA7', '#DDA0DD', '#87CEEB'];
+                    
+                    if (window.topKRegions_{viewer_id} && window.topKRegions_{viewer_id}.length > 0) {{
+                        window.topKRegions_{viewer_id}.forEach((region, index) => {{
+                            const color = colors[index % colors.length];
+                            const regionStyle = {{}};
+                            if (style !== 'surface') {{
+                                regionStyle[style] = {{ color: color }};
+                                window.viewer_{viewer_id}.setStyle({{ resi: region.covered_residues }}, regionStyle);
+                            }}
+                        }});
+                        
+                        if (style === 'surface') {{
+                            window.viewer_{viewer_id}.addSurface($3Dmol.SurfaceType.VDW, {{
+                                opacity: 0.8,
+                                color: '#e6e6f7'
+                            }});
+                            
+                            window.topKRegions_{viewer_id}.forEach((region, index) => {{
+                                const color = colors[index % colors.length];
+                                window.viewer_{viewer_id}.addSurface($3Dmol.SurfaceType.VDW, {{
+                                    opacity: 1.0,
+                                    color: color
+                                }}, {{ resi: region.covered_residues }});
+                            }});
+                        }}
+                    }}
+                }}
+                
+                // Add spheres if requested
+                if (showSpheres && window.topKRegions_{viewer_id} && window.topKRegions_{viewer_id}.length > 0) {{
+                    const colors = ['#FF6B6B', '#96CEB4', '#4ECDC4', '#45B7D1', '#FFEAA7', '#DDA0DD', '#87CEEB'];
+                    const sphereCount = document.getElementById('sphereCount_{viewer_id}').value;
+                    
+                    // Determine which spheres to show
+                    let spheresToShow = [];
+                    if (sphereCount === 'custom') {{
+                        const selectedIndices = getSelectedSphereIndices_{viewer_id}();
+                        spheresToShow = selectedIndices.map(idx => ({{ region: window.topKRegions_{viewer_id}[idx], index: idx }}));
+                    }} else {{
+                        let numSpheres = sphereCount === 'all' ? window.topKRegions_{viewer_id}.length : parseInt(sphereCount);
+                        numSpheres = Math.min(numSpheres, window.topKRegions_{viewer_id}.length);
+                        spheresToShow = window.topKRegions_{viewer_id}.slice(0, numSpheres).map((region, index) => ({{ region, index }}));
+                    }}
+                    
+                    spheresToShow.forEach(({{ region, index }}) => {{
+                        const color = colors[index % colors.length];
+                        const centerResidues = window.viewer_{viewer_id}.getModel(0).selectedAtoms({{
+                            resi: region.center_residue,
+                            atom: 'CA'
+                        }});
+                        
+                        if (centerResidues.length > 0) {{
+                            const centerAtom = centerResidues[0];
+                            const centerCoords = {{ x: centerAtom.x, y: centerAtom.y, z: centerAtom.z }};
+                            
+                            // Add wireframe sphere
+                            window.viewer_{viewer_id}.addSphere({{
+                                center: centerCoords,
+                                radius: region.radius,
+                                color: color,
+                                wireframe: true,
+                                linewidth: 2.0
+                            }});
+                            
+                            // Add center point
+                            window.viewer_{viewer_id}.addSphere({{
+                                center: centerCoords,
+                                radius: 0.7,
+                                color: '#FFD700',
+                                wireframe: false
+                            }});
+                        }}
+                    }});
+                }}
+                
+                window.viewer_{viewer_id}.zoomTo();
+                window.viewer_{viewer_id}.render();
+            }} catch (error) {{
+                console.error('Error updating visualization:', error);
+            }}
+        }}
+        
+        // Color interpolation helper functions
+        function interpolateColor(color1, color2, factor) {{
+            const c1 = hexToRgb(color1);
+            const c2 = hexToRgb(color2);
+            
+            const r = Math.round(c1.r + factor * (c2.r - c1.r));
+            const g = Math.round(c1.g + factor * (c2.g - c1.g));
+            const b = Math.round(c1.b + factor * (c2.b - c1.b));
+            
+            return rgbToHex(r, g, b);
+        }}
+        
+        function hexToRgb(hex) {{
+            const result = /^#?([a-f\d]{{2}})([a-f\d]{{2}})([a-f\d]{{2}})$/i.exec(hex);
+            return result ? {{
+                r: parseInt(result[1], 16),
+                g: parseInt(result[2], 16),
+                b: parseInt(result[3], 16)
+            }} : null;
+        }}
+        
+        function rgbToHex(r, g, b) {{
+            return "#" + ((1 << 24) + (r << 16) + (g << 8) + b).toString(16).slice(1);
+        }}
+        
+        function resetView_{viewer_id}() {{
+            if (window.viewer_{viewer_id}) {{
+                window.viewer_{viewer_id}.zoomTo();
+                window.viewer_{viewer_id}.render();
+            }}
+        }}
+        
+        function saveImage_{viewer_id}() {{
+            if (window.viewer_{viewer_id}) {{
+                window.viewer_{viewer_id}.pngURI(function(uri) {{
+                    const link = document.createElement('a');
+                    link.href = uri;
+                    link.download = '{protein_id}_structure.png';
+                    link.click();
+                }});
+            }}
+        }}
+        
+        // Handle sphere count selection change
+        function handleSphereCountChange_{viewer_id}() {{
+            const sphereCount = document.getElementById('sphereCount_{viewer_id}').value;
+            const customSelectionDiv = document.getElementById('customSphereSelection_{viewer_id}');
+            
+            if (sphereCount === 'custom') {{
+                customSelectionDiv.style.display = 'block';
+                generateSphereCheckboxes_{viewer_id}();
+            }} else {{
+                customSelectionDiv.style.display = 'none';
+            }}
+            
+            updateVisualization_{viewer_id}();
+        }}
+        
+        // Generate sphere checkboxes for custom selection
+        function generateSphereCheckboxes_{viewer_id}() {{
+            if (!window.topKRegions_{viewer_id} || window.topKRegions_{viewer_id}.length === 0) {{
+                return;
+            }}
+            
+            const regions = window.topKRegions_{viewer_id};
+            const container = document.getElementById('sphereCheckboxes_{viewer_id}');
+            container.innerHTML = '';
+            
+            regions.forEach((region, index) => {{
+                const sphereNum = index + 1;
+                const checkboxId = `sphere_{{sphereNum}}_{viewer_id}`;
+                const colors = ['#FF6B6B', '#96CEB4', '#4ECDC4', '#45B7D1', '#FFEAA7', '#DDA0DD', '#87CEEB'];
+                const sphereColor = colors[index % colors.length];
+                
+                const checkboxContainer = document.createElement('div');
+                checkboxContainer.style.cssText = `
+                    display: flex;
+                    align-items: center;
+                    padding: 5px 10px;
+                    border: 1px solid #ddd;
+                    border-radius: 4px;
+                    background: white;
+                    cursor: pointer;
+                    user-select: none;
+                `;
+                checkboxContainer.setAttribute('data-sphere', sphereNum);
+                
+                const checkbox = document.createElement('input');
+                checkbox.type = 'checkbox';
+                checkbox.id = checkboxId;
+                checkbox.checked = sphereNum <= 5; // Default: show first 5
+                checkbox.style.marginRight = '5px';
+                
+                const colorBox = document.createElement('div');
+                colorBox.style.cssText = `
+                    width: 16px;
+                    height: 16px;
+                    background-color: ${{sphereColor}};
+                    border: 1px solid #333;
+                    border-radius: 2px;
+                    margin-right: 5px;
+                `;
+                
+                const label = document.createElement('label');
+                label.setAttribute('for', checkboxId);
+                label.textContent = `Sphere ${{sphereNum}} (R${{region.center_residue}})`;
+                label.style.cursor = 'pointer';
+                label.style.fontSize = '14px';
+                
+                checkboxContainer.appendChild(checkbox);
+                checkboxContainer.appendChild(colorBox);
+                checkboxContainer.appendChild(label);
+                container.appendChild(checkboxContainer);
+                
+                // Add click handler
+                checkboxContainer.addEventListener('click', function(e) {{
+                    if (e.target.type !== 'checkbox') {{
+                        checkbox.checked = !checkbox.checked;
+                    }}
+                    
+                    if (checkbox.checked) {{
+                        checkboxContainer.style.backgroundColor = '#f0f8ff';
+                        checkboxContainer.style.borderColor = '#4a90e2';
+                    }} else {{
+                        checkboxContainer.style.backgroundColor = 'white';
+                        checkboxContainer.style.borderColor = '#ddd';
+                    }}
+                    
+                    updateVisualization_{viewer_id}();
+                }});
+                
+                // Initialize visual state
+                if (checkbox.checked) {{
+                    checkboxContainer.style.backgroundColor = '#f0f8ff';
+                    checkboxContainer.style.borderColor = '#4a90e2';
+                }}
+            }});
+        }}
+        
+        // Get selected sphere indices for custom mode
+        function getSelectedSphereIndices_{viewer_id}() {{
+            const selected = [];
+            const checkboxes = document.querySelectorAll('#sphereCheckboxes_{viewer_id} input[type="checkbox"]:checked');
+            checkboxes.forEach(function(checkbox) {{
+                // Get sphere number from the data-sphere attribute of the container
+                const container = checkbox.closest('[data-sphere]');
+                if (container) {{
+                    const sphereNum = parseInt(container.getAttribute('data-sphere'));
+                    selected.push(sphereNum - 1); // Convert to 0-based index
+                }}
+            }});
+            return selected;
+        }}
+        
+        // Start initialization
+        wait3Dmol_{viewer_id}();
+    </script>
+    """
+    
+    return html_content
+
+def predict_epitopes(pdb_id: str, pdb_file, chain_id: str, radius: float, k: int, 
+                    encoder: str, device_config: str, use_threshold: bool, threshold: float,
+                    auto_cleanup: bool, progress: gr.Progress = None) -> Tuple[str, str, str, str, str, str]:
+    """
+    Main prediction function that handles the epitope prediction workflow
+    """
+    try:
+        # Input validation
+        if not pdb_file and not pdb_id:
+            return "Error: Please provide either a PDB ID or upload a PDB file", "", "", "", "", ""
+        
+        if pdb_id and not validate_pdb_id(pdb_id):
+            return "Error: PDB ID must be exactly 4 characters (letters and numbers)", "", "", "", "", ""
+        
+        if not validate_chain_id(chain_id):
+            return "Error: Chain ID must be exactly 1 character", "", "", "", "", ""
+        
+        # Update progress
+        if progress:
+            progress(0.1, desc="Initializing prediction...")
+        
+        # Process device configuration
+        device_id = -1 if device_config == "CPU Only" else int(device_config.split(" ")[1])
+        use_gpu = device_id >= 0
+        
+        # Load protein structure
+        if progress:
+            progress(0.2, desc="Loading protein structure...")
+        
+        antigen_chain = None
+        temp_file_path = None
+        
+        try:
+            if pdb_file:
+                # Handle uploaded file
+                if progress:
+                        progress(0.25, desc="Processing uploaded PDB file...")
+                
+                # Debug: print type and attributes of pdb_file
+                print(f"🔍 Debug: pdb_file type = {type(pdb_file)}")
+                print(f"🔍 Debug: pdb_file attributes = {dir(pdb_file)}")
+                
+                # Extract PDB ID from filename if not provided
+                if not pdb_id:
+                    if hasattr(pdb_file, 'name'):
+                        pdb_id = Path(pdb_file.name).stem.split('_')[0][:4]
+                    else:
+                        pdb_id = "UNKN"  # Default fallback
+                
+                # Save uploaded file to data/pdb/ directory with proper naming
+                timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+                filename = f"{pdb_id}_{chain_id}_{timestamp}.pdb"
+                temp_file_path = PDB_DATA_DIR / filename
+                
+                # Properly read and write the uploaded file
+                try:
+                    if hasattr(pdb_file, 'name') and os.path.isfile(pdb_file.name):
+                        # pdb_file is a file object with .name attribute
+                        print(f"📁 Processing file object: {pdb_file.name}")
+                        with open(pdb_file.name, "rb") as src:
+                            with open(temp_file_path, "wb") as dst:
+                                dst.write(src.read())
+                    elif hasattr(pdb_file, 'read'):
+                        # pdb_file is a file-like object
+                        print(f"📄 Processing file-like object")
+                        with open(temp_file_path, "wb") as f:
+                            f.write(pdb_file.read())
+                    else:
+                        # pdb_file is a string (file path)
+                        print(f"📍 Processing file path: {pdb_file}")
+                        with open(str(pdb_file), "rb") as src:
+                            with open(temp_file_path, "wb") as dst:
+                                dst.write(src.read())
+                    
+                    print(f"✅ PDB file saved to: {temp_file_path}")
+                    
+                except Exception as file_error:
+                    print(f"❌ Error processing uploaded file: {file_error}")
+                    return f"Error processing uploaded file: {str(file_error)}", "", "", "", "", ""
+                
+                antigen_chain = AntigenChain.from_pdb(
+                    path=str(temp_file_path),
+                    chain_id=chain_id,
+                    id=pdb_id
+                )
+            else:
+                # Load from PDB ID
+                if progress:
+                    progress(0.25, desc=f"Downloading PDB structure {pdb_id}...")
+                
+                antigen_chain = AntigenChain.from_pdb(
+                    chain_id=chain_id,
+                    id=pdb_id
+                )
+                
+        except Exception as e:
+            return f"Error loading protein structure: {str(e)}", "", "", "", "", ""
+        
+        if antigen_chain is None:
+            return "Error: Failed to load protein structure", "", "", "", "", ""
+        
+        # Run prediction
+        if progress:
+            progress(0.4, desc="Running epitope prediction...")
+        
+        try:
+            # Use threshold only if checkbox is checked
+            final_threshold = threshold if use_threshold else None
+            
+            predict_results = antigen_chain.predict(
+                model_path=DEFAULT_MODEL_PATH,
+                device_id=device_id,
+                radius=radius,
+                k=k,
+                threshold=final_threshold,
+                verbose=True,
+                encoder=encoder,
+                use_gpu=use_gpu,
+                auto_cleanup=auto_cleanup
+            )
+        except Exception as e:
+            error_msg = f"Error during prediction: {str(e)}"
+            print(f"Prediction error: {error_msg}")
+            import traceback
+            traceback.print_exc()
+            return error_msg, "", "", "", "", ""
+        
+        if progress:
+            progress(0.8, desc="Processing results...")
+        
+        # Process results
+        if not predict_results:
+            return "Error: No prediction results generated", "", "", "", "", ""
+        
+        # Extract prediction data
+        predicted_epitopes = predict_results.get("predicted_epitopes", [])
+        predictions = predict_results.get("predictions", {})
+        top_k_centers = predict_results.get("top_k_centers", [])
+        top_k_region_residues = predict_results.get("top_k_region_residues", [])
+        top_k_regions = predict_results.get("top_k_regions", [])
+        
+        # Calculate summary statistics
+        protein_length = len(antigen_chain.sequence)
+        epitope_count = len(predicted_epitopes)
+        region_count = len(top_k_regions)
+        coverage_rate = (len(top_k_region_residues) / protein_length) * 100 if protein_length > 0 else 0
+        
+        # Create summary text
+        summary_text = f"""
+## Prediction Results for {pdb_id}_{chain_id}
+
+### Protein Information
+- **PDB ID**: {pdb_id}
+- **Chain**: {chain_id}
+- **Length**: {protein_length} residues
+- **Sequence**: <div style="word-wrap: break-word; word-break: break-all; white-space: pre-wrap; max-width: 100%; font-family: monospace; background: #f5f5f5; padding: 8px; border-radius: 4px; margin: 5px 0; display: inline-block;">{antigen_chain.sequence}</div>
+
+### Prediction Summary
+- **Predicted Epitopes**: {epitope_count}
+- **Top-k Regions**: {region_count}
+- **Coverage Rate**: {coverage_rate:.1f}%
+
+### Top-k Region Centers
+{', '.join(map(str, top_k_centers))}
+
+### Predicted Epitope Residues
+{', '.join(map(str, predicted_epitopes))}
+
+### Binding Region Residues (Top-k Union)
+{', '.join(map(str, top_k_region_residues))}
+        """
+        
+        # Create epitope list text with residue names
+        epitope_text = f"Predicted Epitope Residues ({len(predicted_epitopes)}):\n"
+        epitope_lines = []
+        for res in predicted_epitopes:
+            # Get residue index from residue number
+            if res in antigen_chain.resnum_to_index:
+                res_idx = antigen_chain.resnum_to_index[res]
+                res_name = antigen_chain.sequence[res_idx]
+                epitope_lines.append(f"Residue {res} ({res_name})")
+            else:
+                epitope_lines.append(f"Residue {res}")
+        epitope_text += "\n".join(epitope_lines)
+        
+        # Create binding region text with residue names
+        binding_text = f"Binding Region Residues ({len(top_k_region_residues)}):\n"
+        binding_lines = []
+        for res in top_k_region_residues:
+            # Get residue index from residue number
+            if res in antigen_chain.resnum_to_index:
+                res_idx = antigen_chain.resnum_to_index[res]
+                res_name = antigen_chain.sequence[res_idx]
+                binding_lines.append(f"Residue {res} ({res_name})")
+            else:
+                binding_lines.append(f"Residue {res}")
+        binding_text += "\n".join(binding_lines)
+        
+        # Create downloadable files
+        if progress:
+            progress(0.9, desc="Preparing download files...")
+        
+        # JSON file
+        json_data = {
+            "protein_info": {
+                "id": pdb_id,
+                "chain_id": chain_id,
+                "length": protein_length,
+                "sequence": antigen_chain.sequence
+            },
+            "prediction": {
+                "predicted_epitopes": predicted_epitopes,
+                "predictions": predictions,
+                "top_k_centers": top_k_centers,
+                "top_k_region_residues": top_k_region_residues,
+                "top_k_regions": [
+                    {
+                        "center_idx": region.get('center_idx', 0),
+                        "graph_pred": region.get('graph_pred', 0),
+                        "covered_indices": region.get('covered_indices', [])
+                    }
+                    for region in top_k_regions
+                ],
+                "coverage_rate": coverage_rate,
+                "mean_region_value": 0 # No longer calculated
+            },
+            "parameters": {
+                "radius": radius,
+                "k": k,
+                "encoder": encoder,
+                "device_config": device_config,
+                "use_threshold": use_threshold,
+                "threshold": final_threshold,
+                "auto_cleanup": auto_cleanup
+            }
+        }
+        
+        # Save JSON file
+        json_file_path = tempfile.mktemp(suffix=".json")
+        with open(json_file_path, "w") as f:
+            json.dump(json_data, f, indent=2)
+        
+        # CSV file  
+        csv_data = []
+        for i, residue_num in enumerate(antigen_chain.residue_index):
+            residue_num = int(residue_num)
+            csv_data.append({
+                "Residue_Number": residue_num,
+                "Residue_Type": antigen_chain.sequence[i],
+                "Prediction_Probability": predictions.get(residue_num, 0.0),
+                "Is_Predicted_Epitope": 1 if residue_num in predicted_epitopes else 0,
+                "Is_In_TopK_Regions": 1 if residue_num in top_k_region_residues else 0
+            })
+        
+        csv_df = pd.DataFrame(csv_data)
+        csv_file_path = tempfile.mktemp(suffix=".csv")
+        csv_df.to_csv(csv_file_path, index=False)
+        
+        # Create 3D visualization
+        if progress:
+            progress(0.95, desc="Creating 3D visualization...")
+        
+        # Generate PDB string for visualization HTML file
+        html_file_path = None
+        try:
+            pdb_str = generate_pdb_string(antigen_chain)
+            html_content = create_pdb_visualization_html(
+                pdb_str, predicted_epitopes, predictions, f"{pdb_id}_{chain_id}", top_k_regions
+            )
+            
+            # Save HTML file to data directory for download
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            html_filename = f"{pdb_id}_{chain_id}_visualization_{timestamp}.html"
+            html_file_path = PDB_DATA_DIR / html_filename
+            
+            with open(html_file_path, "w", encoding='utf-8') as f:
+                f.write(html_content)
+                
+            print(f"✅ 3D visualization HTML saved to: {html_file_path}")
+                
+        except Exception as e:
+            html_file_path = None
+            print(f"Warning: Could not create 3D visualization: {str(e)}")
+        
+        # Clean up temporary files if auto_cleanup is enabled
+        if auto_cleanup and temp_file_path and os.path.exists(temp_file_path):
+            os.remove(temp_file_path)
+            print(f"🧹 Cleaned up temporary file: {temp_file_path}")
+        elif temp_file_path and os.path.exists(temp_file_path):
+            print(f"📁 PDB file retained at: {temp_file_path}")
+        
+        if progress:
+            progress(1.0, desc="Prediction completed!")
+        
+        # Return all results including HTML file path for download
+        return (
+            summary_text,
+            epitope_text,
+            binding_text,
+            str(html_file_path) if html_file_path else None,  # HTML file moved to 4th position
+            json_file_path,
+            csv_file_path
+        )
+        
+    except Exception as e:
+        import traceback
+        error_msg = f"Error: {str(e)}\n\nTraceback:\n{traceback.format_exc()}"
+        return error_msg, "", "", "", "", ""
+
+def generate_pdb_string(antigen_chain) -> str:
+    """Generate PDB string for 3D visualization"""
+    from esm.utils import residue_constants as RC
+    
+    pdb_str = "MODEL        1\n"
+    atom_num = 1
+    
+    for res_idx in range(len(antigen_chain.sequence)):
+        one_letter = antigen_chain.sequence[res_idx]
+        resname = antigen_chain.convert_letter_1to3(one_letter)
+        resnum = antigen_chain.residue_index[res_idx]
+        
+        mask = antigen_chain.atom37_mask[res_idx]
+        coords = antigen_chain.atom37_positions[res_idx][mask]
+        atoms = [name for name, exists in zip(RC.atom_types, mask) if exists]
+        
+        for atom_name, coord in zip(atoms, coords):
+            x, y, z = coord
+            pdb_str += (f"ATOM  {atom_num:5d}  {atom_name:<3s} {resname:>3s} {antigen_chain.chain_id:1s}{resnum:4d}"
+                       f"    {x:8.3f}{y:8.3f}{z:8.3f}  1.00  0.00\n")
+            atom_num += 1
+    
+    pdb_str += "ENDMDL\n"
+    return pdb_str
+
+def create_interface():
+    """Create the Gradio interface"""
+
+    with gr.Blocks(css="""
+        .container {
+            max-width: 1200px;
+            margin: 0 auto;
+            padding: 20px;
+        }
+        .header {
+            text-align: center;
+            margin-bottom: 30px;
+            padding: 20px;
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            color: white;
+            border-radius: 10px;
+        }
+        .header h1 {
+            font-size: 2.5em;
+            margin-bottom: 10px;
+        }
+        .form-row {
+            display: flex;
+            gap: 20px;
+            align-items: end;
+        }
+        .form-row > * {
+            flex: 1;
+        }
+        .section {
+            margin: 20px 0;
+            padding: 15px;
+            background: #f8f9fa;
+            border-radius: 8px;
+            border-left: 4px solid #007bff;
+        }
+        .section h2 {
+            color: #333;
+            margin-bottom: 15px;
+        }
+        .results-section {
+            margin-top: 30px;
+            padding: 20px;
+            background: #f0f8ff;
+            border-radius: 8px;
+            border: 1px solid #e0e8f0;
+        }
+        .download-section {
+            margin-top: 20px;
+            padding: 15px;
+            background: #f9f9f9;
+            border-radius: 8px;
+        }
+        .download-section h3 {
+            color: #333;
+            margin-bottom: 10px;
+        }
+        """) as interface:
+        
+        # Header
+        gr.HTML("""
+        <div class="header">
+            <h1>🧬 B-cell Epitope Prediction Server</h1>
+            <p>Predict epitopes using the ReCEP model</p>
+        </div>
+        """)
+
+        with gr.Row():
+            with gr.Column(scale=1):
+                gr.HTML("<div class='section'><h2>📋 Input Protein Structure</h2></div>")
+
+                input_method = gr.Radio(
+                    choices=["PDB ID", "Upload PDB File"],
+                    value="PDB ID",
+                    label="Input Method"
+                )
+
+                pdb_id = gr.Textbox(
+                    label="PDB ID", 
+                    placeholder="e.g., 5I9Q", 
+                    max_lines=1, 
+                    visible=True
+                )
+                pdb_file = gr.File(
+                    label="Upload PDB File", 
+                    file_types=[".pdb", ".ent"], 
+                    visible=False
+                )
+                chain_id = gr.Textbox(
+                    label="Chain ID", 
+                    value="A", 
+                    max_lines=1
+                )
+
+                with gr.Accordion("🔧 Advanced Parameters", open=False):
+                    radius = gr.Slider(
+                        label="Radius (Å)", 
+                        minimum=1.0, 
+                        maximum=50.0, 
+                        step=0.1, 
+                        value=19.0
+                    )
+                    k = gr.Slider(
+                        label="Top-k Regions", 
+                        minimum=1, 
+                        maximum=20, 
+                        step=1, 
+                        value=7
+                    )
+                    encoder = gr.Dropdown(
+                        label="Encoder", 
+                        choices=["esmc", "esm2"], 
+                        value="esmc"
+                    )
+                    device_config = gr.Dropdown(
+                        label="Device Configuration", 
+                        choices=["CPU Only", "GPU 0", "GPU 1", "GPU 2", "GPU 3"], 
+                        value="CPU Only"
+                    )
+                    use_threshold = gr.Checkbox(
+                        label="Use Custom Threshold", 
+                        value=False
+                    )
+                    threshold = gr.Number(
+                        label="Threshold Value", 
+                        value=0.366, 
+                        visible=False
+                    )
+                    auto_cleanup = gr.Checkbox(
+                        label="Auto-cleanup Generated Data", 
+                        value=True
+                    )
+
+                predict_btn = gr.Button("🧮 Predict Epitopes", variant="primary", size="lg")
+
+            with gr.Column(scale=2):
+                gr.HTML("<div class='section'><h2>📊 Prediction Results</h2></div>")
+
+                results_text = gr.Markdown(label="Prediction Summary", visible=True)
+
+                # 3D Visualization download (moved to top)
+                gr.HTML("<div style='margin: 15px 0; padding: 10px; background: #f0f8ff; border-left: 4px solid #4a90e2; border-radius: 5px;'><h3 style='margin: 0 0 8px 0; color: #333;'>🧬 3D Visualization</h3><p style='margin: 0; color: #666;'>You can download the HTML to visualize the prediction results and the spheres used.</p></div>")
+                html_download = gr.File(
+                    label="Download Interactive 3D Visualization HTML",
+                    visible=True
+                )
+
+                with gr.Row():
+                    epitope_list = gr.Textbox(
+                        label="Predicted Epitope Residues", 
+                        max_lines=10, 
+                        interactive=False
+                    )
+                    binding_regions = gr.Textbox(
+                        label="Binding Region Residues", 
+                        max_lines=10, 
+                        interactive=False
+                    )
+
+                gr.HTML("<div class='download-section'><h3>📥 Download Data Results</h3></div>")
+                with gr.Row():
+                    json_download = gr.File(
+                        label="JSON Results",
+                        visible=True
+                    )
+                    csv_download = gr.File(
+                        label="CSV Results",
+                        visible=True
+                    )
+
+        def toggle_input_method(method):
+            return (gr.update(visible=method == "PDB ID"),
+                    gr.update(visible=method == "Upload PDB File"))
+
+        def toggle_threshold(use_threshold):
+            return gr.update(visible=use_threshold)
+
+        input_method.change(toggle_input_method, inputs=[input_method], outputs=[pdb_id, pdb_file])
+        use_threshold.change(toggle_threshold, inputs=[use_threshold], outputs=[threshold])
+
+        predict_btn.click(
+            predict_epitopes,
+            inputs=[
+                pdb_id, pdb_file, chain_id, radius, k, encoder, 
+                device_config, use_threshold, threshold, auto_cleanup
+            ],
+            outputs=[
+                results_text, epitope_list, binding_regions, 
+                html_download, json_download, csv_download
+            ],
+            show_progress=True
+        )
+
+        gr.HTML("""
+        <div style="text-align: center; margin-top: 30px; padding: 20px; background: #f0f0f0; border-radius: 10px;">
+            <p>© 2024 B-cell Epitope Prediction Server | Powered by ReCEP model</p>
+            <p>🚀 Advanced AI-powered epitope prediction with interactive 3D visualization</p>
+            <p><strong>Features:</strong> PDB ID/File support • ESM-C encoder • GPU acceleration • 3D visualization • Multiple export formats</p>
+        </div>
+        """)
+
+    return interface
+
+
+if __name__ == "__main__":
+    # Create and launch the interface
+    try:
+        interface = create_interface()
+        
+        # Check if running on Hugging Face Spaces
+        is_spaces = os.getenv("SPACE_ID") is not None
+        
+        interface.launch(
+            server_name="0.0.0.0",
+            server_port=7860,
+            share=is_spaces,  # Use share=True on Spaces, False locally
+            show_error=True,
+            max_threads=4 if is_spaces else 8
+        )
+    except Exception as e:
+        print(f"Error launching application: {e}")
+        print("Please ensure all dependencies are installed correctly.")
+        import traceback
+        traceback.print_exc()
diff --git a/config.yaml b/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f2c785e0c9683a74a70e983ae4ef3c701ae80641
--- /dev/null
+++ b/config.yaml
@@ -0,0 +1,16 @@
+title: B-cell Epitope Prediction Server
+emoji: 🧬
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 4.44.1
+app_file: app.py
+pinned: false
+license: mit
+short_description: "AI-powered B-cell epitope prediction using the ReCEP model"
+tags:
+  - bioinformatics
+  - protein-structure
+  - epitope-prediction
+  - machine-learning
+  - computational-biology 
\ No newline at end of file
diff --git a/data/epitopes/data_splits.json b/data/epitopes/data_splits.json
new file mode 100644
index 0000000000000000000000000000000000000000..9912407b607400f3f791833e8ef0ff017d11cb24
--- /dev/null
+++ b/data/epitopes/data_splits.json
@@ -0,0 +1,2878 @@
+{
+  "train": [
+    [
+      "6elu",
+      "A"
+    ],
+    [
+      "4zfg",
+      "A"
+    ],
+    [
+      "8tfl",
+      "B"
+    ],
+    [
+      "6gv4",
+      "B"
+    ],
+    [
+      "3lh2",
+      "V"
+    ],
+    [
+      "5b3j",
+      "C"
+    ],
+    [
+      "5bv7",
+      "A"
+    ],
+    [
+      "6u6u",
+      "R"
+    ],
+    [
+      "1fj1",
+      "E"
+    ],
+    [
+      "8tv3",
+      "A"
+    ],
+    [
+      "3vg9",
+      "A"
+    ],
+    [
+      "7kql",
+      "T"
+    ],
+    [
+      "1bgx",
+      "T"
+    ],
+    [
+      "9ima",
+      "A"
+    ],
+    [
+      "5fcu",
+      "G"
+    ],
+    [
+      "6v4n",
+      "W"
+    ],
+    [
+      "8u1c",
+      "A"
+    ],
+    [
+      "7tpd",
+      "A"
+    ],
+    [
+      "9ivk",
+      "A"
+    ],
+    [
+      "8zd5",
+      "D"
+    ],
+    [
+      "8r8d",
+      "B"
+    ],
+    [
+      "8yxi",
+      "A"
+    ],
+    [
+      "6y9b",
+      "C"
+    ],
+    [
+      "7tuf",
+      "C"
+    ],
+    [
+      "8gkl",
+      "E"
+    ],
+    [
+      "1lk3",
+      "B"
+    ],
+    [
+      "6wgl",
+      "C"
+    ],
+    [
+      "1egj",
+      "A"
+    ],
+    [
+      "6k65",
+      "A"
+    ],
+    [
+      "7lxx",
+      "A"
+    ],
+    [
+      "3kr3",
+      "D"
+    ],
+    [
+      "4f37",
+      "A"
+    ],
+    [
+      "6fgb",
+      "A"
+    ],
+    [
+      "5e8e",
+      "H"
+    ],
+    [
+      "6iw2",
+      "D"
+    ],
+    [
+      "7lf8",
+      "A"
+    ],
+    [
+      "1nsn",
+      "S"
+    ],
+    [
+      "4plj",
+      "A"
+    ],
+    [
+      "3rkd",
+      "A"
+    ],
+    [
+      "8y0r",
+      "2"
+    ],
+    [
+      "8y0q",
+      "2"
+    ],
+    [
+      "6hx4",
+      "A"
+    ],
+    [
+      "6wzk",
+      "E"
+    ],
+    [
+      "6wzj",
+      "E"
+    ],
+    [
+      "5ob5",
+      "A"
+    ],
+    [
+      "5hys",
+      "G"
+    ],
+    [
+      "7shu",
+      "A"
+    ],
+    [
+      "5veb",
+      "Y"
+    ],
+    [
+      "7zli",
+      "A"
+    ],
+    [
+      "6wbv",
+      "A"
+    ],
+    [
+      "4rdq",
+      "C"
+    ],
+    [
+      "8caf",
+      "E"
+    ],
+    [
+      "4hwb",
+      "A"
+    ],
+    [
+      "6ck9",
+      "G"
+    ],
+    [
+      "6mug",
+      "G"
+    ],
+    [
+      "6vzi",
+      "G"
+    ],
+    [
+      "4ma7",
+      "A"
+    ],
+    [
+      "4yxl",
+      "A"
+    ],
+    [
+      "1tqb",
+      "A"
+    ],
+    [
+      "4yxk",
+      "A"
+    ],
+    [
+      "6sv2",
+      "A"
+    ],
+    [
+      "8dyx",
+      "I"
+    ],
+    [
+      "7l7r",
+      "G"
+    ],
+    [
+      "6umx",
+      "A"
+    ],
+    [
+      "6wtu",
+      "A"
+    ],
+    [
+      "3l95",
+      "X"
+    ],
+    [
+      "8v2e",
+      "C"
+    ],
+    [
+      "4ala",
+      "C"
+    ],
+    [
+      "6wo4",
+      "E"
+    ],
+    [
+      "6uyf",
+      "E"
+    ],
+    [
+      "7jtg",
+      "E"
+    ],
+    [
+      "6bkc",
+      "E"
+    ],
+    [
+      "5ldn",
+      "A"
+    ],
+    [
+      "6nms",
+      "C"
+    ],
+    [
+      "4edw",
+      "V"
+    ],
+    [
+      "5vyf",
+      "F"
+    ],
+    [
+      "6qb6",
+      "A"
+    ],
+    [
+      "3cvh",
+      "M"
+    ],
+    [
+      "8tq8",
+      "C"
+    ],
+    [
+      "8tqa",
+      "A"
+    ],
+    [
+      "4xvu",
+      "H"
+    ],
+    [
+      "4xwo",
+      "A"
+    ],
+    [
+      "2wuc",
+      "A"
+    ],
+    [
+      "8jxs",
+      "A"
+    ],
+    [
+      "8hrx",
+      "B"
+    ],
+    [
+      "4ypg",
+      "D"
+    ],
+    [
+      "8urf",
+      "A"
+    ],
+    [
+      "8j8r",
+      "B"
+    ],
+    [
+      "4cni",
+      "D"
+    ],
+    [
+      "4o9h",
+      "A"
+    ],
+    [
+      "4zs7",
+      "A"
+    ],
+    [
+      "5ukr",
+      "G"
+    ],
+    [
+      "4a6y",
+      "A"
+    ],
+    [
+      "7kq7",
+      "B"
+    ],
+    [
+      "8wre",
+      "A"
+    ],
+    [
+      "3qwo",
+      "C"
+    ],
+    [
+      "3gi9",
+      "C"
+    ],
+    [
+      "8f5i",
+      "A"
+    ],
+    [
+      "3s35",
+      "X"
+    ],
+    [
+      "1ncc",
+      "N"
+    ],
+    [
+      "6lxj",
+      "D"
+    ],
+    [
+      "4kxz",
+      "D"
+    ],
+    [
+      "7v23",
+      "B"
+    ],
+    [
+      "9c44",
+      "C"
+    ],
+    [
+      "7k9j",
+      "C"
+    ],
+    [
+      "8hhy",
+      "A"
+    ],
+    [
+      "7e7x",
+      "A"
+    ],
+    [
+      "6ogx",
+      "G"
+    ],
+    [
+      "7yk4",
+      "B"
+    ],
+    [
+      "4dw2",
+      "U"
+    ],
+    [
+      "5k9k",
+      "F"
+    ],
+    [
+      "8ut3",
+      "C"
+    ],
+    [
+      "4o58",
+      "A"
+    ],
+    [
+      "6umg",
+      "C"
+    ],
+    [
+      "5ikc",
+      "N"
+    ],
+    [
+      "6tyb",
+      "G"
+    ],
+    [
+      "7dm2",
+      "A"
+    ],
+    [
+      "3v6o",
+      "B"
+    ],
+    [
+      "4u6v",
+      "B"
+    ],
+    [
+      "4khx",
+      "A"
+    ],
+    [
+      "5w2b",
+      "A"
+    ],
+    [
+      "6mlk",
+      "A"
+    ],
+    [
+      "4ywg",
+      "G"
+    ],
+    [
+      "8b7h",
+      "A"
+    ],
+    [
+      "7upw",
+      "A"
+    ],
+    [
+      "8r1d",
+      "C"
+    ],
+    [
+      "7q0i",
+      "D"
+    ],
+    [
+      "7ttx",
+      "A"
+    ],
+    [
+      "8y6a",
+      "B"
+    ],
+    [
+      "6xkq",
+      "A"
+    ],
+    [
+      "3o2d",
+      "A"
+    ],
+    [
+      "1xf5",
+      "L"
+    ],
+    [
+      "5u3d",
+      "E"
+    ],
+    [
+      "8d9y",
+      "K"
+    ],
+    [
+      "1iqd",
+      "C"
+    ],
+    [
+      "8c7m",
+      "B"
+    ],
+    [
+      "7mlh",
+      "F"
+    ],
+    [
+      "6oor",
+      "A"
+    ],
+    [
+      "3uc0",
+      "B"
+    ],
+    [
+      "2hfg",
+      "R"
+    ],
+    [
+      "8too",
+      "I"
+    ],
+    [
+      "7a0x",
+      "B"
+    ],
+    [
+      "4krp",
+      "A"
+    ],
+    [
+      "3ks0",
+      "A"
+    ],
+    [
+      "8vgm",
+      "A"
+    ],
+    [
+      "6o9i",
+      "C"
+    ],
+    [
+      "8y6i",
+      "A"
+    ],
+    [
+      "6xsw",
+      "X"
+    ],
+    [
+      "8tbq",
+      "P"
+    ],
+    [
+      "6mvl",
+      "A"
+    ],
+    [
+      "5epm",
+      "C"
+    ],
+    [
+      "6ppg",
+      "G"
+    ],
+    [
+      "8ruu",
+      "Y"
+    ],
+    [
+      "6wmw",
+      "B"
+    ],
+    [
+      "6svl",
+      "J"
+    ],
+    [
+      "6vtw",
+      "A"
+    ],
+    [
+      "3b9k",
+      "F"
+    ],
+    [
+      "7nx3",
+      "F"
+    ],
+    [
+      "5hbv",
+      "B"
+    ],
+    [
+      "3grw",
+      "A"
+    ],
+    [
+      "4mwf",
+      "D"
+    ],
+    [
+      "6meh",
+      "C"
+    ],
+    [
+      "6uyd",
+      "F"
+    ],
+    [
+      "6wo5",
+      "E"
+    ],
+    [
+      "1xiw",
+      "B"
+    ],
+    [
+      "7bbj",
+      "B"
+    ],
+    [
+      "6xcj",
+      "G"
+    ],
+    [
+      "8jlx",
+      "A"
+    ],
+    [
+      "8jlw",
+      "A"
+    ],
+    [
+      "7s7i",
+      "B"
+    ],
+    [
+      "6ddm",
+      "C"
+    ],
+    [
+      "4yqx",
+      "M"
+    ],
+    [
+      "4yue",
+      "C"
+    ],
+    [
+      "8dcn",
+      "F"
+    ],
+    [
+      "4i3r",
+      "G"
+    ],
+    [
+      "7n3c",
+      "C"
+    ],
+    [
+      "7sue",
+      "D"
+    ],
+    [
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+      "C"
+    ],
+    [
+      "4aei",
+      "C"
+    ],
+    [
+      "7cj2",
+      "A"
+    ],
+    [
+      "5xez",
+      "A"
+    ],
+    [
+      "5yoy",
+      "A"
+    ],
+    [
+      "5wux",
+      "E"
+    ],
+    [
+      "5mo9",
+      "X"
+    ],
+    [
+      "4np4",
+      "A"
+    ],
+    [
+      "6pi7",
+      "A"
+    ],
+    [
+      "7usl",
+      "C"
+    ],
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+      "E"
+    ],
+    [
+      "1ob1",
+      "F"
+    ],
+    [
+      "8dfh",
+      "A"
+    ],
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+      "6xqw",
+      "E"
+    ],
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+      "4f2m",
+      "E"
+    ],
+    [
+      "2j88",
+      "A"
+    ],
+    [
+      "4ps4",
+      "A"
+    ],
+    [
+      "8slb",
+      "A"
+    ],
+    [
+      "8jkf",
+      "A"
+    ],
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+      "C"
+    ],
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+      "B"
+    ],
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+      "6wfw",
+      "G"
+    ],
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+      "6u8c",
+      "B"
+    ],
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+      "F"
+    ],
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+      "D"
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+      "L"
+    ],
+    [
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+      "H"
+    ],
+    [
+      "6xxv",
+      "C"
+    ],
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+      "C"
+    ],
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+      "5w5z",
+      "A"
+    ],
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+      "I"
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+      "A"
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+      "A"
+    ],
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+      "C"
+    ],
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+      "A"
+    ],
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+      "C"
+    ],
+    [
+      "8dn7",
+      "C"
+    ],
+    [
+      "5k59",
+      "B"
+    ],
+    [
+      "7xy8",
+      "A"
+    ],
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+      "A"
+    ],
+    [
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+      "A"
+    ],
+    [
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+      "C"
+    ],
+    [
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+      "D"
+    ],
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+      "A"
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+      "C"
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+      "1"
+    ],
+    [
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+      "I"
+    ],
+    [
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+      "D"
+    ],
+    [
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+      "C"
+    ],
+    [
+      "8y6h",
+      "A"
+    ],
+    [
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+      "E"
+    ],
+    [
+      "3hi6",
+      "A"
+    ],
+    [
+      "5jq6",
+      "A"
+    ],
+    [
+      "6ks0",
+      "A"
+    ],
+    [
+      "5f3b",
+      "C"
+    ],
+    [
+      "5th9",
+      "B"
+    ],
+    [
+      "5kw9",
+      "A"
+    ],
+    [
+      "6z3q",
+      "A"
+    ],
+    [
+      "5wi9",
+      "A"
+    ],
+    [
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+      "A"
+    ],
+    [
+      "3hae",
+      "J"
+    ],
+    [
+      "7t0l",
+      "D"
+    ],
+    [
+      "6id4",
+      "E"
+    ],
+    [
+      "3l5x",
+      "A"
+    ],
+    [
+      "5l6y",
+      "C"
+    ],
+    [
+      "7rew",
+      "I"
+    ],
+    [
+      "3l5w",
+      "J"
+    ],
+    [
+      "6yio",
+      "B"
+    ],
+    [
+      "3nfp",
+      "I"
+    ],
+    [
+      "2hmi",
+      "B"
+    ],
+    [
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+      "E"
+    ],
+    [
+      "6wh9",
+      "D"
+    ],
+    [
+      "7rt9",
+      "Y"
+    ],
+    [
+      "7ec5",
+      "C"
+    ],
+    [
+      "6vvu",
+      "B"
+    ],
+    [
+      "6x3x",
+      "D"
+    ],
+    [
+      "7zoz",
+      "A"
+    ],
+    [
+      "5tl5",
+      "A"
+    ],
+    [
+      "5gzn",
+      "E"
+    ],
+    [
+      "7vux",
+      "A"
+    ],
+    [
+      "6jbt",
+      "F"
+    ],
+    [
+      "5wt9",
+      "G"
+    ],
+    [
+      "6j15",
+      "D"
+    ],
+    [
+      "5jxe",
+      "B"
+    ],
+    [
+      "6j14",
+      "G"
+    ],
+    [
+      "1rjl",
+      "C"
+    ],
+    [
+      "8uky",
+      "D"
+    ],
+    [
+      "7wg3",
+      "K"
+    ],
+    [
+      "7x28",
+      "F"
+    ],
+    [
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+      "B"
+    ],
+    [
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+      "A"
+    ],
+    [
+      "4hc1",
+      "A"
+    ],
+    [
+      "7lsg",
+      "C"
+    ],
+    [
+      "6j5d",
+      "A"
+    ],
+    [
+      "7kyl",
+      "Z"
+    ],
+    [
+      "7lse",
+      "E"
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+    ],
+    [
+      "4uu9",
+      "D"
+    ],
+    [
+      "7lr3",
+      "D"
+    ],
+    [
+      "7lr4",
+      "D"
+    ],
+    [
+      "1mhp",
+      "B"
+    ],
+    [
+      "1ztx",
+      "E"
+    ],
+    [
+      "6i8s",
+      "D"
+    ],
+    [
+      "7ujd",
+      "A"
+    ],
+    [
+      "3gbn",
+      "A"
+    ],
+    [
+      "8txp",
+      "A"
+    ],
+    [
+      "6uyn",
+      "A"
+    ],
+    [
+      "4cad",
+      "I"
+    ],
+    [
+      "1yjd",
+      "C"
+    ],
+    [
+      "5u8r",
+      "A"
+    ],
+    [
+      "5d72",
+      "B"
+    ],
+    [
+      "6bfq",
+      "G"
+    ],
+    [
+      "8a1e",
+      "A"
+    ],
+    [
+      "4irz",
+      "A"
+    ],
+    [
+      "6o3b",
+      "C"
+    ],
+    [
+      "7x8p",
+      "D"
+    ],
+    [
+      "2yss",
+      "C"
+    ],
+    [
+      "1uac",
+      "Y"
+    ],
+    [
+      "1fbi",
+      "Y"
+    ],
+    [
+      "5vjo",
+      "F"
+    ],
+    [
+      "1jhl",
+      "A"
+    ],
+    [
+      "5o1r",
+      "A"
+    ],
+    [
+      "7yru",
+      "A"
+    ],
+    [
+      "2qqn",
+      "A"
+    ],
+    [
+      "5w4l",
+      "G"
+    ],
+    [
+      "6bf4",
+      "A"
+    ],
+    [
+      "5h35",
+      "C"
+    ],
+    [
+      "5cbe",
+      "F"
+    ],
+    [
+      "5w5x",
+      "A"
+    ],
+    [
+      "7lfa",
+      "C"
+    ],
+    [
+      "7lf7",
+      "M"
+    ],
+    [
+      "8cdd",
+      "B"
+    ],
+    [
+      "7phw",
+      "D"
+    ],
+    [
+      "6hga",
+      "B"
+    ],
+    [
+      "6h3t",
+      "B"
+    ],
+    [
+      "5lcv",
+      "A"
+    ],
+    [
+      "5i9q",
+      "A"
+    ],
+    [
+      "6rlo",
+      "J"
+    ],
+    [
+      "2xra",
+      "A"
+    ],
+    [
+      "4nzr",
+      "M"
+    ],
+    [
+      "1sy6",
+      "A"
+    ],
+    [
+      "1xiw",
+      "A"
+    ],
+    [
+      "8vsj",
+      "A"
+    ],
+    [
+      "6m58",
+      "A"
+    ],
+    [
+      "7q6c",
+      "A"
+    ],
+    [
+      "8u03",
+      "C"
+    ],
+    [
+      "4wv1",
+      "F"
+    ],
+    [
+      "5bo1",
+      "A"
+    ],
+    [
+      "3liz",
+      "A"
+    ],
+    [
+      "7uij",
+      "D"
+    ],
+    [
+      "5tq0",
+      "A"
+    ],
+    [
+      "6uym",
+      "E"
+    ],
+    [
+      "4g7v",
+      "S"
+    ],
+    [
+      "8j80",
+      "A"
+    ],
+    [
+      "7unb",
+      "R"
+    ],
+    [
+      "6e63",
+      "A"
+    ],
+    [
+      "5vkd",
+      "A"
+    ],
+    [
+      "5nmv",
+      "K"
+    ],
+    [
+      "6wzm",
+      "F"
+    ],
+    [
+      "7tuy",
+      "R"
+    ],
+    [
+      "7ahu",
+      "D"
+    ],
+    [
+      "7zqt",
+      "E"
+    ],
+    [
+      "8w84",
+      "C"
+    ],
+    [
+      "8vyn",
+      "C"
+    ],
+    [
+      "4ot1",
+      "A"
+    ],
+    [
+      "7sem",
+      "F"
+    ],
+    [
+      "8t9z",
+      "A"
+    ],
+    [
+      "6rps",
+      "A"
+    ],
+    [
+      "6gku",
+      "A"
+    ],
+    [
+      "7uvi",
+      "C"
+    ],
+    [
+      "7uvs",
+      "C"
+    ],
+    [
+      "8vzn",
+      "A"
+    ],
+    [
+      "5d8j",
+      "A"
+    ],
+    [
+      "3t2n",
+      "A"
+    ],
+    [
+      "8tq9",
+      "B"
+    ],
+    [
+      "4qww",
+      "B"
+    ],
+    [
+      "1uj3",
+      "C"
+    ],
+    [
+      "7xnf",
+      "A"
+    ],
+    [
+      "6mi2",
+      "C"
+    ],
+    [
+      "6a3w",
+      "L"
+    ],
+    [
+      "3zkn",
+      "B"
+    ],
+    [
+      "7ly0",
+      "A"
+    ],
+    [
+      "7lxw",
+      "A"
+    ],
+    [
+      "4uta",
+      "B"
+    ],
+    [
+      "6flb",
+      "G"
+    ],
+    [
+      "2j5l",
+      "A"
+    ],
+    [
+      "6mto",
+      "T"
+    ],
+    [
+      "8tzw",
+      "C"
+    ],
+    [
+      "4m62",
+      "T"
+    ],
+    [
+      "6cw2",
+      "D"
+    ],
+    [
+      "6cw3",
+      "F"
+    ],
+    [
+      "3hmx",
+      "A"
+    ],
+    [
+      "4dtg",
+      "K"
+    ],
+    [
+      "4rrp",
+      "P"
+    ],
+    [
+      "5eii",
+      "I"
+    ],
+    [
+      "8qya",
+      "A"
+    ],
+    [
+      "8a44",
+      "A"
+    ],
+    [
+      "7zjl",
+      "A"
+    ],
+    [
+      "8a99",
+      "C"
+    ],
+    [
+      "7mjk",
+      "C"
+    ],
+    [
+      "7q6e",
+      "A"
+    ],
+    [
+      "8h07",
+      "B"
+    ],
+    [
+      "6p67",
+      "G"
+    ],
+    [
+      "1wej",
+      "F"
+    ],
+    [
+      "4ogy",
+      "A"
+    ],
+    [
+      "8fgx",
+      "C"
+    ],
+    [
+      "1kb5",
+      "A"
+    ],
+    [
+      "4f15",
+      "J"
+    ],
+    [
+      "4leo",
+      "C"
+    ],
+    [
+      "8vdl",
+      "C"
+    ],
+    [
+      "6h2y",
+      "D"
+    ],
+    [
+      "8bk2",
+      "B"
+    ],
+    [
+      "5d93",
+      "A"
+    ],
+    [
+      "4ij3",
+      "A"
+    ],
+    [
+      "3r1g",
+      "B"
+    ],
+    [
+      "6vn1",
+      "C"
+    ],
+    [
+      "6qig",
+      "A"
+    ],
+    [
+      "7k7h",
+      "A"
+    ],
+    [
+      "8hpk",
+      "A"
+    ],
+    [
+      "5e8d",
+      "A"
+    ],
+    [
+      "5bk2",
+      "A"
+    ],
+    [
+      "6nyq",
+      "C"
+    ],
+    [
+      "2zch",
+      "P"
+    ],
+    [
+      "8v91",
+      "B"
+    ],
+    [
+      "1fns",
+      "A"
+    ],
+    [
+      "1yy9",
+      "A"
+    ],
+    [
+      "4jqi",
+      "A"
+    ]
+  ]
+}
\ No newline at end of file
diff --git a/data/epitopes/epitopes.csv b/data/epitopes/epitopes.csv
new file mode 100644
index 0000000000000000000000000000000000000000..632076396cf2d7cdbef056324f31e145da28180b
--- /dev/null
+++ b/data/epitopes/epitopes.csv
@@ -0,0 +1,757 @@
+antigen_name,sequence,binary_label
+6ktr_C,GWGDPIRLRHLYTSGPHGLSSCFLRIRADGVVDCARGQSAHSLLEIKAVALRTVAIKGVHSVRYLCMGADGKMQGLLQYSEEDCAFEEEIRPDGYNVYRSEKHRLPVSLSSAKQRQLYKNRGFLPLSHFLPMLPMV,1111111110000000000000000000000000000000000010000000000000000000000000000000000000000000010000000000000000000000000000000000000001111111
+7o9s_A,SLRNVYDMKIECPHTVSENSVIGYVELPPMPLADTAQMVPESSCSMDNHQSINTITKYTQVIWRGKADPGQSSQNSFETVSTEVDLKGTCVLKHKMVEESYRSRKSITCYDLSCNSTFCKPTLYMIVPIHACNMMKSCLIALGPYRVQVVYERTYCMTGVLIEGKCFVPDVSIIKHGIFDIASVHVVCFFVAVKGNTYKLFEQVKKSFESTCNDTENKVQGYYICIVGGNSAPIYVPTLDDFRSMEAFTGIFKSPHGEDSYSIVGPANAKVPHSASSDTLSLIAYSGIPSYSSLSILTSSTDAKHVFSPGLFPKLNHTNCDKSAIPLTWTGMIDLPGYYE,1111100000000000000000000000010001000000111000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001010110101000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
+2bdn_A,AINAPVTCCYNFTNRKISVQRLASYRRITSSKCPKEAVIFKTIVAKEICADPKQKWVQDSMDHLDKQT,00000000000010111101100010111010011111000111010000011000010011001100
+8d1t_A,LVPGLVNLGNTCFMNSLLQGLSACPAFIRWLEEFTSQYSRDQKEPPSHQYLSLTLLHLLKALSCQEVTDDEVLDASCLLDVLRMYRWQISSFEEQDAHELFHVITSSLEDERDGSGSHWKSQHPFHGRLTSNMVCKHCEHQSPVRFDTFDSLSLSIPAATWGHPLTLDHCLHHFISSESVRDVVCDNCTKIEAKGTLNRTTFVKQLKLGKLPQCLCIHLQRLSWSSHGTPLKRHEHVQFNEDLSMDEYKYHSNASTYLFRLMAVVVHHGDMHSGHFVTYRRSPPSARNPLSTSNQWLWVSDDTVRKASLQEVLSSSAYLLFYERVLEVLF,000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001111110101100100000000000000000000000000000000000000000000000000000000000000000101000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
+9b2w_D,RITHDVGIKPLNPDDFWRCTSGLPSLMKTPKIRLMPGPGLLAMPTTVDGCVRTPSLVINDLIYAYTSNLITRGCQDIGKSYQVLQIGIITVNSDLVPDLNPRISHTFNINDNRKSCSLALLNTDVYQLCSTPKVDERSDYASSGIEDIVLDIVNYDGSISTTRFKNNNISFDQPYAALYPSVGPGIYYKGKIIFLGYGGLEHPINENVICNTTGCPGKTQRDCNQASHSPWFSDRRMVNSIIVADKNSIPKLKVWTISMRQNYWGSEGRLLLLGNKIYIYTRSTSWHSKLQLGIIDITDYSDIRIKWTWHNVLSRPGNNECPWGHSCPDGCITGVYTDAYPLNPTGSIVSSVILDSQKSRVNPVITYSTATERVNELAILNRTLSAGYTTTSCITHYNKGYCFHIVEINHKSLNTFQPMLFKTEIPKSCS,1111011010000010011110000010000000000000000000000000000000001000000000000000000000000000001111100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000011110100000000000000000000001000110
+6mej_C,PGAKQNIQLINTNGSWHLNRTALNCNDSLNTGWLAGLFYHHKFNSSGCPERMASCRPLTDFDQGWGPISHANGSGPDQRPYCWHYPPRPCGIVPAKTVCGPVYCFTPSPVVVGTTDRAGAPAYNWGENDTDVFVLNNTRPPLGNWFGCTWMNSTGFTKACGAPPCAILYCPTDCFRKHPEATYSRCGSGPWITPRCLVDYPYRLWHYPCTINYTVFKIRMYVGGVEHRLEAACN,001111000000000000000011111111111110001001110000000000000000000000000000000000000000000000000000000000000000000000000000000010100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
+8dk6_E,YPPRQCGVVSAKTVCGPVYCFTPSPVVVGTTDRLGAPTYTWGENETDVFLLNSTRPPLGSWFGCTWMNSSGYTKTCGAPPCTTYLKCGSGPWLTPRCLIDYPYRLWHYPCTVNYTIFKIRMYVGGVEHRLTAACNFTR,000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000101010000101110000000
+8vgn_I,MRESKTLGAVQIMNGLFHIALGGLLMIPAGIYAPICVTVWYPLWGGIMYIISGSLLAAVKGKMIMNSLSLFAAISGMILSIMDILNIKISHFLKMESLNFIRAHTPYINIYNCEPANPSEKNSPSTQYCYSIQSLFLGILSVMLIFAFFQELVIAGIVENEWKRT,000000000000000000000000000000100000000000000000000000000000000000000000000000000000000000000000000000000110000101111111100000000000000000000000000000000000000000000
+8f60_C,CDVQLYIKRQSEHHILAGDPFELECPVKYCANRPHVTWCKLNGTTCVKLEDRQTSWKEEKNISFFILHFEPVLPNDNGSYRCSANFQSNLIESHSTTLYVTDVKSA,0101111110000111111110000000000110100000101110000000111111000000000001101100000010001111111111100000111111
+8udz_A,IDMELVKRKRIEAIRGQILSKLRLASPPSQGEVPPGPLPEAVLALYNSTRDRVAGDYYAKEVTRVLMVEQSTHSIYMFFNTSELREAVPEPVLLSRAELRLLLKVEQHVELYQLSNRLLAPSWLSFDVTGVVRQWLSEIEGFRLSAHCSCDSRDNTLQVDINNRPFLLLMATPLTNYCFSSTEKNCCVRQLYIDFRKDLGWKWIHEPKGYHANFCLGPCPPCCVPQALEPLPIVYYVGRKPKVEQLSNMIVRSCKCS,01011001001100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
+7kpj_E,EVQNVKINYYDEDAEKQVAEVPVQVSIDTSCVNMAILTRYMPEGYALVSSDCIIRDGYVYVSVKKDVEIREAVLHITFETPNGEVVTTETVTAEGADGEDAVFRLGVDFNLPTGYKLSNDRDQVTEITIPFGSTGGHTMVVEKGDLSSIVKIQFVDAENNDEVVAGGDYFVDGDGDGIFHTREITEWVPEGYELQEVGDFQVELYKETPLQLSVTKIK,00000000000000000000000000010110000000000000000000011111000000000000000000000000000000000000000000000001000000000000100001001100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
+7o52_U,DVRVRKIKPLSEIHSGNSVSLQCDVQFFWEKNGRLLGKESQLNFDSISPEDAGSYSCWVTASKAWTLEVLYAPRRLRVSMSPGDQVMEGKSATLTCESDANPPVSHYTWFDWNNQSLPYHSQKLRLEPVKVQHSGAYWCQGTNSVGKGRSPLSTLTVYYSPE,000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000011101001010110111110011100001100010000000000000000000000000
+5b71_E,EQTYVISAPKIFRVGASENIVIQVYGYTEAFDATISIKSYPDKKFSYSSGHVHLSSENKFQNSAILTIQPVSYVYLEVVSKHFSKSKRMPITY,000000000000000000000000000111111100010000000100001110101000000000000000000000111100000000000
+4zff_D,HHEVVKFMDVYQRSYCHPIETLVDIFQEYPDEIEYIFKPSCVPLMRCGGCCNDEGLECVPTEESNITMQIMRIKPHQGQHIGEMSFLQHNKCECRPKKD,000000010011001000000000000000010011010000000000000110110000000000001111111111111111000000000001000
+4uu9_D,STLQKKIEEIAAKYKHSVVKKCCYDGACVNNDETCEQRAARISLGPRCIKAFTECCVVASQLRANI,000000000000001111101100100100001000010011111111000100000000000000
+7lr3_D,ATITHVTIPNDCASNECVLIIHVWNNNKFVGSQFSCSIACTNKETDQLASHINPIAPVRAFIGPNKNYAFYFIIKFLINKEITTLCKAIVKDSNGKESIEEFELQSKES,0000000000000000000000000000000010101000000000000000000000100000000000000000000000000000101111111000000000000
+7lr4_D,ATITHVTIPNDCANSNSNECVLIIHVWNNNKFVGSQFSCSIACTNNPIAPVRAFIGPNKNYAFYFIIKFLINKEITTLCKAIVKDSNGKECSIEEFELQ,000000100000000000101010100000000000000000000000101000000001111101000000000000000000000000000000000
+7lr4_C,ATITHVTIPNDCANECVLIIHVWNNNKFVGSQFSCSIACTNASHINPIAPVRAFIGPNKNYAFYFIIKFLINEITTLCKAIVKDSNGKECSIEEFELQS,000000100010001010101000000000000000000000000000001100000001111101000000000000000000000000000000000
+1mhp_B,TQLDIVIVLDGSNSIYPWESVIAFLNDLLKRMDIGPKQTQVGIVQYGENVTHEFNLNKYSSTEEVLVAANKIVQRGGRQTMTALGIDTARKEAFTEARGARRGVKKVMVIVTDGESHDNYRLKQVIQDCEDENIQRFSIAILGHYNRGNLSTEKFVEEIKSIASEPTEKHFFNVSDELALVTIVKALGERIF,000000000001110100000000000000000000000000000001000000000000000000000000011111110000000000000000000000000000000000111011000000000000000000000000101001000000000000000000000000000000000000000000
+1ztx_E,TTYGVCSKAFKFLGTPADTGHGTVVLELQYTGTDGPCKVPISSVASLNDLTPVGRLVTVNPFVSVATANAKVLIELEPPFGDSYIVVGRGEQQINHHWHKS,00100011110000000000000000000011110000000000001101000000000000000111100000000000000000001111000000000
+6i8s_D,SYVAHLASDFGVRVFQQVAQASKDRNVVFSPYGVASVLAMLQLTTGGETQQQIQAAMGFKIDDKGMAPALRHLYKELMGPWNKDEISTTDAIFVQRDLKLVQGFMPHFFRLFRSTVKQVDFSEVERARFIINDWVKTHTKGMISHLLGTGAVDQLTRLVLVNALYFNGQWKTPFPDSSTHRRLFHKSDGSTVSVPMMAQTNKFNYTEFTTPDGHYYDILELPYHGDTLSMFIAAPYEKEVPLSALTNILSAQLISHWKGNMTRLPRLLVLPKFSLETEVDLRKPLENLGMTDMFRQFQADFTSLSDQEPLHVALALQKVKIEVNESGTARMAPEEIIIDRPFLFVVRHNPTGTVLFMGQVMEP,000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000111000000010000000000000000000010000000000000000000000000011111000000000000000000000000000000000000000000000000000000000000011111110000000000000000000000000000
+7ujd_A,SRFPEALRLALMLNDMELVEDIFTSCKDVVVQKQMAFMLGRHGVFLELSEDVEEYEDLTEIMSNVQLNSNFLALARELDIMEPKVPDDIYSARMNLASSFVNGFVNAAFGQDKLLTDDGNKWLYKNKDHGMLSAAASLGMILLWDVDGGLTQIDKYLYSSEDYIKSGALLACGIVNSGVRNECDPALALLSDFVLHNSNTMRLGSIFGLGLAYAGSNREDVLTLLLPVMGDSKSSMEVAGVTALACGMIAVGSCNGDVTSTILQTIMEKSETELKDTYARWLPLGLGLNHLGKGEAIEAILAALEVVSEPFRSFANTLVDVCAYAGSGNVLKVQQLLHQGVAVLGIALIAMGEEIGAEMALRTFGHLLRYGEPTLRRAVPLALALISVSNPRLNILDTLSKFSHDADPEVSYNSIFAMGMVGSGTNNARLAAMLRQLAQYHAKDPNNLFMVRLAQGLTHLGKGTLTLCPYHSDRQLMSQVAVAGLLTVLVSFLDVRNIILGKSHYVLYGLVAAMQPRMLVTFDEELRPLPVSVRVGQAFQTHTTPVLLAHGERAELATEEFLPVTPILEGFVILRKN,0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001110000000000000000000000000010011001100100000000000000000000000000100110110000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
+3gbn_A,ADPGDTICIGYHANNSTDTVDTVLEKNVTVTHSVNLLEDSHNGKLCKLKGIAPLQLGKCNIAGWLLGNPECDLLLTASSWSYIVETSNSENGTCYPGDFIDYEELREQLSSVSSFEKFEIFPKTSSWPNHETTKGVTAACSYAGASSFYRNLLWLTKKGSSYPKLSKSYVNNKGKEVLVLWGVHHPPTGTDQQSLYQNADAYVSVGSSKYNRRFTPEIAARPKVRDQAGRMNYYWTLLEPGDTITFEATGNLIAPWYAFALNRGSGSGIITSDAPVHDCNTKCQTPHGAINSSLPFQNIHPVTIGECPKYVRSTKLRMATGLRNIPSI,0000000000000000000000000000000101110000000000000000000000000000000000000000000000000000000000000000000000000000000000000111110110000000000000000000000000000111111101110100000000000000000000000000010000000000000000000000000000000000000000000000001010000000000000000000000000000000000000000000110000000000000000000000000000000000
+8txp_A,PGDTLCIGYHANNSTDTVDTVLEKNVTVTHSVNLLEDKHNGKLCKLRGVAPLHLGKCNIAGWILGNPECESLSTASSWSYIVETPSSDNGTCYPGDFIDYEELREQLSSVSSFERFEIFPKTSSWPNHDSNKGVTAACPHAGAKSFYKNLIWLVKKGNSYPKLSKSYINDKGKEVLVLWGIHHPSTSADQQSLYQNADTYVFVGSSRYSKKFKPEIAIRPKVRDQEGRMNYYWTLVEPGDKITFEATGNLVVPRYAFAMERNAGSGIIISDTPVHDCNTTCQTPKGAINTSLPFQNIHPITIGKCPKYVKSTKLRLATGLRNIPSI,00000000000000000000000000000101110000000000000000000000010100000000000111110000000011110010111110010000000000001111100101100000001101110010111100000001010001000001110100101000000001001111101110000000000000000000111111111101111100000000000000000000000000100000000000000000000000000000000001111000000000000000000000000100000000
+6uyn_A,DADKICIGYHANNSTDTVDTVLEKNVTVTHSVNLLENKHNGKLCKLRGVAPLHLGKCNIAGWLLGNPECESLATASSWSYIVETSSSNNGTCYPGDFINYEELREQLSSVSSFEKFEIFPKTSSWPNHETNKGVTAACPHAGTNSFYKNLIWLVKKENSYPKINISYTNNRGKEVLVLWAIHHPPTSTDQQSLYQNANSYVFVGSSRYSRKFEPEIATRPKVRGQAGRMNYYWTLVEPGDKITFEATGNLVVPRYAFALKRNSGSGIIISETPVHDCDTTCQTPNGAINTSLPFQNIHPVTIGECPKYVKSTKLRMATGLRNIPSI,00000000000000000000000000000101110000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000111000000000000000000000000100000000
+4cad_I,YNPKLYFLSTFVVTYILWFTGAYLSFSSTYSGIYMLIMLPGLMAPFIISTILIAKSLKKDFINRLFNLKLINLKTIPVVFLLMPAVILLSILLSIPFGGSISQFQFSGSTDFVPVLFLLLLAATFEELGWRGYAFDSLQSRYSLFKASILFGIFWSLWHFPLIFVNNSYQYEIFNQSIWYGLNFFLSILPMGIIITWMCLKNRKSIILAIIFHFLINLNQELLAITQDTKIIETGVLFLVAAAIILYDKKMFFE,00000000000000000000000010000011010000000000000000000000000000000000000000000000000000000000000011000000001110110010001000000000000000000000000000000000000000000000011010011111100100000000000000000000000000000000000000011001111100000000000000000000000000
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+6phb_I,ETGAKVTVDTVCKRGFLIQMSGHLECKCENDLVLVNEETCEEKVLKCDEKTVNKPCGDFSKCIKIDGNPVSYACKCNLGYDMVNNVCIPNECKQVTCGNGKCILDTSNPVKTGVCSCNIGKVPNVQDQNKCSKDGETKCSLKCLKEQETCKAVDGIYKCDCKDGFIIICT,00011111111011010011111110111111000111001111110111100100000000000001110100000010100111000110111101001000111111111111010000101101110000000111011100000011111101010000000000
+6phc_I,AKVTVDTVCKRGFLIQMSGHLECKCENDLVLVNEETCEEKVLKCDEKTVNKPCGDFSKCICKCNLGYDMVNNVCIPNECKQVTCGNGKCILDTSNPVKTGVCSCNIGKVPNVQDQNKCSKDGETKCSLKCLKEQETCKAVDGIYKCDCKDGFIISICT,11111111000000011011100000000000111000000000000000000000000000000000000000000000000000000000100000000000000000000000000000001111111000000000000000000000000000
+6wix_G,AENLWVTVYYGVPVWRDADTTLFCASDAHNVWATHACVPTDPNPQEIHLDNVTEKFNMWKNNMVEQMHTDIISLWDQSLKPCVKLTPLCVTLHCTNFNPREEIKNCSFNMTTELRDKKQRVHSLFYKLDIVQINSEYRLINCNTSACTQACPKVSFEPIPIHYCAPAGFAILKCKDEEFNGTGPCKNVSTVQCTHGIKPVVSTQLLLNGSLAKKEVKIRSENITNNVKTIIVQLVNPVIINCTRPNNNTRKSIRIGPGQAFYATGAIIGDIRQAHCNVSRSDWNKTLQQVARQLRKHFVKTIIFTNSSGGDLEVTTHSFNCGGEFFYCNTSGLFNSTWENITLPCRIKQIINMWQRTGQCMYAPPIPGVISCVSNITGLLLTRDGNETFRPGGGDMRDNWRSELYKYKVVKIEPLGVAPTRCKRR,00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000011100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000111111101000000000000000000000000000000000000000000000000000000000000000000101000000000000000000000000000000000000000000000000000000000000000000000000000000000
+8k3c_B,ISQSTASINENVNEKCKFTLPPLKIHECNISCPNPLPFREYRPQTEGVSNLVGLPNNICLQKTSNQILKPKLISYTLPVVGQSGTCITDPLLAMDEGYFAYSHLERIGSCSRGVSKQRIIGVGEVLDRGDEVPSLFMTNVWTPPNPNTVYHCSAVYNNEFYYVLCAVSTVGDPILNSTYWSGSLMMTRLAVKPKSNGGGYNQHQLALRSIEKGRYDKVMPYGPSGIKQGDTLYFPAVGFLVRTEFKYNDSNCPITKCQYSKPENCRLSMGIRPNSHYILRSGLLKYNLSDGENPKVVFIEISDQRLSIGSPSKIYDSLGQPVFYQASFSWDTMIKFGDVLTVNPLVVNWRNNTVISRPGQSQCPRFNTCPEICWEGVYNDAFLIDRINWISAGVFLDSNQTAENPVFTVFKDNEILYRAQLASEDTNAQKTITNCFLLKNKIWCISLVEIYDTGDNVIRPKLFAVKIPEQC,000000000000000000000000000000000000000000000000000000000000000000000000000000000011000011000000000000000011111111000000000000000000000000000000000000100000000000000000000101100000000000000000000000000000000000000000000010000000000000000000000000000000000000001000000000111100000000000000000000000000000000000000000000000000000100000000000000000000000000000111110000000011111110000000000000000000000111100000000000000000000000101100000000000000000010100011010000000000000
+8ffe_A,APLLLYANRRDLRLVDATNGKENATIVVGGLEDAAAVDFVFSHGLIYWSDVSEEAIKRTEFNKTESVQNVVVSGLLSPDGLACDWLGEKLYWTDSETNRIEVSNLDGSLRKVLFWQELDQPRAIALDPSSGFMYWTDWGEVPKIERAGMDGSSRFIIINSEIYWPNGLTLDYEEQKLYWADAKLNFIHKSNLDGTNRQAVVKGSLPHPFALTLFEDILYWTDWSTHSILACNKYTGEGLREIHSDIFSPMDIHAFSQQRQPNATNPCGIDNGGCSHLCLMSPVKPFYQCACPTGVKLLENGKTCKDGATELLLLARRTDLRRISLDTPDFTDIVLQLEDIRHAIAIDYDPVEGYIYWTDDEVRAIRRSFIDGSGSQFVVTAQIAHPDGIAVDWVARNLYWTDTGTDRIEVTRLNGTMRKILISEDLEEPRAIVLDPMVGYMYWTDWGEIPKIERAALDGSDRVVLVNTSLGWPNGLALDYDEGKIYWGDAKTDKIEVMNTDGTGRRVLVEDKIPHIFGFTLLGDYVYWTDWQRRSIERVHKRSAEREVIIDQLPDLMGLKATNVHRVIGSNPCAEENGGCSHLCLYRPQGLRCACPIGFELISDMKTCIVPEH,0000000011000000000000000000000100100000000000000010010000000000000000000001101000000000000000000000000000000000000000000100000000000000010000000000000000000000000101000000000000000110000000000000000000000110100000000000001100000000000000000000001001000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000010100000000000000011000000000000000000000001010000000000000000000000000000000000000000001000000000000000100000000000000000000000001010000000000000000000000000000000000000000100000000000000010000000000000000000000000100000000000000000000000000000000000000000000000000000000
+8g4t_A,AVGIGAVFLGFLGAAGSTMGAASMTLTVQARNLLSGLTVWGIKQLQARVLAVERYLRDQQLLGIWGCSGKLICCTNVPWNSSWSNRNLSEIWDNMTWLQWDKEISNYTQIIYGLLEESQNQQEKNEQDLLALD,1111111110001000100010010000001000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
+5otj_D,DDMERIFKRFDTNGDGKISLSELTDALRTLGSTSADEVQRMMAEIDTDGDGFIDFNEFISFCNANPGLM,000000000000000010111000000000000001001101110111111100000000000000000
+5lsp_P,GCRHFQSCSQCLSAPPFVQCGWCHDKCVRSEECLSGTWTQQICLPAIYKVFPNSAPLEGGTRLTICGWDFGFRRNNKFDLKKTRVLLGNESCTLTLSESTMNTLKCTVGPAMNKHFNMSIIISNGHGTTQYSTFSYVDPVITSISPKYGPMAGGTLLTLTGNYLNSGNSRHISIGGKTCTLKSVSNSILECYTPAQTISTEFAVKLKIDLANRETSIFSYRE,000000000000000000000000000000000000000000000000000000000000000000000000011010001101000000000111100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
+6s5a_A,APEAAGGPSVFLFPPKPKDTLMISRTPEVTCVVVDVSHEDPEVKFNWYVDGVEVHNAKTKPREEQYNSTYRVVSVLTVLHQDWLNGKEYKCKVSNKALGAPIEKTISKAKGQPREPQVYTLPPSRDELTKNQVSLTCLVKGFYPSDIAVEWESNGQPENNYKTTPPVLDSDGSFFLYSKLTVDKSRWQQGNVFSCSVMHEALHNHYTQKSLSL,011111110000000000001111000000000000111111000000000000000000000000000000000000011001000000010011111110000000000000000000000000000000000000000000000000010110000000000000000000000000000000000001010001001111111111010
+3vi4_D,NRCLKANAKSCGECIQAGPNCGWCTNSTFARCDDLEALKKKGCPPDDIENPRGSKDIKKNKNVTNRSKGTAEKLKPEDIHQIQPQQLVLRLRSGEPQTFTLKFKRAEDYPIDLYYLMDLSYSMKDDLENVKSLGTDLMNEMRRITSDFRIGFGSFVEKTVMPYISTTPAKLRNPCTSEQNCTTPFSYKNVLSLTNKGEVFNELVGKQRISGNLDSPEGGFDAIMQVAVCGSLIGWRNVTRLLVFSTDAGFHFAGDGKLGGIVLPNDGQCHLENNMYTMSHYYDYPSIAHLVQKLSENNIQTIFAVTEEFQPVYKELKNLIPKSAVGTLSANSSNVIQLIIDAYNSLSSEVILENGKLSEGVTISYKSYCKNGVNGTGENGRKCSNISIGDEVQFEISITSNKCPKKDSDSFKIRPLGFTEEVEVILQYICECE,0000000000000000000000000000000000000000000000000000000000000000111111111010000000000000000000000000000000110000000000000000000000000000001001111100100000000000000000000000000000000000000011111111111001000000000000000000000000000011111100000000000000000000000000000000000011000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000011000000000000000000000000000000000000000000000
+5nh3_B,SETQECLFFNANWEKDRTNQTGVEPCYGDKDKRRHCFATWKNISGSIEIVKQGCWLDDINCYDRTDCVEKKDSPEVYFCCCEGNMCNEKFSYFPE,00000000000000110000000000000010000010100000000001100111111101000000000000010100000000000000000
+3i50_E,SNRDFLEGVSGATWVDLVLEGDSCVTIMSKDKPTIDVKMMNMEAANLAEVRSYCYLATVSDLSTKAACPTMGEAHNDKRADPAFVCRQGVVDRGWGNGCGLFGKGSIDTCAKFACSTKAIGRTILKENIKYEVAIFVHQAGRFSITPAAPSYTLKLGEYGEVTVDCEPRSGIDTNAYYVMTVGTKTFLVHREWFMDLNLPWSSAGSTVWRNRETLMEFEEPHATKQSVIALGSQEGALHQALAGAIPVEFSSNTVKLTSGHLKCRVKMEKLQL,000000000000000000000000000000000000000000000000000000000000000000011111100000000000000000000000011110110000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
+4wfg_B,RSTTLLALLALVLLYLVSGALVFRALEQPHEQQAQRELGEVREKFLRAHPCVSDQELGLLIKEVADALGGGADPETQSSHSAWDLGSAFFFSGTIITTIGYGNVALRTDAGRLFCIFYALVGIPLFGILLAGVGDRLGSSLRHGIGHIEAIFLKWHVPPELVRVLSAMLFLLIGCLLFVLTPTFVFCYMEDWSKLEAIYFVIVTLTTVGFGDYVAGADPRQDSPAYQPLVWFWILLGLAYFASVLTTIGNWLRVV,000000000000000000000000000000000000000001100110111111110000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
+6pis_B,RSTTLLALLALVLLYLVSGALVFQALEQPHEQQAQKKMDHGRDQFLRDHPCVSQKSLEDFIKLLVEALGGGANPSSAWNLGSAFFFSGTIITTIGYGNIVLHTDAGRLFCIFYALVGIPLFGMLLAGVGDRLGSSLRRGIGHIEAIFLKWHVPPGLVRSLSAVLFLLIGCLLFVLTPTFVFSYMESWSKLEAIYFVIVTLTTVGFGDYVPPAYQPLVWFWILFGLAYFASVLTTIGNWL,00000000000000000000000000000100100110011001101111111011000000100000000000000000000000000000000000000100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
+6s8j_E,SIPLGVIHNSALQVSDVDKLVCRDKLSSTNQLRSVGLNLEGNGVATDVPSATKRWGFRSGVPPKVVNYEAGEWAENCYNLEIKKPDGSECLPAAPDGIRGFPRCRYVHKVSGTGPCAGDFAFHKEGAFFLYDRLASTVIYRGTTFAEGVVAFLILPQAKKDFFYYSTTIRYQATGFGTNETEYLFE,000000000000000000000000000000000000000000000000000000000000000000000000000000000011111110000000000000000000000011101000000000000000000000000000000000000000000000000000000101100101010000
+3nps_A,VVGGTDADEGEWPWQVSLHALGQGHICGASLISPNWLVSAAHCYIDDRGFRYSDPTQWTAFLGLHDQSQRSAPGVQERRLKRIISHPFFNDFTFDYDIALLELEKPAEYSSMVRPISLPDASHVFPAGKAIWVTGWGHTQYGGTGALILQKGEIRVINQTTCENLLPQQITPRMMCVGFLSGGVDSCQGDSGGPLSSVEADGRIFQAGVVSWGDGCAQRNKPGVYTRLPLFRDWIKENTGV,0000000000000000000000000110000000000000010011111111110000000000000000000000000000000000011111000000000000000000000000000000000000000000010111100000000000000000000000111111100000000000111100100000000000000000000111110000001000000000000000000
+1kyo_P,KSTYRTPNFDDVLKENNDADKGRSYAYFMVGAMGLLSSAGAKSTVETFISSMTATADVLAMAKVEVNLAAIPLGKNVVVKWQGKPVFIRHRTPHEIQEANSVDMSALKDPQTDADRVKDPQWLIMLGICTHLGCVPIGEAGDFGGWFCPCHGSHYDISGRIRKGPAPLNLEIPAYEFDGDKVIVG,00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000110110011111000000011100111110000000000000000000000000000000000000000000000000000000000000000
+7quh_A,GDRQYGDGYLLQVQELVTVQEGLSVHVPCSFSYPQDGWTDSDPVHGYWFRAGDRPYQDAPVATNNPDREVQAETQGRFQLLGDIWSNDCSLSIRDARKRDKGSYFFRLERGSMKWSYKSQLNYKTKQLSVFVTAL,011000100000000000000000000000001111111110000000000000000000000000000000000000000000101000000000000000000000000000000000000000000000000
+8cz5_A,ISPADLLTTPVLTGVGTDNRWNGEIVGLQPVPGGFSTCNRHWNLNGSTFGWSSPRFAAIDHDRGNASYPGSSSSNVLELWYASAGSAADNPISQIAPDGFPDMSFVPFSGTTVPTAGWVGFGGIWNSSNGAPFVTTVQAYELGFATGAPSNPQPTTTTSGAQIVAKSIYGVATGINQATAGLFVMASGVISTPNSSAITYTPQPNRIVNAPGTPAAAPIGKNTPIMFASVVRRTGDINAEAGSTNGTQYGAGSQPLPVTVGLSLNNYSSALMPGQFFVWQLNFASGFMELGLSVDGYFYAGTGASATLIDLSELVDIRPVGPRPSTSTLVYNL,000000000000000000000000000000000000000000000000000000000000001010111110000000001010000000000000000000000011100000000000000000000000000000000000011111111000000101000100000000010110000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
+8t03_B,VAKLLLPTLSSLAFLPTVSIATKRRFYMEAMVYLFTMFFVAFSHACDGPGLSVLCFMRRDILEYFSIYGTALSMWVSLMALADFDEPQRSTFTMLGVLTIAVRTFHDRWGYGVYSGPIGTATLIIAVKWLKKMKEKKGLYPDKSIYTQQIGPGLCFGALALMLRFFFEEWDYTYVHSFYHCALAMSFVLLLPKVNKKAG,0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001001111101101000000000000000000000000000000000000000000000000000000
+4k94_C,KGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTRCSASVLPVDVQTLNGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAF,000000011110011110011110101000000000000000000000101011111000001000101011000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000100000000000
diff --git a/data/epitopes/epitopes_13.csv b/data/epitopes/epitopes_13.csv
new file mode 100644
index 0000000000000000000000000000000000000000..5b7001f40eab30557e8c8b4a25b36790b9649a8b
--- /dev/null
+++ b/data/epitopes/epitopes_13.csv
@@ -0,0 +1,3455 @@
+antigen_chain,antibody_chains,epitopes,Resolution
+2ATK_C,2ATK_B_light+2ATK_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG, 80_ASP",2.5
+7WN2_A,7WN2_C_light+7WN2_B_heavy,"449_TYR, 452_LEU, 456_PHE, 475_ALA, 478_THR, 479_PRO, 481_ASN, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER, 498_GLN",2.35
+7WN2_R,7WN2_L_light+7WN2_H_heavy,"449_TYR, 452_LEU, 456_PHE, 475_ALA, 478_THR, 479_PRO, 481_ASN, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER, 498_GLN",2.35
+6YIO_B,6YIO_L_light+6YIO_H_heavy,"128_TYR, 129_TYR, 130_GLN, 131_CYS, 136_ARG, 137_ALA, 138_LEU, 139_HIS, 140_ARG, 141_GLY, 142_PRO, 143_ALA, 144_GLU, 14_THR, 161_LEU, 16_LYS, 17_ALA, 18_MET",1.83
+7PS6_E,7PS6_L_light+7PS6_H_heavy,"440_ASN, 443_SER, 444_LYS, 445_VAL, 446_GLY, 498_GLN, 499_PRO, 500_THR, 501_TYR, 502_GLY, 505_TYR",2.26
+7PS6_E,7PS6_D_light+7PS6_C_heavy,"475_ALA, 477_SER, 478_THR, 479_PRO, 480_CYS, 483_VAL, 484_LYS, 485_GLY, 486_PHE, 487_ASN, 488_CYS, 489_TYR",2.26
+8EE1_D,8EE1_H_light+8EE1_G_heavy,"464_PRO, 465_GLN, 466_PRO, 467_ARG, 469_MET, 580_LEU, 581_PRO, 583_PRO, 584_VAL, 586_ALA, 587_GLU, 590_ALA, 591_GLU, 593_ASP, 594_ALA, 595_VAL, 598_GLU, 604_VAL, 607_VAL, 636_ARG, 639_ARG, 640_ALA",2.7
+8EE1_A,8EE1_C_light+8EE1_B_heavy,"463_VAL, 464_PRO, 465_GLN, 466_PRO, 467_ARG, 469_MET, 580_LEU, 581_PRO, 583_PRO, 584_VAL, 586_ALA, 587_GLU, 590_ALA, 591_GLU, 593_ASP, 594_ALA, 604_VAL, 607_VAL, 636_ARG, 639_ARG, 640_ALA",2.7
+9JBQ_C,9JBQ_B_light+9JBQ_A_heavy,"163_ARG, 165_ASP, 166_ALA, 167_GLY, 196_SER, 197_ASN, 198_LEU, 199_ASP, 200_THR, 201_PHE, 202_SER, 203_GLY, 204_LYS, 208_LYS, 209_ASP, 212_SER, 214_SER, 215_PRO, 226_ASP",2.0
+7R58_A,7R58_L_light+7R58_H_heavy,"100_PRO, 109_ASP, 111_THR, 125_LEU, 131_PRO, 132_ALA, 134_TYR, 135_LYS, 136_ASN, 137_PRO, 144_SER, 145_PHE, 146_PRO, 147_ILE, 148_ILE, 153_ALA, 154_HIS, 99_GLN",1.9
+6W00_G,6W00_L_light+6W00_H_heavy,"12_ILE, 15_LYS, 16_THR, 17_LEU, 18_LYS, 19_GLY, 20_GLU, 21_THR, 22_THR, 24_LYS, 38_TYR, 40_ASN, 41_ASP, 42_ASN, 43_GLY, 45_ASP",1.85
+5OCA_B,5OCA_L_light+5OCA_H_heavy,"214_THR, 215_ARG, 216_PHE, 220_ALA, 221_SER, 222_LYS, 255_CYS, 256_GLN, 257_GLY, 258_LYS, 317_ASN, 318_PHE, 348_LEU, 349_GLY, 350_THR, 351_LEU, 367_ASP, 380_VAL, 381_SER, 382_GLN, 383_SER, 384_GLY",2.3
+6O1F_A,6O1F_L_light+6O1F_H_heavy,"109_GLU, 110_GLU, 111_PRO, 36_HIS, 50_GLN, 60_GLY, 61_LEU, 62_ALA, 63_ALA, 65_ARG, 84_PRO, 85_VAL, 86_SER, 87_ARG",2.15
+4HKX_E,4HKX_B_light+4HKX_A_heavy,"134_GLY, 135_VAL, 136_SER, 137_ALA, 153_TRP, 155_THR, 158_ASN, 159_GLY, 160_LEU, 187_ASN, 189_GLY, 190_ASP, 192_ARG, 193_ALA, 194_LEU, 196_HIS, 219_LYS, 225_ASP, 226_GLN",2.5
+6UTE_S,6UTE_D_light+6UTE_C_heavy,"306_SER, 307_LYS, 333_ASP, 346_LEU, 347_ASN, 349_LEU, 388_ARG, 389_GLY, 390_GLU, 391_GLN",2.9
+3MA9_A,3MA9_L_light+3MA9_H_heavy,"19_GLU, 205_VAL, 208_ILE, 209_LYS, 212_GLN, 22_GLN, 23_HIS, 26_GLN, 27_LEU, 28_THR, 30_TRP, 31_GLY, 33_LYS, 34_GLN, 35_LEU, 38_ARG, 50_TRP, 51_MET, 53_TRP, 54_ASP, 58_ASN, 61_THR, 62_SER, 65_HIS, 68_ILE",2.05
+8F60_C,8F60_B_light+8F60_A_heavy,"103_GLU, 104_PRO, 106_LEU, 107_PRO, 134_THR, 135_ASP, 136_VAL, 137_LYS, 138_SER, 139_ALA, 47_HIS, 48_ILE, 49_LEU, 50_ALA, 51_GLY, 52_ASP, 53_PRO, 54_PHE",1.64
+8XSJ_B,8XSJ_L_light+8XSJ_H_heavy,"354_ASN, 355_ARG, 356_LYS, 357_ARG, 360_ASN, 426_PRO, 428_ASP, 462_LYS, 463_PRO, 464_PHE, 465_GLU, 466_ARG, 468_ILE, 518_LEU, 519_HIS, 520_ALA",2.61
+7BQ5_A,7BQ5_L_light+7BQ5_H_heavy,"101_TRP, 102_GLY, 104_GLY, 105_CYS, 106_GLY, 107_LEU, 108_PHE, 109_GLY, 110_LYS, 75_PRO, 76_THR",2.99
+7BQ5_B,7BQ5_D_light+7BQ5_C_heavy,"101_TRP, 102_GLY, 104_GLY, 105_CYS, 106_GLY, 107_LEU, 108_PHE, 109_GLY, 110_LYS, 75_PRO, 76_THR",2.99
+2AEP_A,2AEP_L_light+2AEP_H_heavy,"147_ASP, 150_HIS, 152_ARG, 153_THR, 154_PRO, 197_HIS, 198_ASP, 199_GLU, 218_TRP, 219_SER, 220_LYS, 221_LYS, 222_ILE, 224_ARG, 244_GLY, 245_SER, 246_ALA, 248_GLY, 249_ARG, 250_ALA, 251_ASP",2.1
+1K4C_C,1K4C_B_light+1K4C_A_heavy,"45_TYR, 49_LEU, 50_ALA, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.0
+3VG9_A,3VG9_B_light+3VG9_C_heavy,"102_ARG, 110_LEU, 113_ASN, 216_LEU, 217_PRO, 221_ALA, 222_ARG, 223_SER, 224_THR, 227_LYS, 228_GLU, 231_ALA, 235_LEU, 292_ILE, 293_ARG, 294_GLU, 297_GLN, 298_THR, 301_LYS, 33_LEU, 34_ASN, 35_SER, 36_ASN, 37_LEU, 39_ASN, 41_THR, 42_ASN",2.7
+7V23_C,7V23_T_light+7V23_S_heavy,"144_TYR, 145_TYR, 146_HIS, 147_LYS, 14_GLN, 154_GLU, 156_GLU, 157_PHE, 158_ARG, 246_ARG, 247_SER, 248_TYR, 249_LEU, 250_THR, 251_PRO, 252_GLY, 254_SER",2.96
+7V23_B,7V23_R_light+7V23_Q_heavy,"144_TYR, 145_TYR, 146_HIS, 147_LYS, 148_ASN, 14_GLN, 154_GLU, 156_GLU, 157_PHE, 158_ARG, 161_SER, 16_VAL, 246_ARG, 247_SER, 248_TYR, 249_LEU, 250_THR, 251_PRO, 252_GLY, 253_ASP, 254_SER, 258_TRP",2.96
+7V23_A,7V23_P_light+7V23_O_heavy,"144_TYR, 145_TYR, 146_HIS, 147_LYS, 148_ASN, 14_GLN, 154_GLU, 156_GLU, 157_PHE, 158_ARG, 16_VAL, 246_ARG, 247_SER, 248_TYR, 249_LEU, 250_THR, 251_PRO, 252_GLY, 253_ASP, 254_SER, 258_TRP",2.96
+7MF1_A,7MF1_L_light+7MF1_H_heavy,"346_ARG, 351_TYR, 444_LYS, 449_TYR, 450_ASN, 452_LEU, 470_THR, 472_ILE, 481_ASN, 482_GLY, 483_VAL, 484_GLU, 490_PHE, 492_LEU, 494_SER",2.09
+7MSQ_B,7MSQ_E_light+7MSQ_D_heavy,"369_TYR, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 384_PRO, 385_THR, 404_GLY, 407_VAL, 408_ARG, 414_GLN, 508_TYR",2.29
+7MSQ_A,7MSQ_L_light+7MSQ_H_heavy,"369_TYR, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 384_PRO, 404_GLY, 405_ASP, 407_VAL, 408_ARG, 414_GLN, 503_VAL, 504_GLY, 508_TYR",2.29
+3OR6_C,3OR6_B_light+3OR6_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.7
+5U3D_E,5U3D_A_light+5U3D_B_heavy,"27_LEU, 33_LYS, 35_GLN, 36_THR, 37_ALA, 38_GLU, 39_PHE, 40_LYS, 49_GLU, 52_ARG, 53_TYR, 56_LEU, 57_LEU, 60_VAL, 61_ASN",1.77
+5U3D_C,5U3D_A_light+5U3D_B_heavy,"1_GLY, 2_SER, 3_TYR, 4_ASN, 5_LYS, 8_GLN",1.77
+5TLK_X,5TLK_A_light+5TLK_B_heavy,"11_HIS, 13_TRP, 16_GLY, 17_LYS, 21_GLN, 36_HIS, 37_ARG, 5_LYS, 6_SER, 7_CYS, 8_PRO, 9_GLU",2.7
+5TLK_X,5TLK_C_light+5TLK_D_heavy,"24_GLU, 27_THR, 28_PHE, 30_VAL, 42_CYS, 43_ASP, 44_PRO, 45_CYS, 46_ILE, 47_PRO, 49_VAL, 60_HIS, 63_SER, 64_CYS, 77_THR, 78_ILE",2.7
+5TLK_Y,5TLK_E_light+5TLK_F_heavy,"11_HIS, 13_TRP, 15_GLN, 16_GLY, 17_LYS, 36_HIS, 37_ARG, 5_LYS, 6_SER, 7_CYS, 8_PRO, 9_GLU",2.7
+5TLK_Y,5TLK_G_light+5TLK_H_heavy,"24_GLU, 25_PRO, 27_THR, 28_PHE, 30_VAL, 42_CYS, 43_ASP, 44_PRO, 45_CYS, 46_ILE, 47_PRO, 49_VAL, 60_HIS, 63_SER, 64_CYS, 77_THR",2.7
+5LXG_A,5LXG_L_light+5LXG_H_heavy,"0_SER, 112_HIS, 116_PRO, 117_PRO, 118_MET, 119_PRO, 120_SER, 122_ARG, 89_GLU, 90_GLY, 91_ARG, 92_TRP, 93_ARG, 95_ILE, 96_PRO, 99_VAL",2.73
+4O9H_A,4O9H_L_light+4O9H_H_heavy,"129_LYS, 179_ARG, 182_ARG, 183_GLN, 184_MET, 21_SER, 22_SER, 23_GLU, 26_ASP, 27_LYS, 30_ARG, 73_CYS, 74_PHE, 75_GLN, 78_PHE, 80_GLU",2.42
+5GGS_Y,5GGS_D_light+5GGS_C_heavy,"128_LEU, 129_ALA, 131_LYS, 132_ALA, 134_ILE, 62_SER, 63_PHE, 64_VAL, 66_ASN, 68_TYR, 76_THR, 78_LYS, 82_PHE, 83_PRO, 84_GLU, 85_ASP, 86_ARG, 87_SER, 88_GLN, 89_PRO, 90_GLY",2.0
+5GGS_Z,5GGS_B_light+5GGS_A_heavy,"128_LEU, 131_LYS, 132_ALA, 134_ILE, 60_SER, 62_SER, 63_PHE, 64_VAL, 66_ASN, 68_TYR, 76_THR, 78_LYS, 83_PRO, 85_ASP, 86_ARG, 87_SER, 88_GLN, 89_PRO, 90_GLY",2.0
+4MA7_A,4MA7_L_light+4MA7_H_heavy,"138_MET, 140_HIS, 141_PHE, 142_GLY, 143_ASN, 144_ASP, 145_TRP, 146_GLU, 147_ASP, 204_LYS, 208_ARG, 212_GLN",1.97
+7UZC_A,7UZC_L_light+7UZC_H_heavy,"366_SER, 369_TYR, 370_ASN, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 383_SER, 384_PRO, 385_THR, 388_ASN, 411_ALA, 412_PRO, 413_GLY, 414_GLN, 427_ASP, 428_ASP, 429_PHE, 430_THR",2.2
+7UZC_B,7UZC_F_light+7UZC_E_heavy,"366_SER, 369_TYR, 370_ASN, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 384_PRO, 385_THR, 388_ASN, 411_ALA, 412_PRO, 413_GLY, 414_GLN, 427_ASP, 428_ASP, 429_PHE, 430_THR",2.2
+2CMR_A,2CMR_L_light+2CMR_H_heavy,"143_ARG, 146_ASN, 150_SER, 153_HIS, 199_HIS, 202_GLN, 203_LEU, 204_THR, 206_TRP, 207_GLY, 209_LYS, 210_GLN, 211_LEU, 213_ALA, 214_ARG, 32_ILE, 33_LYS, 36_GLN",2.0
+2YSS_C,2YSS_A_light+2YSS_B_heavy,"100_SER, 101_ASP, 102_GLY, 103_ASN, 107_ALA, 15_HIS, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 62_TRP, 63_TRP, 73_ARG, 74_ASN, 75_LEU, 77_ASN, 89_THR, 93_ASN, 96_LYS, 97_LYS, 98_ILE",2.4
+6Q0H_A,6Q0H_L_light+6Q0H_H_heavy,"133_THR, 134_GLY, 135_VAL, 136_THR, 137_ALA, 145_SER, 153_TRP, 155_THR, 156_GLU, 158_ASN, 159_GLY, 160_LEU, 183_HIS, 185_PRO, 186_SER, 187_ASN, 189_ARG, 190_ASP, 193_ALA, 194_ILE, 196_HIS, 222_LYS, 225_GLY, 226_GLN, 227_GLU, 228_GLY, 95_TYR",2.75
+7U9F_C,7U9F_F_light+7U9F_E_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.7
+7U9F_A,7U9F_L_light+7U9F_H_heavy,"117_GLU, 149_ASN, 154_ILE, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.7
+7M7B_A,7M7B_L_light+7M7B_H_heavy,"369_TYR, 370_ASN, 371_SER, 372_ALA, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 383_SER, 384_PRO, 385_THR, 408_ARG, 414_GLN",2.95
+8X0Y_A,8X0Y_I_light+8X0Y_G_heavy,"403_ARG, 405_ASP, 406_GLU, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 447_GLY, 448_ASN, 455_LEU, 456_PHE, 475_ALA, 476_GLY, 477_SER, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 496_GLY, 498_GLN, 505_TYR",1.94
+8X0Y_B,8X0Y_F_light+8X0Y_E_heavy,"403_ARG, 405_ASP, 406_GLU, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 447_GLY, 448_ASN, 455_LEU, 456_PHE, 475_ALA, 476_GLY, 477_SER, 484_GLU, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 496_GLY, 498_GLN, 505_TYR",1.94
+1YJD_C,1YJD_L_light+1YJD_H_heavy,"100_TYR, 104_TYR, 32_GLU, 34_ARG, 49_VAL, 51_TYR, 59_GLN, 61_TYR, 62_SER, 63_LYS, 64_THR, 65_GLY, 97_GLU, 99_MET",2.7
+2HVK_C,2HVK_B_light+2HVK_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",1.9
+7U2D_A,7U2D_L_light+7U2D_H_heavy,"403_ARG, 405_ASP, 408_ARG, 498_GLN, 500_THR, 501_ASN, 502_GLY, 503_VAL, 504_GLY, 505_TYR, 506_GLN",2.76
+6BKB_E,6BKB_L_light+6BKB_H_heavy,"427_LEU, 429_CYS, 430_ASN, 431_ASP, 432_SER, 434_GLN, 435_THR, 436_GLY, 438_ILE, 439_THR, 441_LEU, 442_PHE, 443_TYR, 529_TRP, 531_GLU",2.8
+7KFX_A,7KFX_L_light+7KFX_H_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",2.23
+1MHP_A,1MHP_L_light+1MHP_H_heavy,"156_SER, 157_ASN, 158_SER, 160_TYR, 192_GLU, 218_GLN, 219_ARG, 220_GLY, 221_GLY, 222_ARG, 223_GLN, 224_THR, 259_GLU, 261_HIS, 263_ASN, 264_TYR",2.8
+1MHP_B,1MHP_Y_light+1MHP_X_heavy,"156_SER, 157_ASN, 158_SER, 160_TYR, 192_GLU, 218_GLN, 219_ARG, 220_GLY, 221_GLY, 222_ARG, 223_GLN, 224_THR, 259_GLU, 261_HIS, 263_ASN, 264_TYR, 289_TYR, 291_ARG, 294_LEU",2.8
+2NY1_A,2NY1_C_light+2NY1_D_heavy,"119_CYS, 120_VAL, 122_LEU, 200_VAL, 202_THR, 203_GLN, 204_ALA, 419_ARG, 421_LYS, 422_GLN, 423_ILE, 434_MET, 437_PRO",1.99
+5W3L_B,5W3L_G_light+5W3L_E_heavy,"202_THR, 203_GLY, 204_GLN, 206_TYR, 58_THR, 59_HIS, 60_THR, 74_ASN, 75_ARG, 76_GLN, 77_ASN, 78_GLU",2.71
+5WHK_A,5WHK_L_light+5WHK_H_heavy,"112_LEU, 113_ASN, 129_GLY, 130_ASP, 131_TRP, 132_PRO, 133_GLU, 135_LEU, 82_LEU, 83_GLY, 84_GLY, 85_LYS, 86_GLY, 88_TYR",2.5
+5UKR_G,5UKR_L_light+5UKR_H_heavy,"105_HIS, 109_ILE, 112_TRP, 113_ASP, 191_VAL, 193_THR, 273_GLY, 274_GLY, 275_ASP, 276_PRO, 277_GLU, 278_ILE, 318_ARG, 320_LYS, 324_ASN, 325_MET, 372_GLY, 373_ASP, 374_MET, 375_ARG",2.71
+5X2O_A,5X2O_L_light+5X2O_H_heavy,"194_GLY, 197_LEU, 198_ASN, 200_ASN, 201_TRP, 202_ARG, 203_TRP, 225_ILE, 226_GLU, 227_ASP, 228_SER, 229_GLU, 230_ILE, 255_ASN, 456_LEU, 460_LYS",2.6
+5X2O_C,5X2O_K_light+5X2O_J_heavy,"194_GLY, 197_LEU, 198_ASN, 200_ASN, 201_TRP, 202_ARG, 203_TRP, 225_ILE, 226_GLU, 227_ASP, 228_SER, 229_GLU, 230_ILE, 255_ASN, 456_LEU, 460_LYS",2.6
+8JQN_a,8JQN_l_light+8JQN_h_heavy,"290_THR, 291_CYS, 303_ALA, 304_SER, 306_ARG, 339_LYS, 340_GLU, 341_PRO, 343_SER, 344_ASN, 346_VAL, 347_ARG",2.8
+7BSD_I,7BSD_L_light+7BSD_H_heavy,"281_PRO, 282_GLY, 301_THR, 303_ALA, 304_SER, 305_GLY, 306_ARG, 307_VAL, 309_GLU, 326_LYS, 327_ASP, 328_GLY",2.53
+7BSD_G,7BSD_B_light+7BSD_A_heavy,"281_PRO, 282_GLY, 301_THR, 303_ALA, 304_SER, 305_GLY, 306_ARG, 307_VAL, 309_GLU, 326_LYS, 327_ASP, 328_GLY",2.53
+7N8I_A,7N8I_L_light+7N8I_H_heavy,"108_THR, 109_THR, 111_ASP, 113_LYS, 114_THR, 236_THR, 237_ARG, 269_TYR, 270_LEU, 28_TYR, 63_THR, 82_PRO, 83_VAL, 85_PRO, 87_ASN, 88_ASP",3.0
+5IF0_I,5IF0_B_light+5IF0_A_heavy,"138_SER, 139_GLY, 140_GLY, 141_ASP, 144_PHE, 25_THR, 26_ARG, 27_GLN, 28_GLY, 29_GLY, 30_TYR, 31_SER, 37_ILE, 39_ARG, 42_GLY, 43_GLY, 44_ASP, 47_ASP, 79_ASP, 81_ARG, 82_ASP, 83_ASN, 84_ALA, 85_LYS",2.44
+5IF0_G,5IF0_L_light+5IF0_H_heavy,"138_SER, 139_GLY, 140_GLY, 141_ASP, 144_PHE, 25_THR, 26_ARG, 27_GLN, 28_GLY, 29_GLY, 31_SER, 39_ARG, 42_GLY, 43_GLY, 44_ASP, 47_ASP, 79_ASP, 81_ARG, 82_ASP, 83_ASN, 84_ALA, 85_LYS",2.44
+6X3X_D,6X3X_L_light+6X3X_K_heavy,"170_GLU, 171_TRP, 172_THR, 173_ARG, 174_GLU, 175_PRO, 176_ALA, 177_ARG, 197_THR, 198_VAL, 199_ASP, 200_SER, 42_LYS, 71_LYS",2.92
+6X3X_B,6X3X_I_light+6X3X_J_heavy,"170_GLU, 171_TRP, 172_THR, 173_ARG, 174_GLU, 175_PRO, 176_ALA, 177_ARG, 197_THR, 198_VAL, 199_ASP, 200_SER, 202_ILE, 42_LYS",2.92
+7URQ_A,7URQ_L_light+7URQ_H_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 500_THR, 502_GLY, 505_TYR",2.05
+7Y3O_A,7Y3O_L_light+7Y3O_H_heavy,"352_ALA, 353_TRP, 355_ARG, 357_ARG, 394_ASN, 396_TYR, 462_LYS, 463_PRO, 464_PHE, 465_GLU, 466_ARG, 468_ILE, 516_GLU, 518_LEU, 519_HIS, 520_ALA",2.1
+7BEP_E,7BEP_L_light+7BEP_H_heavy,"334_ASN, 335_LEU, 337_PRO, 339_GLY, 340_GLU, 343_ASN, 344_ALA, 345_THR, 356_LYS, 357_ARG, 359_SER, 361_CYS, 440_ASN, 441_LEU",2.61
+7BEP_C,7BEP_I_light+7BEP_G_heavy,"334_ASN, 335_LEU, 337_PRO, 339_GLY, 340_GLU, 343_ASN, 344_ALA, 345_THR, 346_ARG, 356_LYS, 357_ARG, 359_SER, 361_CYS, 440_ASN, 441_LEU, 509_ARG",2.61
+6AOD_C,6AOD_A_light+6AOD_B_heavy,"392_PRO, 395_ARG, 396_HIS, 413_HIS, 416_TYR, 456_MET, 457_ALA, 458_GLU, 459_SER, 462_ASP, 503_TYR, 504_ARG, 505_LYS, 506_LEU, 507_ARG, 508_ASP, 509_LYS, 510_ILE, 534_HIS, 551_ASP, 552_ALA, 553_CYS, 554_LYS, 555_GLY, 576_SER, 577_TRP, 578_GLY, 579_GLU, 580_GLY, 581_CYS, 588_GLY",1.8
+6WZK_E,6WZK_B_light+6WZK_A_heavy,"10_GLN, 12_LEU, 13_GLN, 15_LEU, 34_HIS, 35_CYS, 36_ALA, 37_GLU, 41_ILE, 49_LYS, 50_ALA, 51_CYS, 7_LEU, 8_ARG, 9_CYS",1.8
+6WZK_F,6WZK_D_light+6WZK_C_heavy,"10_GLN, 12_LEU, 13_GLN, 14_THR, 15_LEU, 34_HIS, 35_CYS, 36_ALA, 37_GLU, 41_ILE, 49_LYS, 50_ALA, 51_CYS, 6_GLU, 7_LEU, 8_ARG, 9_CYS",1.8
+8C7H_D,8C7H_K_light+8C7H_L_heavy,"284_TYR, 336_TYR, 338_LYS, 341_ALA, 345_GLN",2.7
+8C7H_I,8C7H_K_light+8C7H_L_heavy,"113_THR, 137_TYR, 138_SER, 139_GLY, 140_LEU, 142_GLU, 143_ARG, 162_THR, 163_ARG, 165_THR, 167_HIS",2.7
+7XDB_C,7XDB_H_light+7XDB_I_heavy,"345_THR, 346_ARG, 440_LYS, 441_LEU, 443_SER, 444_LYS, 445_VAL, 448_ASN, 499_PRO",2.62
+7XDB_B,7XDB_F_light+7XDB_G_heavy,"345_THR, 346_ARG, 440_LYS, 441_LEU, 443_SER, 444_LYS, 445_VAL, 448_ASN, 499_PRO",2.62
+7XDB_A,7XDB_E_light+7XDB_D_heavy,"345_THR, 346_ARG, 440_LYS, 441_LEU, 443_SER, 444_LYS, 445_VAL, 448_ASN, 450_ASN, 499_PRO, 500_THR",2.62
+6O9H_C,6O9H_B_light+6O9H_A_heavy,"16_GLU, 19_LYS, 20_MET, 21_LEU, 22_GLU, 23_THR, 24_THR, 25_GLU, 26_PRO, 44_TYR, 57_TRP, 63_ARG, 9_ARG",2.1
+6O9H_D,6O9H_L_light+6O9H_H_heavy,"16_GLU, 19_LYS, 20_MET, 21_LEU, 22_GLU, 23_THR, 24_THR, 25_GLU, 26_PRO, 44_TYR, 57_TRP, 9_ARG",2.1
+6R8X_A,6R8X_B_light+6R8X_C_heavy,"143_TYR, 144_ARG, 145_LYS, 146_LEU, 147_ARG, 195_SER, 214_SER, 215_TRP, 216_GLY, 217_GLU, 224_ARG, 37_SER, 57_HIS, 59_PHE, 95_LYS, 96_MET, 97_ALA, 98_GLU",2.04
+5KAN_F,5KAN_K_light+5KAN_J_heavy,"18_ILE, 19_ASP, 20_GLY, 38_LEU, 39_LYS, 41_THR, 42_GLN, 45_ILE, 48_ILE, 49_ASN, 52_LEU, 53_ASN, 56_ILE, 57_GLU, 59_THR",2.79
+5KAN_B,5KAN_L_light+5KAN_H_heavy,"19_ASP, 20_GLY, 38_LEU, 39_LYS, 41_THR, 42_GLN, 45_ILE, 48_ILE, 49_ASN, 52_LEU, 53_ASN, 56_ILE, 57_GLU, 59_THR, 61_GLU",2.79
+5KAN_D,5KAN_I_light+5KAN_G_heavy,"18_ILE, 19_ASP, 20_GLY, 38_LEU, 39_LYS, 41_THR, 42_GLN, 45_ILE, 46_ASP, 48_ILE, 49_ASN, 52_LEU, 53_ASN, 56_ILE, 57_GLU, 59_THR",2.79
+8DCC_E,8DCC_L_light+8DCC_H_heavy,"444_LYS, 445_VAL, 446_GLY, 447_GLY, 448_ASN, 449_TYR, 450_ASN, 452_LEU, 483_VAL, 484_GLU, 490_PHE, 494_SER",2.6
+3T2N_B,3T2N_M_light+3T2N_I_heavy,"252_GLU, 317_ILE, 320_ASP, 321_VAL, 323_ASN, 324_GLY, 325_ALA, 326_ASP, 327_PHE, 328_TYR, 329_GLY, 330_ASN, 331_GLN, 333_LYS, 341_TYR, 384_ALA, 385_GLN, 386_LYS, 387_PRO",2.55
+3T2N_A,3T2N_L_light+3T2N_H_heavy,"252_GLU, 317_ILE, 320_ASP, 321_VAL, 323_ASN, 324_GLY, 325_ALA, 326_ASP, 327_PHE, 328_TYR, 329_GLY, 330_ASN, 331_GLN, 333_LYS, 341_TYR, 383_LEU, 384_ALA, 385_GLN, 386_LYS, 387_PRO",2.55
+6OOR_A,6OOR_L_light+6OOR_H_heavy,"145_LEU, 146_PRO, 148_LYS, 149_VAL, 150_LEU, 152_ALA, 153_ASP, 80_ASP, 83_GLU, 84_LEU",2.45
+3ZE0_C,3ZE0_F_light+3ZE0_E_heavy,"117_GLU, 149_ASN, 153_ARG, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.95
+3ZE0_A,3ZE0_L_light+3ZE0_H_heavy,"117_GLU, 149_ASN, 154_ILE, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.95
+6Q0O_A,6Q0O_B_light+6Q0O_Z_heavy,"134_GLY, 135_VAL, 145_SER, 153_TRP, 155_THR, 156_GLY, 158_ASN, 159_GLY, 160_LEU, 186_PRO, 187_ASN, 190_ASP, 192_ARG, 193_ALA, 194_LEU, 196_HIS, 198_GLU, 225_ASP, 226_GLN, 227_GLU, 228_GLY, 95_TYR",3.0
+6ELU_D,6ELU_F_light+6ELU_E_heavy,"189_GLU, 193_LYS, 214_LYS, 218_ASP, 237_GLN, 238_LYS, 240_LYS, 241_GLU, 242_LYS, 244_LEU, 245_ASN, 246_THR, 248_VAL, 249_PRO",2.3
+6ELU_J,6ELU_L_light+6ELU_K_heavy,"189_GLU, 193_LYS, 198_LYS, 214_LYS, 218_ASP, 237_GLN, 238_LYS, 240_LYS, 241_GLU, 242_LYS, 244_LEU, 245_ASN, 246_THR, 248_VAL, 249_PRO",2.3
+6ELU_G,6ELU_I_light+6ELU_H_heavy,"189_GLU, 193_LYS, 198_LYS, 214_LYS, 218_ASP, 237_GLN, 238_LYS, 240_LYS, 241_GLU, 242_LYS, 244_LEU, 245_ASN, 246_THR, 248_VAL, 249_PRO",2.3
+6ELU_A,6ELU_C_light+6ELU_B_heavy,"189_GLU, 193_LYS, 198_LYS, 214_LYS, 218_ASP, 237_GLN, 238_LYS, 240_LYS, 241_GLU, 242_LYS, 244_LEU, 245_ASN, 246_THR, 248_VAL, 249_PRO",2.3
+8WPW_R,8WPW_B_light+8WPW_A_heavy,"334_ASN, 335_LEU, 337_PRO, 339_HIS, 340_GLU, 343_ASN, 344_ALA, 345_THR, 356_LYS, 357_ARG, 359_SER, 441_LEU",2.49
+8WPW_R,8WPW_D_light+8WPW_C_heavy,"403_ARG, 405_ASN, 415_THR, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 460_LYS, 473_TYR, 475_ALA, 476_GLY, 477_ASN, 478_LYS, 487_ASN, 489_TYR, 493_GLN, 501_TYR, 505_HIS",2.49
+8WPW_R,8WPW_L_light+8WPW_H_heavy,"369_TYR, 374_PHE, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 390_LEU, 392_PHE, 412_PRO, 427_ASP, 428_ASP, 429_PHE, 517_LEU",2.49
+3CVH_M,3CVH_R_light+3CVH_Q_heavy,"146_LYS, 149_GLN, 150_ALA, 154_GLU, 155_ARG, 158_ALA, 159_TYR, 162_GLY, 163_THR, 166_GLU, 62_ARG, 72_GLN, 73_SER, 75_ARG, 76_VAL, 79_ARG, 80_THR",2.9
+3CVH_A,3CVH_L_light+3CVH_H_heavy,"146_LYS, 149_GLN, 150_ALA, 154_GLU, 155_ARG, 158_ALA, 159_TYR, 162_GLY, 163_THR, 62_ARG, 65_GLN, 68_LYS, 72_GLN, 73_SER, 76_VAL, 79_ARG, 80_THR",2.9
+3EFD_K,3EFD_L_light+3EFD_H_heavy,"142_ARG, 145_HIS, 146_GLU, 149_ASP, 152_GLU, 153_ARG, 156_ASP, 157_ASP",2.6
+8U1C_A,8U1C_L_light+8U1C_H_heavy,"222_ARG, 244_ALA, 245_SER, 274_GLU, 275_GLU, 291_ARG, 293_ASN, 294_ARG, 342_LYS, 344_SER, 345_GLY, 346_GLY, 371_THR, 372_GLU, 373_ARG, 407_TRP, 408_TYR, 431_ASP, 438_HIS",2.89
+7KLH_B,7KLH_M_light+7KLH_I_heavy,"403_ARG, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 460_ASN, 473_TYR, 475_ALA, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 493_GLN, 505_TYR",3.0
+7KLH_A,7KLH_L_light+7KLH_H_heavy,"403_ARG, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 493_GLN, 505_TYR",3.0
+5U3M_A,5U3M_L_light+5U3M_H_heavy,"669_LEU, 670_TRP, 671_ASN, 672_TRP, 673_PHE, 674_ASP, 675_ILE, 676_THR, 679_LEU, 680_TRP, 682_ILE, 683_ARG, 685_LYS",2.42
+6X3T_D,6X3T_L_light+6X3T_K_heavy,"170_GLU, 171_TRP, 172_THR, 173_ARG, 174_GLU, 175_PRO, 176_ALA, 177_ARG, 197_THR, 198_VAL, 199_ASP, 200_SER, 42_LYS, 71_LYS",2.55
+6X3T_B,6X3T_I_light+6X3T_J_heavy,"170_GLU, 171_TRP, 172_THR, 173_ARG, 174_GLU, 175_PRO, 176_ALA, 177_ARG, 197_THR, 198_VAL, 199_ASP, 200_SER, 201_GLY, 42_LYS",2.55
+4OLY_G,4OLY_L_light+4OLY_H_heavy,"102_GLU, 276_ASN, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 371_ILE, 429_GLY, 457_ASP, 458_GLY, 459_GLY, 460_ALA, 461_ASN, 465_ASN, 466_GLU, 467_THR, 469_ARG, 97_LYS",2.35
+7MHX_C,7MHX_B_light+7MHX_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 63_PRO, 64_ARG",2.85
+6O39_C,6O39_A_light+6O39_B_heavy,"120_MET, 123_TYR, 125_PHE, 128_PRO, 130_ARG, 57_HIS, 62_GLU, 64_GLY, 65_LEU, 66_GLU, 68_HIS, 69_GLN, 70_PHE, 71_TRP, 72_PRO, 73_LEU, 75_GLU",1.8
+8VGP_A,8VGP_L_light+8VGP_H_heavy,"433_CYS, 434_ILE, 435_CYS, 440_MET, 449_ALA, 450_CYS, 451_GLY, 452_PRO, 469_PHE, 475_TYR, 480_SER",2.7
+8TBB_D,8TBB_L_light+8TBB_H_heavy,"35_GLY, 36_ALA, 37_CYS, 38_PRO, 39_VAL, 40_PHE, 41_GLU, 42_CYS, 43_GLY, 44_ASN, 45_VAL, 51_GLU, 92_GLN, 93_ILE, 94_PRO, 95_GLY, 96_ILE, 97_MET, 99_ASP",2.5
+4JR9_A,4JR9_L_light+4JR9_H_heavy,"12_GLY, 15_ILE, 16_THR, 17_ASP, 19_ARG, 24_ALA, 25_PHE, 27_GLN, 28_GLN, 29_ARG, 32_ARG",2.6
+5GGT_A,5GGT_L_light+5GGT_H_heavy,"115_MET, 117_SER, 119_GLY, 120_GLY, 121_ALA, 49_ASP, 51_ALA, 52_ALA, 54_ILE, 56_TYR, 66_GLN, 68_VAL, 69_HIS",2.8
+6VC9_A,6VC9_L_light+6VC9_H_heavy,"122_ASN, 123_GLY, 124_VAL, 125_GLU, 147_LYS, 149_PRO, 182_PRO, 183_PHE, 185_SER, 186_ASN, 187_PRO, 188_GLY, 189_THR, 190_ASN, 191_LEU, 192_VAL, 193_PHE, 194_GLU, 374_ARG, 517_ARG, 522_ASP",2.25
+8TUI_A,8TUI_L_light+8TUI_H_heavy,"36_HIS, 38_LEU, 40_HIS, 41_ARG, 44_LYS, 45_PHE, 46_LYS, 47_ASP, 48_THR, 49_LEU, 50_HIS, 51_LEU, 52_ILE, 64_PHE, 65_SER, 72_ASP, 87_PRO, 88_TYR, 90_LEU",2.75
+7RXL_F,7RXL_D_light+7RXL_C_heavy,"314_LYS, 317_LYS, 318_GLU, 320_LEU, 321_ASN, 322_LYS, 324_GLN, 327_LEU, 353_LYS, 354_PRO, 356_ASP, 357_GLU, 358_LEU, 359_ASP, 360_TYR, 364_ILE",1.82
+7RXL_E,7RXL_L_light+7RXL_H_heavy,"314_LYS, 317_LYS, 318_GLU, 320_LEU, 321_ASN, 327_LEU, 353_LYS, 354_PRO, 356_ASP, 357_GLU, 358_LEU, 359_ASP, 360_TYR, 364_ILE",1.82
+7WBZ_A,7WBZ_L_light+7WBZ_H_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 457_ARG, 458_LYS, 459_SER, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",2.42
+5TL5_A,5TL5_L_light+5TL5_H_heavy,"11_HIS, 13_TRP, 15_GLN, 16_GLY, 17_LYS, 21_GLN, 36_HIS, 37_ARG, 5_LYS, 6_SER, 7_CYS, 8_PRO",1.8
+4TTD_A,4TTD_L_light+4TTD_H_heavy,"101_ASP, 102_GLY, 103_ASN, 104_GLY, 106_ASN, 109_VAL, 111_TRP, 112_ARG, 113_ASN, 116_LYS, 117_GLY, 21_ARG, 22_GLY, 23_TYR",2.15
+4TTD_B,4TTD_D_light+4TTD_C_heavy,"101_ASP, 102_GLY, 103_ASN, 104_GLY, 106_ASN, 109_VAL, 111_TRP, 112_ARG, 113_ASN, 116_LYS, 117_GLY, 118_THR, 22_GLY, 23_TYR",2.15
+6DKJ_D,6DKJ_B_light+6DKJ_A_heavy,"113_ARG, 115_HIS, 119_GLU, 123_LYS, 125_GLU, 128_LEU, 129_ASP, 132_LEU, 133_ILE, 31_THR, 32_ALA, 33_GLY, 35_LEU, 36_TYR, 37_GLN, 39_TRP, 66_ASP, 67_MET, 68_TYR, 87_TYR, 90_TRP",1.95
+6DKJ_C,6DKJ_L_light+6DKJ_H_heavy,"113_ARG, 115_HIS, 123_LYS, 125_GLU, 128_LEU, 129_ASP, 31_THR, 32_ALA, 33_GLY, 35_LEU, 36_TYR, 39_TRP, 66_ASP, 67_MET, 68_TYR, 87_TYR, 90_TRP",1.95
+7ORB_X,7ORB_D_light+7ORB_C_heavy,"421_TYR, 455_LEU, 473_TYR, 475_ALA, 476_GLY, 477_SER, 478_THR, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN",2.5
+7ORB_X,7ORB_F_light+7ORB_E_heavy,"346_ARG, 440_ASN, 441_LEU, 443_SER, 444_LYS, 445_VAL, 446_GLY, 447_GLY, 448_ASN, 449_TYR, 450_ASN, 492_LEU, 493_GLN, 494_SER, 495_TYR, 496_GLY",2.5
+7ORB_R,7ORB_L_light+7ORB_H_heavy,"453_TYR, 455_LEU, 473_TYR, 475_ALA, 476_GLY, 477_SER, 478_THR, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN",2.5
+7ORB_R,7ORB_B_light+7ORB_A_heavy,"440_ASN, 441_LEU, 443_SER, 444_LYS, 445_VAL, 446_GLY, 447_GLY, 448_ASN, 449_TYR, 450_ASN, 490_PHE, 492_LEU, 493_GLN, 494_SER, 495_TYR, 496_GLY",2.5
+6XQW_E,6XQW_L_light+6XQW_H_heavy,"10_LYS, 11_GLN, 6_GLN, 8_VAL, 9_LYS",2.99
+8K0D_A,8K0D_F_light+8K0D_E_heavy,"219_ASP, 220_PRO, 239_SER, 240_CYS, 241_SER, 242_ARG, 281_HIS, 302_ASP, 304_ILE, 305_LEU, 351_TYR, 391_LYS, 402_ARG, 403_PRO, 404_ASN, 458_PHE, 490_GLN, 491_SER, 492_GLN, 501_GLU, 502_ILE, 503_CYS, 504_TRP, 505_GLU, 506_GLY, 530_GLN, 531_THR, 532_ALA, 533_GLU, 557_ASN, 559_GLN, 560_LYS, 579_GLU, 581_TYR, 586_ASN",2.94
+8K0D_B,8K0D_D_light+8K0D_C_heavy,"219_ASP, 220_PRO, 240_CYS, 241_SER, 242_ARG, 281_HIS, 302_ASP, 304_ILE, 305_LEU, 351_TYR, 391_LYS, 402_ARG, 403_PRO, 404_ASN, 458_PHE, 490_GLN, 491_SER, 492_GLN, 501_GLU, 502_ILE, 503_CYS, 504_TRP, 505_GLU, 506_GLY, 530_GLN, 531_THR, 532_ALA, 557_ASN, 559_GLN, 560_LYS, 579_GLU, 581_TYR, 586_ASN",2.94
+8BYU_A,8BYU_L_light+8BYU_H_heavy,"116_THR, 117_VAL, 118_PRO, 119_CYS, 120_ASN, 121_LYS, 140_ARG, 141_ASP, 143_PHE, 201_CYS, 202_ASP, 233_GLU, 234_LYS, 236_GLN, 73_ILE, 74_HIS, 75_PRO, 76_GLU, 78_ARG, 79_HIS",1.85
+2JK5_C,2JK5_B_light+2JK5_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.4
+7LSG_C,7LSG_L_light+7LSG_H_heavy,"305_THR, 306_MET, 308_ASP, 309_LYS, 310_THR, 311_LYS, 333_SER, 334_GLY, 335_THR, 336_LYS, 337_PRO, 366_ASN",1.86
+8HPU_A,8HPU_L_light+8HPU_H_heavy,"360_ARG, 451_ASN, 453_ASN, 454_LYS, 455_LEU, 458_LYS, 459_VAL, 460_GLY, 461_GLY, 463_TYR, 512_ARG, 513_PRO, 514_THR, 520_GLN",2.56
+8HPU_A,8HPU_N_light+8HPU_M_heavy,"419_ASN, 429_THR, 430_GLY, 431_ASN, 434_ASP, 435_TYR, 469_LEU, 470_PHE, 471_ARG, 472_LYS, 474_ASN, 487_TYR, 488_GLN, 489_ALA, 490_GLY, 491_ASN, 492_LYS, 499_GLY, 501_ASN, 503_TYR, 507_GLN, 515_TYR, 519_HIS",2.56
+3ZE1_A,3ZE1_L_light+3ZE1_H_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",3.0
+3ZE1_C,3ZE1_F_light+3ZE1_E_heavy,"117_GLU, 149_ASN, 153_ARG, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",3.0
+7BEL_X,7BEL_D_light+7BEL_C_heavy,"353_TRP, 354_ASN, 355_ARG, 356_LYS, 357_ARG, 359_SER, 360_ASN, 396_TYR, 426_PRO, 428_ASP, 462_LYS, 463_PRO, 464_PHE, 465_GLU, 466_ARG, 468_ILE, 516_GLU, 518_LEU, 519_HIS, 523_THR",2.53
+7BEL_R,7BEL_B_light+7BEL_A_heavy,"353_TRP, 354_ASN, 355_ARG, 356_LYS, 357_ARG, 359_SER, 360_ASN, 394_ASN, 396_TYR, 426_PRO, 428_ASP, 462_LYS, 463_PRO, 464_PHE, 465_GLU, 466_ARG, 468_ILE, 516_GLU, 518_LEU, 519_HIS, 520_ALA, 523_THR",2.53
+6WGL_C,6WGL_B_light+6WGL_A_heavy,"38_GLN, 41_PHE, 42_LEU, 43_LEU, 44_SER, 45_GLU, 47_HIS, 66_ASP, 67_ASP, 69_VAL, 71_ALA, 72_ASP",2.82
+7SEM_F,7SEM_C_light+7SEM_B_heavy,"156_ARG, 232_SER, 234_MET, 235_PRO, 236_THR, 237_SER, 240_GLN, 241_ILE, 242_LYS, 243_LEU, 275_ILE, 276_PHE, 279_ILE, 280_ASP, 281_THR, 282_PRO, 312_GLN, 314_ALA, 315_GLY, 36_LEU, 41_THR, 42_GLY, 43_TRP",2.2
+7KPG_S,7KPG_L_light+7KPG_H_heavy,"30_LEU, 31_ILE, 32_PRO, 33_VAL, 34_GLY, 35_PRO, 52_GLN, 53_LYS, 65_ASP, 67_THR, 68_LYS, 69_ARG, 70_ASN, 96_LYS, 97_GLY, 98_SER",2.27
+4I2X_E,4I2X_A_light+4I2X_B_heavy,"108_PRO, 109_GLY, 10_GLU, 11_LYS, 12_LEU, 147_PRO, 149_ASP, 203_VAL, 206_GLN, 207_GLY, 208_ASP, 24_HIS, 26_THR, 28_THR, 6_MET, 72_MET, 73_ASP, 7_ILE, 8_GLN, 9_PRO",2.48
+4I2X_F,4I2X_C_light+4I2X_D_heavy,"108_PRO, 109_GLY, 10_GLU, 11_LYS, 12_LEU, 147_PRO, 149_ASP, 203_VAL, 206_GLN, 207_GLY, 208_ASP, 24_HIS, 26_THR, 28_THR, 5_GLN, 72_MET, 73_ASP, 7_ILE, 8_GLN, 9_PRO",2.48
+3A67_Y,3A67_L_light+3A67_H_heavy,"100_SER, 101_ASP, 102_GLY, 103_ASN, 15_HIS, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 62_TRP, 63_TRP, 73_ARG, 75_LEU, 77_ASN, 89_THR, 93_ASN, 96_LYS, 97_LYS, 98_ILE",1.8
+2J4W_D,2J4W_L_light+2J4W_H_heavy,"427_LYS, 428_GLU, 431_LYS, 432_CYS, 433_PRO, 434_CYS, 435_GLU, 436_PRO, 437_GLU, 450_ASN, 451_CYS, 452_VAL",2.5
+7X63_R,7X63_L_light+7X63_H_heavy,"403_ARG, 415_THR, 416_GLY, 417_ASN, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 501_TYR, 502_GLY, 505_TYR",2.24
+8AHN_A,8AHN_L_light+8AHN_H_heavy,"241_THR, 242_GLU, 244_SER, 303_THR, 310_GLU, 311_THR, 312_THR, 313_GLU, 314_THR, 316_GLN, 334_ARG, 335_LYS, 356_ASP, 357_LYS, 88_SER",1.8
+8F5N_A,8F5N_L_light+8F5N_H_heavy,"122_TYR, 123_VAL, 124_VAL, 125_ASN, 126_ARG, 127_GLU, 129_GLY, 131_PHE, 134_LYS, 45_LYS, 47_LYS, 48_ASP, 49_GLY, 50_LEU, 51_ASP, 93_VAL",1.9
+6D2P_C,6D2P_L_light+6D2P_H_heavy,"138_SER, 139_GLY, 140_GLY, 141_ASP, 144_PHE, 25_THR, 26_ARG, 27_GLN, 28_GLY, 29_GLY, 30_TYR, 31_SER, 37_ILE, 39_ARG, 42_GLY, 43_GLY, 44_ASP, 79_ASP, 81_ARG, 82_ASP, 83_ASN, 84_ALA",2.6
+8GB6_A,8GB6_L_light+8GB6_H_heavy,"345_THR, 346_ARG, 348_ALA, 349_SER, 351_TYR, 352_ALA, 440_ASN, 441_LEU, 444_LYS, 445_VAL, 447_GLY, 448_ASN, 449_TYR, 450_ASN, 452_LEU, 470_THR, 490_PHE, 492_LEU",1.75
+8A99_C,8A99_Q_light+8A99_T_heavy,"351_TYR, 449_TYR, 452_LEU, 456_PHE, 470_THR, 472_ILE, 482_GLY, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 488_CYS, 489_TYR, 490_PHE, 492_LEU, 493_GLN",2.5
+8A99_A,8A99_O_light+8A99_R_heavy,"351_TYR, 449_TYR, 452_LEU, 470_THR, 478_THR, 479_PRO, 480_CYS, 482_GLY, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 488_CYS, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER",2.5
+8A99_B,8A99_P_light+8A99_S_heavy,"351_TYR, 449_TYR, 452_LEU, 470_THR, 474_GLN, 478_THR, 481_ASN, 482_GLY, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 488_CYS, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER",2.5
+8TP5_G,8TP5_K_light+8TP5_J_heavy,"125_PRO, 131_THR, 132_VAL, 133_THR, 134_GLY, 135_VAL, 136_SER, 137_ALA, 142_ASN, 143_GLY, 144_LYS, 145_SER, 153_TRP, 155_THR, 157_LYS, 158_ASN, 189_GLY, 190_ASN, 193_ALA, 194_LEU, 222_LYS, 225_ASP, 226_GLN, 227_GLU",2.9
+8TP5_C,8TP5_F_light+8TP5_E_heavy,"125_PRO, 131_THR, 132_VAL, 133_THR, 134_GLY, 135_VAL, 136_SER, 137_ALA, 142_ASN, 143_GLY, 144_LYS, 145_SER, 153_TRP, 155_THR, 157_LYS, 158_ASN, 189_GLY, 190_ASN, 193_ALA, 194_LEU, 222_LYS, 225_ASP, 226_GLN, 227_GLU",2.9
+8TP5_A,8TP5_L_light+8TP5_H_heavy,"125_PRO, 131_THR, 132_VAL, 133_THR, 134_GLY, 135_VAL, 136_SER, 137_ALA, 142_ASN, 143_GLY, 144_LYS, 145_SER, 153_TRP, 155_THR, 157_LYS, 158_ASN, 189_GLY, 190_ASN, 193_ALA, 194_LEU, 222_LYS, 225_ASP, 226_GLN, 227_GLU",2.9
+1KIP_C,1KIP_A_light+1KIP_B_heavy,"102_GLY, 116_LYS, 117_GLY, 118_THR, 119_ASP, 120_VAL, 121_GLN, 124_ILE, 125_ARG, 18_ASP, 19_ASN, 22_GLY, 23_TYR, 24_SER, 27_ASN",2.1
+1GC1_G,1GC1_L_light+1GC1_H_heavy,"119_CYS, 121_LYS, 200_VAL, 202_THR, 203_GLN, 419_ARG, 421_LYS, 422_GLN, 423_ILE, 434_MET, 437_PRO",2.5
+6IW2_D,6IW2_F_light+6IW2_E_heavy,"102_GLY, 103_ASN, 104_GLY, 239_ALA, 240_ALA, 241_THR, 242_ILE, 243_ARG, 244_VAL, 65_LEU, 66_THR, 67_HIS, 68_VAL, 69_LYS, 70_ILE, 71_ASN, 72_ASP, 73_LYS, 74_CYS, 81_HIS, 84_GLU, 87_GLU, 99_ARG",2.9
+6IW2_G,6IW2_I_light+6IW2_H_heavy,"101_TRP, 102_GLY, 103_ASN, 104_GLY, 240_ALA, 241_THR, 242_ILE, 243_ARG, 244_VAL, 65_LEU, 66_THR, 67_HIS, 68_VAL, 69_LYS, 70_ILE, 71_ASN, 72_ASP, 73_LYS, 74_CYS, 81_HIS, 84_GLU, 87_GLU, 99_ARG",2.9
+6IW2_P,6IW2_R_light+6IW2_Q_heavy,"102_GLY, 103_ASN, 104_GLY, 239_ALA, 240_ALA, 241_THR, 242_ILE, 243_ARG, 244_VAL, 65_LEU, 66_THR, 67_HIS, 68_VAL, 69_LYS, 70_ILE, 71_ASN, 72_ASP, 73_LYS, 74_CYS, 81_HIS, 84_GLU, 87_GLU, 99_ARG",2.9
+6IW2_J,6IW2_L_light+6IW2_K_heavy,"101_TRP, 103_ASN, 104_GLY, 240_ALA, 241_THR, 242_ILE, 243_ARG, 244_VAL, 65_LEU, 66_THR, 67_HIS, 68_VAL, 69_LYS, 70_ILE, 71_ASN, 72_ASP, 73_LYS, 81_HIS, 84_GLU, 87_GLU, 99_ARG",2.9
+6IW2_M,6IW2_O_light+6IW2_N_heavy,"101_TRP, 102_GLY, 103_ASN, 104_GLY, 240_ALA, 241_THR, 242_ILE, 243_ARG, 244_VAL, 65_LEU, 66_THR, 67_HIS, 68_VAL, 69_LYS, 70_ILE, 71_ASN, 72_ASP, 73_LYS, 74_CYS, 81_HIS, 84_GLU, 87_GLU, 99_ARG",2.9
+6IW2_A,6IW2_C_light+6IW2_B_heavy,"101_TRP, 102_GLY, 103_ASN, 104_GLY, 239_ALA, 240_ALA, 241_THR, 242_ILE, 243_ARG, 244_VAL, 65_LEU, 66_THR, 67_HIS, 68_VAL, 69_LYS, 70_ILE, 71_ASN, 72_ASP, 73_LYS, 81_HIS, 84_GLU, 87_GLU, 99_ARG",2.9
+4AG4_A,4AG4_L_light+4AG4_H_heavy,"277_LEU, 279_ALA, 281_GLN, 282_ALA, 284_GLN, 307_ARG, 314_GLU, 320_HIS, 335_SER, 336_VAL, 337_PRO, 339_GLY, 340_GLY, 341_ARG, 342_VAL, 365_ILE, 367_ASP, 369_ALA",2.8
+7XIL_A,7XIL_L_light+7XIL_H_heavy,"403_ARG, 408_ARG, 415_THR, 416_GLY, 417_ASN, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 493_GLN, 501_TYR, 502_GLY, 505_TYR",2.91
+4K3J_B,4K3J_L_light+4K3J_H_heavy,"326_GLY, 327_ALA, 328_GLN, 331_ARG, 332_GLN, 333_ILE, 334_GLY, 335_ALA, 336_SER, 337_LEU, 338_ASN, 339_ASP, 368_LYS, 369_TYR, 426_ARG, 446_ILE, 448_GLY, 449_ASP, 469_ARG",2.8
+7K7H_A,7K7H_L_light+7K7H_H_heavy,"128_CYS, 129_SER, 130_ALA, 131_THR, 132_GLN, 33_TYR, 34_TYR, 35_SER, 36_ASP, 59_LYS, 61_ASN, 62_GLY, 63_SER, 64_GLY",3.0
+7K7H_C,7K7H_L_light+7K7H_H_heavy,"40_SER, 58_VAL, 59_LYS, 60_ALA, 62_GLY, 93_TYR",3.0
+8OXY_A,8OXY_C_light+8OXY_B_heavy,"242_THR, 243_GLY, 244_GLY, 245_ARG, 258_LYS, 259_ASN, 261_LYS, 262_LYS, 263_SER, 264_GLY, 266_SER, 267_PRO, 268_VAL, 269_ARG, 270_TYR, 639_LEU, 640_GLY, 641_ASN, 643_LYS, 644_ILE, 645_ASP",2.0
+8WE4_B,8WE4_L_light+8WE4_H_heavy,"369_TYR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 390_LEU, 392_PHE, 412_PRO, 427_ASP, 429_PHE",2.91
+7VGS_A,7VGS_C_light+7VGS_D_heavy,"123_PRO, 126_GLY, 128_ILE, 138_LEU, 165_PRO, 166_LYS, 178_TYR, 180_LYS, 197_SER, 198_ARG, 199_TYR, 200_ARG, 201_ILE, 202_GLY, 203_ASN",2.8
+7VGS_B,7VGS_E_light+7VGS_F_heavy,"123_PRO, 126_GLY, 128_ILE, 138_LEU, 165_PRO, 166_LYS, 178_TYR, 180_LYS, 197_SER, 198_ARG, 199_TYR, 200_ARG, 201_ILE, 202_GLY, 203_ASN",2.8
+6BY3_C,6BY3_B_light+6BY3_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.37
+4IRZ_A,4IRZ_L_light+4IRZ_H_heavy,"145_LYS, 148_ALA, 149_PRO, 150_CYS, 151_TYR, 152_GLN, 154_TYR, 155_VAL, 160_GLU, 161_ASN, 162_PHE, 163_ALA, 193_PHE, 198_THR, 199_THR, 200_ASN, 201_LYS, 202_TYR, 204_ALA, 206_LEU, 208_LYS, 255_GLU, 256_LYS, 257_GLU, 83_LYS",2.84
+8EOO_D,8EOO_J_light+8EOO_E_heavy,"353_TRP, 354_ASN, 355_ARG, 396_TYR, 426_PRO, 428_ASP, 462_LYS, 463_PRO, 464_PHE, 465_GLU, 466_ARG, 467_ASP, 468_ILE, 469_SER, 471_GLU, 514_SER, 516_GLU, 518_LEU",2.77
+8EOO_D,8EOO_K_light+8EOO_F_heavy,"369_TYR, 370_ASN, 371_SER, 372_ALA, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 383_SER, 384_PRO, 385_THR",2.77
+8EOO_C,8EOO_L_light+8EOO_H_heavy,"353_TRP, 354_ASN, 355_ARG, 396_TYR, 426_PRO, 428_ASP, 462_LYS, 463_PRO, 464_PHE, 465_GLU, 466_ARG, 467_ASP, 468_ILE, 469_SER, 471_GLU, 514_SER, 516_GLU, 518_LEU",2.77
+8EOO_C,8EOO_B_light+8EOO_A_heavy,"369_TYR, 370_ASN, 371_SER, 372_ALA, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 383_SER, 384_PRO",2.77
+1R3I_C,1R3I_L_light+1R3I_H_heavy,"45_TYR, 49_LEU, 50_ALA, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.4
+3NID_C,3NID_F_light+3NID_E_heavy,"149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.3
+3NID_A,3NID_L_light+3NID_H_heavy,"117_GLU, 149_ASN, 154_ILE, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.3
+3STZ_C,3STZ_B_light+3STZ_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 63_PRO, 64_ARG",2.5
+6WBV_A,6WBV_L_light+6WBV_H_heavy,"115_LEU, 116_HIS, 117_LYS, 118_HIS, 119_GLU, 121_LEU, 122_THR, 123_MET, 52_GLU, 53_LEU, 54_TYR, 55_GLY",2.5
+7TPD_C,7TPD_F_light+7TPD_E_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.6
+7TPD_A,7TPD_L_light+7TPD_H_heavy,"149_ASN, 154_ILE, 158_ASN, 205_SER, 206_SER, 208_ARG, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.6
+7EK0_R,7EK0_L_light+7EK0_H_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 459_SER, 460_ASN, 473_TYR, 475_ALA, 484_GLU, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 493_GLN, 494_SER, 495_TYR, 501_TYR, 502_GLY, 505_TYR",2.7
+6MVL_A,6MVL_L_light+6MVL_H_heavy,"147_LEU, 149_VAL, 151_ILE, 153_HIS, 154_HIS, 155_HIS, 156_SER, 157_GLU, 159_ARG, 65_ASP, 69_TYR, 71_THR, 86_ARG, 94_PHE, 95_GLN, 98_HIS",1.61
+8TXP_A,8TXP_L_light+8TXP_H_heavy,"290_THR, 291_SER, 292_LEU, 293_PRO, 318_THR, 38_HIS, 40_VAL, 41_ASN, 42_LEU",2.75
+8TXP_B,8TXP_L_light+8TXP_H_heavy,"21_TRP, 38_LEU, 39_LYS, 42_GLN, 45_ILE, 46_ASP, 48_ILE, 49_THR, 50_ASN, 52_VAL, 53_ASN, 56_ILE, 57_GLU, 59_MET, 60_ASN, 61_THR",2.75
+8AV9_A,8AV9_L_light+8AV9_H_heavy,"205_ALA, 208_ARG, 308_LYS, 310_ARG, 311_GLY, 313_ASP, 317_GLU, 318_PHE, 320_HIS, 322_GLU",1.99
+5UEA_X,5UEA_B_light+5UEA_A_heavy,"10_LYS, 11_VAL, 12_LEU, 136_VAL, 13_ASN, 14_ASN, 15_PRO, 16_ALA, 17_LYS, 20_ASP, 21_PRO, 23_GLU, 25_GLU, 60_ILE, 62_VAL, 63_GLY, 66_PRO, 70_ASN, 71_LYS, 72_PHE, 73_VAL, 75_SER",1.7
+5UEA_D,5UEA_L_light+5UEA_H_heavy,"10_LYS, 11_VAL, 12_LEU, 136_VAL, 13_ASN, 14_ASN, 15_PRO, 16_ALA, 17_LYS, 20_ASP, 21_PRO, 23_GLU, 25_GLU, 59_ILE, 61_VAL, 62_GLY, 63_PRO, 65_PRO, 69_ASN, 70_LYS, 71_PHE, 72_VAL, 74_SER",1.7
+8JLW_A,8JLW_L_light+8JLW_H_heavy,"1165_CYS, 1166_THR, 1191_TRP, 1196_THR, 1197_TRP, 1198_CYS, 1199_TRP, 1200_GLY, 1201_VAL, 1203_THR",3.0
+6ADB_A,6ADB_D_light+6ADB_C_heavy,"246_ASP, 248_PRO, 249_LEU, 250_ASN, 380_PRO, 381_GLN, 382_TYR, 383_HIS",2.69
+6ADB_B,6ADB_F_light+6ADB_E_heavy,"243_LYS, 244_LEU, 246_ASP, 248_PRO, 249_LEU, 250_ASN, 380_PRO, 381_GLN, 382_TYR, 383_HIS",2.69
+9ATM_R,9ATM_L_light+9ATM_H_heavy,"403_ARG, 405_ASN, 409_GLN, 415_THR, 416_GLY, 417_ASN, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_LEU, 457_ARG, 458_LYS, 459_SER, 460_LYS, 473_TYR, 475_ALA, 476_GLY, 477_ASN, 487_ASN, 489_TYR, 493_GLN, 505_HIS",1.9
+9ATM_R,9ATM_M_light+9ATM_I_heavy,"353_TRP, 355_ARG, 357_ARG, 396_TYR, 426_PRO, 427_ASP, 428_ASP, 459_SER, 462_LYS, 463_PRO, 464_PHE, 514_SER, 516_GLU, 518_LEU, 519_HIS, 520_ALA",1.9
+8CBF_E,8CBF_B_light+8CBF_A_heavy,"333_THR, 334_ASN, 335_LEU, 336_CYS, 337_PRO, 338_PHE, 339_GLY, 340_GLU, 342_PHE, 343_ASN, 345_THR, 362_VAL, 363_ALA, 364_ASP, 365_TYR, 367_VAL, 368_LEU, 444_LYS, 527_LYS",2.33
+8CBF_E,8CBF_L_light+8CBF_H_heavy,"405_ASP, 408_ARG, 409_GLN, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 459_SER, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 489_TYR, 493_GLN, 505_TYR",2.33
+7L2E_C,7L2E_K_light+7L2E_I_heavy,"142_GLY, 143_VAL, 144_TYR, 147_LYS, 14_GLN, 156_GLU, 158_ARG, 15_CYS, 16_VAL, 17_ASN, 19_THR, 244_LEU, 245_HIS, 246_ARG, 247_SER, 248_TYR, 249_LEU, 250_THR, 251_PRO, 252_GLY, 256_SER",2.97
+7L2E_B,7L2E_J_light+7L2E_G_heavy,"142_GLY, 143_VAL, 144_TYR, 147_LYS, 14_GLN, 156_GLU, 158_ARG, 15_CYS, 16_VAL, 17_ASN, 19_THR, 244_LEU, 245_HIS, 246_ARG, 247_SER, 248_TYR, 249_LEU, 250_THR, 251_PRO, 252_GLY, 256_SER",2.97
+7L2E_A,7L2E_L_light+7L2E_H_heavy,"142_GLY, 143_VAL, 144_TYR, 147_LYS, 14_GLN, 156_GLU, 158_ARG, 15_CYS, 16_VAL, 17_ASN, 19_THR, 244_LEU, 245_HIS, 246_ARG, 247_SER, 248_TYR, 249_LEU, 250_THR, 251_PRO, 252_GLY, 256_SER",2.97
+2YC1_F,2YC1_E_light+2YC1_D_heavy,"14_TYR, 15_GLU, 16_CYS, 17_LEU, 18_LYS, 19_LEU, 22_ASN, 24_TYR, 27_ARG, 42_TYR, 43_ALA, 44_PHE, 45_ALA, 7_ASP",1.9
+2YC1_C,2YC1_B_light+2YC1_A_heavy,"14_TYR, 15_GLU, 16_CYS, 17_LEU, 18_LYS, 19_LEU, 22_ASN, 24_TYR, 27_ARG, 42_TYR, 43_ALA, 44_PHE, 45_ALA, 7_ASP",1.9
+4JPK_A,4JPK_L_light+4JPK_H_heavy,"138_SER, 139_GLY, 140_GLY, 141_ASP, 144_PHE, 25_THR, 26_ARG, 27_ASP, 28_GLY, 29_GLY, 30_VAL, 31_SER, 32_ASN, 35_THR, 37_ILE, 39_ARG, 42_GLY, 43_GLY, 44_ASP, 79_ASP, 81_ARG, 82_ASP, 83_ASN, 84_ALA, 85_LYS",2.4
+5T5B_E,5T5B_L_light+5T5B_H_heavy,"118_GLY, 119_ASN, 120_TRP, 121_PHE, 124_THR, 125_ASN, 127_LEU, 128_TRP, 131_LYS, 64_LEU, 66_VAL, 79_LYS",2.07
+5T5B_O,5T5B_B_light+5T5B_A_heavy,"118_GLY, 119_ASN, 120_TRP, 121_PHE, 124_THR, 125_ASN, 127_LEU, 128_TRP, 131_LYS, 134_ILE, 135_GLN, 64_LEU, 66_VAL, 73_ILE, 79_LYS",2.07
+8VCR_B,8VCR_L_light+8VCR_H_heavy,"969_ASN, 973_ILE, 982_SER, 983_ARG, 984_LEU, 985_ASP, 988_GLU, 992_GLN",3.0
+8VCR_A,8VCR_G_light+8VCR_E_heavy,"969_ASN, 973_ILE, 982_SER, 983_ARG, 984_LEU, 985_ASP, 988_GLU, 992_GLN",3.0
+8VCR_C,8VCR_F_light+8VCR_D_heavy,"969_ASN, 973_ILE, 982_SER, 983_ARG, 984_LEU, 985_ASP, 988_GLU, 992_GLN",3.0
+7CHO_E,7CHO_L_light+7CHO_H_heavy,"403_ARG, 405_ASP, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 502_GLY, 505_TYR",2.56
+7CHO_A,7CHO_C_light+7CHO_B_heavy,"403_ARG, 405_ASP, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 502_GLY, 504_GLY, 505_TYR",2.56
+8ONI_B,8ONI_L_light+8ONI_H_heavy,"10_HIS, 12_VAL, 13_GLU, 16_TYR, 17_LEU, 1_PHE, 21_GLU, 24_PHE, 26_TYR, 27_THR, 2_VAL, 4_GLN, 5_HIS, 6_LEU, 7_CYS, 8_GLY, 9_SER",2.3
+8ONI_F,8ONI_D_light+8ONI_C_heavy,"10_HIS, 12_VAL, 13_GLU, 16_TYR, 17_LEU, 1_PHE, 24_PHE, 26_TYR, 28_PRO, 2_VAL, 4_GLN, 5_HIS, 6_LEU, 7_CYS, 8_GLY, 9_SER",2.3
+7UIJ_D,7UIJ_B_light+7UIJ_A_heavy,"156_LYS, 160_LEU, 161_LYS, 162_THR, 163_ASN, 166_LYS, 171_GLU, 174_GLY, 175_LYS, 178_GLU, 181_GLU, 185_LYS, 188_LYS, 70_ASP, 71_GLU, 74_ALA, 75_LYS, 79_LYS, 85_ASN",2.7
+7UIJ_C,7UIJ_L_light+7UIJ_H_heavy,"156_LYS, 160_LEU, 161_LYS, 162_THR, 163_ASN, 166_LYS, 174_GLY, 175_LYS, 178_GLU, 181_GLU, 185_LYS, 188_LYS, 71_GLU, 74_ALA, 75_LYS, 79_LYS, 85_ASN",2.7
+5T85_G,5T85_L_light+5T85_H_heavy,"10_GLN, 118_GLY, 119_ASN, 120_TRP, 121_PHE, 124_THR, 125_ASN, 127_LEU, 128_TRP, 131_LYS, 64_LEU, 66_VAL, 9_MET",2.37
+8U31_A,8U31_B_light+8U31_C_heavy,"126_ILE, 127_SER, 128_LEU, 130_PRO, 131_LYS, 132_ALA, 133_GLN, 134_ILE, 135_LYS, 136_GLU, 139_ARG, 32_TRP, 66_ASN, 68_TYR, 76_THR, 78_LYS",2.73
+4KKL_B,4KKL_F_light+4KKL_E_heavy,"243_LYS, 246_ASP, 248_PRO, 249_LEU, 250_ASN, 380_PRO, 381_GLN, 382_TYR, 383_HIS",2.85
+4KKL_A,4KKL_D_light+4KKL_C_heavy,"246_ASP, 248_PRO, 249_LEU, 250_ASN, 380_PRO, 381_GLN, 382_TYR, 383_HIS",2.85
+6V4P_B,6V4P_D_light+6V4P_C_heavy,"122_TYR, 125_LYS, 126_ASP, 128_LEU, 129_TRP, 130_SER, 132_GLN, 171_GLU, 175_ASN, 178_TYR, 180_MET, 181_LYS, 182_THR, 183_THR, 184_CYS, 211_SER, 212_VAL, 335_MET, 336_ASP",2.8
+6XC7_A,6XC7_L_light+6XC7_H_heavy,"369_TYR, 370_ASN, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 390_LEU, 392_PHE, 428_ASP, 430_THR, 517_LEU",2.88
+6XC7_A,6XC7_D_light+6XC7_C_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 496_GLY, 501_ASN, 505_TYR",2.88
+4RWY_A,4RWY_L_light+4RWY_H_heavy,"102_GLU, 122_LEU, 276_ASN, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 283_THR, 366_GLY, 367_GLY, 368_ASP, 371_ILE, 425_ASN, 426_MET, 427_TRP, 429_GLU, 430_VAL, 431_GLY, 432_LYS, 455_THR, 456_ARG, 457_ASP, 459_GLY, 460_LYS, 461_ASP, 472_GLY, 473_GLY, 474_ASP, 476_ARG, 480_ARG, 96_TRP, 97_LYS, 98_ASN",2.13
+7LY0_A,7LY0_L_light+7LY0_H_heavy,"140_PHE, 144_TYR, 145_TYR, 146_HIS, 147_LYS, 148_ASN, 155_SER, 158_ARG, 16_VAL, 17_ASN, 246_ARG, 249_LEU, 250_THR, 251_PRO, 252_GLY, 253_ASP, 254_SER",2.6
+4G3Y_C,4G3Y_L_light+4G3Y_H_heavy,"105_THR, 107_GLU, 108_GLY, 109_ALA, 110_GLU, 111_ALA, 137_ASN, 138_ARG, 139_PRO, 140_ASP, 141_TYR, 67_GLN, 70_PRO, 71_SER, 73_HIS, 75_LEU, 77_THR, 97_ILE",2.6
+8J7T_A,8J7T_C_light+8J7T_E_heavy,"313_GLN, 314_GLN, 315_LEU, 316_GLN, 317_GLY, 318_VAL, 319_TYR, 343_ALA, 344_PRO, 345_ASP, 346_ALA, 347_ASP, 349_ARG, 350_TRP, 353_SER, 354_GLN, 356_HIS, 357_ASN, 373_PHE, 375_ALA, 376_MET",2.2
+8J7T_B,8J7T_D_light+8J7T_F_heavy,"313_GLN, 314_GLN, 315_LEU, 316_GLN, 317_GLY, 318_VAL, 319_TYR, 343_ALA, 344_PRO, 345_ASP, 346_ALA, 347_ASP, 349_ARG, 350_TRP, 353_SER, 354_GLN, 356_HIS, 357_ASN, 373_PHE, 375_ALA, 376_MET",2.2
+7T01_A,7T01_L_light+7T01_H_heavy,"439_ASN, 440_ASN, 443_SER, 444_LYS, 445_VAL, 446_GLY, 447_GLY, 449_TYR, 498_GLN, 499_PRO, 500_THR",2.69
+8E1G_B,8E1G_T_light+8E1G_V_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 459_SER, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 478_THR, 486_PHE, 487_ASN, 489_TYR, 501_ASN, 502_GLY, 505_TYR",2.57
+8E1G_A,8E1G_L_light+8E1G_H_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 478_THR, 486_PHE, 487_ASN, 489_TYR, 501_ASN, 502_GLY, 505_TYR",2.57
+5W06_T,5W06_L_light+5W06_H_heavy,"101_THR, 103_TYR, 104_LEU, 129_ASP, 136_ARG, 141_LEU, 142_SER, 144_ARG, 145_ASP, 146_VAL, 148_GLY, 149_LYS, 150_ASP, 172_THR, 174_GLU, 194_PRO, 195_SER, 197_THR, 65_LYS, 66_ASP, 67_VAL, 68_LYS, 69_GLN, 70_THR",2.6
+4OM1_G,4OM1_L_light+4OM1_H_heavy,"102_GLU, 276_ASN, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 357_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 370_GLU, 371_ILE, 425_ASN, 426_MET, 427_TRP, 429_GLY, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_ALA, 461_ASN, 462_ASN, 463_THR, 465_ASN, 466_GLU, 467_THR, 469_ARG, 473_GLY, 97_LYS",2.13
+7A0X_B,7A0X_F_light+7A0X_E_heavy,"118_SER, 119_ASP, 120_PRO, 121_VAL, 124_GLU, 277_GLN, 286_SER, 287_MET, 349_ALA, 351_GLY, 352_ASN, 353_VAL, 354_ASP, 355_PRO, 356_GLU, 357_LEU, 360_TRP, 39_HIS, 43_GLU",2.37
+7A0X_A,7A0X_D_light+7A0X_C_heavy,"118_SER, 119_ASP, 120_PRO, 121_VAL, 122_THR, 124_GLU, 277_GLN, 278_GLY, 286_SER, 287_MET, 349_ALA, 351_GLY, 352_ASN, 353_VAL, 354_ASP, 355_PRO, 356_GLU, 357_LEU, 360_TRP, 39_HIS, 43_GLU",2.37
+8J7W_B,8J7W_D_light+8J7W_F_heavy,"313_GLN, 314_GLN, 315_LEU, 316_GLN, 317_GLY, 318_VAL, 319_TYR, 343_ALA, 344_PRO, 345_ASP, 346_ALA, 347_ASP, 349_ARG, 350_TRP, 353_SER, 354_GLN, 356_HIS, 357_ASN, 373_PHE, 376_MET",2.92
+8J7W_A,8J7W_C_light+8J7W_E_heavy,"313_GLN, 314_GLN, 315_LEU, 316_GLN, 317_GLY, 318_VAL, 319_TYR, 343_ALA, 344_PRO, 345_ASP, 346_ALA, 347_ASP, 349_ARG, 350_TRP, 353_SER, 354_GLN, 356_HIS, 373_PHE, 376_MET",2.92
+1HEZ_E,1HEZ_A_light+1HEZ_B_heavy,"824_LYS, 834_ILE, 835_GLN, 836_THR, 837_ALA, 838_GLU, 839_PHE, 840_LYS, 849_GLU, 852_ARG, 853_TYR, 856_LEU",2.7
+1HEZ_E,1HEZ_C_light+1HEZ_D_heavy,"843_PHE, 844_GLU, 847_THR, 848_ALA, 851_TYR, 852_ARG, 855_ASP, 864_TYR, 865_THR, 866_ALA, 867_ASP, 868_LEU, 869_GLU, 871_GLY, 872_GLY",2.7
+6IAP_A,6IAP_D_light+6IAP_E_heavy,"32_VAL, 33_GLU, 35_GLN, 37_HIS, 42_LEU, 47_ARG, 48_PRO, 49_LYS, 51_PRO, 75_ILE, 77_ARG, 78_VAL, 79_GLY, 80_GLU, 81_LEU, 82_TRP",2.9
+6IAP_A,6IAP_L_light+6IAP_H_heavy,"123_ASP, 124_THR, 177_PHE, 180_GLU, 18_GLU, 65_ASN, 66_SER, 67_ARG, 96_MET, 97_TYR, 98_ASP, 99_THR",2.9
+5JHL_A,5JHL_L_light+5JHL_H_heavy,"101_TRP, 102_GLY, 104_GLY, 105_CYS, 106_GLY, 107_LEU, 108_PHE, 76_THR, 77_GLN",3.0
+4RGO_S,4RGO_L_light+4RGO_H_heavy,"135_ARG, 137_PHE, 139_ASP, 140_GLY, 141_LYS, 142_ASN, 145_SER, 186_TYR, 188_LYS, 190_ILE, 229_LYS, 231_GLU, 233_TYR, 235_THR, 238_LYS, 5_ASP, 6_PRO, 8_PRO",1.8
+5W5Z_A,5W5Z_L_light+5W5Z_H_heavy,"33_ASP, 34_ARG, 35_ALA, 36_GLY, 37_ARG, 38_MET, 39_GLY, 40_GLY, 43_PRO, 44_GLU",2.0
+3B2U_S,3B2U_U_light+3B2U_T_heavy,"348_LEU, 349_PRO, 353_ARG, 384_GLN, 408_GLN, 409_HIS, 412_PHE, 417_VAL, 418_SER, 438_ILE, 440_SER, 441_GLY, 443_LYS, 464_THR, 465_LYS, 466_ILE, 467_ILE, 468_SER, 469_ASN, 471_GLY, 473_ASN",2.58
+3B2U_I,3B2U_K_light+3B2U_J_heavy,"349_PRO, 384_GLN, 408_GLN, 409_HIS, 412_PHE, 417_VAL, 418_SER, 438_ILE, 440_SER, 441_GLY, 443_LYS, 464_THR, 465_LYS, 466_ILE, 467_ILE, 468_SER, 469_ASN, 470_ARG, 471_GLY, 473_ASN",2.58
+3B2U_P,3B2U_R_light+3B2U_Q_heavy,"349_PRO, 384_GLN, 408_GLN, 409_HIS, 412_PHE, 417_VAL, 418_SER, 438_ILE, 440_SER, 441_GLY, 443_LYS, 464_THR, 465_LYS, 466_ILE, 467_ILE, 468_SER, 469_ASN, 471_GLY, 473_ASN",2.58
+3B2U_V,3B2U_X_light+3B2U_W_heavy,"349_PRO, 384_GLN, 408_GLN, 409_HIS, 411_GLN, 412_PHE, 417_VAL, 418_SER, 438_ILE, 440_SER, 441_GLY, 443_LYS, 464_THR, 465_LYS, 466_ILE, 467_ILE, 468_SER, 469_ASN, 473_ASN",2.58
+3B2U_A,3B2U_L_light+3B2U_H_heavy,"348_LEU, 349_PRO, 384_GLN, 408_GLN, 409_HIS, 412_PHE, 417_VAL, 418_SER, 438_ILE, 440_SER, 441_GLY, 443_LYS, 464_THR, 465_LYS, 466_ILE, 467_ILE, 468_SER, 469_ASN, 473_ASN",2.58
+3B2U_E,3B2U_G_light+3B2U_F_heavy,"349_PRO, 384_GLN, 409_HIS, 412_PHE, 417_VAL, 418_SER, 438_ILE, 440_SER, 441_GLY, 443_LYS, 464_THR, 465_LYS, 466_ILE, 467_ILE, 468_SER, 469_ASN, 471_GLY, 473_ASN",2.58
+3B2U_B,3B2U_D_light+3B2U_C_heavy,"348_LEU, 349_PRO, 353_ARG, 384_GLN, 408_GLN, 409_HIS, 412_PHE, 417_VAL, 418_SER, 438_ILE, 440_SER, 441_GLY, 443_LYS, 464_THR, 465_LYS, 466_ILE, 467_ILE, 468_SER, 469_ASN, 473_ASN",2.58
+3B2U_M,3B2U_O_light+3B2U_N_heavy,"349_PRO, 384_GLN, 408_GLN, 409_HIS, 412_PHE, 417_VAL, 418_SER, 438_ILE, 440_SER, 441_GLY, 443_LYS, 464_THR, 465_LYS, 466_ILE, 467_ILE, 468_SER, 469_ASN, 473_ASN",2.58
+6LXJ_D,6LXJ_N_light+6LXJ_M_heavy,"118_ARG, 119_GLU, 149_ILE, 150_HIS, 151_ASP, 152_ARG, 179_TRP, 223_ILE, 228_GLU, 293_ARG, 344_ASN, 368_ARG, 402_TYR, 427_ILE, 430_ARG, 431_PRO, 432_LYS, 434_ASP, 437_TRP",2.9
+6LXJ_C,6LXJ_K_light+6LXJ_J_heavy,"118_ARG, 149_ILE, 150_HIS, 151_ASP, 152_ARG, 179_TRP, 180_SER, 223_ILE, 228_GLU, 293_ARG, 343_ASN, 344_ASN, 368_ARG, 402_TYR, 427_ILE, 430_ARG, 431_PRO, 432_LYS, 434_ASP, 437_TRP",2.9
+6LXJ_B,6LXJ_F_light+6LXJ_E_heavy,"118_ARG, 149_ILE, 150_HIS, 151_ASP, 152_ARG, 179_TRP, 228_GLU, 293_ARG, 344_ASN, 368_ARG, 402_TYR, 427_ILE, 430_ARG, 431_PRO, 432_LYS, 434_ASP, 437_TRP",2.9
+6LXJ_A,6LXJ_L_light+6LXJ_H_heavy,"118_ARG, 149_ILE, 150_HIS, 151_ASP, 152_ARG, 179_TRP, 180_SER, 223_ILE, 228_GLU, 293_ARG, 343_ASN, 344_ASN, 368_ARG, 402_TYR, 427_ILE, 430_ARG, 431_PRO, 432_LYS, 434_ASP, 437_TRP",2.9
+7XNF_A,7XNF_L_light+7XNF_H_heavy,"403_LYS, 415_THR, 416_GLY, 417_VAL, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 457_ARG, 458_LYS, 460_LYS, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 487_ASN, 489_TYR, 493_GLU, 505_TYR",2.79
+8DN6_A,8DN6_D_light+8DN6_C_heavy,"252_ASP, 253_ARG, 270_MET, 271_ASP, 274_MET, 276_ASP, 280_LEU, 282_TYR, 283_TYR, 284_ARG, 286_LEU, 287_GLU, 319_LYS, 320_VAL, 321_SER, 322_ALA, 325_LEU, 326_GLN, 329_ARG, 346_VAL, 347_VAL, 348_ASN, 349_PHE, 351_ASN, 354_THR, 355_LYS",3.0
+8DN6_B,8DN6_F_light+8DN6_E_heavy,"271_ASP, 272_SER, 273_ASP, 276_ASP, 279_LYS, 280_LEU, 282_TYR, 283_TYR, 284_ARG, 286_LEU, 287_GLU, 318_GLY, 319_LYS, 320_VAL, 321_SER, 322_ALA, 323_ARG, 326_GLN, 329_ARG, 345_GLN, 346_VAL, 347_VAL, 348_ASN, 349_PHE, 351_ASN, 354_THR, 436_GLU",3.0
+8EB2_G,8EB2_O_light+8EB2_N_heavy,"105_SER, 106_ASP, 107_TRP, 108_ARG, 109_PHE, 110_LEU, 161_GLU, 165_VAL, 166_GLU, 169_ARG, 173_GLU, 176_LYS",2.9
+8EB2_D,8EB2_L_light+8EB2_J_heavy,"106_ASP, 107_TRP, 108_ARG, 109_PHE, 110_LEU, 161_GLU, 162_GLY, 166_GLU, 169_ARG, 173_GLU, 176_LYS",2.9
+8EB2_A,8EB2_M_light+8EB2_K_heavy,"105_SER, 106_ASP, 107_TRP, 108_ARG, 109_PHE, 110_LEU, 161_GLU, 162_GLY, 166_GLU, 169_ARG, 173_GLU, 176_LYS, 180_GLN",2.9
+8WSQ_F,8WSQ_B_light+8WSQ_A_heavy,"426_ASN, 428_ASN, 429_ARG, 432_ILE, 433_LYS, 441_TYR, 442_VAL, 443_SER, 444_ASN, 445_LYS, 446_GLY, 447_VAL, 463_GLU, 464_GLY, 465_LYS, 466_SER, 467_LEU, 468_TYR, 470_LYS",2.9
+8X0T_A,8X0T_L_light+8X0T_H_heavy,"121_LEU, 122_MET, 125_TYR, 126_GLY, 127_PHE, 128_ALA, 130_PRO, 132_ARG, 67_LEU, 68_GLU, 70_HIS, 71_GLN, 73_TRP, 74_PRO, 75_LEU, 77_GLU, 78_ILE",2.5
+6GLW_B,6GLW_D_light+6GLW_C_heavy,"115_ARG, 116_ILE, 117_LYS, 119_GLU, 120_ALA, 42_THR, 49_ASP, 68_GLU, 69_TYR, 70_GLY, 71_ALA, 73_LYS",1.9
+6GLW_A,6GLW_L_light+6GLW_H_heavy,"114_HIS, 115_ARG, 116_ILE, 117_LYS, 119_GLU, 120_ALA, 42_THR, 49_ASP, 68_GLU, 69_TYR, 70_GLY, 71_ALA, 73_LYS",1.9
+6NHA_A,6NHA_L_light+6NHA_H_heavy,"112_LEU, 113_ASN, 115_GLU, 116_GLU, 130_ASP, 131_TRP, 132_PRO, 133_GLU, 85_LYS, 86_GLY, 87_PRO",2.38
+5VK6_C,5VK6_B_light+5VK6_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.25
+6U6U_R,6U6U_L_light+6U6U_H_heavy,"113_PHE, 114_GLU, 117_TRP, 118_CYS, 119_ASP, 120_THR, 121_SER, 122_ILE, 123_GLY, 124_GLY, 125_LEU, 126_PRO, 127_ASN, 128_LEU, 132_TYR, 164_ASP, 165_CYS, 166_ASN, 190_ARG, 191_GLY, 192_ASN, 211_GLY, 212_ILE, 213_THR, 87_TRP",2.31
+7LFB_X,7LFB_L_light+7LFB_H_heavy,"152_GLU, 155_ILE, 156_ARG, 157_ARG, 159_ARG, 160_ALA, 163_ASP, 164_GLY, 165_VAL, 166_GLN, 167_LYS, 168_VAL",1.91
+3O2D_A,3O2D_L_light+3O2D_H_heavy,"120_SER, 122_PRO, 123_GLY, 124_SER, 125_SER, 126_PRO, 127_SER, 140_GLY, 141_GLY, 142_LYS, 162_LEU, 163_GLN, 164_ASN, 165_GLN, 166_LYS, 31_SER, 3_VAL, 50_LYS, 76_ILE, 77_GLU, 79_SER, 80_ASP, 94_GLN, 96_LEU",2.19
+7WVG_B,7WVG_D_light+7WVG_C_heavy,"153_LYS, 16_GLY, 18_ILE, 19_ASP, 20_GLY, 21_TRP, 35_ALA, 38_LYS, 41_THR, 42_GLN, 45_VAL, 46_ASP, 48_ILE, 49_THR",2.6
+8Y31_E,8Y31_A_light+8Y31_B_heavy,"261_TRP, 263_HIS, 265_PHE, 266_LEU, 274_LEU, 275_TYR, 276_LYS, 278_LYS, 279_GLN, 280_ILE, 281_PRO, 282_ASP, 284_GLU, 294_PRO, 296_ASN, 297_VAL, 298_PHE, 299_GLN, 300_LYS, 301_GLY, 302_ILE, 303_TYR",2.68
+8Y31_F,8Y31_C_light+8Y31_D_heavy,"261_TRP, 263_HIS, 265_PHE, 275_TYR, 276_LYS, 278_LYS, 279_GLN, 280_ILE, 281_PRO, 282_ASP, 284_GLU, 294_PRO, 296_ASN, 297_VAL, 298_PHE, 299_GLN, 300_LYS, 301_GLY, 302_ILE, 303_TYR",2.68
+6XSW_C,6XSW_B_light+6XSW_A_heavy,"1396_LYS, 1399_ASP, 1400_GLN, 1401_ARG, 1402_CYS, 1403_ASP, 1404_ARG, 1417_ASP, 1418_CYS, 1420_LEU, 1519_LEU, 1520_LEU, 1524_GLU, 1560_HIS, 1577_GLU, 1616_SER, 1620_ARG, 1621_LEU, 1622_ASP, 1623_PHE, 1624_PRO, 1626_PRO, 1627_LEU, 1628_ARG",2.98
+6XSW_J,6XSW_I_light+6XSW_G_heavy,"1399_ASP, 1400_GLN, 1401_ARG, 1402_CYS, 1403_ASP, 1404_ARG, 1417_ASP, 1418_CYS, 1420_LEU, 1421_SER, 1519_LEU, 1520_LEU, 1524_GLU, 1616_SER, 1620_ARG, 1621_LEU, 1622_ASP, 1623_PHE, 1624_PRO, 1625_TYR, 1626_PRO, 1627_LEU, 1628_ARG",2.98
+6XSW_X,6XSW_L_light+6XSW_H_heavy,"1396_LYS, 1399_ASP, 1400_GLN, 1401_ARG, 1402_CYS, 1403_ASP, 1404_ARG, 1417_ASP, 1418_CYS, 1420_LEU, 1421_SER, 1519_LEU, 1520_LEU, 1524_GLU, 1560_HIS, 1577_GLU, 1579_ILE, 1616_SER, 1621_LEU, 1622_ASP, 1623_PHE, 1624_PRO, 1626_PRO, 1627_LEU, 1628_ARG",2.98
+6XSW_F,6XSW_E_light+6XSW_D_heavy,"1399_ASP, 1400_GLN, 1401_ARG, 1402_CYS, 1404_ARG, 1416_GLY, 1417_ASP, 1418_CYS, 1420_LEU, 1421_SER, 1519_LEU, 1520_LEU, 1524_GLU, 1560_HIS, 1577_GLU, 1579_ILE, 1621_LEU, 1622_ASP, 1623_PHE, 1624_PRO, 1625_TYR, 1626_PRO, 1627_LEU, 1628_ARG",2.98
+7THO_A,7THO_L_light+7THO_H_heavy,"117_GLU, 149_ASN, 154_ILE, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.75
+7THO_C,7THO_F_light+7THO_E_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.75
+7L0L_E,7L0L_J_light+7L0L_G_heavy,"18_VAL, 19_ASP, 20_GLY, 21_TRP, 38_LYS, 41_THR, 42_GLN, 45_PHE, 49_THR, 52_VAL, 56_ILE",2.85
+7L0L_B,7L0L_L_light+7L0L_H_heavy,"18_VAL, 19_ASP, 20_GLY, 21_TRP, 38_LYS, 41_THR, 42_GLN, 45_PHE, 49_THR, 52_VAL, 56_ILE",2.85
+7L0L_F,7L0L_K_light+7L0L_I_heavy,"18_VAL, 19_ASP, 20_GLY, 21_TRP, 38_LYS, 41_THR, 42_GLN, 45_PHE, 49_THR, 52_VAL, 56_ILE",2.85
+6ID4_B,6ID4_L_light+6ID4_H_heavy,"34_ASP, 35_ILE, 36_GLU, 45_ARG, 83_ASN, 85_VAL",2.4
+6ID4_A,6ID4_L_light+6ID4_H_heavy,"14_ARG, 15_PRO, 16_GLY, 17_ARG, 18_GLY, 19_GLU, 39_ASP, 40_ALA, 41_ALA, 43_GLN, 88_SER, 90_ASP, 92_SER",2.4
+6ID4_F,6ID4_D_light+6ID4_C_heavy,"34_ASP, 35_ILE, 36_GLU, 45_ARG, 83_ASN, 84_HIS, 85_VAL",2.4
+6ID4_E,6ID4_D_light+6ID4_C_heavy,"14_ARG, 15_PRO, 16_GLY, 17_ARG, 18_GLY, 19_GLU, 39_ASP, 40_ALA, 41_ALA, 88_SER, 90_ASP, 92_SER",2.4
+1JRH_I,1JRH_L_light+1JRH_H_heavy,"47_LYS, 49_TYR, 50_GLY, 51_VAL, 52_LYS, 53_ASN, 54_SER, 55_GLU",2.8
+8E6J_D,8E6J_K_light+8E6J_G_heavy,"283_ASP, 356_TYR, 357_ASP, 358_ASN, 359_GLY, 361_TRP, 378_TRP, 380_PRO, 415_LEU, 416_ASP, 417_CYS, 450_ASN, 90_SER, 91_LEU, 92_CYS, 93_PRO, 94_ILE, 95_SER",2.7
+8E6J_A,8E6J_L_light+8E6J_H_heavy,"283_ASP, 356_TYR, 357_ASP, 358_ASN, 359_GLY, 361_TRP, 378_TRP, 380_PRO, 415_LEU, 416_ASP, 417_CYS, 450_ASN, 90_SER, 91_LEU, 92_CYS, 93_PRO, 94_ILE, 95_SER",2.7
+8E6J_B,8E6J_I_light+8E6J_E_heavy,"283_ASP, 356_TYR, 357_ASP, 358_ASN, 359_GLY, 361_TRP, 378_TRP, 380_PRO, 415_LEU, 416_ASP, 417_CYS, 450_ASN, 90_SER, 91_LEU, 92_CYS, 93_PRO, 94_ILE, 95_SER",2.7
+8E6J_C,8E6J_J_light+8E6J_F_heavy,"283_ASP, 356_TYR, 357_ASP, 358_ASN, 359_GLY, 361_TRP, 378_TRP, 380_PRO, 415_LEU, 416_ASP, 417_CYS, 450_ASN, 90_SER, 91_LEU, 92_CYS, 93_PRO, 94_ILE, 95_SER",2.7
+8HWS_A,8HWS_L_light+8HWS_H_heavy,"415_THR, 417_ASN, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 459_SER, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_ASN, 487_ASN, 489_TYR, 493_GLN, 501_TYR, 502_GLY, 505_HIS",2.36
+8HWS_A,8HWS_E_light+8HWS_D_heavy,"369_TYR, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 384_PRO, 385_THR, 386_LYS, 390_LEU, 412_PRO, 427_ASP, 429_PHE",2.36
+8HWS_A,8HWS_J_light+8HWS_I_heavy,"334_ASN, 335_LEU, 337_PRO, 339_ASP, 340_GLU, 343_ASN, 344_ALA, 345_THR, 346_ARG, 356_LYS, 359_SER, 441_LEU",2.36
+6M3B_A,6M3B_B_light+6M3B_C_heavy,"169_SER, 170_GLU, 171_VAL, 172_MET, 173_SER, 174_ASN, 175_MET, 190_ALA, 191_CYS, 192_GLU, 195_SER, 213_VAL, 214_SER, 215_TRP, 216_GLY, 217_GLU, 218_GLY, 60_ASP, 96_LYS, 97_SER, 98_THR, 99_THR",2.2
+5X2Q_C,5X2Q_K_light+5X2Q_J_heavy,"194_GLY, 197_LEU, 198_ASN, 200_ASN, 201_TRP, 202_ARG, 203_TRP, 225_ILE, 226_GLU, 227_ASP, 228_SER, 229_GLU, 230_ILE, 255_ASN, 455_HIS, 456_LEU, 460_LYS",2.6
+5X2Q_A,5X2Q_L_light+5X2Q_H_heavy,"194_GLY, 197_LEU, 198_ASN, 200_ASN, 201_TRP, 202_ARG, 203_TRP, 225_ILE, 226_GLU, 227_ASP, 228_SER, 229_GLU, 230_ILE, 255_ASN, 456_LEU, 460_LYS",2.6
+6NZ7_F,6NZ7_I_light+6NZ7_G_heavy,"18_ILE, 19_ASP, 20_GLY, 21_TRP, 42_GLN, 46_ASP, 48_ILE, 49_ASN, 50_GLY, 53_ASN",2.95
+6NZ7_B,6NZ7_L_light+6NZ7_H_heavy,"18_ILE, 19_ASP, 20_GLY, 21_TRP, 42_GLN, 45_ILE, 46_ASP, 48_ILE, 49_ASN, 50_GLY, 53_ASN",2.95
+6NZ7_E,6NZ7_I_light+6NZ7_G_heavy,"18_HIS, 280_GLU, 290_ASN, 318_THR, 38_ASN",2.95
+2ADF_A,2ADF_L_light+2ADF_H_heavy,"1009_ASP, 1012_GLY, 1013_PHE, 1016_ARG, 1017_TYR, 1022_MET, 1023_HIS, 975_ILE, 976_THR, 977_THR, 978_ILE, 979_ASP, 981_PRO, 982_TRP, 983_ASN",1.9
+4QCI_D,4QCI_A_light+4QCI_B_heavy,"40_TRP, 71_GLN, 73_ARG, 75_ILE, 76_GLU, 77_ILE, 79_ARG, 80_LYS, 81_LYS, 82_PRO, 83_ILE, 84_PHE, 85_LYS, 86_LYS",2.3
+4QCI_C,4QCI_L_light+4QCI_H_heavy,"38_LEU, 39_VAL, 40_TRP, 71_GLN, 73_ARG, 75_ILE, 77_ILE, 79_ARG, 80_LYS, 81_LYS, 82_PRO, 83_ILE, 84_PHE, 85_LYS, 86_LYS",2.3
+8BCZ_R,8BCZ_L_light+8BCZ_H_heavy,"403_ARG, 405_ASP, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 459_SER, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 501_ASN, 502_GLY, 505_TYR",2.9
+8BCZ_R,8BCZ_D_light+8BCZ_C_heavy,"353_TRP, 354_ASN, 355_ARG, 356_LYS, 357_ARG, 359_SER, 360_ASN, 394_ASN, 396_TYR, 426_PRO, 428_ASP, 462_LYS, 463_PRO, 464_PHE, 465_GLU, 466_ARG, 468_ILE, 516_GLU, 518_LEU, 519_HIS",2.9
+8BCZ_R,8BCZ_F_light+8BCZ_E_heavy,"366_SER, 369_TYR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 389_ASP, 390_LEU, 412_PRO, 427_ASP, 428_ASP, 429_PHE, 517_LEU",2.9
+8BCZ_R,8BCZ_B_light+8BCZ_A_heavy,"345_THR, 346_ARG, 351_TYR, 440_ASN, 441_LEU, 443_SER, 444_LYS, 445_VAL, 446_GLY, 449_TYR, 450_ASN, 452_ARG, 470_THR, 482_GLY, 484_GLU, 490_PHE",2.9
+7CJ2_A,7CJ2_D_light+7CJ2_C_heavy,"104_PRO, 105_PHE, 108_THR, 109_HIS, 12_GLN, 13_TYR, 14_ARG, 16_GLY, 19_SER, 21_PHE, 23_ASP, 41_SER, 42_ASN, 46_ASP, 50_TRP, 51_ASN, 54_THR, 55_LEU, 58_MET",2.7
+7CJ2_B,7CJ2_L_light+7CJ2_K_heavy,"104_PRO, 105_PHE, 108_THR, 109_HIS, 12_GLN, 13_TYR, 14_ARG, 16_GLY, 19_SER, 21_PHE, 23_ASP, 41_SER, 42_ASN, 46_ASP, 50_TRP, 51_ASN, 53_VAL, 54_THR, 55_LEU, 58_MET",2.7
+6OC7_C,6OC7_L_light+6OC7_H_heavy,"11_THR, 12_LEU, 13_LYS, 14_GLY, 15_GLU, 16_THR, 17_THR, 33_TYR, 35_ASN, 36_ASP, 37_ASN, 38_GLY",1.3
+9DF0_A,9DF0_L_light+9DF0_H_heavy,"313_SER, 315_HIS, 316_MET, 317_ASP, 318_PHE, 319_GLY, 320_GLU, 324_ASP, 350_THR, 351_GLY, 394_TYR",2.8
+1XF5_L,1XF5_A_light+1XF5_B_heavy,"33_LYS, 34_ILE, 35_GLN, 36_THR, 37_ALA, 38_GLU, 39_PHE, 40_LYS, 52_ARG, 53_TYR, 56_LEU, 57_LEU",2.6
+1XF5_M,1XF5_C_light+1XF5_D_heavy,"17_PRO, 19_GLU, 33_LYS, 34_ILE, 35_GLN, 36_THR, 37_ALA, 38_GLU, 39_PHE, 40_LYS, 49_GLU, 53_TYR, 56_LEU",2.6
+6E62_A,6E62_C_light+6E62_B_heavy,"345_PRO, 346_GLY, 347_ASP, 349_ILE, 350_PRO, 351_ASP, 355_GLN, 364_LEU, 365_GLU, 366_PRO, 367_SER, 369_ILE, 371_TYR, 392_LYS, 394_LYS, 411_ILE, 413_LYS, 415_ASP, 416_LYS",2.7
+6E62_P,6E62_L_light+6E62_H_heavy,"346_GLY, 347_ASP, 349_ILE, 351_ASP, 355_GLN, 364_LEU, 365_GLU, 366_PRO, 367_SER, 369_ILE, 371_TYR, 390_ASP, 392_LYS, 394_LYS, 411_ILE, 413_LYS, 414_LYS, 415_ASP, 416_LYS",2.7
+3THM_F,3THM_L_light+3THM_H_heavy,"36_LEU, 39_ASP, 40_GLY, 41_GLN, 42_PHE, 43_CYS, 44_HIS, 45_LYS, 46_PRO, 47_CYS, 48_PRO, 49_PRO, 59_VAL, 60_ASN, 78_LYS, 79_ALA, 80_HIS, 81_PHE, 86_ARG",2.1
+8DB4_F,8DB4_H_light+8DB4_G_heavy,"111_GLN, 112_ILE, 114_GLU, 115_ASN, 34_GLN, 35_SER, 37_LEU, 38_GLU, 39_ARG, 40_ALA, 42_LEU, 43_ARG, 46_GLU",2.3
+8DB4_F,8DB4_J_light+8DB4_I_heavy,"100_ASN, 102_GLN, 131_ARG, 134_ARG, 138_GLN, 142_LEU, 143_ARG, 144_ALA, 145_PRO, 146_GLN, 147_ARG, 148_CYS",2.3
+8DB4_E,8DB4_B_light+8DB4_A_heavy,"111_GLN, 112_ILE, 114_GLU, 115_ASN, 118_ASP, 119_ARG, 34_GLN, 35_SER, 37_LEU, 38_GLU, 39_ARG, 40_ALA, 41_ASN, 42_LEU, 43_ARG, 46_GLU",2.3
+8DB4_E,8DB4_D_light+8DB4_C_heavy,"100_ASN, 102_GLN, 131_ARG, 134_ARG, 138_GLN, 142_LEU, 143_ARG, 144_ALA, 145_PRO, 146_GLN, 147_ARG, 148_CYS",2.3
+1UAC_Y,1UAC_L_light+1UAC_H_heavy,"100_SER, 101_GLY, 102_GLY, 15_LEU, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 62_TRP, 63_TRP, 73_LYS, 75_LEU, 77_ASN, 89_THR, 93_ASN, 96_LYS, 97_LYS, 98_ILE",1.7
+7KF0_V,7KF0_L_light+7KF0_H_heavy,"48_LYS, 81_MET, 83_ILE, 84_LYS, 85_PRO, 86_HIS, 87_GLN, 88_GLY, 89_GLN, 90_HIS, 91_ILE, 92_GLY, 93_GLU",2.32
+7KF0_C,7KF0_B_light+7KF0_A_heavy,"48_LYS, 81_MET, 83_ILE, 84_LYS, 85_PRO, 86_HIS, 87_GLN, 88_GLY, 89_GLN, 90_HIS, 91_ILE, 92_GLY, 93_GLU",2.32
+2NY2_A,2NY2_C_light+2NY2_D_heavy,"119_CYS, 120_VAL, 122_LEU, 200_VAL, 202_THR, 203_GLN, 419_ARG, 421_LYS, 422_GLN, 423_ILE, 434_MET, 437_PRO",2.0
+7YD1_C,7YD1_B_light+7YD1_A_heavy,"345_THR, 439_ASN, 440_LYS, 441_LEU, 443_SER, 444_LYS, 445_VAL, 446_SER, 447_GLY, 450_ASN, 498_ARG, 499_PRO, 500_THR, 502_GLY, 506_GLN",2.99
+8R1D_C,8R1D_E_light+8R1D_D_heavy,"532_LYS, 551_GLU, 552_SER, 553_ASN, 554_LYS, 555_LYS, 556_PHE, 574_ARG, 578_THR, 579_LEU, 580_GLU, 581_ILE",2.37
+8R1D_B,8R1D_G_light+8R1D_F_heavy,"532_LYS, 551_GLU, 552_SER, 554_LYS, 555_LYS, 556_PHE, 574_ARG, 578_THR, 579_LEU, 580_GLU, 581_ILE",2.37
+8R1D_A,8R1D_L_light+8R1D_H_heavy,"532_LYS, 551_GLU, 552_SER, 553_ASN, 554_LYS, 555_LYS, 556_PHE, 558_PRO, 574_ARG, 578_THR, 579_LEU, 580_GLU, 581_ILE",2.37
+5J9P_C,5J9P_B_light+5J9P_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 63_PRO, 64_ARG",2.85
+1NCC_N,1NCC_L_light+1NCC_H_heavy,"326_PRO, 327_ARG, 328_PRO, 329_ASN, 343_GLY, 344_ASN, 345_ASN, 347_ASN, 366_ILE, 367_SER, 368_ARG, 369_ALA, 370_SER, 372_SER, 399_LEU, 400_ASN, 401_THR, 402_ASP, 403_TRP, 431_PRO, 432_LYS",2.5
+8TRT_D,8TRT_C_light+8TRT_B_heavy,"278_PRO, 279_GLY, 280_PHE, 281_PHE, 288_SER, 289_PRO, 291_LEU, 292_GLU, 293_CYS, 294_PRO, 295_GLU, 296_HIS, 297_THR, 298_LEU, 299_PRO, 310_GLU, 313_PHE, 322_SER",3.0
+8TRT_E,8TRT_G_light+8TRT_A_heavy,"277_SER, 278_PRO, 279_GLY, 280_PHE, 288_SER, 289_PRO, 291_LEU, 292_GLU, 293_CYS, 294_PRO, 295_GLU, 296_HIS, 297_THR, 298_LEU, 299_PRO, 310_GLU, 313_PHE, 322_SER",3.0
+6MEH_C,6MEH_L_light+6MEH_H_heavy,"421_HIS, 427_LEU, 429_CYS, 430_ASN, 431_ASP, 432_SER, 434_HIS, 435_THR, 436_GLY, 437_PHE, 438_LEU, 439_ALA, 440_ALA, 442_PHE, 443_TYR, 445_HIS, 446_LYS, 447_PHE, 448_ASN, 529_TRP, 531_GLU",1.99
+3TJE_F,3TJE_L_light+3TJE_H_heavy,"39_ASP, 40_GLY, 41_GLN, 42_PHE, 43_CYS, 44_HIS, 45_LYS, 46_PRO, 47_CYS, 48_PRO, 49_PRO, 59_VAL, 60_ASN, 75_TYR, 77_ASP, 78_LYS, 79_ALA, 80_HIS, 81_PHE, 86_ARG",1.93
+6NNF_G,6NNF_L_light+6NNF_H_heavy,"322_ILE, 323_ILE, 324_GLY, 325_ASP, 326_ILE, 327_ARG, 328_GLN, 330_HIS, 415_THR, 417_PRO",2.76
+6NNF_G,6NNF_V_light+6NNF_U_heavy,"198_THR, 276_ASN, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 283_ASN, 365_SER, 366_GLY, 367_GLY, 368_ASP, 371_VAL, 428_GLN, 430_ILE, 455_THR, 456_ARG, 457_ASP, 462_ASN, 465_THR, 466_GLU, 467_THR, 469_ARG, 473_GLY",2.76
+7E7Y_E,7E7Y_D_light+7E7Y_C_heavy,"475_ALA, 476_GLY, 477_SER, 478_THR, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 488_CYS, 489_TYR",2.41
+7E7Y_R,7E7Y_B_light+7E7Y_A_heavy,"456_PHE, 473_TYR, 475_ALA, 476_GLY, 477_SER, 478_THR, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 488_CYS, 489_TYR",2.41
+7KI6_A,7KI6_L_light+7KI6_H_heavy,"160_LYS, 166_GLU, 244_ARG, 246_LEU, 247_GLY, 248_TYR, 249_ALA, 250_THR, 251_GLU, 282_PHE, 284_ILE, 51_ASN, 52_PRO, 53_LEU, 54_THR, 55_LYS",2.8
+7KI6_B,7KI6_C_light+7KI6_D_heavy,"160_LYS, 166_GLU, 244_ARG, 246_LEU, 247_GLY, 248_TYR, 249_ALA, 250_THR, 251_GLU, 282_PHE, 284_ILE, 51_ASN, 52_PRO, 53_LEU, 54_THR, 55_LYS",2.8
+7KI6_E,7KI6_F_light+7KI6_G_heavy,"160_LYS, 166_GLU, 244_ARG, 246_LEU, 247_GLY, 248_TYR, 249_ALA, 250_THR, 251_GLU, 282_PHE, 284_ILE, 51_ASN, 52_PRO, 53_LEU, 54_THR, 55_LYS",2.8
+2H8P_C,2H8P_B_light+2H8P_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.25
+8HES_C,8HES_L_light+8HES_H_heavy,"403_ARG, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 449_TYR, 452_LEU, 453_TYR, 455_LEU, 456_PHE, 475_ALA, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 488_CYS, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER, 505_TYR",2.2
+6XQ2_D,6XQ2_E_light+6XQ2_F_heavy,"100_TYR, 101_ASP, 102_VAL, 105_TYR, 219_PHE, 220_ARG, 221_PRO, 222_ARG, 223_ILE, 224_ARG, 225_ASN, 229_ARG, 230_ILE, 269_ARG, 91_SER, 92_LYS, 93_ALA, 95_SER, 99_PRO",3.0
+6XQ2_A,6XQ2_B_light+6XQ2_C_heavy,"100_TYR, 101_ASP, 102_VAL, 103_PRO, 105_TYR, 65_THR, 91_SER, 93_ALA, 95_SER, 99_PRO",3.0
+1KYO_P,1KYO_V_light+1KYO_U_heavy,"123_PRO, 124_HIS, 126_ILE, 127_GLN, 130_ASN, 131_SER, 132_VAL, 133_ASP, 134_MET, 142_THR, 143_ASP, 144_ALA, 147_VAL, 148_LYS, 149_ASP, 150_PRO, 151_GLN",2.97
+1KYO_E,1KYO_K_light+1KYO_J_heavy,"123_PRO, 126_ILE, 127_GLN, 130_ASN, 131_SER, 132_VAL, 133_ASP, 134_MET, 142_THR, 143_ASP, 144_ALA, 147_VAL, 148_LYS, 149_ASP, 150_PRO, 151_GLN",2.97
+7UKT_A,7UKT_L_light+7UKT_H_heavy,"117_GLU, 149_ASN, 154_ILE, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.37
+7UKT_C,7UKT_F_light+7UKT_E_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.37
+6M38_A,6M38_L_light+6M38_H_heavy,"337_TYR, 498_TYR, 502_ARG, 505_GLU, 506_ASP, 508_ARG, 509_ASP, 511_ILE, 512_GLY, 513_PHE, 514_PRO, 598_ARG, 599_ASP, 90_HIS",3.0
+7UJD_A,7UJD_E_light+7UJD_F_heavy,"394_ASP, 395_GLY, 396_ASN, 423_ASP, 426_LEU, 427_THR, 430_ASP, 431_LYS, 434_TYR, 461_PRO, 464_ALA, 465_LEU, 467_SER, 468_ASP",2.5
+7S0X_F,7S0X_D_light+7S0X_C_heavy,"314_LYS, 317_LYS, 318_GLU, 320_LEU, 321_ASN, 324_GLN, 327_LEU, 353_LYS, 354_PRO, 355_LYS, 356_ASP, 357_GLU, 358_LEU, 359_ASP, 360_TYR",2.8
+7S0X_E,7S0X_L_light+7S0X_H_heavy,"314_LYS, 317_LYS, 318_GLU, 320_LEU, 321_ASN, 324_GLN, 327_LEU, 353_LYS, 354_PRO, 355_LYS, 356_ASP, 357_GLU, 358_LEU, 360_TYR, 364_ILE",2.8
+7X08_C,7X08_N_light+7X08_J_heavy,"456_PHE, 475_ALA, 476_GLY, 477_SER, 478_THR, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR",2.7
+7X08_B,7X08_M_light+7X08_I_heavy,"456_PHE, 475_ALA, 476_GLY, 477_SER, 478_THR, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR",2.7
+7X08_A,7X08_L_light+7X08_H_heavy,"456_PHE, 475_ALA, 476_GLY, 477_SER, 478_THR, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR",2.7
+8VVM_I,8VVM_G_light+8VVM_A_heavy,"101_THR, 102_ALA, 103_ALA, 105_GLY, 107_PRO, 110_HIS, 112_VAL, 54_SER, 58_GLN, 60_GLU, 61_ASP, 62_GLU, 63_PRO, 64_TRP, 66_LEU, 95_PRO, 97_GLU, 99_ARG",2.9
+8VVM_I,8VVM_C_light+8VVM_B_heavy,"149_PRO, 32_ARG, 34_GLU, 65_LYS, 86_PRO, 87_THR, 88_ALA, 91_SER, 92_SER",2.9
+7U2E_A,7U2E_L_light+7U2E_H_heavy,"405_ASP, 408_ARG, 498_GLN, 500_THR, 501_ASN, 502_GLY, 503_VAL, 504_GLY, 505_TYR, 506_GLN",2.85
+4YXH_A,4YXH_L_light+4YXH_H_heavy,"130_LEU, 132_HIS, 133_PHE, 134_GLY, 135_ASN, 136_ASP, 137_TYR, 138_GLU, 139_ASP, 196_LYS, 200_ARG, 204_GLN",2.7
+6LXI_B,6LXI_D_light+6LXI_C_heavy,"118_ARG, 149_VAL, 150_LYS, 151_ASP, 152_ARG, 179_TRP, 180_SER, 223_ILE, 228_GLU, 293_ARG, 344_ASN, 368_ARG, 402_TYR, 427_ILE, 430_GLN, 431_PRO, 432_LYS, 434_ASN",2.5
+6LXI_A,6LXI_L_light+6LXI_H_heavy,"118_ARG, 149_VAL, 150_LYS, 151_ASP, 152_ARG, 179_TRP, 180_SER, 222_ASN, 223_ILE, 228_GLU, 293_ARG, 343_ALA, 344_ASN, 368_ARG, 402_TYR, 430_GLN, 431_PRO, 432_LYS",2.5
+7X28_G,7X28_K_light+7X28_H_heavy,"502_LYS, 506_LEU, 510_ASP, 511_ARG, 513_GLU, 535_TRP, 536_GLU, 538_GLY, 539_ASP, 540_TYR, 541_TYR, 542_ARG, 553_TRP, 555_VAL, 557_SER",2.49
+7X28_F,7X28_D_light+7X28_C_heavy,"502_LYS, 506_LEU, 510_ASP, 511_ARG, 512_THR, 513_GLU, 535_TRP, 536_GLU, 538_GLY, 539_ASP, 540_TYR, 541_TYR, 542_ARG, 553_TRP, 555_VAL, 557_SER",2.49
+1G7L_C,1G7L_A_light+1G7L_B_heavy,"102_GLY, 116_LYS, 117_GLY, 118_THR, 119_ASP, 120_VAL, 121_GLN, 124_ILE, 125_ARG, 18_ASP, 19_ASN, 22_GLY, 23_TYR, 24_SER, 27_ASN",2.0
+7E86_C,7E86_B_light+7E86_A_heavy,"403_ARG, 405_ASP, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 484_GLU, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 493_GLN, 494_SER, 496_GLY, 498_GLN, 501_ASN, 502_GLY, 505_TYR",2.9
+8YX9_D,8YX9_F_light+8YX9_E_heavy,"30_GLN, 36_GLN, 37_CYS, 38_CYS, 39_SER, 49_SER, 52_THR, 53_GLU, 54_PHE, 55_THR, 56_GLU, 58_GLU",2.8
+8YX9_K,8YX9_I_light+8YX9_G_heavy,"30_GLN, 36_GLN, 37_CYS, 38_CYS, 39_SER, 49_SER, 52_THR, 53_GLU, 54_PHE, 55_THR, 56_GLU, 58_GLU",2.8
+8YX9_J,8YX9_C_light+8YX9_B_heavy,"30_GLN, 36_GLN, 37_CYS, 38_CYS, 39_SER, 49_SER, 52_THR, 53_GLU, 54_PHE, 55_THR, 56_GLU, 58_GLU",2.8
+8YX9_A,8YX9_L_light+8YX9_H_heavy,"30_GLN, 36_GLN, 37_CYS, 38_CYS, 39_SER, 49_SER, 52_THR, 53_GLU, 54_PHE, 55_THR, 56_GLU, 58_GLU",2.8
+2ITD_C,2ITD_B_light+2ITD_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.7
+4I9W_A,4I9W_D_light+4I9W_E_heavy,"69_ARG, 70_GLU, 73_LEU, 74_ARG, 76_HIS, 77_PRO, 78_CYS, 79_VAL, 80_SER, 81_ASP, 82_GLN, 83_GLU",2.75
+4I9W_B,4I9W_F_light+4I9W_G_heavy,"69_ARG, 70_GLU, 73_LEU, 74_ARG, 76_HIS, 77_PRO, 78_CYS, 79_VAL, 80_SER, 81_ASP, 82_GLN, 83_GLU",2.75
+7Z0X_R,7Z0X_L_light+7Z0X_H_heavy,"456_PHE, 475_ALA, 476_GLY, 477_SER, 478_THR, 479_PRO, 481_ASN, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 488_CYS, 489_TYR, 490_PHE",1.8
+2HFG_R,2HFG_L_light+2HFG_H_heavy,"23_GLU, 24_CYS, 25_PHE, 26_ASP, 27_PRO, 28_LEU, 29_VAL, 31_HIS, 33_VAL, 34_ALA, 35_CYS, 36_GLY, 37_LEU, 38_LEU, 39_ARG",2.61
+4F15_D,4F15_F_light+4F15_E_heavy,"112_GLU, 113_ARG, 114_PHE, 115_GLU, 118_PRO, 120_THR, 121_SER, 163_LYS, 164_SER, 166_ILE, 169_LYS, 253_TYR, 72_THR",2.81
+4F15_G,4F15_I_light+4F15_H_heavy,"112_GLU, 113_ARG, 114_PHE, 115_GLU, 118_PRO, 120_THR, 121_SER, 163_LYS, 164_SER, 166_ILE, 253_TYR, 72_THR",2.81
+4F15_J,4F15_L_light+4F15_K_heavy,"112_GLU, 113_ARG, 114_PHE, 115_GLU, 116_ILE, 118_PRO, 120_THR, 121_SER, 146_LYS, 163_LYS, 164_SER, 253_TYR, 72_THR, 73_ALA",2.81
+4F15_A,4F15_C_light+4F15_B_heavy,"112_GLU, 113_ARG, 114_PHE, 115_GLU, 118_PRO, 120_THR, 121_SER, 163_LYS, 164_SER, 166_ILE, 253_TYR",2.81
+7KQ7_B,7KQ7_L_light+7KQ7_H_heavy,"129_TYR, 130_MET, 33_GLN, 34_ASP, 35_GLN, 36_TYR, 37_GLU, 41_ASP, 68_HIS, 69_PHE, 70_MET, 72_ASP, 73_ASP, 92_SER, 93_PHE, 94_LEU",2.2
+4YWG_G,4YWG_L_light+4YWG_H_heavy,"153_ARG, 154_VAL, 175_THR, 176_PHE, 177_TYR, 179_LEU, 180_ASP, 194_ILE, 229_ASP, 230_ASP, 231_ALA, 232_THR",3.0
+4YWG_Q,4YWG_M_light+4YWG_I_heavy,"152_THR, 153_ARG, 154_VAL, 175_THR, 176_PHE, 177_TYR, 179_LEU, 180_ASP, 194_ILE, 230_ASP, 231_ALA, 232_THR",3.0
+6W0G_C,6W0G_B_light+6W0G_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.6
+1ZWI_C,1ZWI_B_light+1ZWI_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.5
+7QNW_E,7QNW_L_light+7QNW_H_heavy,"366_SER, 369_TYR, 370_ASN, 373_PRO, 375_PHE, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 389_ASP, 390_LEU, 412_PRO, 414_GLN, 427_ASP, 428_ASP, 429_PHE",2.4
+7QNW_E,7QNW_B_light+7QNW_A_heavy,"405_ASP, 408_ARG, 439_ASN, 440_LYS, 443_SER, 445_VAL, 498_ARG, 499_PRO, 500_THR, 501_TYR, 502_GLY, 503_VAL, 504_GLY, 505_HIS",2.4
+7YV1_A,7YV1_L_light+7YV1_H_heavy,"21_ILE, 25_GLN, 27_HIS, 28_PHE, 29_VAL, 30_ASP, 31_GLU, 32_TYR, 33_ASP, 34_PRO, 36_ILE, 38_ASP, 40_TYR",1.45
+7F6Z_R,7F6Z_L_light+7F6Z_H_heavy,"403_ARG, 417_ASN, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 493_GLN, 494_SER, 501_TYR, 502_GLY, 505_TYR",3.0
+7R6W_R,7R6W_B_light+7R6W_A_heavy,"333_THR, 334_ASN, 335_LEU, 337_PRO, 339_GLY, 340_GLU, 343_ASN, 344_ALA, 345_THR, 356_LYS, 357_ARG, 361_CYS",1.83
+7R6W_R,7R6W_L_light+7R6W_H_heavy,"369_TYR, 371_SER, 372_ALA, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 382_VAL, 383_SER, 384_PRO, 403_ARG, 404_GLY, 405_ASP, 408_ARG, 501_ASN, 502_GLY, 503_VAL, 504_GLY, 505_TYR, 508_TYR",1.83
+6NYQ_C,6NYQ_L_light+6NYQ_H_heavy,"101_PRO, 242_GLY, 243_PRO, 244_ASP, 305_SER, 307_LEU, 308_ALA, 309_SER, 313_HIS, 318_GLN, 323_SER, 324_GLN, 325_ASN, 326_ASN",1.85
+7JKT_G,7JKT_L_light+7JKT_H_heavy,"276_ASN, 278_THR, 279_ASP, 280_ASN, 281_ALA, 365_SER, 366_GLY, 367_GLY, 368_ASP, 370_GLU, 371_ILE, 425_ASN, 426_MET, 455_THR, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_SER, 461_SER, 468_THR, 470_ARG, 474_GLY",2.75
+3WFD_C,3WFD_L_light+3WFD_H_heavy,"101_ILE, 105_ASN, 106_VAL, 107_PRO, 109_ARG, 94_PHE, 97_ALA, 98_TRP",2.3
+3WFD_B,3WFD_L_light+3WFD_H_heavy,"421_GLY, 423_ALA, 424_MET, 425_THR, 427_MET, 428_ALA, 431_ASP",2.3
+7KPJ_E,7KPJ_B_light+7KPJ_A_heavy,"123_LEU, 128_ASP, 131_THR, 132_GLU, 34_ASP, 36_SER, 37_CYS, 58_CYS, 59_ILE, 60_ILE, 61_ARG, 62_ASP",2.1
+7KPJ_F,7KPJ_D_light+7KPJ_C_heavy,"110_ARG, 123_LEU, 128_ASP, 132_GLU, 34_ASP, 36_SER, 37_CYS, 58_CYS, 59_ILE, 61_ARG, 62_ASP",2.1
+6U2F_A,6U2F_L_light+6U2F_H_heavy,"535_SER, 536_VAL, 537_HIS, 538_THR, 539_ALA, 540_PRO, 541_PRO, 542_ALA, 543_GLU, 544_ALA, 545_SER, 546_MET, 547_GLY, 548_THR, 549_ARG, 550_VAL, 551_HIS, 553_HIS, 554_GLN, 555_GLN, 557_HIS, 567_GLU, 568_VAL, 569_GLU, 593_GLU, 595_SER",2.94
+5T5F_A,5T5F_L_light+5T5F_H_heavy,"115_GLN, 117_SER, 118_GLU, 119_HIS, 121_GLY, 122_LYS, 123_MET, 82_ILE, 84_VAL, 85_ASP, 87_GLN, 89_ILE, 91_LEU",2.98
+7A3U_A,7A3U_L_light+7A3U_H_heavy,"140_MET, 148_HIS, 150_GLY, 151_MET, 152_ILE, 278_ASP, 283_ARG, 2_ARG, 315_THR, 316_LYS, 370_GLU, 371_ASN, 373_LYS, 44_GLU, 46_VAL",3.0
+5VAG_A,5VAG_B_light+5VAG_C_heavy,"109_LYS, 111_VAL, 112_ASN, 114_GLU, 115_ALA, 118_GLN, 119_ILE, 122_GLU, 184_PRO, 217_ASN, 218_TYR, 219_GLN, 220_GLN, 221_SER, 222_PHE, 242_HIS, 243_TRP, 244_LEU, 245_MET, 247_ASN, 269_LEU",1.9
+5UEM_G,5UEM_L_light+5UEM_H_heavy,"102_GLU, 122_LEU, 123_THR, 124_GLY, 198_GLY, 200_VAL, 279_ASN, 280_ASN, 282_LYS, 367_GLY, 368_ASP, 371_ILE, 429_GLY, 430_THR, 431_GLY, 473_GLY, 474_ASN, 476_LYS, 97_LYS",2.7
+7DK2_I,7DK2_H_light+7DK2_G_heavy,"403_ARG, 405_ASP, 406_GLU, 409_GLN, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 449_TYR, 453_TYR, 455_LEU, 456_PHE, 473_TYR, 475_ALA, 476_GLY, 477_SER, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",3.0
+7DK2_F,7DK2_E_light+7DK2_D_heavy,"403_ARG, 405_ASP, 406_GLU, 408_ARG, 409_GLN, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 449_TYR, 455_LEU, 456_PHE, 473_TYR, 475_ALA, 476_GLY, 477_SER, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 496_GLY, 498_GLN, 501_ASN, 502_GLY, 505_TYR",3.0
+7DK2_C,7DK2_B_light+7DK2_A_heavy,"403_ARG, 405_ASP, 406_GLU, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 449_TYR, 455_LEU, 456_PHE, 473_TYR, 475_ALA, 476_GLY, 477_SER, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 496_GLY, 498_GLN, 501_ASN, 502_GLY, 505_TYR",3.0
+7DK2_L,7DK2_K_light+7DK2_J_heavy,"403_ARG, 405_ASP, 406_GLU, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 449_TYR, 455_LEU, 456_PHE, 473_TYR, 475_ALA, 476_GLY, 477_SER, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 496_GLY, 498_GLN, 501_ASN, 502_GLY, 505_TYR",3.0
+6JEP_E,6JEP_L_light+6JEP_H_heavy,"309_THR, 310_ALA, 311_ALA, 312_PHE, 313_THR, 331_GLN, 332_TYR, 333_ALA, 334_GLY, 335_THR, 336_ASP, 370_GLU, 371_ASN, 393_GLU, 394_LYS",2.32
+6JEP_F,6JEP_I_light+6JEP_K_heavy,"309_THR, 310_ALA, 311_ALA, 312_PHE, 313_THR, 331_GLN, 332_TYR, 333_ALA, 334_GLY, 335_THR, 336_ASP, 369_THR, 370_GLU, 371_ASN, 393_GLU, 394_LYS",2.32
+9B2C_A,9B2C_L_light+9B2C_H_heavy,"316_MET, 318_PHE, 319_GLY, 320_GLU, 321_ALA, 322_ARG, 393_THR, 394_TYR, 395_VAL, 396_TRP, 397_ASN, 398_TYR, 399_LEU, 400_LEU, 401_ARG",3.0
+7AMP_B,7AMP_L_light+7AMP_H_heavy,"116_GLU, 117_ASP, 119_ASN, 120_LYS, 223_GLU, 224_TRP, 225_THR, 226_GLN, 227_ASP, 228_ARG",2.64
+7TXW_A,7TXW_L_light+7TXW_H_heavy,"31_LYS, 32_ARG, 47_GLU, 48_ASN, 49_ASP, 50_LEU, 59_GLU, 60_GLU, 61_LYS, 62_VAL, 63_LEU, 64_LYS, 69_THR",2.17
+3SE9_G,3SE9_L_light+3SE9_H_heavy,"124_GLY, 275_GLU, 276_ASN, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 283_THR, 365_SER, 366_GLY, 367_GLY, 368_ASP, 371_ILE, 429_GLY, 431_GLY, 455_THR, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 461_ASN, 462_ASN, 469_ARG, 473_GLY, 476_LYS, 97_LYS",2.0
+8DCN_F,8DCN_E_light+8DCN_D_heavy,"810_LEU, 812_TYR, 813_SER, 816_LYS, 817_ASP, 821_LYS, 843_ARG, 846_PHE, 847_THR, 848_GLY, 849_GLY, 850_GLU",2.6
+8DCN_C,8DCN_B_light+8DCN_A_heavy,"810_LEU, 812_TYR, 813_SER, 816_LYS, 817_ASP, 821_LYS, 843_ARG, 845_TYR, 846_PHE, 847_THR, 848_GLY, 849_GLY, 850_GLU",2.6
+3MAC_A,3MAC_L_light+3MAC_H_heavy,"19_GLU, 208_ILE, 209_LYS, 212_GLN, 22_GLN, 23_HIS, 24_LEU, 26_GLN, 27_LEU, 28_THR, 30_TRP, 31_GLY, 33_LYS, 34_GLN, 35_LEU, 38_ARG, 51_MET, 54_ASP, 61_THR, 65_HIS, 68_ILE",2.5
+1UA6_Y,1UA6_L_light+1UA6_H_heavy,"100_SER, 101_ASP, 102_GLY, 15_HIS, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 62_TRP, 63_TRP, 73_ARG, 75_LEU, 89_THR, 93_ASN, 96_LYS, 97_LYS, 98_ILE",1.9
+5NMV_K,5NMV_L_light+5NMV_H_heavy,"16_LEU, 17_PRO, 19_LEU, 20_LYS, 21_LEU, 22_MET, 24_SER, 25_PHE, 35_CYS, 37_ALA, 39_MET, 41_ARG, 56_TYR, 57_GLY, 58_GLY, 59_CYS, 60_GLU, 61_GLY, 62_ASN, 63_GLN, 65_ARG, 66_PHE, 67_GLU, 68_SER, 71_GLU, 74_LYS, 75_MET",1.65
+8C2R_A,8C2R_E_light+8C2R_F_heavy,"403_ARG, 415_THR, 417_ASN, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_ASN, 478_LYS, 486_PHE, 487_ASN, 489_TYR, 493_ARG, 496_SER, 501_TYR, 505_HIS",2.56
+8C2R_B,8C2R_L_light+8C2R_R_heavy,"415_THR, 417_ASN, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_ASN, 486_PHE, 487_ASN, 489_TYR, 493_ARG, 496_SER, 501_TYR, 505_HIS",2.56
+3D85_C,3D85_A_light+3D85_B_heavy,"106_HIS, 133_PRO, 134_SER, 135_GLN, 136_PRO, 137_TRP, 139_ARG, 140_LEU, 82_GLU, 86_GLY, 87_SER, 88_ASP, 91_THR, 92_GLY, 93_GLU, 94_PRO, 95_SER",1.9
+1NCB_N,1NCB_L_light+1NCB_H_heavy,"326_PRO, 327_ARG, 328_PRO, 329_ASP, 343_GLY, 344_ASN, 347_ASN, 366_ILE, 367_SER, 368_ILE, 369_ALA, 370_SER, 372_SER, 399_LEU, 400_ASN, 401_THR, 402_ASP, 403_TRP, 431_PRO, 432_LYS",2.5
+8EUV_D,8EUV_J_light+8EUV_I_heavy,"512_ALA, 513_VAL, 514_GLY, 515_ILE, 516_GLY, 517_ALA, 518_VAL, 519_PHE, 520_LEU",2.6
+8EUV_A,8EUV_H_light+8EUV_G_heavy,"229_LYS, 80_ASN, 84_ILE, 85_HIS, 87_GLU",2.6
+8EUV_B,8EUV_H_light+8EUV_G_heavy,"512_ALA, 513_VAL, 514_GLY, 515_ILE, 516_GLY, 517_ALA, 518_VAL, 519_PHE, 520_LEU",2.6
+8EUV_F,8EUV_L_light+8EUV_K_heavy,"512_ALA, 513_VAL, 514_GLY, 515_ILE, 516_GLY, 517_ALA, 518_VAL, 519_PHE, 520_LEU",2.6
+8QYA_A,8QYA_L_light+8QYA_H_heavy,"13_TYR, 15_ASP, 16_SER, 17_LEU, 18_LEU, 19_HIS, 20_ALA, 21_CYS, 22_ILE, 23_PRO, 26_LEU, 27_ARG, 30_SER, 31_ASN, 32_THR, 34_PRO",2.72
+7AMR_B,7AMR_L_light+7AMR_H_heavy,"116_GLU, 117_ASP, 119_ASN, 120_LYS, 186_ASP, 188_ARG, 223_GLU, 224_TRP, 225_THR, 226_GLN, 227_ASP, 228_ARG",1.95
+6P3R_E,6P3R_D_light+6P3R_C_heavy,"102_GLY, 103_ASP, 104_PHE, 107_TYR, 111_LYS, 223_ALA, 224_THR, 225_ARG, 226_SER, 227_LYS, 228_VAL, 229_ASN, 234_ARG, 90_GLU, 91_LYS, 94_PRO",3.0
+6P3R_L,6P3R_B_light+6P3R_A_heavy,"102_GLY, 103_ASP, 104_PHE, 107_TYR, 222_ILE, 223_ALA, 224_THR, 225_ARG, 226_SER, 227_LYS, 228_VAL, 229_ASN, 234_ARG, 91_LYS, 92_ALA, 93_ASN, 97_ASP",3.0
+7KFW_B,7KFW_D_light+7KFW_C_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 484_GLU, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",2.79
+7KFW_A,7KFW_L_light+7KFW_H_heavy,"403_ARG, 408_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",2.79
+7KFW_E,7KFW_G_light+7KFW_F_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 496_GLY, 501_ASN, 502_GLY, 505_TYR",2.79
+8VGK_D,8VGK_L_light+8VGK_K_heavy,"107_GLU, 109_GLU, 110_GLU, 36_HIS, 38_TRP, 50_GLN, 60_GLY, 61_LEU, 62_ALA, 65_ARG, 81_GLN, 82_LEU, 83_LEU, 84_PRO, 85_VAL, 86_SER",2.4
+8VGK_A,8VGK_G_light+8VGK_H_heavy,"107_GLU, 109_GLU, 110_GLU, 36_HIS, 38_TRP, 50_GLN, 60_GLY, 61_LEU, 62_ALA, 65_ARG, 81_GLN, 82_LEU, 83_LEU, 84_PRO, 85_VAL, 86_SER",2.4
+8VGK_B,8VGK_I_light+8VGK_J_heavy,"107_GLU, 109_GLU, 110_GLU, 36_HIS, 38_TRP, 50_GLN, 60_GLY, 61_LEU, 62_ALA, 65_ARG, 81_GLN, 82_LEU, 83_LEU, 84_PRO, 85_VAL, 86_SER",2.4
+8VGK_C,8VGK_E_light+8VGK_F_heavy,"107_GLU, 109_GLU, 110_GLU, 36_HIS, 38_TRP, 50_GLN, 60_GLY, 61_LEU, 62_ALA, 65_ARG, 81_GLN, 82_LEU, 83_LEU, 84_PRO, 85_VAL, 86_SER",2.4
+5K9K_I,5K9K_B_light+5K9K_A_heavy,"18_HIS, 318_THR, 344_GLU, 345_GLY, 347_ILE, 348_ASP, 349_GLY, 350_TRP, 363_GLN, 364_ALA, 367_LEU, 370_THR, 371_GLN, 374_ILE, 375_ASP, 377_ILE, 38_ASN",2.97
+5K9K_F,5K9K_L_light+5K9K_H_heavy,"318_THR, 344_GLU, 345_GLY, 347_ILE, 348_ASP, 349_GLY, 350_TRP, 354_ARG, 363_GLN, 364_ALA, 365_ALA, 367_LEU, 370_THR, 371_GLN, 374_ILE, 377_ILE, 38_ASN",2.97
+6J14_G,6J14_B_light+6J14_A_heavy,"126_ILE, 127_SER, 128_LEU, 129_ALA, 130_PRO, 131_LYS, 132_ALA, 133_GLN, 134_ILE, 78_LYS",1.4
+2XTJ_C,2XTJ_B_light+2XTJ_D_heavy,"108_THR, 109_VAL, 110_ALA, 139_TYR, 141_ARG, 164_GLU, 165_GLN, 169_ASP, 170_SER, 171_THR, 172_TYR",2.7
+2XTJ_A,2XTJ_B_light+2XTJ_D_heavy,"155_PRO, 192_ASP, 194_ARG, 197_GLU, 220_ALA, 237_ARG, 238_ASP, 239_ALA, 372_SER, 374_ASP, 375_CYS, 376_SER, 377_THR, 378_CYS, 379_PHE, 380_VAL, 381_SER",2.7
+8KEO_B,8KEO_F_light+8KEO_G_heavy,"456_PHE, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 478_LYS, 479_PRO, 481_ASN, 483_VAL, 486_PHE, 487_ASN, 489_TYR, 493_ARG",2.78
+8KEO_C,8KEO_H_light+8KEO_I_heavy,"456_PHE, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 478_LYS, 479_PRO, 481_ASN, 483_VAL, 486_PHE, 487_ASN, 489_TYR, 493_ARG",2.78
+8KEO_A,8KEO_D_light+8KEO_E_heavy,"456_PHE, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 478_LYS, 479_PRO, 481_ASN, 483_VAL, 486_PHE, 487_ASN, 489_TYR, 493_ARG",2.78
+8G3Q_G,8G3Q_B_light+8G3Q_C_heavy,"118_ARG, 149_VAL, 150_ARG, 151_ASP, 152_ARG, 178_TRP, 179_SER, 199_LYS, 220_LYS, 221_GLU, 222_ILE, 224_ARG, 227_GLU, 246_ALA, 247_SER, 248_GLY, 292_ARG, 294_ASN, 295_TRP, 344_GLU, 346_GLY, 347_HIS, 371_ARG, 406_TYR, 431_LYS",2.3
+8G3Q_E,8G3Q_D_light+8G3Q_I_heavy,"118_ARG, 149_VAL, 150_ARG, 151_ASP, 152_ARG, 178_TRP, 179_SER, 199_LYS, 221_GLU, 222_ILE, 224_ARG, 227_GLU, 246_ALA, 247_SER, 248_GLY, 292_ARG, 294_ASN, 295_TRP, 344_GLU, 346_GLY, 347_HIS, 371_ARG, 406_TYR, 431_LYS",2.3
+8G3Q_F,8G3Q_L_light+8G3Q_A_heavy,"118_ARG, 149_VAL, 150_ARG, 151_ASP, 152_ARG, 178_TRP, 179_SER, 199_LYS, 220_LYS, 221_GLU, 222_ILE, 224_ARG, 227_GLU, 246_ALA, 247_SER, 248_GLY, 292_ARG, 294_ASN, 295_TRP, 344_GLU, 346_GLY, 347_HIS, 371_ARG, 406_TYR, 431_LYS",2.3
+7UNB_R,7UNB_L_light+7UNB_H_heavy,"347_ASP, 349_ILE, 350_PRO, 351_ASP, 354_PHE, 355_GLN, 357_TYR, 358_GLN, 360_GLU, 362_GLU, 363_GLU, 364_LEU, 365_GLU, 366_PRO, 367_SER, 368_ASN, 369_ILE, 370_VAL, 371_TYR, 374_SER, 375_GLN, 413_LYS",2.18
+7UNB_R,7UNB_E_light+7UNB_F_heavy,"312_ASP, 314_LEU, 318_LEU, 319_VAL, 320_ASP, 321_ASP, 322_SER, 323_ALA, 324_HIS, 325_ILE, 326_SER, 327_CYS, 328_ASN, 361_SER, 363_GLU",2.18
+6Z3Q_C,6Z3Q_L_light+6Z3Q_H_heavy,"206_ILE, 209_PRO, 210_ASN, 211_THR, 213_TYR, 58_VAL, 72_PRO, 74_SER, 75_ALA, 76_GLN, 77_ALA, 79_LYS, 81_GLU",2.7
+6Z3Q_A,6Z3Q_L_light+6Z3Q_H_heavy,"292_ASN, 293_ALA, 294_ILE, 295_THR, 296_THR",2.7
+5TE7_G,5TE7_L_light+5TE7_H_heavy,"124_GLY, 198_GLY, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 370_GLU, 371_ILE, 425_ARG, 426_MET, 427_TRP, 430_VAL, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_LYS, 469_ARG, 473_GLY, 474_ASP, 475_MET, 476_ARG, 97_LYS",2.15
+6NMV_S,6NMV_L_light+6NMV_H_heavy,"102_VAL, 103_GLU, 106_SER, 111_GLU, 39_PHE, 40_ARG, 42_ALA, 43_GLY, 44_PRO, 45_GLY, 46_ARG, 47_GLU, 87_GLY, 88_THR",2.61
+7BEN_E,7BEN_L_light+7BEN_H_heavy,"455_LEU, 456_PHE, 458_LYS, 473_TYR, 475_ALA, 476_GLY, 477_SER, 478_THR, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN",2.5
+7BEN_E,7BEN_B_light+7BEN_A_heavy,"440_ASN, 441_LEU, 443_SER, 444_LYS, 445_VAL, 446_GLY, 447_GLY, 448_ASN, 449_TYR, 450_ASN, 452_LEU, 490_PHE, 492_LEU, 493_GLN, 494_SER, 495_TYR",2.5
+7BEN_C,7BEN_F_light+7BEN_D_heavy,"455_LEU, 458_LYS, 473_TYR, 475_ALA, 476_GLY, 477_SER, 478_THR, 485_GLY, 486_PHE, 487_ASN, 488_CYS, 489_TYR, 493_GLN",2.5
+7BEN_C,7BEN_I_light+7BEN_G_heavy,"440_ASN, 441_LEU, 443_SER, 444_LYS, 445_VAL, 446_GLY, 447_GLY, 448_ASN, 449_TYR, 450_ASN, 490_PHE, 492_LEU, 493_GLN, 494_SER, 495_TYR",2.5
+8VYN_C,8VYN_M_light+8VYN_L_heavy,"131_ARG, 346_LEU, 359_GLU, 360_ALA, 361_GLU, 362_ASP, 378_LYS, 379_LYS, 380_GLN, 381_GLU, 382_VAL, 383_ASN, 386_ASP, 388_ALA, 422_GLU, 428_VAL",2.8
+8VYN_C,8VYN_O_light+8VYN_N_heavy,"280_TYR, 281_ASN, 282_GLY, 283_THR, 284_ASN, 285_ARG, 286_ASN, 290_PHE, 291_GLY, 292_GLU, 293_ASN, 294_ALA, 297_PHE, 298_PHE, 299_ILE, 321_LEU",2.8
+8VYN_B,8VYN_I_light+8VYN_H_heavy,"131_ARG, 346_LEU, 359_GLU, 360_ALA, 361_GLU, 362_ASP, 378_LYS, 379_LYS, 380_GLN, 381_GLU, 382_VAL, 383_ASN, 386_ASP, 388_ALA, 422_GLU, 428_VAL",2.8
+8VYN_B,8VYN_K_light+8VYN_J_heavy,"280_TYR, 281_ASN, 282_GLY, 283_THR, 284_ASN, 285_ARG, 286_ASN, 290_PHE, 291_GLY, 292_GLU, 293_ASN, 294_ALA, 297_PHE, 298_PHE, 299_ILE, 321_LEU",2.8
+8VYN_A,8VYN_E_light+8VYN_D_heavy,"131_ARG, 346_LEU, 359_GLU, 360_ALA, 361_GLU, 362_ASP, 378_LYS, 379_LYS, 380_GLN, 381_GLU, 382_VAL, 383_ASN, 386_ASP, 388_ALA, 422_GLU, 428_VAL",2.8
+8VYN_A,8VYN_G_light+8VYN_F_heavy,"280_TYR, 281_ASN, 282_GLY, 283_THR, 284_ASN, 285_ARG, 286_ASN, 290_PHE, 291_GLY, 292_GLU, 293_ASN, 294_ALA, 297_PHE, 298_PHE, 299_ILE, 321_LEU",2.8
+5C0N_B,5C0N_D_light+5C0N_C_heavy,"10_LEU, 11_VAL, 129_GLU, 131_ALA, 36_ALA, 37_LYS, 56_THR, 9_LYS",3.0
+5C0N_A,5C0N_L_light+5C0N_H_heavy,"10_LEU, 11_VAL, 129_GLU, 131_ALA, 35_MET, 36_ALA, 37_LYS, 56_THR, 9_LYS",3.0
+7X25_I,7X25_K_light+7X25_H_heavy,"502_LYS, 506_LEU, 511_ARG, 513_GLU, 536_GLU, 538_GLY, 539_ASP, 540_TYR, 541_TYR, 542_ARG, 553_TRP, 555_VAL, 557_SER",2.49
+7X25_G,7X25_C_light+7X25_A_heavy,"502_LYS, 506_LEU, 511_ARG, 513_GLU, 536_GLU, 538_GLY, 539_ASP, 540_TYR, 541_TYR, 542_ARG, 553_TRP, 555_VAL, 557_SER",2.49
+7X25_J,7X25_F_light+7X25_E_heavy,"502_LYS, 506_LEU, 536_GLU, 538_GLY, 539_ASP, 540_TYR, 541_TYR, 542_ARG, 553_TRP, 555_VAL, 557_SER",2.49
+4Z7O_A,4Z7O_L_light+4Z7O_H_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.85
+4Z7O_C,4Z7O_F_light+4Z7O_E_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.85
+5BV7_A,5BV7_L_light+5BV7_H_heavy,"256_ARG, 257_MET, 258_ALA, 259_TRP, 260_PRO, 315_TYR, 317_VAL, 318_GLY, 319_LEU, 320_PRO, 342_GLU, 343_ASP, 350_THR, 351_ARG, 354_GLU, 355_LEU, 357_GLY, 358_LEU, 360_GLN, 367_VAL, 369_LEU, 371_PRO, 373_HIS",2.45
+5BV7_A,5BV7_B_light+5BV7_C_heavy,"40_LEU, 48_TRP, 49_MET, 50_CYS, 51_TYR, 53_LYS, 54_THR, 58_PHE, 60_ILE, 65_ASN, 68_LEU, 69_PRO, 71_GLY, 74_CYS, 75_TRP, 78_ASN",2.45
+8DXU_R,8DXU_L_light+8DXU_H_heavy,"456_PHE, 474_GLN, 476_GLY, 477_SER, 478_THR, 479_PRO, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR",2.73
+8DXU_R,8DXU_B_light+8DXU_A_heavy,"340_GLU, 344_ALA, 345_THR, 346_ARG, 347_PHE, 348_ALA, 351_TYR, 352_ALA, 353_TRP, 354_ASN, 355_ARG, 356_LYS, 357_ARG, 359_SER, 464_PHE, 466_ARG, 468_ILE, 469_SER, 471_GLU",2.73
+4JQI_A,4JQI_L_light+4JQI_H_heavy,"210_HIS, 211_GLY, 213_PRO, 275_THR, 276_PRO, 277_PHE, 278_LEU, 279_ALA, 282_ARG, 297_ASP, 298_THR, 299_ASN, 300_LEU, 353_HIS, 354_PRO, 358_GLU, 361_PRO, 7_ARG",2.6
+1IQD_C,1IQD_A_light+1IQD_B_heavy,"2196_PHE, 2197_THR, 2198_ASN, 2199_MET, 2200_PHE, 2215_ARG, 2220_ARG, 2222_GLN, 2223_VAL, 2250_SER, 2251_LEU, 2252_LEU, 2253_THR, 2255_MET, 2315_HIS, 2316_GLN",2.0
+4I77_Z,4I77_L_light+4I77_H_heavy,"32_SER, 52_ILE, 53_ASN, 54_VAL, 55_SER, 56_GLY, 57_CYS, 58_SER, 59_ALA, 60_ILE, 61_GLU, 62_LYS, 64_GLN, 65_ARG, 66_MET, 68_SER, 69_GLY, 74_LYS, 76_SER, 77_ALA",1.9
+6UJ9_A,6UJ9_L_light+6UJ9_H_heavy,"142_ILE, 146_LYS, 149_ALA, 150_ALA, 151_ARG, 152_GLU, 65_GLN, 68_LYS, 69_ALA, 72_GLN, 73_THR, 76_GLU, 79_ARG, 80_ASN, 82_ARG, 83_GLY, 84_TYR, 86_ASN, 89_GLU",2.9
+7SBU_A,7SBU_L_light+7SBU_H_heavy,"455_LEU, 456_PHE, 475_ALA, 476_GLY, 477_SER, 478_THR, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 492_LEU, 493_GLN",2.53
+9C44_C,9C44_G_light+9C44_F_heavy,"108_THR, 236_THR, 237_ARG, 269_TYR, 26_PRO, 270_LEU, 28_TYR, 323_THR, 324_GLU, 532_ASN, 634_ARG, 63_THR, 82_PRO, 83_VAL, 85_PRO, 87_ASN, 88_ASP",2.6
+9C44_B,9C44_D_light+9C44_E_heavy,"108_THR, 236_THR, 237_ARG, 269_TYR, 26_PRO, 270_LEU, 28_TYR, 323_THR, 324_GLU, 532_ASN, 634_ARG, 63_THR, 82_PRO, 83_VAL, 85_PRO, 87_ASN, 88_ASP",2.6
+9C44_A,9C44_L_light+9C44_H_heavy,"108_THR, 236_THR, 237_ARG, 269_TYR, 26_PRO, 270_LEU, 28_TYR, 323_THR, 324_GLU, 532_ASN, 634_ARG, 63_THR, 82_PRO, 83_VAL, 85_PRO, 87_ASN, 88_ASP",2.6
+2HVJ_C,2HVJ_B_light+2HVJ_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 63_PRO, 64_ARG",2.75
+8KHC_A,8KHC_J_light+8KHC_H_heavy,"346_ARG, 348_ALA, 352_ALA, 353_TRP, 354_ASN, 355_ARG, 357_ARG, 396_TYR, 462_LYS, 463_PRO, 464_PHE, 465_GLU, 466_ARG, 467_ASP, 468_ILE, 469_SER, 470_THR, 471_GLU, 519_HIS",3.0
+8KHC_B,8KHC_G_light+8KHC_F_heavy,"346_ARG, 348_ALA, 351_TYR, 352_ALA, 354_ASN, 355_ARG, 357_ARG, 396_TYR, 462_LYS, 463_PRO, 464_PHE, 465_GLU, 466_ARG, 467_ASP, 468_ILE, 469_SER, 470_THR, 471_GLU",3.0
+5B3J_D,5B3J_L_light+5B3J_H_heavy,"31_SER, 32_PRO, 33_PRO, 34_SER, 55_GLU, 56_LYS, 57_ASP, 58_ASP, 59_PHE, 60_HIS, 61_HIS, 65_VAL, 66_PRO, 67_ARG, 92_ARG",2.9
+5B3J_A,5B3J_L_light+5B3J_H_heavy,"34_SER, 36_LYS, 66_THR, 67_HIS, 68_ARG, 69_PRO, 70_ASN, 95_PRO, 96_PRO",2.9
+5B3J_B,5B3J_F_light+5B3J_E_heavy,"34_SER, 66_THR, 67_HIS, 69_PRO, 70_ASN, 95_PRO, 96_PRO",2.9
+5B3J_C,5B3J_F_light+5B3J_E_heavy,"31_SER, 32_PRO, 33_PRO, 34_SER, 55_GLU, 56_LYS, 57_ASP, 58_ASP, 59_PHE, 60_HIS, 61_HIS, 65_VAL, 66_PRO, 67_ARG, 92_ARG",2.9
+4KJQ_A,4KJQ_D_light+4KJQ_C_heavy,"243_LYS, 246_ASP, 248_PRO, 249_LEU, 250_ASN, 380_PRO, 381_GLN, 382_TYR, 383_HIS",2.88
+4KJQ_B,4KJQ_F_light+4KJQ_E_heavy,"243_LYS, 246_ASP, 248_PRO, 249_LEU, 250_ASN, 380_PRO, 381_GLN, 382_TYR, 383_HIS",2.88
+3WFB_C,3WFB_L_light+3WFB_H_heavy,"101_ILE, 104_LEU, 105_ASN, 106_VAL, 107_PRO, 109_ARG, 145_GLU, 94_PHE, 97_ALA, 98_TRP",2.7
+3WFB_B,3WFB_L_light+3WFB_H_heavy,"421_GLY, 422_ALA, 423_ALA, 424_MET, 425_THR, 427_MET, 428_ALA, 431_ASP",2.7
+1FJ1_E,1FJ1_C_light+1FJ1_D_heavy,"206_SER, 207_SER, 208_ALA, 209_ALA, 211_LYS, 213_THR, 214_ALA, 215_ALA, 226_THR, 228_ASN, 229_SER, 230_LYS, 249_ASP, 250_SER, 251_ASN, 252_GLY, 253_THR",2.68
+1FJ1_F,1FJ1_A_light+1FJ1_B_heavy,"206_SER, 207_SER, 208_ALA, 209_ALA, 211_LYS, 213_THR, 214_ALA, 215_ALA, 226_THR, 228_ASN, 229_SER, 230_LYS, 249_ASP, 250_SER, 251_ASN, 252_GLY, 253_THR",2.68
+9DX6_A,9DX6_C_light+9DX6_B_heavy,"116_THR, 117_GLY, 118_ASP, 128_PHE, 129_PRO, 130_ASN, 131_ALA, 132_ASN, 133_ASP, 134_HIS, 146_ARG, 147_TYR, 153_MET, 168_SER, 169_PHE, 170_VAL, 171_MET, 172_ALA, 181_HIS, 196_TYR, 313_ARG, 314_GLN, 315_ASN, 316_ASN, 317_ARG, 318_GLU, 321_LYS, 83_GLU",2.4
+4KK8_A,4KK8_D_light+4KK8_C_heavy,"243_LYS, 246_ASP, 248_PRO, 249_LEU, 250_ASN, 380_PRO, 381_GLN, 382_TYR, 383_HIS",2.86
+4KK8_B,4KK8_F_light+4KK8_E_heavy,"243_LYS, 246_ASP, 248_PRO, 249_LEU, 250_ASN, 380_PRO, 381_GLN, 382_TYR, 383_HIS",2.86
+2ZCH_P,2ZCH_L_light+2ZCH_H_heavy,"101_HIS, 125_THR, 175_LYS, 178_LYS, 179_PHE, 233_HIS, 234_TYR, 235_ARG, 236_LYS, 237_TRP, 239_LYS, 91_HIS, 92_PRO, 93_LEU, 94_TYR, 98_ASP",2.83
+6PHB_I,6PHB_C_light+6PHB_D_heavy,"27_ASN, 28_ASP, 29_LEU, 39_GLU, 40_LYS, 41_VAL, 42_LEU, 43_LYS, 46_GLU, 47_LYS, 48_THR, 51_LYS",2.0
+6PHB_E,6PHB_A_light+6PHB_B_heavy,"27_ASN, 28_ASP, 29_LEU, 39_GLU, 40_LYS, 41_VAL, 42_LEU, 43_LYS, 46_GLU, 47_LYS, 51_LYS",2.0
+5XWD_A,5XWD_D_light+5XWD_H_heavy,"322_LYS, 323_ASP, 325_LEU, 346_HIS, 348_LEU, 349_PRO, 350_VAL, 353_ARG, 355_ASP, 357_PHE, 380_PHE, 382_LEU, 384_GLN, 407_LYS, 408_GLN, 409_HIS, 417_VAL, 418_SER, 438_ILE, 440_SER, 441_GLY, 443_LYS, 465_LYS, 467_ILE, 468_SER",2.89
+8BH5_E,8BH5_L_light+8BH5_H_heavy,"403_ARG, 415_THR, 416_GLY, 417_ASN, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_ASN, 486_PHE, 487_ASN, 489_TYR, 493_ARG, 500_THR, 501_TYR, 502_GLY, 505_HIS",2.38
+8EE0_A,8EE0_L_light+8EE0_H_heavy,"10_ALA, 11_GLU, 12_TYR, 14_ARG, 15_ARG, 16_ALA, 18_LEU, 19_ASP, 22_ALA, 2_ALA, 3_SER, 5_ASP, 6_SER, 778_ARG, 779_ARG, 7_GLU, 8_LYS, 9_VAL",2.65
+4R8W_B,4R8W_L_light+4R8W_H_heavy,"340_ASP, 341_GLY, 359_TYR, 360_LYS, 363_GLN, 366_ILE, 369_ILE, 370_THR, 373_LEU, 374_ASN, 378_GLU, 381_ASN",2.8
+3KLH_B,3KLH_C_light+3KLH_D_heavy,"199_ARG, 222_GLN, 223_LYS, 224_GLU, 225_PRO, 226_PRO, 227_PHE, 228_LEU, 229_TRP, 230_MET, 358_ARG",2.9
+8HWT_A,8HWT_L_light+8HWT_H_heavy,"405_ASN, 420_ASP, 421_TYR, 455_LEU, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_ASN, 486_PHE, 487_ASN, 489_TYR, 493_ARG, 501_TYR, 505_HIS",2.91
+8HWT_A,8HWT_E_light+8HWT_D_heavy,"369_TYR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 384_PRO, 385_THR, 386_LYS, 391_CYS, 392_PHE, 412_PRO, 427_ASP, 428_ASP, 429_PHE, 517_LEU",2.91
+4N9G_D,4N9G_F_light+4N9G_E_heavy,"69_SER, 72_ASN, 73_ASP, 74_MET, 75_PRO, 76_ILE, 77_THR, 78_ASN, 79_ASP, 81_LYS, 85_SER",2.5
+4N9G_Z,4N9G_N_light+4N9G_M_heavy,"68_LEU, 69_SER, 72_ASN, 73_ASP, 74_MET, 75_PRO, 76_ILE, 77_THR, 78_ASN, 79_ASP, 81_LYS, 82_LYS, 85_SER",2.5
+4N9G_Y,4N9G_L_light+4N9G_H_heavy,"68_LEU, 69_SER, 72_ASN, 73_ASP, 74_MET, 75_PRO, 76_ILE, 77_THR, 78_ASN, 79_ASP, 81_LYS, 82_LYS, 85_SER",2.5
+4N9G_C,4N9G_B_light+4N9G_A_heavy,"69_SER, 72_ASN, 73_ASP, 74_MET, 75_PRO, 76_ILE, 77_THR, 78_ASN, 79_ASP, 81_LYS, 82_LYS",2.5
+6PE8_T,6PE8_L_light+6PE8_H_heavy,"62_CYS, 64_GLU, 65_SER, 79_GLN, 80_HIS, 81_LYS, 82_TYR, 83_CYS, 90_ARG, 91_VAL, 92_GLN, 93_GLN, 94_LYS, 96_THR, 98_GLU, 99_THR",2.84
+6PE8_U,6PE8_B_light+6PE8_A_heavy,"62_CYS, 64_GLU, 65_SER, 81_LYS, 82_TYR, 83_CYS, 91_VAL, 92_GLN, 93_GLN, 94_LYS, 96_THR, 98_GLU, 99_THR",2.84
+8R9Z_A,8R9Z_C_light+8R9Z_B_heavy,"318_PHE, 319_GLY, 320_GLU, 357_ARG, 358_THR, 359_ASP, 389_LYS, 395_VAL, 396_TRP, 397_ASN, 398_TYR, 399_LEU, 401_ARG, 403_ARG",2.9
+8R9Z_A,8R9Z_L_light+8R9Z_H_heavy,"359_ASP, 360_THR, 361_CYS, 362_SER, 372_MET, 374_PHE, 385_ALA, 386_CYS, 387_GLU, 408_ALA, 409_VAL, 410_GLU",2.9
+1FBI_Y,1FBI_P_light+1FBI_Q_heavy,"100_SER, 101_ASP, 15_HIS, 16_GLY, 20_TYR, 21_ARG, 61_ARG, 62_TRP, 73_ARG, 75_LEU, 77_ASN, 93_ASN, 96_LYS, 97_LYS",3.0
+1FBI_X,1FBI_L_light+1FBI_H_heavy,"100_SER, 101_ASP, 14_ARG, 15_HIS, 16_GLY, 20_TYR, 21_ARG, 63_TRP, 71_GLY, 73_ARG, 75_LEU, 77_ASN, 93_ASN, 96_LYS, 97_LYS",3.0
+8CDD_B,8CDD_D_light+8CDD_E_heavy,"112_GLU, 113_GLU, 135_ARG, 137_TYR, 139_ASN, 143_GLU, 145_ASN, 146_ASN, 147_ALA, 174_ARG, 203_ASP, 204_LYS, 206_GLU, 207_THR",3.0
+2XQB_A,2XQB_L_light+2XQB_H_heavy,"22_ASP, 23_ALA, 26_TYR, 38_THR, 41_LYS, 44_LEU, 45_LEU, 46_GLU, 48_GLN, 49_VAL, 52_LEU, 53_GLU, 55_GLY, 64_GLU, 67_ILE, 68_ILE, 71_ASN, 72_ASN, 75_SER, 88_CYS, 89_GLU, 93_GLU",2.6
+4HKZ_E,4HKZ_A_light+4HKZ_B_heavy,"27_LEU, 33_LYS, 35_GLN, 36_THR, 37_ALA, 38_GLU, 39_PHE, 40_LYS, 49_GLU, 52_ARG, 53_TYR, 56_LEU, 57_LEU, 60_VAL",2.08
+4HKZ_H,4HKZ_A_light+4HKZ_B_heavy,"23_ALA, 24_GLN, 27_GLY, 28_PHE, 30_GLN, 31_SER, 34_ASP, 35_ASP, 37_SER, 38_GLN, 41_ASN, 42_VAL, 45_GLU, 49_LEU",2.08
+3PNW_C,3PNW_A_light+3PNW_B_heavy,"566_TYR, 567_TRP, 579_ALA, 580_LEU, 581_HIS, 582_SER, 586_THR, 587_ALA, 588_VAL, 590_LYS, 592_ILE, 593_ASP, 594_TYR, 595_GLY, 597_TYR, 598_GLU, 599_GLU",2.05
+3PNW_R,3PNW_P_light+3PNW_Q_heavy,"566_TYR, 567_TRP, 579_ALA, 580_LEU, 581_HIS, 582_SER, 586_THR, 587_ALA, 588_VAL, 590_LYS, 592_ILE, 593_ASP, 594_TYR, 595_GLY, 596_ASN, 597_TYR, 598_GLU, 599_GLU",2.05
+3PNW_U,3PNW_S_light+3PNW_T_heavy,"566_TYR, 567_TRP, 579_ALA, 580_LEU, 581_HIS, 582_SER, 586_THR, 587_ALA, 588_VAL, 590_LYS, 592_ILE, 593_ASP, 594_TYR, 595_GLY, 597_TYR, 598_GLU, 599_GLU",2.05
+3PNW_X,3PNW_V_light+3PNW_W_heavy,"566_TYR, 567_TRP, 579_ALA, 580_LEU, 581_HIS, 582_SER, 586_THR, 587_ALA, 588_VAL, 590_LYS, 592_ILE, 593_ASP, 594_TYR, 595_GLY, 596_ASN, 597_TYR, 598_GLU, 599_GLU",2.05
+3PNW_I,3PNW_G_light+3PNW_H_heavy,"566_TYR, 567_TRP, 568_GLU, 579_ALA, 580_LEU, 581_HIS, 582_SER, 586_THR, 587_ALA, 588_VAL, 590_LYS, 592_ILE, 593_ASP, 594_TYR, 595_GLY, 596_ASN, 597_TYR, 599_GLU",2.05
+3PNW_L,3PNW_J_light+3PNW_K_heavy,"566_TYR, 567_TRP, 568_GLU, 579_ALA, 580_LEU, 581_HIS, 582_SER, 586_THR, 587_ALA, 588_VAL, 590_LYS, 592_ILE, 593_ASP, 594_TYR, 595_GLY, 597_TYR, 598_GLU, 599_GLU",2.05
+3PNW_F,3PNW_D_light+3PNW_E_heavy,"566_TYR, 567_TRP, 578_GLU, 579_ALA, 580_LEU, 581_HIS, 582_SER, 586_THR, 587_ALA, 588_VAL, 590_LYS, 592_ILE, 593_ASP, 594_TYR, 595_GLY, 596_ASN, 597_TYR, 598_GLU, 599_GLU",2.05
+3PNW_O,3PNW_M_light+3PNW_N_heavy,"566_TYR, 567_TRP, 579_ALA, 580_LEU, 581_HIS, 582_SER, 586_THR, 587_ALA, 588_VAL, 590_LYS, 592_ILE, 593_ASP, 594_TYR, 595_GLY, 596_ASN, 597_TYR, 598_GLU, 599_GLU",2.05
+1R3L_C,1R3L_A_light+1R3L_B_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.41
+6Q0I_A,6Q0I_L_light+6Q0I_H_heavy,"135_VAL, 136_THR, 137_ALA, 145_SER, 153_TRP, 155_THR, 156_GLU, 158_ASN, 159_GLY, 160_LEU, 183_HIS, 185_PRO, 186_SER, 187_ASN, 189_ARG, 190_ASP, 192_ARG, 193_ALA, 194_ILE, 196_HIS, 225_GLY, 226_GLN, 227_GLU, 95_TYR",2.7
+7Y0C_E,7Y0C_N_light+7Y0C_M_heavy,"332_ILE, 333_THR, 334_ASN, 335_LEU, 336_CYS, 337_PRO, 338_PHE, 339_ASP, 340_GLU, 342_PHE, 343_ASN, 345_THR, 362_VAL, 363_ALA, 364_ASP, 367_VAL, 368_LEU",2.94
+7Y0C_R,7Y0C_B_light+7Y0C_A_heavy,"333_THR, 334_ASN, 335_LEU, 336_CYS, 337_PRO, 338_PHE, 339_ASP, 340_GLU, 342_PHE, 343_ASN, 344_ALA, 345_THR, 362_VAL, 363_ALA, 364_ASP, 366_SER, 367_VAL, 527_PRO",2.94
+7OX4_C,7OX4_B_light+7OX4_A_heavy,"22_SER, 25_TRP, 28_ARG, 29_ASP, 32_TYR, 35_GLU, 36_ASN, 87_PRO, 88_VAL, 91_ARG, 94_ARG, 95_ILE, 98_VAL, 99_LEU",1.8
+1FNS_A,1FNS_L_light+1FNS_H_heavy,"627_PRO, 628_GLN, 629_ARG, 631_SER, 632_ARG, 633_ASN, 634_PHE, 635_VAL, 636_ARG, 660_LYS, 661_GLN, 664_LEU",2.0
+7CEB_B,7CEB_D_light+7CEB_C_heavy,"161_ARG, 201_ASN, 202_VAL, 203_LEU, 204_SER, 205_LEU, 206_THR, 207_ASN, 208_LYS, 209_GLY, 210_GLU, 211_VAL, 214_GLU, 243_SER, 244_LEU, 245_ILE, 246_GLY, 247_TRP, 248_ARG, 285_ASN, 286_ASN",2.89
+8DJG_F,8DJG_B_light+8DJG_A_heavy,"103_PRO, 112_GLY, 113_THR, 114_TYR, 115_LYS, 36_CYS, 37_GLU, 38_SER, 39_TYR, 67_ASP, 68_SER, 69_ASP, 72_GLN",2.65
+8DJG_E,8DJG_D_light+8DJG_C_heavy,"103_PRO, 112_GLY, 113_THR, 114_TYR, 115_LYS, 36_CYS, 37_GLU, 38_SER, 39_TYR, 67_ASP, 68_SER, 69_ASP, 70_PRO, 72_GLN",2.65
+4D9Q_A,4D9Q_L_light+4D9Q_H_heavy,"129_ARG, 132_PRO, 164_ASP, 165_ARG, 166_ALA, 167_THR, 169_ASN, 170_ARG, 173_ASP, 174_GLY, 176_ILE, 178_GLN, 223_ARG",2.28
+4D9Q_B,4D9Q_D_light+4D9Q_E_heavy,"129_ARG, 132_PRO, 164_ASP, 165_ARG, 166_ALA, 167_THR, 169_ASN, 170_ARG, 173_ASP, 174_GLY, 176_ILE, 178_GLN, 223_ARG",2.28
+7EAM_A,7EAM_L_light+7EAM_H_heavy,"346_ARG, 351_TYR, 352_ALA, 353_TRP, 354_ASN, 355_ARG, 357_ARG, 464_PHE, 466_ARG, 468_ILE, 469_SER, 470_THR, 471_GLU, 516_GLU, 518_LEU, 519_HIS, 520_ALA",1.4
+7EAM_B,7EAM_D_light+7EAM_C_heavy,"346_ARG, 351_TYR, 352_ALA, 353_TRP, 354_ASN, 355_ARG, 357_ARG, 464_PHE, 466_ARG, 468_ILE, 469_SER, 470_THR, 471_GLU, 516_GLU, 518_LEU, 519_HIS, 520_ALA",1.4
+7ZQT_E,7ZQT_H_light+7ZQT_G_heavy,"191_GLY, 192_GLU, 206_ASP, 207_GLN, 208_ASN, 231_VAL, 233_ASP, 234_SER, 235_GLN, 242_ARG, 243_VAL, 244_SER, 245_TYR, 246_THR",2.7
+7ZQT_A,7ZQT_D_light+7ZQT_C_heavy,"191_GLY, 192_GLU, 206_ASP, 207_GLN, 208_ASN, 233_ASP, 234_SER, 235_GLN, 242_ARG, 243_VAL, 244_SER, 245_TYR, 246_THR",2.7
+6URM_F,6URM_L_light+6URM_H_heavy,"133_ASN, 137_ALA, 143_GLY, 144_ALA, 145_LYS, 159_ASN, 186_SER, 187_THR, 188_SER, 189_ALA, 190_ASP, 193_SER, 194_LEU, 226_GLN, 227_GLU, 228_GLY, 98_TYR",2.65
+6URM_C,6URM_E_light+6URM_D_heavy,"133_ASN, 137_ALA, 143_GLY, 144_ALA, 145_LYS, 159_ASN, 186_SER, 187_THR, 188_SER, 189_ALA, 190_ASP, 193_SER, 219_ILE, 225_ASP, 226_GLN, 227_GLU, 228_GLY, 98_TYR",2.65
+6MUG_G,6MUG_L_light+6MUG_H_heavy,"322_ILE, 323_ILE, 324_GLY, 325_ASN, 326_ILE, 327_ARG, 328_GLN, 330_HIS, 415_THR, 417_GLN",2.95
+3IDX_G,3IDX_L_light+3IDX_H_heavy,"281_ALA, 283_THR, 365_SER, 366_GLY, 367_GLY, 368_ASP, 369_PRO, 370_GLU, 371_ILE, 372_VAL, 373_THR, 384_TYR, 419_ARG, 421_LYS, 425_ASN, 474_ASP",2.5
+7KYO_B,7KYO_L_light+7KYO_H_heavy,"107_LYS, 109_LYS, 112_LYS, 113_LYS, 116_GLU, 117_ALA, 119_GLU, 120_ILE, 148_LEU, 151_GLU, 152_ALA, 174_ASN, 178_ASP, 179_LEU, 180_LYS, 181_LYS, 182_ALA, 183_GLY, 184_LYS, 27_PRO",2.85
+6WAS_G,6WAS_B_light+6WAS_A_heavy,"133_GLN, 155_LYS, 178_ARG, 182_VAL, 183_PRO, 184_LEU, 185_GLU, 186_GLU",1.9
+6WAS_J,6WAS_L_light+6WAS_H_heavy,"178_ARG, 182_VAL, 183_PRO, 184_LEU, 185_GLU, 186_GLU",1.9
+5U5F_C,5U5F_A_light+5U5F_B_heavy,"23_ALA, 24_GLN, 27_GLY, 28_PHE, 30_GLN, 31_SER, 34_ASP, 35_ASP, 37_SER, 38_GLN, 41_ASN, 42_VAL, 45_GLU, 49_LEU",1.81
+5U5F_E,5U5F_A_light+5U5F_B_heavy,"33_LYS, 34_ILE, 35_GLN, 36_THR, 37_ALA, 38_GLU, 39_PHE, 40_LYS, 49_GLU, 53_TYR, 56_LEU, 57_LEU, 60_VAL",1.81
+4OKV_E,4OKV_D_light+4OKV_C_heavy,"226_SER, 228_GLU, 229_GLU, 230_CYS, 231_SER, 232_LYS, 233_ASP, 234_LYS, 235_ASN, 236_ASN, 239_CYS, 242_GLU",1.8
+4OKV_F,4OKV_B_light+4OKV_A_heavy,"226_SER, 228_GLU, 229_GLU, 230_CYS, 231_SER, 232_LYS, 233_ASP, 234_LYS, 235_ASN, 236_ASN, 239_CYS, 242_GLU",1.8
+7ENF_B,7ENF_O_light+7ENF_P_heavy,"345_THR, 346_ARG, 347_PHE, 348_ALA, 349_SER, 351_TYR, 440_ASN, 441_LEU, 442_ASP, 443_SER, 444_LYS, 445_VAL, 448_ASN, 450_ASN, 451_TYR, 509_ARG",2.76
+4ZSO_F,4ZSO_C_light+4ZSO_D_heavy,"110_PRO, 111_GLY, 113_GLN, 115_GLU, 116_GLU, 203_MET, 204_ASP, 41_ASN, 67_TRP, 72_PHE, 74_LYS, 75_GLU, 76_VAL, 87_GLU, 89_PHE, 90_ARG, 91_PRO, 92_GLY",2.5
+4ZSO_E,4ZSO_A_light+4ZSO_B_heavy,"110_PRO, 111_GLY, 113_GLN, 115_GLU, 116_GLU, 203_MET, 41_ASN, 67_TRP, 72_PHE, 74_LYS, 75_GLU, 76_VAL, 87_GLU, 89_PHE, 90_ARG, 91_PRO, 92_GLY",2.5
+5SX4_M,5SX4_I_light+5SX4_J_heavy,"348_LEU, 349_PRO, 353_ARG, 384_GLN, 408_GLN, 409_HIS, 417_VAL, 418_SER, 420_ASP, 438_ILE, 440_SER, 441_GLY, 443_LYS, 464_THR, 465_LYS, 466_ILE, 467_ILE, 468_SER, 469_ASN, 471_GLY",2.8
+5SX4_N,5SX4_L_light+5SX4_H_heavy,"349_PRO, 384_GLN, 409_HIS, 417_VAL, 418_SER, 420_ASP, 438_ILE, 440_SER, 441_GLY, 443_LYS, 464_THR, 465_LYS, 466_ILE, 467_ILE, 468_SER, 469_ASN, 471_GLY, 473_ASN",2.8
+8JEO_A,8JEO_C_light+8JEO_B_heavy,"111_HIS, 55_THR, 56_GLN, 58_ASN, 60_GLU, 63_ASP, 64_GLN, 65_LEU, 76_HIS, 77_ILE, 78_SER, 79_PRO, 80_SER, 82_LYS",2.06
+8JEO_D,8JEO_F_light+8JEO_E_heavy,"111_HIS, 55_THR, 56_GLN, 58_ASN, 60_GLU, 65_LEU, 73_LEU, 76_HIS, 77_ILE, 78_SER, 79_PRO, 80_SER, 82_LYS",2.06
+5VKE_C,5VKE_B_light+5VKE_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.37
+5X2N_A,5X2N_L_light+5X2N_H_heavy,"194_GLY, 197_LEU, 198_ASN, 200_ASN, 201_TRP, 202_ARG, 203_TRP, 225_ILE, 226_GLU, 227_ASP, 228_SER, 229_GLU, 230_ILE, 255_ASN, 453_SER, 454_ILE, 455_HIS, 456_LEU, 460_LYS",2.2
+5X2N_C,5X2N_K_light+5X2N_J_heavy,"194_GLY, 197_LEU, 198_ASN, 200_ASN, 201_TRP, 202_ARG, 203_TRP, 225_ILE, 226_GLU, 227_ASP, 228_SER, 229_GLU, 230_ILE, 255_ASN, 453_SER, 454_ILE, 455_HIS, 456_LEU, 460_LYS",2.2
+8EEE_E,8EEE_L_light+8EEE_H_heavy,"118_LYS, 67_ASP, 73_ARG, 81_TYR, 82_LEU, 83_ASP, 84_LYS, 86_SER, 87_ASP, 88_THR, 90_TYR",2.82
+8EEE_Z,8EEE_B_light+8EEE_A_heavy,"67_ASP, 69_ALA, 73_ARG, 81_TYR, 82_LEU, 83_ASP, 84_LYS, 85_GLN, 86_SER, 87_ASP, 88_THR, 90_TYR",2.82
+3NFP_I,3NFP_L_light+3NFP_H_heavy,"118_ILE, 120_HIS, 149_MET, 150_THR, 151_HIS, 152_GLY, 153_LYS, 154_THR, 155_ARG, 1_GLU, 25_MET, 27_ASN, 2_LEU, 42_LEU, 43_TYR, 4_ASP, 5_ASP, 6_ASP",2.86
+3NFP_K,3NFP_B_light+3NFP_A_heavy,"118_ILE, 120_HIS, 149_MET, 152_GLY, 153_LYS, 154_THR, 1_GLU, 25_MET, 27_ASN, 2_LEU, 42_LEU, 43_TYR, 4_ASP, 5_ASP, 6_ASP",2.86
+3MXW_A,3MXW_L_light+3MXW_H_heavy,"123_ARG, 125_THR, 126_GLU, 131_ASP, 132_GLY, 133_HIS, 134_HIS, 135_SER, 136_GLU, 147_ASP, 149_THR, 153_ARG, 155_ARG, 174_TYR, 176_GLU, 177_SER, 178_LYS, 179_ALA, 180_HIS, 44_TYR, 45_LYS, 87_LYS",1.83
+7XRZ_Y,7XRZ_A_light+7XRZ_B_heavy,"100_ALA, 18_GLU, 67_ILE, 70_GLY, 71_GLN, 74_ASP, 78_LEU, 83_LYS, 85_LYS, 86_GLU, 88_GLN, 89_ALA, 92_GLU, 93_GLN, 95_LYS, 96_THR, 97_THR, 99_ASN",2.1
+7XRZ_X,7XRZ_L_light+7XRZ_H_heavy,"100_ALA, 18_GLU, 67_ILE, 70_GLY, 71_GLN, 74_ASP, 77_LYS, 78_LEU, 83_LYS, 85_LYS, 86_GLU, 88_GLN, 89_ALA, 90_ALA, 92_GLU, 93_GLN, 95_LYS, 96_THR, 97_THR, 99_ASN",2.1
+3EOA_I,3EOA_L_light+3EOA_H_heavy,"144_PRO, 147_PHE, 148_GLN, 151_LEU, 155_LYS, 191_ASP, 193_ASP, 194_ALA, 197_LYS, 198_HIS, 199_VAL, 200_LYS, 201_HIS, 203_LEU",2.8
+3EOA_J,3EOA_A_light+3EOA_B_heavy,"144_PRO, 147_PHE, 148_GLN, 151_LEU, 155_LYS, 191_ASP, 193_ASP, 194_ALA, 197_LYS, 198_HIS, 199_VAL, 200_LYS, 201_HIS, 203_LEU",2.8
+8F6L_C,8F6L_B_light+8F6L_A_heavy,"118_ASN, 65_ASN, 66_ARG, 68_HIS, 86_GLN, 87_THR, 88_SER, 89_TRP, 90_LYS, 91_GLU",1.85
+6WO4_E,6WO4_L_light+6WO4_H_heavy,"427_LEU, 428_ASN, 429_CYS, 430_ASN, 431_ASP, 432_SER, 434_GLN, 438_ILE, 439_THR, 442_PHE, 443_TYR, 529_TRP, 531_GLU",2.35
+7M7W_R,7M7W_L_light+7M7W_H_heavy,"369_TYR, 370_ASN, 371_SER, 372_ALA, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 382_VAL, 383_SER, 384_PRO, 385_THR, 404_GLY, 405_ASP, 408_ARG, 500_THR, 501_ASN, 502_GLY, 503_VAL, 504_GLY, 506_GLN",2.65
+7M7W_R,7M7W_F_light+7M7W_E_heavy,"353_TRP, 355_ARG, 357_ARG, 396_TYR, 426_PRO, 427_ASP, 428_ASP, 429_PHE, 462_LYS, 463_PRO, 464_PHE, 466_ARG, 514_SER, 516_GLU, 518_LEU, 519_HIS, 520_ALA, 521_PRO",2.65
+7M7W_S,7M7W_B_light+7M7W_A_heavy,"369_TYR, 370_ASN, 371_SER, 372_ALA, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 382_VAL, 383_SER, 384_PRO, 385_THR, 404_GLY, 405_ASP, 408_ARG, 502_GLY, 503_VAL, 504_GLY",2.65
+7M7W_S,7M7W_D_light+7M7W_C_heavy,"353_TRP, 355_ARG, 357_ARG, 393_THR, 396_TYR, 426_PRO, 428_ASP, 429_PHE, 462_LYS, 463_PRO, 464_PHE, 514_SER, 516_GLU, 518_LEU, 519_HIS, 520_ALA, 521_PRO",2.65
+1KIQ_C,1KIQ_A_light+1KIQ_B_heavy,"102_GLY, 116_LYS, 117_GLY, 118_THR, 119_ASP, 120_VAL, 121_GLN, 124_ILE, 125_ARG, 18_ASP, 19_ASN, 22_GLY, 23_TYR, 24_SER, 27_ASN",1.85
+7MJK_C,7MJK_I_light+7MJK_H_heavy,"403_ARG, 408_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 493_GLN, 501_TYR, 505_TYR",2.73
+7MJK_B,7MJK_G_light+7MJK_F_heavy,"403_ARG, 408_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 493_GLN, 501_TYR, 505_TYR",2.73
+7MJK_A,7MJK_E_light+7MJK_D_heavy,"403_ARG, 408_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 493_GLN, 501_TYR, 505_TYR",2.73
+4XAK_B,4XAK_E_light+4XAK_D_heavy,"502_LYS, 506_PHE, 510_ASP, 511_ARG, 513_GLU, 535_TRP, 536_GLU, 538_GLY, 539_ASP, 540_TYR, 541_TYR, 542_ARG, 553_TRP, 555_VAL, 556_ALA, 557_SER",2.45
+4XAK_A,4XAK_L_light+4XAK_H_heavy,"502_LYS, 506_PHE, 510_ASP, 513_GLU, 535_TRP, 536_GLU, 538_GLY, 539_ASP, 540_TYR, 541_TYR, 542_ARG, 553_TRP, 555_VAL, 556_ALA, 557_SER",2.45
+7SYZ_A,7SYZ_L_light+7SYZ_H_heavy,"384_ILE, 385_ILE, 386_HIS, 387_CYS, 388_LYS, 389_TYR, 390_SER, 391_LYS, 392_ALA, 435_ARG, 463_MET, 482_ASN, 483_SER, 543_ASN",2.86
+6J11_A,6J11_L_light+6J11_H_heavy,"188_GLU, 18_TYR, 190_ARG, 191_SER, 20_ASP, 226_ASN, 22_GLY, 234_LEU, 235_ARG, 236_ASN, 23_PRO, 24_ASP, 25_SER, 26_VAL, 27_LYS, 28_SER",3.0
+6J11_B,6J11_G_light+6J11_F_heavy,"186_ILE, 188_GLU, 18_TYR, 190_ARG, 191_SER, 19_VAL, 20_ASP, 226_ASN, 22_GLY, 230_GLU, 234_LEU, 235_ARG, 236_ASN, 23_PRO, 24_ASP, 25_SER, 26_VAL, 27_LYS, 28_SER",3.0
+6J11_C,6J11_E_light+6J11_D_heavy,"188_GLU, 18_TYR, 190_ARG, 191_SER, 19_VAL, 20_ASP, 226_ASN, 22_GLY, 234_LEU, 235_ARG, 236_ASN, 23_PRO, 24_ASP, 26_VAL, 27_LYS, 28_SER",3.0
+8TPA_C,8TPA_E_light+8TPA_D_heavy,"271_ASN, 273_PRO, 285_GLN, 296_ASN, 297_VAL, 298_HIS, 299_PRO, 308_TYR, 310_ARG, 311_SER, 45_ASP, 47_HIS, 49_GLY, 50_LYS, 92_ASN",3.0
+8TPA_K,8TPA_J_light+8TPA_I_heavy,"75_ARG, 78_GLU, 79_ASN, 86_ASP, 89_LEU, 90_ASP",3.0
+8TPA_B,8TPA_L_light+8TPA_H_heavy,"75_ARG, 78_GLU, 79_ASN, 86_ASP, 89_LEU, 90_ASP",3.0
+8TPA_G,8TPA_J_light+8TPA_I_heavy,"271_ASN, 273_PRO, 285_GLN, 296_ASN, 297_VAL, 298_HIS, 299_PRO, 308_TYR, 310_ARG, 311_SER, 45_ASP, 47_HIS, 49_GLY, 50_LYS, 92_ASN",3.0
+8TPA_A,8TPA_L_light+8TPA_H_heavy,"271_ASN, 273_PRO, 285_GLN, 296_ASN, 297_VAL, 298_HIS, 299_PRO, 308_TYR, 310_ARG, 311_SER, 45_ASP, 47_HIS, 49_GLY, 50_LYS, 92_ASN",3.0
+8TPA_F,8TPA_E_light+8TPA_D_heavy,"75_ARG, 78_GLU, 79_ASN, 86_ASP, 89_LEU, 90_ASP",3.0
+7KET_C,7KET_B_light+7KET_A_heavy,"112_GLU, 113_GLN, 120_SER, 121_GLY, 122_LYS, 123_MET, 125_ALA, 127_ARG, 47_LYS, 48_LEU, 49_ALA, 50_ALA, 51_GLN, 52_GLY, 54_GLU, 77_ASP, 78_PHE, 79_ILE, 87_GLN, 88_LEU, 90_THR, 93_SER, 94_GLY, 95_GLU",2.0
+7WPF_A,7WPF_Y_light+7WPF_X_heavy,"346_ARG, 351_TYR, 352_ALA, 443_SER, 444_LYS, 447_GLY, 448_ASN, 449_TYR, 450_ASN, 468_ILE, 470_THR, 482_GLY, 483_VAL, 490_PHE, 491_PRO",2.92
+7WPF_C,7WPF_V_light+7WPF_U_heavy,"345_THR, 346_ARG, 347_PHE, 348_ALA, 349_SER, 351_TYR, 352_ALA, 440_LYS, 441_LEU, 444_LYS, 449_TYR, 450_ASN, 451_TYR, 452_LEU, 468_ILE, 470_THR, 472_ILE, 490_PHE, 492_LEU, 493_ARG",2.92
+7WPF_B,7WPF_S_light+7WPF_R_heavy,"346_ARG, 348_ALA, 351_TYR, 352_ALA, 449_TYR, 450_ASN, 452_LEU, 482_GLY, 483_VAL, 490_PHE",2.92
+3WFC_C,3WFC_L_light+3WFC_H_heavy,"101_ILE, 105_ASN, 106_VAL, 107_PRO, 109_ARG, 94_PHE, 97_ALA, 98_TRP",2.5
+3WFC_B,3WFC_L_light+3WFC_H_heavy,"421_GLY, 422_ALA, 423_ALA, 424_MET, 425_THR, 427_MET, 428_ALA, 431_ASP",2.5
+5I9Q_G,5I9Q_L_light+5I9Q_H_heavy,"123_THR, 197_ASN, 275_LYS, 276_ASN, 278_ARG, 279_ASP, 280_ASN, 281_ALA, 282_LYS, 364_SER, 365_GLY, 366_GLY, 367_ASP, 370_ILE, 430_VAL, 455_LEU, 457_ASP, 458_GLY, 459_GLY, 460_ASP, 461_THR, 462_THR, 469_ARG, 471_SER, 472_GLY, 473_GLY, 474_ASP",3.0
+5I9Q_A,5I9Q_C_light+5I9Q_B_heavy,"123_THR, 197_ASN, 276_ASN, 278_ARG, 279_ASP, 280_ASN, 281_ALA, 282_LYS, 364_SER, 365_GLY, 366_GLY, 367_ASP, 370_ILE, 430_VAL, 455_LEU, 458_GLY, 459_GLY, 460_ASP, 461_THR, 471_SER, 472_GLY, 473_GLY, 474_ASP",3.0
+2DQG_Y,2DQG_L_light+2DQG_H_heavy,"100_SER, 101_ASP, 102_GLY, 103_ASN, 15_HIS, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 62_TRP, 63_TRP, 73_ARG, 75_LEU, 77_ASN, 89_THR, 93_ASN, 96_LYS, 97_LYS, 98_ILE",2.3
+2VDP_A,2VDP_L_light+2VDP_H_heavy,"149_ASN, 154_ILE, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.8
+8DD2_D,8DD2_L_light+8DD2_K_heavy,"124_ASP, 170_GLU, 171_TRP, 172_THR, 173_ARG, 174_GLU, 175_PRO, 176_ALA, 177_ARG, 197_THR, 198_VAL, 199_ASP, 200_SER",2.9
+8DD2_B,8DD2_I_light+8DD2_J_heavy,"170_GLU, 171_TRP, 172_THR, 173_ARG, 174_GLU, 175_PRO, 176_ALA, 177_ARG, 197_THR, 198_VAL, 199_ASP, 200_SER",2.9
+7KEZ_V,7KEZ_L_light+7KEZ_H_heavy,"48_LYS, 81_MET, 85_PRO, 86_HIS, 87_GLN, 88_GLY, 89_GLN, 90_HIS, 91_ILE, 92_GLY, 93_GLU",2.31
+7AMQ_B,7AMQ_L_light+7AMQ_H_heavy,"119_LYS, 120_ASN, 122_PHE, 188_ARG, 223_GLU, 224_TRP, 225_THR, 226_GLN, 227_ASP, 228_ARG",2.35
+5TZT_D,5TZT_B_light+5TZT_A_heavy,"101_LEU, 102_THR, 103_ARG, 104_GLU, 27_ASN, 35_GLU, 37_TYR, 3_LEU",2.89
+5TZT_C,5TZT_L_light+5TZT_H_heavy,"101_LEU, 102_THR, 103_ARG, 104_GLU, 27_ASN, 29_GLU, 3_LEU, 97_GLU",2.89
+4LST_G,4LST_L_light+4LST_H_heavy,"124_GLY, 276_ASN, 278_THR, 279_ASP, 280_ASN, 281_ALA, 282_LYS, 357_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 371_ILE, 430_VAL, 431_GLY, 432_ARG, 455_THR, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_ASN, 461_ASP, 464_ASP, 465_THR, 466_GLU, 467_THR, 469_ARG, 476_ARG",2.55
+8GB8_E,8GB8_L_light+8GB8_H_heavy,"375_PHE, 404_GLY, 405_ASN, 437_ASN, 439_ASN, 440_LYS, 499_PRO, 500_THR, 501_TYR, 502_GLY, 503_VAL, 504_GLY, 505_HIS, 506_GLN, 508_TYR",2.3
+7CHB_R,7CHB_L_light+7CHB_H_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 503_VAL, 505_TYR",2.4
+8HPQ_B,8HPQ_Q_light+8HPQ_P_heavy,"360_ARG, 451_ASN, 453_ASN, 454_LYS, 455_LEU, 458_LYS, 459_VAL, 460_GLY, 461_GLY, 463_TYR, 512_ARG, 513_PRO, 514_THR, 520_GLN",2.85
+8HPQ_B,8HPQ_F_light+8HPQ_E_heavy,"419_ASN, 429_THR, 430_GLY, 431_ASN, 434_ASP, 435_TYR, 469_LEU, 470_PHE, 471_ARG, 472_LYS, 474_ASN, 487_TYR, 488_GLN, 489_ALA, 490_GLY, 491_ASN, 492_LYS, 499_GLY, 501_ASN, 503_TYR, 507_GLN, 515_TYR, 519_HIS",2.85
+8HPQ_C,8HPQ_J_light+8HPQ_I_heavy,"360_ARG, 451_ASN, 453_ASN, 454_LYS, 455_LEU, 458_LYS, 459_VAL, 460_GLY, 461_GLY, 463_TYR, 512_ARG, 513_PRO, 514_THR, 520_GLN",2.85
+8HPQ_C,8HPQ_S_light+8HPQ_R_heavy,"419_ASN, 429_THR, 430_GLY, 431_ASN, 434_ASP, 435_TYR, 469_LEU, 470_PHE, 471_ARG, 472_LYS, 474_ASN, 487_TYR, 488_GLN, 489_ALA, 490_GLY, 491_ASN, 492_LYS, 499_GLY, 501_ASN, 503_TYR, 507_GLN, 515_TYR, 519_HIS",2.85
+8HPQ_A,8HPQ_L_light+8HPQ_H_heavy,"360_ARG, 451_ASN, 453_ASN, 454_LYS, 455_LEU, 458_LYS, 459_VAL, 460_GLY, 461_GLY, 463_TYR, 512_ARG, 513_PRO, 514_THR, 520_GLN",2.85
+8HPQ_A,8HPQ_N_light+8HPQ_M_heavy,"419_ASN, 429_THR, 430_GLY, 431_ASN, 434_ASP, 435_TYR, 469_LEU, 470_PHE, 471_ARG, 472_LYS, 474_ASN, 487_TYR, 488_GLN, 489_ALA, 490_GLY, 491_ASN, 492_LYS, 499_GLY, 501_ASN, 503_TYR, 507_GLN, 515_TYR, 519_HIS",2.85
+8HPK_A,8HPK_L_light+8HPK_H_heavy,"244_VAL, 245_PRO, 248_ARG, 249_SER, 250_LEU, 251_GLY, 371_THR, 372_ASN, 374_GLY, 375_TRP, 42_ASN, 45_LYS, 46_ASP, 47_ASN, 49_GLY, 50_VAL, 51_SER",3.0
+5K9Q_A,5K9Q_L_light+5K9Q_H_heavy,"280_GLU, 290_ASN, 318_THR, 40_THR, 55_PRO, 56_HIS",2.5
+5K9Q_P,5K9Q_V_light+5K9Q_U_heavy,"19_ASP, 21_TRP, 38_LEU, 39_LYS, 41_THR, 42_GLN, 45_ILE, 48_ILE, 49_ASN, 52_LEU, 53_ASN, 58_LYS, 59_THR",2.5
+5K9Q_N,5K9Q_T_light+5K9Q_S_heavy,"19_ASP, 21_TRP, 38_LEU, 39_LYS, 41_THR, 42_GLN, 45_ILE, 48_ILE, 49_ASN, 52_LEU, 53_ASN, 56_ILE, 57_GLU, 58_LYS, 59_THR",2.5
+5K9Q_D,5K9Q_I_light+5K9Q_G_heavy,"19_ASP, 21_TRP, 38_LEU, 39_LYS, 41_THR, 42_GLN, 45_ILE, 49_ASN, 52_LEU, 53_ASN, 56_ILE, 58_LYS, 59_THR",2.5
+5K9Q_B,5K9Q_L_light+5K9Q_H_heavy,"19_ASP, 20_GLY, 21_TRP, 38_LEU, 39_LYS, 41_THR, 42_GLN, 45_ILE, 48_ILE, 49_ASN, 52_LEU, 53_ASN, 56_ILE, 58_LYS, 59_THR",2.5
+5K9Q_R,5K9Q_Y_light+5K9Q_X_heavy,"19_ASP, 20_GLY, 21_TRP, 38_LEU, 39_LYS, 41_THR, 42_GLN, 45_ILE, 49_ASN, 52_LEU, 53_ASN, 57_GLU, 58_LYS, 59_THR",2.5
+5K9Q_F,5K9Q_K_light+5K9Q_J_heavy,"19_ASP, 20_GLY, 21_TRP, 38_LEU, 39_LYS, 41_THR, 42_GLN, 45_ILE, 48_ILE, 49_ASN, 52_LEU, 53_ASN, 57_GLU, 58_LYS, 59_THR",2.5
+5K9Q_Q,5K9Q_Y_light+5K9Q_X_heavy,"280_GLU, 318_THR, 40_THR, 55_PRO, 56_HIS",2.5
+5K9Q_O,5K9Q_V_light+5K9Q_U_heavy,"280_GLU, 290_ASN, 318_THR, 55_PRO, 56_HIS",2.5
+5EBW_C,5EBW_B_light+5EBW_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.3
+8FFE_A,8FFE_L_light+8FFE_H_heavy,"141_ARG, 157_TRP, 183_TRP, 185_ASN, 201_ALA, 202_LYS, 225_PRO, 226_HIS, 228_PHE, 242_TRP, 243_SER, 266_PHE, 269_MET, 28_ARG, 29_ARG, 361_HIS, 363_ILE, 379_ASP, 380_GLU, 404_HIS, 406_ASP, 449_ARG, 465_TRP, 491_TRP, 493_ASN, 51_GLU, 534_HIS, 54_ALA, 550_TRP, 576_MET, 70_VAL, 73_GLU, 95_LEU, 96_SER, 98_ASP",1.72
+8DG9_C,8DG9_E_light+8DG9_D_heavy,"262_ASN, 263_ASP, 264_MET, 265_PRO, 266_ILE, 267_THR, 268_ASN, 269_ASP, 26_GLN, 270_GLN, 271_LYS, 272_LYS, 273_LEU, 306_TYR, 307_GLY, 308_VAL, 309_ILE, 310_ASP, 311_THR, 312_PRO, 346_ALA, 347_GLY, 364_ARG, 45_LEU",2.24
+8DG9_B,8DG9_G_light+8DG9_F_heavy,"262_ASN, 263_ASP, 264_MET, 265_PRO, 266_ILE, 267_THR, 268_ASN, 269_ASP, 26_GLN, 270_GLN, 271_LYS, 272_LYS, 273_LEU, 306_TYR, 307_GLY, 309_ILE, 310_ASP, 312_PRO, 344_ASP, 345_ASN, 346_ALA, 347_GLY, 364_ARG, 45_LEU",2.24
+8DG9_A,8DG9_L_light+8DG9_H_heavy,"178_VAL, 262_ASN, 263_ASP, 264_MET, 265_PRO, 266_ILE, 267_THR, 268_ASN, 269_ASP, 26_GLN, 270_GLN, 271_LYS, 272_LYS, 273_LEU, 306_TYR, 307_GLY, 308_VAL, 309_ILE, 310_ASP, 312_PRO, 344_ASP, 346_ALA, 347_GLY, 364_ARG, 45_LEU",2.24
+1VFB_C,1VFB_A_light+1VFB_B_heavy,"102_GLY, 103_ASN, 116_LYS, 117_GLY, 118_THR, 119_ASP, 120_VAL, 121_GLN, 124_ILE, 125_ARG, 126_GLY, 129_LEU, 18_ASP, 19_ASN, 22_GLY, 23_TYR, 24_SER, 25_LEU, 27_ASN",1.8
+8R9Y_A,8R9Y_C_light+8R9Y_B_heavy,"318_PHE, 319_GLY, 320_GLU, 357_ARG, 358_THR, 359_ASP, 389_LYS, 395_VAL, 396_TRP, 397_ASN, 398_TYR, 399_LEU, 400_LEU, 401_ARG, 403_ARG",3.0
+8R9Y_A,8R9Y_L_light+8R9Y_H_heavy,"359_ASP, 360_THR, 361_CYS, 362_SER, 372_MET, 385_ALA, 386_CYS, 387_GLU, 408_ALA, 409_VAL, 410_GLU",3.0
+5UCB_B,5UCB_L_light+5UCB_H_heavy,"107_ARG, 109_GLY, 10_VAL, 110_TYR, 111_TYR, 112_VAL, 113_ASN, 137_ASN, 139_LEU, 140_ALA, 141_GLU, 142_LYS, 143_PRO, 144_ARG, 145_VAL, 146_THR, 147_ARG, 148_PHE, 28_GLU, 44_VAL, 46_SER, 47_SER, 48_ARG, 49_SER, 4_SER, 50_LEU, 51_ASP, 5_LEU, 6_LEU, 7_GLY, 8_ILE, 91_VAL, 92_THR, 93_VAL, 95_LEU, 9_LYS",1.52
+5UEK_A,5UEK_L_light+5UEK_H_heavy,"110_GLY, 112_TYR, 113_VAL, 114_ASN, 138_ASN, 140_LEU, 142_GLU, 143_LYS, 144_PRO, 145_ARG, 146_VAL, 147_THR, 148_ARG, 149_PHE, 47_SER, 49_ARG, 50_SER, 51_LEU, 52_ASP, 6_LEU, 7_LEU, 8_GLY, 94_VAL, 96_LEU",1.7
+8U2C_A,8U2C_E_light+8U2C_F_heavy,"599_GLY, 600_ASN, 601_ASN, 603_CYS, 604_ASN, 605_TYR, 611_GLU, 612_GLU, 615_VAL, 616_LYS, 620_ASP, 621_LEU, 622_SER",2.5
+8U2C_B,8U2C_C_light+8U2C_D_heavy,"598_THR, 599_GLY, 600_ASN, 601_ASN, 603_CYS, 604_ASN, 605_TYR, 611_GLU, 612_GLU, 615_VAL, 616_LYS, 620_ASP, 621_LEU, 622_SER",2.5
+7DHA_A,7DHA_B_light+7DHA_C_heavy,"158_ASP, 159_VAL, 161_HIS, 192_GLN, 193_THR, 195_GLU, 197_TRP, 228_GLN, 229_PHE, 230_SER, 231_CYS, 232_LYS, 240_PHE, 247_PRO, 248_GLU, 252_CYS, 255_GLU, 256_ILE, 76_ASN, 77_LYS, 91_PHE, 95_GLN, 97_ASP, 98_MET",2.55
+7DAA_A,7DAA_L_light+7DAA_H_heavy,"110_VAL, 111_LYS, 114_GLU, 116_ILE, 120_GLU, 121_THR, 122_ALA, 123_MET, 125_VAL, 127_LYS, 159_PHE, 161_SER, 163_SER, 164_GLN, 165_GLY, 166_ARG, 168_GLU, 170_HIS, 172_GLU",2.51
+8KDM_B,8KDM_D_light+8KDM_E_heavy,"342_ARG, 344_LYS, 380_SER, 381_ASN, 383_TYR, 414_ASP, 415_ASP, 449_ARG, 450_PRO, 451_PHE, 452_GLU, 500_SER, 502_GLU, 503_LEU, 504_LEU, 505_ASN, 506_ALA",2.87
+8KDM_A,8KDM_F_light+8KDM_G_heavy,"342_ARG, 344_LYS, 380_SER, 383_TYR, 414_ASP, 415_ASP, 449_ARG, 450_PRO, 451_PHE, 452_GLU, 502_GLU, 503_LEU, 504_LEU, 505_ASN, 506_ALA",2.87
+4JRE_D,4JRE_L_light+4JRE_H_heavy,"15_ILE, 16_THR, 17_ASP, 19_ARG, 22_ASP, 24_ALA, 25_PHE, 27_GLN, 28_GLN, 29_ARG, 32_ARG",2.8
+4JRE_A,4JRE_C_light+4JRE_B_heavy,"15_ILE, 16_THR, 17_ASP, 19_ARG, 22_ASP, 24_ALA, 25_PHE, 27_GLN, 28_GLN, 29_ARG, 32_ARG",2.8
+6S3D_P,6S3D_L_light+6S3D_H_heavy,"12_ASP, 13_LYS, 14_GLN, 16_LEU, 17_PRO, 18_ILE, 20_ASN, 21_LYS, 30_VAL, 53_GLY, 54_LEU, 55_LYS, 56_GLU, 57_ILE, 58_LYS, 8_LYS",3.0
+6S3D_M,6S3D_D_light+6S3D_C_heavy,"10_TYR, 12_ASP, 13_LYS, 14_GLN, 16_LEU, 17_PRO, 18_ILE, 20_ASN, 21_LYS, 53_GLY, 54_LEU, 55_LYS, 56_GLU, 57_ILE, 58_LYS",3.0
+6S3D_N,6S3D_B_light+6S3D_A_heavy,"12_ASP, 13_LYS, 14_GLN, 16_LEU, 17_PRO, 18_ILE, 19_VAL, 20_ASN, 21_LYS, 53_GLY, 54_LEU, 55_LYS, 56_GLU, 57_ILE, 58_LYS",3.0
+6S3D_O,6S3D_F_light+6S3D_E_heavy,"10_TYR, 12_ASP, 13_LYS, 14_GLN, 16_LEU, 17_PRO, 18_ILE, 20_ASN, 21_LYS, 53_GLY, 54_LEU, 55_LYS, 56_GLU, 57_ILE, 58_LYS",3.0
+4LCU_C,4LCU_B_light+4LCU_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.75
+4XP9_C,4XP9_L_light+4XP9_H_heavy,"337_TYR, 498_TYR, 502_ARG, 505_GLU, 506_ASP, 508_ARG, 509_ASP, 511_ILE, 512_GLY, 513_PHE, 514_PRO, 598_ARG, 599_ASP, 90_HIS",2.8
+2DQI_Y,2DQI_L_light+2DQI_H_heavy,"100_SER, 101_ASP, 102_GLY, 103_ASN, 15_HIS, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 62_TRP, 63_TRP, 73_ARG, 75_LEU, 89_THR, 93_ASN, 96_LYS, 97_LYS, 98_ILE",2.0
+1UWX_A,1UWX_L_light+1UWX_H_heavy,"16_THR, 17_LEU, 18_LYS, 19_GLY, 20_GLU, 21_THR, 22_THR, 38_TYR, 40_ASN, 41_ASP, 42_ASN, 43_GLY",2.2
+1UWX_B,1UWX_K_light+1UWX_M_heavy,"16_THR, 17_LEU, 18_LYS, 19_GLY, 20_GLU, 21_THR, 22_THR, 38_TYR, 40_ASN, 41_ASP, 42_ASN, 64_GLU",2.2
+4S1Q_G,4S1Q_L_light+4S1Q_H_heavy,"122_LEU, 123_THR, 124_GLY, 198_GLY, 276_ASN, 278_THR, 279_ASN, 280_ASN, 281_ALA, 283_THR, 357_LYS, 360_ILE, 365_SER, 366_GLY, 367_GLY, 368_ASP, 370_GLU, 371_ILE, 425_ASN, 426_MET, 427_TRP, 429_GLY, 430_THR, 431_GLY, 432_GLN, 455_THR, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_ALA, 461_ASN, 462_ASN, 463_THR, 465_ASN, 466_GLU, 467_THR, 469_ARG, 472_GLY, 473_GLY",2.4
+7O9S_A,7O9S_L_light+7O9S_H_heavy,"158_LEU, 160_ALA, 162_GLY, 163_PRO, 165_ARG, 167_GLN, 18_SER, 19_LEU, 20_ARG, 21_ASN, 22_VAL, 49_MET, 53_ASP, 60_GLU, 61_SER, 62_SER",2.7
+7U8E_A,7U8E_L_light+7U8E_H_heavy,"378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 412_PRO, 413_GLY, 414_GLN, 415_THR, 424_LYS, 426_PRO, 427_ASP, 428_ASP, 430_THR, 517_LEU",2.29
+7ZXU_E,7ZXU_L_light+7ZXU_H_heavy,"415_THR, 416_GLY, 417_ASN, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 459_SER, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_ASN, 487_ASN, 489_TYR, 493_GLN, 500_THR, 501_TYR, 502_GLY, 505_HIS",1.89
+4DN4_M,4DN4_L_light+4DN4_H_heavy,"16_THR, 18_ARG, 19_LYS, 20_ILE, 21_SER, 23_GLN, 24_ARG, 45_THR, 46_ILE, 47_VAL, 49_LYS",2.8
+5USH_X,5USH_L_light+5USH_H_heavy,"100_LYS, 109_LYS, 110_HIS, 111_ASP, 112_ASP, 115_ILE, 168_THR, 170_LEU, 171_GLY, 179_ASP, 181_VAL, 18_ASN, 20_ARG, 21_LEU, 228_ASP, 229_THR, 232_TYR, 2_PRO, 3_GLN, 4_GLN, 56_PHE, 58_PRO, 61_TYR",2.3
+5USH_A,5USH_E_light+5USH_D_heavy,"101_TYR, 109_LYS, 110_HIS, 111_ASP, 112_ASP, 115_ILE, 168_THR, 170_LEU, 171_GLY, 179_ASP, 181_VAL, 18_ASN, 19_ALA, 20_ARG, 228_ASP, 229_THR, 230_GLU, 232_TYR, 3_GLN, 4_GLN, 56_PHE, 58_PRO, 61_TYR",2.3
+6PI7_D,6PI7_E_light+6PI7_F_heavy,"309_GLU, 311_TYR, 324_GLN, 326_VAL, 330_SER, 331_LEU, 333_LEU, 334_ASP, 338_ASN, 367_ASN, 368_GLY, 369_SER, 371_TYR, 391_PHE, 393_ASP, 448_TRP, 449_LYS, 450_PRO",2.8
+6PI7_A,6PI7_B_light+6PI7_C_heavy,"309_GLU, 311_TYR, 313_SER, 324_GLN, 326_VAL, 330_SER, 331_LEU, 333_LEU, 334_ASP, 337_VAL, 338_ASN, 345_GLU, 346_ASN, 367_ASN, 368_GLY, 369_SER, 371_TYR, 391_PHE, 393_ASP, 446_ALA, 448_TRP, 449_LYS, 450_PRO",2.8
+3GI9_C,3GI9_L_light+3GI9_H_heavy,"1_MET, 307_GLU, 309_PRO, 310_GLU, 311_PHE, 312_PHE, 313_GLU, 316_VAL, 318_PHE, 319_LYS, 3_LEU, 430_ASN, 435_SER, 436_LEU, 437_GLU, 4_LYS",2.48
+6WO3_E,6WO3_L_light+6WO3_H_heavy,"427_LEU, 428_ASN, 429_CYS, 430_ASN, 438_ILE, 439_THR, 442_PHE, 443_TYR, 500_ARG, 528_ASN, 529_TRP, 531_GLU",2.38
+8W0W_C,8W0W_L_light+8W0W_H_heavy,"427_LEU, 429_CYS, 430_ASN, 431_ASP, 432_SER, 433_LEU, 434_HIS, 435_THR, 436_GLY, 437_PHE, 438_LEU, 440_ALA, 442_PHE, 443_TYR, 446_LYS, 447_PHE, 448_ASN, 529_TRP, 531_GLU, 615_LEU, 616_TRP, 619_PRO",2.13
+4J6R_G,4J6R_L_light+4J6R_H_heavy,"122_LEU, 124_GLY, 198_GLY, 276_ASN, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 356_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 370_GLU, 371_ILE, 425_ASN, 426_MET, 427_TRP, 430_ALA, 457_ASP, 458_GLY, 459_GLY, 460_LYS, 461_ASP, 462_ASN, 463_ASN, 469_ARG, 473_GLY, 474_ASP, 475_MET, 97_LYS",1.64
+6VEP_E,6VEP_D_light+6VEP_C_heavy,"222_PRO, 223_THR, 233_LEU, 234_ASP, 236_ARG, 237_CYS, 238_VAL, 239_GLU, 240_THR, 241_CYS, 242_PRO, 243_PRO, 244_PRO, 245_TYR, 247_HIS, 281_ASN, 282_ASN",2.9
+6VEP_K,6VEP_J_light+6VEP_I_heavy,"222_PRO, 223_THR, 233_LEU, 234_ASP, 236_ARG, 237_CYS, 238_VAL, 239_GLU, 240_THR, 241_CYS, 242_PRO, 243_PRO, 244_PRO, 245_TYR, 247_HIS, 281_ASN, 282_ASN",2.9
+6VEP_W,6VEP_V_light+6VEP_U_heavy,"222_PRO, 223_THR, 233_LEU, 234_ASP, 236_ARG, 237_CYS, 238_VAL, 239_GLU, 240_THR, 241_CYS, 242_PRO, 243_PRO, 244_PRO, 245_TYR, 247_HIS, 281_ASN, 282_ASN",2.9
+6VEP_Q,6VEP_P_light+6VEP_O_heavy,"222_PRO, 223_THR, 233_LEU, 234_ASP, 236_ARG, 238_VAL, 239_GLU, 240_THR, 241_CYS, 242_PRO, 243_PRO, 244_PRO, 245_TYR, 247_HIS, 281_ASN, 282_ASN",2.9
+7DC8_C,7DC8_A_light+7DC8_B_heavy,"182_GLU, 183_GLY, 247_SER, 248_PHE, 249_TYR, 250_ARG, 252_ARG, 272_ASP, 273_LEU, 296_GLU, 297_GLU, 298_PHE, 300_GLN",2.76
+7DC8_F,7DC8_D_light+7DC8_E_heavy,"182_GLU, 183_GLY, 247_SER, 248_PHE, 249_TYR, 250_ARG, 272_ASP, 273_LEU, 296_GLU, 297_GLU, 298_PHE, 300_GLN",2.76
+7SHZ_G,7SHZ_J_light+7SHZ_I_heavy,"373_THR, 374_TRP, 375_SER, 376_ARG, 377_ALA, 378_SER, 379_GLY, 380_LYS, 381_PRO, 412_GLU, 414_GLU, 417_GLN, 419_ARG, 426_PRO, 427_ARG, 428_ALA, 430_MET",3.0
+7SHZ_B,7SHZ_F_light+7SHZ_E_heavy,"373_THR, 374_TRP, 375_SER, 376_ARG, 377_ALA, 378_SER, 379_GLY, 380_LYS, 381_PRO, 417_GLN, 419_ARG, 421_THR, 427_ARG, 428_ALA, 430_MET",3.0
+7SHZ_H,7SHZ_L_light+7SHZ_K_heavy,"373_THR, 374_TRP, 375_SER, 376_ARG, 377_ALA, 378_SER, 379_GLY, 380_LYS, 381_PRO, 417_GLN, 419_ARG, 421_THR, 427_ARG, 428_ALA, 430_MET",3.0
+7SHZ_A,7SHZ_D_light+7SHZ_C_heavy,"373_THR, 374_TRP, 375_SER, 376_ARG, 377_ALA, 378_SER, 379_GLY, 380_LYS, 417_GLN, 419_ARG, 426_PRO, 427_ARG, 428_ALA, 430_MET",3.0
+3UC0_B,3UC0_M_light+3UC0_I_heavy,"155_THR, 160_VAL, 161_THR, 162_ALA, 163_MET, 168_SER, 170_SER, 171_VAL, 172_GLU, 173_VAL, 174_LYS, 176_PRO, 177_ASP, 179_GLY, 180_GLU, 291_LYS, 293_ARG",2.71
+8XSF_B,8XSF_L_light+8XSF_H_heavy,"355_ARG, 357_ARG, 396_TYR, 426_PRO, 428_ASP, 462_LYS, 463_PRO, 464_PHE, 465_GLU, 466_ARG, 516_GLU, 519_HIS",2.16
+8TCO_A,8TCO_D_light+8TCO_E_heavy,"54_GLU, 58_GLN, 59_CYS, 61_TYR, 64_SER, 65_LEU, 66_ARG, 67_ASN",2.8
+8TCO_B,8TCO_D_light+8TCO_E_heavy,"53_ARG, 54_CYS, 55_LEU, 56_LEU, 57_GLY, 58_GLU, 59_VAL, 60_PHE, 61_GLN",2.8
+8TCO_C,8TCO_G_light+8TCO_F_heavy,"101_ILE, 103_ASN, 211_LEU, 214_ARG, 215_TYR, 217_GLN, 218_ARG, 219_ASN, 221_THR, 225_TYR, 237_VAL, 238_PRO, 239_LYS, 240_TYR, 241_ILE, 243_GLY, 244_THR, 90_GLU, 91_PHE, 93_GLN, 95_TYR",2.8
+5F96_G,5F96_L_light+5F96_H_heavy,"275_GLU, 276_ASN, 277_LEU, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 371_ILE, 429_GLY, 455_THR, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 469_ARG, 472_GLY, 480_ARG, 96_TRP",2.24
+1IC4_Y,1IC4_L_light+1IC4_H_heavy,"100_SER, 101_ASP, 102_GLY, 103_ASN, 15_HIS, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 62_TRP, 63_TRP, 73_ARG, 75_LEU, 89_THR, 93_ASN, 96_LYS, 97_LYS, 98_ILE",2.5
+1XGQ_C,1XGQ_A_light+1XGQ_B_heavy,"100_SER, 101_ASP, 102_GLY, 14_ARG, 15_HIS, 16_GLY, 18_ASP, 19_ASN, 20_TYR, 21_ARG, 63_TRP, 73_ARG, 75_LEU, 77_ASN, 89_THR, 93_ASN, 96_LYS, 97_LYS, 98_ILE",2.1
+4WFG_B,4WFG_F_light+4WFG_G_heavy,"69_ARG, 70_GLU, 73_LEU, 74_ARG, 76_HIS, 77_PRO, 78_CYS, 79_VAL, 80_SER, 81_ASP, 82_GLN, 83_GLU",3.0
+4WFG_A,4WFG_D_light+4WFG_E_heavy,"69_ARG, 70_GLU, 73_LEU, 74_ARG, 76_HIS, 77_PRO, 78_CYS, 79_VAL, 80_SER, 81_ASP, 82_GLN, 83_GLU",3.0
+6XPX_A,6XPX_C_light+6XPX_B_heavy,"136_SER, 137_ASN, 138_ALA, 140_LYS, 216_ASN, 217_ILE, 218_GLY, 219_SER, 220_ARG, 221_PRO, 222_TRP, 223_VAL, 224_ARG, 225_GLY, 226_LEU, 229_ARG",2.6
+2DQH_Y,2DQH_L_light+2DQH_H_heavy,"100_SER, 101_ASP, 102_GLY, 103_ASN, 15_HIS, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 62_TRP, 63_TRP, 73_ARG, 75_LEU, 77_ASN, 89_THR, 93_ASN, 96_LYS, 97_LYS, 98_ILE",2.3
+7RXI_A,7RXI_L_light+7RXI_H_heavy,"314_LYS, 315_HIS, 317_LYS, 318_GLU, 320_LEU, 321_ASN, 324_GLN, 325_ASN, 327_LEU, 354_PRO, 355_LYS, 356_ASP, 357_GLU, 358_LEU, 359_ASP, 360_TYR, 364_ILE",2.15
+2HMI_B,2HMI_C_light+2HMI_D_heavy,"199_ARG, 223_LYS, 224_GLU, 225_PRO, 226_PRO, 227_PHE, 229_TRP, 230_MET, 358_ARG",2.8
+1G7J_C,1G7J_A_light+1G7J_B_heavy,"102_GLY, 116_LYS, 117_GLY, 118_THR, 119_ASP, 120_VAL, 121_GLN, 124_ILE, 125_ARG, 18_ASP, 19_ASN, 22_GLY, 23_TYR, 24_SER, 27_ASN",1.75
+7KN7_A,7KN7_L_light+7KN7_H_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 486_PHE, 487_ASN, 489_TYR, 495_TYR, 501_ASN, 505_TYR",2.73
+7URS_A,7URS_L_light+7URS_H_heavy,"403_ARG, 405_ASP, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 496_GLY, 500_THR, 501_ASN, 502_GLY, 505_TYR",2.4
+8HQ7_A,8HQ7_N_light+8HQ7_M_heavy,"403_ARG, 415_THR, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 478_THR, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 501_ASN, 505_TYR",2.7
+8HQ7_A,8HQ7_L_light+8HQ7_H_heavy,"437_ASN, 439_ASN, 440_ASN, 441_LEU, 444_LYS, 445_VAL, 446_GLY, 447_GLY, 450_ASN, 498_GLN, 499_PRO, 500_THR",2.7
+7ND9_B,7ND9_L_light+7ND9_H_heavy,"455_LEU, 456_PHE, 458_LYS, 473_TYR, 475_ALA, 476_GLY, 477_SER, 478_THR, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN",2.8
+8CII_E,8CII_L_light+8CII_H_heavy,"339_GLY, 343_ASN, 344_ALA, 345_THR, 439_ASN, 440_ASN, 441_LEU, 443_SER, 444_LYS, 445_VAL, 499_PRO",2.7
+8CII_E,8CII_B_light+8CII_A_heavy,"351_TYR, 449_TYR, 452_ARG, 456_PHE, 470_THR, 472_ILE, 478_LYS, 482_GLY, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 488_CYS, 489_TYR, 490_PHE, 492_LEU",2.7
+1KB9_E,1KB9_K_light+1KB9_J_heavy,"123_PRO, 124_HIS, 126_ILE, 127_GLN, 130_ASN, 131_SER, 132_VAL, 133_ASP, 134_MET, 142_THR, 143_ASP, 144_ALA, 147_VAL, 148_LYS, 149_ASP, 150_PRO, 151_GLN",2.3
+8G3N_G,8G3N_F_light+8G3N_E_heavy,"118_ARG, 149_VAL, 150_ARG, 151_ASP, 152_ARG, 178_TRP, 179_SER, 199_LYS, 221_GLU, 222_ILE, 227_GLU, 246_ALA, 247_SER, 248_GLY, 292_ARG, 294_ASN, 295_TRP, 344_GLU, 345_GLY, 346_GLY, 347_HIS, 371_ARG, 406_TYR",2.9
+8G3N_J,8G3N_D_light+8G3N_A_heavy,"118_ARG, 149_VAL, 150_ARG, 151_ASP, 152_ARG, 178_TRP, 179_SER, 199_LYS, 221_GLU, 222_ILE, 227_GLU, 246_ALA, 247_SER, 248_GLY, 292_ARG, 294_ASN, 295_TRP, 344_GLU, 345_GLY, 346_GLY, 347_HIS, 371_ARG, 406_TYR",2.9
+8G3N_K,8G3N_H_light+8G3N_B_heavy,"118_ARG, 149_VAL, 150_ARG, 151_ASP, 152_ARG, 178_TRP, 179_SER, 199_LYS, 221_GLU, 222_ILE, 227_GLU, 246_ALA, 247_SER, 248_GLY, 292_ARG, 294_ASN, 295_TRP, 344_GLU, 345_GLY, 346_GLY, 347_HIS, 371_ARG, 406_TYR",2.9
+8G3N_L,8G3N_I_light+8G3N_C_heavy,"118_ARG, 149_VAL, 150_ARG, 151_ASP, 152_ARG, 178_TRP, 179_SER, 199_LYS, 221_GLU, 222_ILE, 227_GLU, 246_ALA, 247_SER, 248_GLY, 292_ARG, 294_ASN, 295_TRP, 344_GLU, 345_GLY, 346_GLY, 347_HIS, 371_ARG, 406_TYR",2.9
+6FLB_G,6FLB_L_light+6FLB_H_heavy,"301_MET, 303_THR, 305_LYS, 327_GLU, 328_GLY, 336_PRO, 337_PHE, 338_GLU, 345_ARG, 381_GLY, 382_VAL, 383_GLU, 384_PRO, 385_GLY, 386_GLN",2.2
+7AMS_B,7AMS_L_light+7AMS_H_heavy,"119_LYS, 120_ASN, 122_PHE, 186_ASP, 188_ARG, 223_GLU, 224_TRP, 225_THR, 226_GLN, 227_ASP, 228_ARG",2.42
+7X1M_M,7X1M_B_light+7X1M_A_heavy,"334_ASN, 335_LEU, 337_PRO, 339_ASP, 340_GLU, 343_ASN, 344_ALA, 345_THR, 346_ARG, 356_LYS, 440_LYS, 441_LEU",2.74
+7X1M_M,7X1M_D_light+7X1M_C_heavy,"403_ARG, 405_ASP, 408_ARG, 415_THR, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_ASN, 486_PHE, 487_ASN, 489_TYR, 493_ARG, 501_TYR, 502_GLY, 505_HIS",2.74
+7X1M_M,7X1M_L_light+7X1M_H_heavy,"369_TYR, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 390_LEU, 392_PHE, 412_PRO, 427_ASP, 428_ASP, 429_PHE, 517_LEU",2.74
+7KN6_A,7KN6_L_light+7KN6_H_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 486_PHE, 487_ASN, 489_TYR, 496_GLY, 501_ASN, 505_TYR",2.55
+4XVT_G,4XVT_L_light+4XVT_H_heavy,"102_GLU, 275_GLU, 276_ASN, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 371_ILE, 430_THR, 455_THR, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 461_ASN, 469_ARG, 474_ASN, 476_LYS, 97_LYS",1.69
+4JKP_G,4JKP_L_light+4JKP_H_heavy,"102_GLU, 122_LEU, 124_GLY, 275_GLU, 276_ASN, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 370_GLU, 371_ILE, 425_ASN, 427_TRP, 431_GLY, 432_GLN, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 467_THR, 469_ARG, 473_GLY, 476_LYS, 97_LYS, 99_ASN",2.82
+6MTQ_T,6MTQ_L_light+6MTQ_H_heavy,"100_ALA, 101_GLY, 119_ASN, 120_TRP, 121_PHE, 122_ASP, 123_ILE, 124_THR, 125_LYS, 127_LEU, 128_TRP, 131_LYS, 135_GLN, 60_PHE, 73_ILE, 75_HIS, 78_SER",2.7
+6MU8_G,6MU8_L_light+6MU8_H_heavy,"135_THR, 136_ASN, 137_ALA, 138_ILE, 139_THR, 322_ILE, 323_ILE, 324_GLY, 325_ASP, 326_ILE, 327_ARG, 328_GLN, 330_HIS, 415_THR, 417_PRO",2.99
+6PLK_E,6PLK_L_light+6PLK_H_heavy,"306_SER, 307_LEU, 309_THR, 310_ALA, 311_ALA, 336_ASP, 340_LYS, 391_VAL, 392_GLY, 393_GLU, 394_LYS",2.3
+6PLK_F,6PLK_M_light+6PLK_I_heavy,"306_SER, 307_LEU, 309_THR, 310_ALA, 336_ASP, 350_GLN, 351_THR, 352_LEU, 391_VAL, 392_GLY, 393_GLU, 394_LYS",2.3
+5U6A_E,5U6A_A_light+5U6A_B_heavy,"33_LYS, 34_ILE, 35_GLN, 36_THR, 37_ALA, 38_GLU, 39_PHE, 49_GLU, 52_ARG, 53_TYR, 56_LEU, 57_LEU, 60_VAL, 61_ASN",1.74
+5U6A_C,5U6A_A_light+5U6A_B_heavy,"23_ALA, 24_GLN, 27_GLY, 28_PHE, 30_GLN, 31_SER, 34_ASP, 35_ASP, 37_SER, 38_GLN, 41_ASN, 42_VAL, 45_GLU, 49_LEU",1.74
+7QU2_C,7QU2_B_light+7QU2_A_heavy,"111_SER, 113_ASP, 115_ILE, 117_VAL, 119_LEU, 121_GLN, 122_TYR, 137_LYS, 165_ARG, 168_THR, 169_LYS, 170_THR, 171_GLU, 216_LYS, 218_GLN",2.5
+5UMN_B,5UMN_D_light+5UMN_C_heavy,"131_THR, 133_ASN, 134_GLY, 135_GLY, 136_SER, 137_ASN, 145_SER, 153_TRP, 155_THR, 156_LYS, 183_HIS, 189_GLN, 190_GLU, 193_SER, 194_LEU, 226_LEU, 98_TYR",1.97
+5UMN_A,5UMN_F_light+5UMN_E_heavy,"131_THR, 133_ASN, 134_GLY, 135_GLY, 136_SER, 137_ASN, 145_SER, 153_TRP, 155_THR, 156_LYS, 189_GLN, 190_GLU, 193_SER, 194_LEU, 226_LEU, 98_TYR",1.97
+6OSV_K,6OSV_L_light+6OSV_H_heavy,"343_GLN, 344_HIS, 345_PRO, 346_HIS, 347_SER, 348_HIS, 349_HIS, 350_LEU, 351_SER, 353_THR, 354_ASP, 355_PHE, 371_GLN, 376_TRP, 378_PHE, 382_LYS, 385_ARG, 386_MET",1.34
+3ZE2_A,3ZE2_F_light+3ZE2_E_heavy,"117_GLU, 149_ASN, 154_ILE, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.35
+3ZE2_C,3ZE2_L_light+3ZE2_H_heavy,"117_GLU, 149_ASN, 154_ILE, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 217_SER, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.35
+8GKL_E,8GKL_L_light+8GKL_H_heavy,"136_ASN, 139_LYS, 140_GLU, 142_GLY, 143_PRO, 144_TYR, 145_THR, 49_ASN, 50_LEU, 51_LYS, 66_THR, 69_ARG, 70_VAL, 73_SER",2.6
+7SUE_D,7SUE_A_light+7SUE_B_heavy,"123_TYR, 124_GLY, 125_ALA, 126_ASN, 135_THR, 136_GLU, 137_GLY, 138_ALA, 139_LEU, 140_ASN, 68_ARG, 69_GLY",2.9
+7SUE_C,7SUE_L_light+7SUE_H_heavy,"123_TYR, 124_GLY, 125_ALA, 126_ASN, 135_THR, 136_GLU, 137_GLY, 138_ALA, 139_LEU, 140_ASN, 68_ARG",2.9
+7SUE_K,7SUE_G_light+7SUE_I_heavy,"123_TYR, 124_GLY, 125_ALA, 126_ASN, 135_THR, 137_GLY, 138_ALA, 139_LEU, 140_ASN, 68_ARG",2.9
+7SUE_J,7SUE_E_light+7SUE_F_heavy,"123_TYR, 124_GLY, 125_ALA, 126_ASN, 135_THR, 136_GLU, 137_GLY, 138_ALA, 139_LEU, 140_ASN, 68_ARG",2.9
+6KS1_A,6KS1_L_light+6KS1_H_heavy,"0_PHE, 100_GLU, 101_GLY, 102_ARG, 103_TRP, 104_ARG, 106_ILE, 107_PRO, 110_VAL, 123_HIS, 127_PRO, 128_PRO, 129_MET, 130_PRO, 131_SER",2.4
+7BEK_E,7BEK_L_light+7BEK_H_heavy,"403_ARG, 405_ASP, 406_GLU, 408_ARG, 409_GLN, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",2.04
+2NYY_A,2NYY_C_light+2NYY_D_heavy,"1056_LYS, 1057_LEU, 1058_ASP, 1059_GLY, 1060_CYS, 1061_ARG, 1062_ASP, 1063_THR, 1064_HIS, 1065_ARG, 1289_ASP, 1292_GLY, 1294_ARG, 902_SER, 917_PHE, 918_ASN, 919_LEU, 920_GLU, 921_SER, 953_PHE, 954_ASN, 955_SER, 956_ILE",2.61
+7S7I_D,7S7I_V_light+7S7I_U_heavy,"240_ARG, 241_GLN, 242_ASP, 243_GLY, 244_VAL, 245_SER, 246_LEU, 247_SER, 250_THR, 273_CYS, 276_GLU, 278_GLN, 279_ARG, 280_PHE, 283_TYR, 290_HIS, 292_THR",2.4
+7S7I_C,7S7I_N_light+7S7I_J_heavy,"240_ARG, 241_GLN, 242_ASP, 243_GLY, 244_VAL, 245_SER, 246_LEU, 247_SER, 250_THR, 273_CYS, 276_GLU, 278_GLN, 279_ARG, 280_PHE, 283_TYR, 290_HIS, 292_THR",2.4
+7S7I_B,7S7I_M_light+7S7I_I_heavy,"240_ARG, 241_GLN, 242_ASP, 243_GLY, 244_VAL, 245_SER, 246_LEU, 247_SER, 250_THR, 273_CYS, 276_GLU, 278_GLN, 279_ARG, 283_TYR, 290_HIS, 292_THR",2.4
+7S7I_A,7S7I_L_light+7S7I_H_heavy,"240_ARG, 241_GLN, 242_ASP, 243_GLY, 244_VAL, 245_SER, 246_LEU, 247_SER, 250_THR, 273_CYS, 276_GLU, 278_GLN, 279_ARG, 280_PHE, 283_TYR, 290_HIS, 292_THR",2.4
+7S7I_F,7S7I_Z_light+7S7I_Y_heavy,"240_ARG, 241_GLN, 242_ASP, 243_GLY, 244_VAL, 245_SER, 246_LEU, 247_SER, 250_THR, 273_CYS, 276_GLU, 278_GLN, 279_ARG, 280_PHE, 283_TYR, 290_HIS, 292_THR",2.4
+7S7I_E,7S7I_X_light+7S7I_W_heavy,"240_ARG, 241_GLN, 242_ASP, 243_GLY, 244_VAL, 245_SER, 246_LEU, 247_SER, 250_THR, 271_ARG, 273_CYS, 276_GLU, 278_GLN, 279_ARG, 283_TYR, 290_HIS, 292_THR",2.4
+4OT1_A,4OT1_L_light+4OT1_H_heavy,"131_ARG, 359_GLU, 360_ALA, 361_GLU, 362_ASP, 379_LYS, 380_GLN, 381_GLU, 382_VAL, 383_ASN, 386_ASP, 388_ALA, 422_GLU",2.11
+5VCN_A,5VCN_E_light+5VCN_F_heavy,"107_ASN, 108_ALA, 109_GLN, 110_ARG, 111_PHE, 112_GLY, 113_ILE, 114_SER, 221_ILE, 222_LEU, 47_TYR, 51_ARG, 53_GLN, 54_SER, 55_LEU, 83_ILE, 84_GLN, 85_HIS, 90_GLN, 93_TYR",3.0
+5VCN_B,5VCN_C_light+5VCN_D_heavy,"107_ASN, 108_ALA, 109_GLN, 110_ARG, 111_PHE, 112_GLY, 113_ILE, 114_SER, 221_ILE, 222_LEU, 47_TYR, 51_ARG, 53_GLN, 54_SER, 55_LEU, 83_ILE, 84_GLN, 85_HIS, 86_ASN, 87_GLY, 90_GLN, 93_TYR",3.0
+8CWI_A,8CWI_L_light+8CWI_H_heavy,"456_PHE, 474_GLN, 476_GLY, 477_SER, 478_THR, 479_PRO, 483_VAL, 485_GLY, 486_PHE, 487_ASN, 488_CYS, 489_TYR",1.87
+6OHG_A,6OHG_B_light+6OHG_C_heavy,"581_LYS, 582_TYR, 583_ALA, 584_SER, 585_GLN, 586_ASN, 594_ASP, 596_THR, 597_ASP, 598_GLN, 600_LYS, 601_PRO, 602_THR, 603_GLU, 604_SER, 607_LYS, 609_LYS",2.39
+1YYM_G,1YYM_L_light+1YYM_H_heavy,"119_CYS, 120_VAL, 122_LEU, 200_VAL, 202_THR, 203_GLN, 204_ALA, 419_ARG, 420_ILE, 421_LYS, 422_GLN, 423_ILE, 434_MET, 437_PRO",2.2
+1YYM_P,1YYM_Q_light+1YYM_R_heavy,"1119_CYS, 1120_VAL, 1122_LEU, 1202_THR, 1203_GLN, 1204_ALA, 1419_ARG, 1420_ILE, 1421_LYS, 1422_GLN, 1423_ILE, 1434_MET, 1437_PRO",2.2
+8JKH_b,8JKH_n_light+8JKH_m_heavy,"101_PRO, 102_GLN, 146_LYS, 174_ASP, 175_TYR, 176_SER, 177_LEU, 178_GLU, 224_GLU, 227_LYS, 245_LYS, 99_ARG",2.64
+8JKH_a,8JKH_l_light+8JKH_h_heavy,"101_PRO, 102_GLN, 146_LYS, 174_ASP, 175_TYR, 176_SER, 177_LEU, 178_GLU, 224_GLU, 227_LYS, 245_LYS, 251_LEU, 99_ARG",2.64
+8JKH_A,8JKH_L_light+8JKH_H_heavy,"101_PRO, 102_GLN, 146_LYS, 174_ASP, 175_TYR, 176_SER, 177_LEU, 178_GLU, 224_GLU, 227_LYS",2.64
+8JKH_B,8JKH_N_light+8JKH_M_heavy,"100_GLY, 101_PRO, 102_GLN, 146_LYS, 174_ASP, 175_TYR, 176_SER, 177_LEU, 178_GLU, 224_GLU, 227_LYS, 245_LYS, 251_LEU, 99_ARG",2.64
+2IBZ_E,2IBZ_Y_light+2IBZ_X_heavy,"123_PRO, 124_HIS, 126_ILE, 127_GLN, 130_ASN, 131_SER, 132_VAL, 133_ASP, 134_MET, 142_THR, 143_ASP, 144_ALA, 147_VAL, 148_LYS, 149_ASP, 150_PRO, 151_GLN",2.3
+7NEG_E,7NEG_L_light+7NEG_H_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 498_GLN, 501_TYR, 502_GLY, 505_TYR",2.19
+7TR4_A,7TR4_H_light+7TR4_L_heavy,"146_LYS, 149_ALA, 150_ALA, 151_HIS, 154_GLU, 65_ARG, 66_LYS, 69_ALA, 72_GLN, 73_THR",2.3
+6Z7W_N,6Z7W_E_light+6Z7W_F_heavy,"10_HIS, 14_ALA, 17_LEU, 18_VAL, 5_HIS, 6_LEU, 7_CYS",2.42
+6Z7W_P,6Z7W_G_light+6Z7W_H_heavy,"10_HIS, 14_ALA, 4_GLN, 5_HIS, 6_LEU, 7_CYS",2.42
+8ELP_A,8ELP_L_light+8ELP_H_heavy,"403_ARG, 405_ASP, 408_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",2.83
+7A3N_A,7A3N_L_light+7A3N_H_heavy,"148_HIS, 150_GLY, 151_MET, 152_ILE, 164_ARG, 283_ARG, 2_ARG, 316_LYS, 370_GLU, 371_ASN, 373_LYS, 44_GLU, 46_VAL",2.1
+8HPF_A,8HPF_L_light+8HPF_H_heavy,"362_ARG, 453_ASN, 455_ASN, 456_LYS, 457_LEU, 460_LYS, 461_VAL, 462_GLY, 463_GLY, 465_TYR, 466_ASN, 514_ARG, 515_PRO, 516_THR, 522_GLN",2.34
+8HPF_A,8HPF_N_light+8HPF_M_heavy,"421_ASN, 431_THR, 432_GLY, 433_ASN, 436_ASP, 437_TYR, 469_TYR, 471_LEU, 472_PHE, 473_ARG, 474_LYS, 476_ASN, 489_TYR, 490_GLN, 491_ALA, 492_GLY, 493_ASN, 494_LYS, 500_ALA, 502_PHE, 503_ASN, 505_TYR, 509_ARG, 517_TYR, 518_GLY, 521_HIS",2.34
+5E8E_H,5E8E_A_light+5E8E_B_heavy,"339_PHE, 344_GLN, 345_GLU, 346_LEU, 380_LEU, 382_ARG, 388_ARG, 389_THR, 390_ARG, 391_TYR, 393_ARG, 398_ILE",1.9
+6J5F_A,6J5F_L_light+6J5F_H_heavy,"308_ASP, 309_LYS, 310_THR, 311_LYS, 312_PHE, 313_THR, 332_PHE, 333_SER, 334_GLY, 335_THR, 336_LYS, 387_GLU",1.8
+6P67_I,6P67_F_light+6P67_E_heavy,"144_MET, 155_GLU, 156_ASN, 160_HIS, 186_ARG, 188_ILE, 194_LYS, 196_PHE, 197_TRP, 200_TRP, 21_SER, 40_GLU",2.9
+6P67_G,6P67_L_light+6P67_H_heavy,"144_MET, 155_GLU, 156_ASN, 158_TRP, 160_HIS, 186_ARG, 188_ILE, 196_PHE, 197_TRP, 40_GLU",2.9
+6P67_J,6P67_D_light+6P67_C_heavy,"114_ASP, 115_LEU, 116_SER, 117_VAL, 118_VAL, 120_ARG, 121_GLU, 122_GLY, 129_THR, 180_MET, 204_TYR, 205_TYR, 206_PHE, 207_ARG, 209_PRO",2.9
+6P67_K,6P67_B_light+6P67_A_heavy,"114_ASP, 115_LEU, 116_SER, 117_VAL, 118_VAL, 119_TYR, 120_ARG, 121_GLU, 122_GLY, 129_THR, 180_MET, 204_TYR, 205_TYR, 206_PHE, 207_ARG, 209_PRO",2.9
+6B9J_Y,6B9J_B_light+6B9J_A_heavy,"104_TYR, 105_GLU, 108_LYS, 142_ARG, 143_SER, 144_ALA, 145_ASN, 146_THR, 147_SER, 175_ASN, 203_LEU, 34_THR, 35_THR, 37_GLN, 39_THR, 44_ARG, 46_ASN, 64_SER, 65_SER",2.9
+6B9J_X,6B9J_L_light+6B9J_H_heavy,"104_TYR, 105_GLU, 108_LYS, 142_ARG, 143_SER, 144_ALA, 145_ASN, 146_THR, 147_SER, 175_ASN, 203_LEU, 204_SER, 34_THR, 35_THR, 37_GLN, 39_THR, 44_ARG, 46_ASN, 64_SER, 65_SER",2.9
+8DS5_A,8DS5_B_light+8DS5_C_heavy,"28_PRO, 29_GLU, 31_HIS, 37_LYS, 38_LEU, 54_ASP, 55_GLN, 56_HIS, 57_ARG, 58_LYS",1.93
+2IH3_C,2IH3_B_light+2IH3_A_heavy,"45_TYR, 49_LEU, 50_ALA, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",1.72
+6XC4_Z,6XC4_Y_light+6XC4_X_heavy,"403_ARG, 405_ASP, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 495_TYR, 501_ASN, 505_TYR",2.34
+6XC4_A,6XC4_L_light+6XC4_H_heavy,"403_ARG, 405_ASP, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 495_TYR, 501_ASN, 505_TYR",2.34
+7TTM_A,7TTM_L_light+7TTM_H_heavy,"369_TYR, 370_ASN, 371_SER, 372_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 385_THR, 405_ASP, 408_ARG, 412_PRO, 413_GLY, 414_GLN, 415_THR, 416_GLY, 427_ASP, 428_ASP, 429_PHE, 460_LYS, 504_HIS",2.24
+5SY8_O,5SY8_L_light+5SY8_H_heavy,"118_GLY, 119_ASN, 120_TRP, 121_PHE, 124_THR, 125_ASN, 127_LEU, 128_TRP, 131_LYS, 134_ILE, 64_LEU, 73_ILE, 79_LYS",1.62
+8OL9_H,8OL9_A_light+8OL9_B_heavy,"208_ALA, 211_LEU, 212_ARG, 213_SER, 214_THR, 215_LYS, 216_PHE, 217_THR, 219_TYR",2.6
+1XGT_C,1XGT_A_light+1XGT_B_heavy,"100_SER, 101_ASP, 102_GLY, 15_HIS, 16_GLY, 18_ASP, 19_ASN, 20_TYR, 21_ARG, 63_TRP, 73_ARG, 75_LEU, 89_THR, 93_ASN, 96_LYS, 97_LYS",2.1
+7E8M_E,7E8M_L_light+7E8M_H_heavy,"403_ARG, 415_THR, 416_GLY, 417_ASN, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 500_THR, 501_TYR, 502_GLY, 505_TYR",2.09
+7UVI_F,7UVI_E_light+7UVI_D_heavy,"580_ASP, 581_LYS, 582_TYR, 583_ALA, 584_SER, 585_GLN, 594_ASP, 595_PHE, 597_ASP, 598_GLN, 600_LYS, 602_THR, 609_LYS, 611_CYS, 612_GLU, 635_LEU",2.92
+7UVI_C,7UVI_B_light+7UVI_A_heavy,"580_ASP, 581_LYS, 582_TYR, 583_ALA, 584_SER, 585_GLN, 593_CYS, 594_ASP, 595_PHE, 597_ASP, 598_GLN, 600_LYS, 601_PRO, 602_THR, 603_GLU, 609_LYS, 611_CYS, 612_GLU, 635_LEU",2.92
+1R3K_C,1R3K_A_light+1R3K_B_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 63_PRO, 64_ARG",2.8
+7KQG_A,7KQG_C_light+7KQG_B_heavy,"136_ARG, 137_LEU, 138_GLY, 139_THR, 140_SER, 141_GLY, 149_LYS, 150_SER, 158_TRP, 160_VAL, 161_PRO, 163_ASP, 197_GLN, 201_LEU, 237_LEU, 238_PRO, 239_GLN",2.6
+8ACI_A,8ACI_L_light+8ACI_H_heavy,"101_THR, 102_PRO, 103_ARG, 104_LEU, 105_GLY, 106_SER, 108_PRO, 114_SER, 116_GLU, 159_GLY, 178_SER, 179_SER, 181_LEU, 56_TYR, 85_LYS, 88_ARG, 90_PRO, 91_ALA, 92_PRO, 93_VAL, 99_ILE",1.85
+9IMA_A,9IMA_D_light+9IMA_C_heavy,"156_MET, 157_MET, 158_PHE, 159_VAL, 160_ASN, 161_MET, 162_THR, 163_PRO, 164_CYS, 165_GLN, 166_LEU, 234_GLN, 235_PRO, 238_ASP, 239_ASP, 83_GLU, 84_LEU, 85_ASN, 86_GLN, 87_GLN",2.65
+2W0F_C,2W0F_B_light+2W0F_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 63_PRO, 64_ARG",2.4
+7LAB_C,7LAB_L_light+7LAB_H_heavy,"186_PHE, 211_ASN, 212_LEU, 213_VAL, 214_ARG, 215_ASP, 216_LEU, 217_PRO, 218_GLN, 263_ALA, 27_ALA, 28_TYR, 29_THR, 30_ASN, 32_PHE, 59_PHE, 605_SER, 606_ASN, 607_GLN, 61_ASN, 64_TRP, 66_HIS, 689_SER, 68_ILE, 69_HIS, 97_LYS",2.97
+7LAB_B,7LAB_X_light+7LAB_Y_heavy,"186_PHE, 187_LYS, 211_ASN, 212_LEU, 213_VAL, 214_ARG, 215_ASP, 216_LEU, 217_PRO, 218_GLN, 27_ALA, 28_TYR, 29_THR, 30_ASN, 32_PHE, 59_PHE, 605_SER, 606_ASN, 64_TRP, 66_HIS, 689_SER, 68_ILE, 69_HIS, 97_LYS",2.97
+7LAB_A,7LAB_S_light+7LAB_T_heavy,"186_PHE, 187_LYS, 211_ASN, 212_LEU, 213_VAL, 214_ARG, 215_ASP, 216_LEU, 217_PRO, 218_GLN, 27_ALA, 28_TYR, 29_THR, 30_ASN, 32_PHE, 59_PHE, 606_ASN, 64_TRP, 66_HIS, 689_SER, 68_ILE, 69_HIS, 97_LYS",2.97
+7L7D_E,7L7D_L_light+7L7D_H_heavy,"455_LEU, 475_ALA, 476_GLY, 477_SER, 478_THR, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 488_CYS, 489_TYR, 493_GLN",2.5
+6P50_C,6P50_L_light+6P50_H_heavy,"100_LYS, 52_GLU, 54_CYS, 55_GLY, 59_GLU, 61_LYS, 62_CYS, 84_LYS, 85_SER, 86_ASN, 88_CYS, 90_LYS, 93_GLU, 94_LYS, 95_SER, 98_CYS",2.9
+5W0D_A,5W0D_C_light+5W0D_B_heavy,"133_THR, 134_GLY, 135_VAL, 136_SER, 137_ALA, 145_SER, 153_TRP, 155_THR, 158_ASN, 183_HIS, 186_PRO, 187_ASN, 190_ASP, 193_ALA, 194_LEU, 226_GLN, 95_TYR",1.9
+7X29_C,7X29_G_light+7X29_F_heavy,"506_LEU, 509_ASP, 510_ASP, 511_ARG, 512_THR, 513_GLU, 535_TRP, 536_GLU, 538_GLY, 539_ASP, 540_TYR, 541_TYR, 542_ARG, 555_VAL, 556_ALA, 557_SER",2.49
+7X29_B,7X29_K_light+7X29_H_heavy,"502_LYS, 506_LEU, 510_ASP, 511_ARG, 513_GLU, 536_GLU, 538_GLY, 539_ASP, 540_TYR, 541_TYR, 542_ARG, 553_TRP, 555_VAL, 556_ALA, 557_SER",2.49
+6Y9B_C,6Y9B_L_light+6Y9B_H_heavy,"103_GLN, 104_GLN, 105_TYR, 107_TYR, 194_ASN, 195_TRP, 198_GLN, 201_GLN, 202_GLN, 203_ASN, 204_LYS, 205_GLU, 207_ALA",2.97
+6Y9B_B,6Y9B_N_light+6Y9B_J_heavy,"103_GLN, 104_GLN, 105_TYR, 107_TYR, 194_ASN, 195_TRP, 198_GLN, 201_GLN, 202_GLN, 203_ASN, 204_LYS, 205_GLU, 207_ALA",2.97
+6Y9B_A,6Y9B_M_light+6Y9B_I_heavy,"103_GLN, 104_GLN, 105_TYR, 107_TYR, 194_ASN, 195_TRP, 198_GLN, 201_GLN, 202_GLN, 203_ASN, 204_LYS, 205_GLU, 207_ALA",2.97
+1WEJ_F,1WEJ_L_light+1WEJ_H_heavy,"100_LYS, 103_ASN, 104_GLU, 36_PHE, 37_GLY, 58_THR, 60_LYS, 61_GLU, 62_GLU, 96_ALA, 99_LYS",1.8
+7BWJ_E,7BWJ_L_light+7BWJ_H_heavy,"346_ARG, 444_LYS, 446_GLY, 447_GLY, 448_ASN, 449_TYR, 450_ASN, 452_LEU, 483_VAL, 484_GLU, 485_GLY, 490_PHE, 494_SER",2.85
+8S6M_R,8S6M_L_light+8S6M_H_heavy,"403_ARG, 405_ASN, 409_GLN, 415_THR, 416_GLY, 417_ASN, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 459_SER, 460_LYS, 473_TYR, 475_ALA, 476_GLY, 477_ASN, 487_ASN, 489_TYR, 493_GLN, 505_HIS",1.67
+8S6M_R,8S6M_M_light+8S6M_I_heavy,"353_TRP, 355_ARG, 357_ARG, 396_TYR, 426_PRO, 427_ASP, 428_ASP, 429_PHE, 459_SER, 462_LYS, 463_PRO, 464_PHE, 514_SER, 516_GLU, 518_LEU, 519_HIS, 520_ALA",1.67
+7CM4_A,7CM4_L_light+7CM4_H_heavy,"403_ARG, 417_LYS, 449_TYR, 450_ASN, 452_LEU, 453_TYR, 455_LEU, 456_PHE, 484_GLU, 485_GLY, 486_PHE, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER, 495_TYR, 505_TYR",2.71
+7RXJ_I,7RXJ_L_light+7RXJ_H_heavy,"314_LYS, 317_LYS, 318_GLU, 320_LEU, 321_ASN, 322_LYS, 324_GLN, 327_LEU, 354_PRO, 355_LYS, 356_ASP, 357_GLU, 358_LEU, 359_ASP, 360_TYR, 364_ILE",2.35
+7RXJ_G,7RXJ_B_light+7RXJ_A_heavy,"314_LYS, 317_LYS, 318_GLU, 320_LEU, 321_ASN, 324_GLN, 327_LEU, 354_PRO, 355_LYS, 356_ASP, 357_GLU, 358_LEU, 359_ASP, 360_TYR, 364_ILE",2.35
+1J1X_Y,1J1X_L_light+1J1X_H_heavy,"100_SER, 101_ASP, 102_GLY, 103_ASN, 15_HIS, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 62_TRP, 63_TRP, 73_ARG, 75_LEU, 77_ASN, 89_THR, 93_ASN, 96_LYS, 97_LYS, 98_ILE",1.8
+7STR_C,7STR_L_light+7STR_H_heavy,"113_LEU, 120_GLY, 122_PRO, 123_TYR, 134_ALA, 136_GLU, 137_GLY, 138_ALA, 139_LEU, 140_ASN, 141_THR, 142_PRO, 144_ASP, 68_ARG, 81_ASP, 84_ILE",1.5
+2R29_A,2R29_L_light+2R29_H_heavy,"305_LYS, 306_PHE, 307_LYS, 308_VAL, 309_VAL, 310_LYS, 311_GLU, 312_ILE, 325_GLN, 327_GLU, 362_ASP, 364_PRO, 387_LEU, 388_LYS, 389_LEU, 390_ASN",3.0
+8PG0_A,8PG0_L_light+8PG0_H_heavy,"331_SER, 334_THR, 335_ASN, 336_PRO, 561_LYS, 562_ILE, 563_VAL, 564_GLN, 565_PRO, 646_ALA, 649_LYS, 650_LYS, 651_PHE, 652_GLN, 653_GLY",2.97
+6OKM_R,6OKM_L_light+6OKM_H_heavy,"68_GLY, 69_PRO, 70_GLY, 71_PHE, 83_PRO, 86_TRP, 87_CYS, 88_ASN, 89_LEU, 90_ARG, 95_ARG, 96_LYS, 97_GLN, 98_LEU",2.1
+8TOO_I,8TOO_A_light+8TOO_B_heavy,"161_SER, 183_LYS, 185_TYR, 187_ILE, 188_PHE, 196_SER, 198_PHE, 199_SER, 200_THR, 201_VAL, 203_VAL, 206_HIS, 210_PHE, 212_LYS",2.6
+8TOO_J,8TOO_C_light+8TOO_D_heavy,"161_SER, 183_LYS, 185_TYR, 187_ILE, 188_PHE, 194_TYR, 196_SER, 198_PHE, 199_SER, 200_THR, 201_VAL, 203_VAL, 206_HIS, 210_PHE, 212_LYS",2.6
+8TOO_K,8TOO_F_light+8TOO_G_heavy,"161_SER, 183_LYS, 185_TYR, 187_ILE, 188_PHE, 196_SER, 198_PHE, 199_SER, 200_THR, 201_VAL, 203_VAL, 206_HIS, 210_PHE, 212_LYS",2.6
+8TOO_L,8TOO_E_light+8TOO_H_heavy,"161_SER, 183_LYS, 185_TYR, 187_ILE, 188_PHE, 196_SER, 198_PHE, 199_SER, 200_THR, 201_VAL, 203_VAL, 206_HIS, 210_PHE",2.6
+1TQB_A,1TQB_C_light+1TQB_B_heavy,"127_GLY, 128_LEU, 156_ASN, 158_TYR, 159_ARG, 185_ILE, 188_LYS, 189_GLN, 191_THR, 192_VAL, 193_THR, 194_THR, 195_THR, 196_THR, 197_LYS, 198_GLY, 199_GLU, 200_ASN",2.55
+6NMS_S,6NMS_L_light+6NMS_H_heavy,"103_GLU, 106_SER, 107_GLY, 108_ALA, 109_GLY, 10_ASP, 111_GLU, 113_SER, 116_ALA, 11_LYS, 14_LEU, 42_ALA, 43_GLY, 88_THR, 8_GLN, 90_TYR, 9_PRO",2.11
+6NMS_C,6NMS_A_light+6NMS_B_heavy,"103_GLU, 106_SER, 107_GLY, 108_ALA, 109_GLY, 10_ASP, 111_GLU, 113_SER, 115_ARG, 11_LYS, 12_SER, 14_LEU, 42_ALA, 43_GLY, 44_PRO, 46_ARG, 6_VAL, 88_THR, 8_GLN, 90_TYR, 9_PRO",2.11
+6WH9_D,6WH9_F_light+6WH9_E_heavy,"1512_ARG, 1698_ARG, 1699_SER, 1702_ASP, 1724_ALA, 1726_ASP, 1728_THR, 1729_ASP, 1756_LEU, 1758_ASP, 1759_GLY, 1760_THR, 1763_THR, 1765_THR, 1768_ARG, 1771_ARG, 1772_ALA, 1774_ARG, 1809_GLY",2.75
+6WH9_G,6WH9_C_light+6WH9_B_heavy,"1512_ARG, 1698_ARG, 1699_SER, 1702_ASP, 1704_ASP, 1724_ALA, 1726_ASP, 1728_THR, 1729_ASP, 1756_LEU, 1757_ASP, 1758_ASP, 1759_GLY, 1760_THR, 1763_THR, 1765_THR, 1768_ARG, 1771_ARG, 1774_ARG, 1808_PRO, 1809_GLY",2.75
+6WH9_A,6WH9_I_light+6WH9_H_heavy,"1512_ARG, 1697_SER, 1698_ARG, 1699_SER, 1701_PRO, 1702_ASP, 1704_ASP, 1724_ALA, 1726_ASP, 1728_THR, 1729_ASP, 1756_LEU, 1758_ASP, 1759_GLY, 1760_THR, 1763_THR, 1767_GLU, 1768_ARG, 1771_ARG, 1774_ARG, 1809_GLY",2.75
+3ZKN_B,3ZKN_D_light+3ZKN_C_heavy,"255_ASP, 258_VAL, 262_ALA, 263_ARG, 265_SER, 267_ILE, 268_PRO, 269_GLU, 270_PHE, 271_SER, 272_ASP, 275_TRP, 276_THR, 277_GLY, 278_SER, 280_LEU, 281_ALA, 283_TRP, 289_PRO, 293_PHE, 332_TYR, 334_PHE",2.0
+3ZKN_A,3ZKN_L_light+3ZKN_H_heavy,"254_PHE, 255_ASP, 258_VAL, 259_GLU, 261_VAL, 262_ALA, 263_ARG, 265_SER, 267_ILE, 268_PRO, 269_GLU, 270_PHE, 271_SER, 272_ASP, 275_TRP, 276_THR, 277_GLY, 278_SER, 280_LEU, 281_ALA, 283_TRP, 289_PRO, 293_PHE, 332_TYR, 334_PHE",2.0
+3ZKM_B,3ZKM_D_light+3ZKM_C_heavy,"254_PHE, 255_ASP, 258_VAL, 259_GLU, 261_VAL, 262_ALA, 263_ARG, 265_SER, 267_ILE, 268_PRO, 269_GLU, 270_PHE, 271_SER, 272_ASP, 275_TRP, 276_THR, 277_GLY, 278_SER, 280_LEU, 281_ALA, 283_TRP, 289_PRO, 293_PHE, 332_TYR, 334_PHE",1.85
+3ZKM_A,3ZKM_L_light+3ZKM_H_heavy,"254_PHE, 255_ASP, 258_VAL, 259_GLU, 261_VAL, 262_ALA, 263_ARG, 265_SER, 267_ILE, 268_PRO, 269_GLU, 270_PHE, 271_SER, 272_ASP, 275_TRP, 276_THR, 277_GLY, 278_SER, 280_LEU, 281_ALA, 283_TRP, 289_PRO, 293_PHE, 319_ILE, 332_TYR, 334_PHE",1.85
+7LSF_E,7LSF_L_light+7LSF_H_heavy,"305_THR, 306_MET, 308_ASP, 309_LYS, 310_THR, 311_LYS, 333_SER, 334_GLY, 335_THR, 336_LYS, 337_PRO, 366_ASN",2.24
+6NNJ_G,6NNJ_L_light+6NNJ_H_heavy,"135_THR, 322_ILE, 324_GLY, 325_ASP, 326_ILE, 327_ARG, 328_GLN, 415_THR",2.6
+6NNJ_G,6NNJ_V_light+6NNJ_U_heavy,"109_ILE, 194_ILE, 198_THR, 276_ASN, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 371_VAL, 428_GLN, 430_ILE, 455_THR, 456_ARG, 457_ASP, 458_GLY, 465_THR, 472_GLY, 476_ARG",2.6
+8TV1_C,8TV1_E_light+8TV1_D_heavy,"101_THR, 103_ARG, 108_PHE, 109_PRO, 156_PHE, 157_GLU, 158_ALA, 159_ARG, 190_ALA, 49_GLY, 53_ASP, 54_LEU, 55_MET, 56_GLN, 57_ASN, 58_ILE, 59_MET, 60_ASN, 61_ASP, 64_ILE, 65_TYR, 66_MET, 68_SER, 70_CYS, 71_ASN, 72_VAL, 73_MET, 74_SER",2.6
+1S5H_C,1S5H_A_light+1S5H_B_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.2
+2NY6_A,2NY6_C_light+2NY6_D_heavy,"119_CYS, 120_VAL, 122_LEU, 200_VAL, 202_THR, 203_GLN, 204_ALA, 419_ARG, 421_LYS, 422_GLN, 423_ILE, 434_MET, 437_PRO",2.8
+7UDH_A,7UDH_L_light+7UDH_H_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.0
+7UDH_C,7UDH_F_light+7UDH_E_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 205_SER, 206_SER, 208_ARG, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.0
+8G4T_I,8G4T_L_light+8G4T_H_heavy,"79_PRO, 80_ASN, 81_PRO, 82_GLN, 83_GLU, 85_HIS, 87_GLU",2.81
+8G4T_A,8G4T_E_light+8G4T_D_heavy,"513_VAL, 514_GLY, 515_ILE, 516_GLY, 517_ALA, 518_VAL, 519_PHE, 520_LEU, 542_ARG",2.81
+8G4T_F,8G4T_L_light+8G4T_H_heavy,"513_VAL, 514_GLY, 515_ILE, 516_GLY, 517_ALA, 518_VAL, 519_PHE, 520_LEU, 542_ARG",2.81
+8G4T_G,8G4T_K_light+8G4T_J_heavy,"79_PRO, 80_ASN, 81_PRO, 82_GLN, 83_GLU, 85_HIS, 87_GLU",2.81
+8G4T_B,8G4T_K_light+8G4T_J_heavy,"513_VAL, 514_GLY, 515_ILE, 516_GLY, 517_ALA, 518_VAL, 519_PHE, 520_LEU, 542_ARG",2.81
+8G4T_C,8G4T_E_light+8G4T_D_heavy,"79_PRO, 80_ASN, 81_PRO, 82_GLN, 83_GLU, 85_HIS, 87_GLU",2.81
+1G7M_C,1G7M_A_light+1G7M_B_heavy,"102_GLY, 103_ASN, 116_LYS, 117_GLY, 118_THR, 119_ASP, 120_VAL, 121_GLN, 124_ILE, 18_ASP, 19_ASN, 22_GLY, 23_TYR, 24_SER, 27_ASN",1.9
+2AEQ_A,2AEQ_L_light+2AEQ_H_heavy,"150_HIS, 152_ARG, 153_THR, 154_PRO, 197_HIS, 198_ASP, 199_GLU, 218_TRP, 219_SER, 220_LYS, 221_LYS, 222_ILE, 224_ARG, 244_GLY, 246_ALA, 248_GLY, 249_ARG, 250_ALA, 251_ASP",3.0
+7E7X_B,7E7X_N_light+7E7X_M_heavy,"144_TYR, 145_TYR, 146_HIS, 147_LYS, 148_ASN, 150_LYS, 246_ARG, 247_SER, 248_TYR, 249_LEU, 250_THR, 251_PRO, 253_ASP, 254_SER, 255_SER",2.78
+7E7X_A,7E7X_L_light+7E7X_H_heavy,"144_TYR, 145_TYR, 146_HIS, 147_LYS, 148_ASN, 150_LYS, 152_TRP, 246_ARG, 247_SER, 248_TYR, 249_LEU, 250_THR, 251_PRO, 253_ASP, 254_SER, 255_SER",2.78
+6XQ0_D,6XQ0_F_light+6XQ0_E_heavy,"100_GLY, 101_ASP, 102_PHE, 105_TYR, 214_LYS, 215_PRO, 216_GLU, 217_ILE, 218_ALA, 219_ILE, 221_PRO, 223_VAL, 229_ARG, 64_ASN, 66_ALA, 90_PRO, 91_SER, 92_SER, 93_ASP, 96_THR",2.3
+6XQ0_A,6XQ0_C_light+6XQ0_B_heavy,"100_GLY, 101_ASP, 102_PHE, 105_TYR, 214_LYS, 215_PRO, 216_GLU, 217_ILE, 218_ALA, 219_ILE, 220_ARG, 221_PRO, 223_VAL, 229_ARG, 64_ASN, 66_ALA, 90_PRO, 91_SER, 92_SER, 93_ASP, 96_THR, 99_PRO",2.3
+6C9U_A,6C9U_L_light+6C9U_H_heavy,"12_LEU, 13_ARG, 16_THR, 23_ARG, 27_ARG, 327_LEU, 328_GLU, 331_ASP, 5_SER, 8_VAL, 9_ALA",2.09
+7UZ7_A,7UZ7_L_light+7UZ7_H_heavy,"370_ASN, 372_ALA, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 381_GLY, 382_VAL, 383_SER, 385_THR, 386_LYS, 390_LEU, 392_PHE, 408_ARG, 413_GLY, 427_ASP, 428_ASP, 430_THR",2.9
+7UZ7_B,7UZ7_N_light+7UZ7_M_heavy,"370_ASN, 372_ALA, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 381_GLY, 382_VAL, 383_SER, 385_THR, 386_LYS, 390_LEU, 392_PHE, 408_ARG, 413_GLY, 427_ASP, 428_ASP, 430_THR, 517_LEU",2.9
+7UZ7_C,7UZ7_Q_light+7UZ7_P_heavy,"370_ASN, 372_ALA, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 381_GLY, 382_VAL, 383_SER, 385_THR, 386_LYS, 390_LEU, 392_PHE, 408_ARG, 413_GLY, 427_ASP, 428_ASP, 430_THR",2.9
+4LSP_G,4LSP_L_light+4LSP_H_heavy,"276_ASN, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 354_ASN, 357_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 371_ILE, 432_GLN, 455_THR, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_ALA, 461_ASN, 462_ASN, 463_THR, 466_GLU, 473_GLY, 476_LYS",2.15
+8E8R_1,8E8R_L_light+8E8R_H_heavy,"101_ALA, 102_GLN, 103_LYS, 105_PHE, 106_ALA, 107_MET, 108_TRP, 109_ARG, 111_THR, 114_ASP, 168_LYS, 224_GLN, 229_LEU, 240_VAL, 283_PRO, 87_VAL, 88_ALA, 89_ILE, 91_GLU",2.66
+8E8R_2,8E8R_L_light+8E8R_H_heavy,"137_ASP, 138_LYS, 139_GLN, 140_ARG, 141_TYR, 166_VAL",2.66
+2R0L_A,2R0L_L_light+2R0L_H_heavy,"100_ASP, 101_HIS, 177_SER, 178_PRO, 179_ASN, 233_ASN, 241_ARG, 59_PHE, 60_SER, 61_ARG, 87_LYS, 88_TYR, 89_ILE, 90_PRO, 91_TYR, 92_THR, 93_LEU, 95_SER, 97_PHE, 98_ASN",2.2
+4H88_A,4H88_L_light+4H88_H_heavy,"137_MET, 139_HIS, 140_PHE, 141_GLY, 142_ASN, 143_ASP, 144_TRP, 145_GLU, 146_ASP, 203_LYS, 207_ARG, 211_GLN",1.9
+8QRG_E,8QRG_L_light+8QRG_H_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 459_SER, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 498_GLN, 501_ASN, 502_GLY, 505_TYR",2.3
+5TIH_A,5TIH_L_light+5TIH_H_heavy,"103_THR, 116_ASN, 117_ASN, 118_ALA, 119_GLU, 41_GLY, 42_GLU, 45_GLU, 63_ASP, 66_LYS, 67_GLU, 68_THR, 69_ASP, 70_LEU, 71_THR, 91_GLU, 99_LYS",2.44
+6OY4_A,6OY4_C_light+6OY4_D_heavy,"100_LYS, 30_HIS, 31_ARG, 32_GLY, 33_LYS, 34_PRO, 36_GLN, 94_VAL, 95_PRO, 96_LYS, 97_ILE, 99_PRO",2.45
+7V5J_C,7V5J_F_light+7V5J_G_heavy,"132_ILE, 134_PRO, 135_SER, 307_ARG, 34_ASP, 35_ILE, 36_GLN, 38_THR, 39_PHE, 91_HIS, 93_THR, 98_GLN, 99_LYS",2.8
+7V5J_B,7V5J_H_light+7V5J_I_heavy,"132_ILE, 134_PRO, 135_SER, 202_THR, 307_ARG, 32_GLU, 34_ASP, 35_ILE, 36_GLN, 38_THR, 39_PHE, 91_HIS, 93_THR, 94_GLY, 95_THR, 96_THR",2.8
+7V5J_A,7V5J_D_light+7V5J_E_heavy,"101_PHE, 132_ILE, 134_PRO, 135_SER, 202_THR, 34_ASP, 35_ILE, 36_GLN, 38_THR, 39_PHE, 42_LYS, 91_HIS, 93_THR, 96_THR, 98_GLN",2.8
+1FDL_Y,1FDL_L_light+1FDL_H_heavy,"116_LYS, 117_GLY, 118_THR, 119_ASP, 120_VAL, 121_GLN, 124_ILE, 125_ARG, 18_ASP, 19_ASN, 22_GLY, 23_TYR, 24_SER, 27_ASN",2.5
+6CXY_C,6CXY_L_light+6CXY_H_heavy,"12_ASN, 14_LYS, 15_GLY, 16_PRO, 17_PHE, 18_PRO, 19_LYS, 20_ASN, 48_VAL, 52_ILE, 61_LYS, 63_THR, 64_GLU, 65_PRO, 66_LEU, 67_ASP, 70_ARG",2.2
+6AQ7_A,6AQ7_L_light+6AQ7_H_heavy,"144_TRP, 145_GLU, 147_ARG, 148_TYR, 151_GLU, 176_HIS, 179_VAL, 180_ASN, 196_ASN, 198_THR, 199_GLU, 200_THR, 202_VAL, 203_LYS, 206_GLU, 210_GLU",1.83
+7X8Q_D,7X8Q_E_light+7X8Q_F_heavy,"120_ILE, 124_PHE, 126_PHE, 40_PRO, 41_MET, 43_LYS, 62_ARG, 63_GLU, 66_ILE, 69_HIS, 70_GLU, 72_ALA, 73_PRO, 76_GLU, 77_TYR, 84_ARG",2.65
+7X8Q_A,7X8Q_B_light+7X8Q_C_heavy,"120_ILE, 124_PHE, 40_PRO, 41_MET, 69_HIS, 70_GLU, 72_ALA, 73_PRO, 75_VAL, 76_GLU, 77_TYR, 84_ARG",2.65
+7EAN_A,7EAN_L_light+7EAN_H_heavy,"351_TYR, 352_ALA, 353_TRP, 354_ASN, 355_ARG, 356_LYS, 357_ARG, 396_TYR, 426_PRO, 457_ARG, 459_SER, 460_ASN, 461_LEU, 462_LYS, 463_PRO, 464_PHE, 465_GLU, 466_ARG, 468_ILE, 469_SER, 470_THR, 471_GLU, 516_GLU, 518_LEU, 520_ALA",1.91
+7UCY_A,7UCY_L_light+7UCY_H_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.35
+7UCY_C,7UCY_F_light+7UCY_E_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN",2.35
+1NFD_D,1NFD_G_light+1NFD_H_heavy,"118_ASP, 120_ARG, 121_ASN, 123_THR, 14_GLU, 223_ASP, 224_LYS, 225_TRP, 226_PRO, 227_GLU, 228_GLY, 229_SER, 230_PRO",2.8
+1NFD_B,1NFD_E_light+1NFD_F_heavy,"118_ASP, 120_ARG, 14_GLU, 221_GLU, 223_ASP, 224_LYS, 226_PRO, 227_GLU, 228_GLY, 229_SER, 230_PRO",2.8
+6B9Z_E,6B9Z_A_light+6B9Z_B_heavy,"33_LYS, 34_ILE, 35_GLN, 36_THR, 37_ALA, 38_GLU, 39_PHE, 40_LYS, 49_GLU, 52_ARG, 53_TYR, 56_LEU, 57_LEU, 60_VAL, 61_ASN",1.82
+6B9Z_C,6B9Z_A_light+6B9Z_B_heavy,"23_ALA, 24_GLN, 27_GLY, 28_PHE, 30_GLN, 31_SER, 34_ASP, 35_ASP, 37_SER, 38_GLN, 41_ASN, 42_VAL, 45_GLU, 49_LEU",1.82
+7S5R_A,7S5R_L_light+7S5R_H_heavy,"369_TYR, 371_SER, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 408_ARG, 409_GLN, 412_PRO, 413_GLY, 414_GLN, 415_THR, 416_GLY, 427_ASP, 428_ASP, 429_PHE",2.45
+7S5R_A,7S5R_C_light+7S5R_B_heavy,"455_LEU, 473_TYR, 475_ALA, 476_GLY, 477_SER, 478_THR, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN",2.45
+1W72_D,1W72_M_light+1W72_I_heavy,"142_ILE, 146_LYS, 155_GLN, 65_ARG, 66_ASN, 68_LYS, 69_ALA, 72_GLN, 73_THR, 75_ARG, 76_ALA, 80_THR, 82_ARG, 84_TYR",2.15
+1W72_A,1W72_L_light+1W72_H_heavy,"145_ARG, 146_LYS, 155_GLN, 65_ARG, 66_ASN, 68_LYS, 69_ALA, 72_GLN, 73_THR, 75_ARG, 76_ALA, 79_GLY, 80_THR, 84_TYR, 89_GLU",2.15
+6WM9_D,6WM9_F_light+6WM9_E_heavy,"173_ASP, 174_ASN, 192_LEU, 193_ARG, 194_PRO, 230_LEU, 231_ASN, 232_GLU, 234_SER, 235_ARG, 238_GLY, 239_ILE, 241_ASN, 245_ASN, 437_LYS, 438_ASP, 441_ASN, 445_LYS",2.45
+6WM9_A,6WM9_C_light+6WM9_B_heavy,"173_ASP, 174_ASN, 191_GLN, 192_LEU, 193_ARG, 194_PRO, 227_ASN, 230_LEU, 231_ASN, 232_GLU, 234_SER, 235_ARG, 238_GLY, 241_ASN, 245_ASN, 434_LYS, 437_LYS, 438_ASP, 441_ASN, 445_LYS",2.45
+7USL_C,7USL_L_light+7USL_H_heavy,"1373_LEU, 1375_ASP, 1377_GLY, 1378_ALA, 1379_GLY, 1380_ARG, 1382_ASP, 1386_GLU, 1387_ALA, 1388_GLY, 1394_THR, 1409_ARG, 1427_VAL, 1428_LEU, 1429_ALA, 1445_GLN, 1446_LEU, 1447_SER, 1449_ASP, 1465_PHE",2.7
+5K59_B,5K59_F_light+5K59_E_heavy,"206_ASN, 207_LEU, 208_TRP, 210_LYS, 211_ASP, 262_TRP, 264_ARG, 267_PHE, 71_ASN, 73_TYR, 74_TRP",2.84
+5K59_A,5K59_L_light+5K59_H_heavy,"206_ASN, 207_LEU, 208_TRP, 210_LYS, 211_ASP, 262_TRP, 264_ARG, 267_PHE, 71_ASN, 73_TYR, 74_TRP",2.84
+6V4O_W,6V4O_E_light+6V4O_D_heavy,"116_ARG, 134_HIS, 147_ARG, 150_ARG, 221_ILE, 245_ALA, 246_SER, 292_ARG, 294_ASN, 329_ASN, 332_SER, 334_THR, 342_ASP, 343_GLU, 346_GLY, 347_GLY, 372_THR, 373_LYS, 374_ARG, 408_TRP, 409_TYR",2.8
+6V4O_I,6V4O_B_light+6V4O_A_heavy,"116_ARG, 134_HIS, 147_ARG, 150_ARG, 221_ILE, 245_ALA, 246_SER, 292_ARG, 294_ASN, 329_ASN, 332_SER, 334_THR, 342_ASP, 343_GLU, 346_GLY, 347_GLY, 372_THR, 373_LYS, 374_ARG, 408_TRP, 409_TYR",2.8
+6V4O_M,6V4O_G_light+6V4O_C_heavy,"116_ARG, 134_HIS, 147_ARG, 150_ARG, 245_ALA, 246_SER, 292_ARG, 294_ASN, 329_ASN, 332_SER, 334_THR, 342_ASP, 343_GLU, 346_GLY, 347_GLY, 372_THR, 373_LYS, 374_ARG, 408_TRP, 409_TYR",2.8
+6V4O_N,6V4O_L_light+6V4O_H_heavy,"116_ARG, 134_HIS, 147_ARG, 150_ARG, 221_ILE, 245_ALA, 246_SER, 292_ARG, 294_ASN, 329_ASN, 332_SER, 334_THR, 342_ASP, 343_GLU, 346_GLY, 347_GLY, 372_THR, 373_LYS, 374_ARG, 408_TRP, 409_TYR",2.8
+2VDM_A,2VDM_L_light+2VDM_H_heavy,"149_ASN, 154_ILE, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.9
+5W5X_A,5W5X_L_light+5W5X_H_heavy,"121_VAL, 122_LEU, 123_LYS, 124_ALA, 125_LEU, 126_SER, 33_ASP, 34_ARG, 35_ALA, 36_GLY, 37_ARG, 38_MET, 39_GLY, 40_GLY, 41_GLU, 42_ALA",2.5
+8T03_B,8T03_F_light+8T03_E_heavy,"137_MET, 140_LYS, 141_LYS, 142_GLY, 143_LEU, 144_TYR, 146_ASP, 147_LYS, 149_ILE",2.72
+8T03_A,8T03_D_light+8T03_C_heavy,"137_MET, 140_LYS, 141_LYS, 142_GLY, 143_LEU, 144_TYR, 146_ASP, 147_LYS, 149_ILE",2.72
+4ZFO_F,4ZFO_B_light+4ZFO_A_heavy,"13_TYR, 15_ASP, 16_SER, 17_LEU, 18_LEU, 19_HIS, 20_ALA, 21_CYS, 22_ILE, 23_PRO, 26_LEU, 27_ARG, 32_THR",1.9
+4ZFO_K,4ZFO_L_light+4ZFO_H_heavy,"13_TYR, 15_ASP, 16_SER, 17_LEU, 18_LEU, 19_HIS, 20_ALA, 22_ILE, 23_PRO, 26_LEU, 27_ARG, 32_THR",1.9
+8BBO_R,8BBO_L_light+8BBO_H_heavy,"345_THR, 346_ARG, 351_TYR, 440_ASN, 441_LEU, 443_SER, 444_LYS, 445_VAL, 446_GLY, 449_TYR, 450_ASN, 452_ARG, 470_THR, 482_GLY, 484_GLU, 490_PHE",2.75
+8TX3_D,8TX3_L_light+8TX3_H_heavy,"15_GLU, 16_GLY, 17_MET, 18_VAL, 19_ASP, 25_ARG, 31_GLY, 32_ARG, 33_GLY, 34_GLN",2.99
+8TX3_I,8TX3_K_light+8TX3_F_heavy,"15_GLU, 16_GLY, 17_MET, 18_VAL, 19_ASP, 25_ARG, 26_HIS, 27_GLN, 31_GLY, 32_ARG, 33_GLY, 34_GLN",2.99
+8TX3_B,8TX3_J_light+8TX3_E_heavy,"15_GLU, 16_GLY, 17_MET, 18_VAL, 19_ASP, 25_ARG, 30_GLU, 31_GLY, 32_ARG, 33_GLY, 34_GLN, 35_ALA, 36_ALA",2.99
+8SGI_A,8SGI_L_light+8SGI_H_heavy,"538_PRO, 539_TYR, 540_LYS, 590_GLU, 591_ILE, 592_GLY, 593_GLU, 595_ARG, 597_VAL, 610_LEU, 628_LYS, 629_VAL, 630_HIS, 631_ALA, 662_ARG, 668_ARG, 671_LEU, 675_THR, 676_LYS",2.9
+5EII_I,5EII_B_light+5EII_A_heavy,"12_LEU, 23_GLU, 25_GLU, 37_ASP, 58_ASP, 60_ILE, 61_LEU, 62_VAL, 63_GLY, 64_PRO, 66_PRO, 70_ASN, 71_LYS, 72_PHE, 73_VAL, 75_SER",2.44
+5EII_G,5EII_L_light+5EII_H_heavy,"12_LEU, 23_GLU, 25_GLU, 37_ASP, 58_ASP, 59_SER, 60_ILE, 61_LEU, 62_VAL, 63_GLY, 64_PRO, 66_PRO, 70_ASN, 71_LYS, 72_PHE, 73_VAL",2.44
+7CZW_C,7CZW_N_light+7CZW_J_heavy,"403_ARG, 417_LYS, 449_TYR, 452_LEU, 453_TYR, 455_LEU, 456_PHE, 484_GLU, 485_GLY, 486_PHE, 488_CYS, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 505_TYR",2.8
+7CZW_A,7CZW_L_light+7CZW_H_heavy,"403_ARG, 417_LYS, 449_TYR, 452_LEU, 453_TYR, 455_LEU, 456_PHE, 484_GLU, 485_GLY, 486_PHE, 488_CYS, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 505_TYR",2.8
+3I50_E,3I50_L_light+3I50_H_heavy,"104_GLY, 105_CYS, 106_GLY, 107_LEU, 109_GLY, 110_LYS, 74_CYS, 75_PRO, 76_THR, 77_MET, 78_GLY, 79_GLU",3.0
+8HRX_A,8HRX_L_light+8HRX_H_heavy,"11_ASN, 12_PHE, 147_ASP, 275_PRO, 277_GLU, 278_VAL, 86_LYS",2.89
+8HRX_B,8HRX_L_light+8HRX_H_heavy,"34_LEU, 36_PRO, 37_HIS, 38_LYS, 39_ASP, 40_ASN, 41_TRP, 42_PRO, 45_ASN, 46_LYS",2.89
+4OLV_G,4OLV_L_light+4OLV_H_heavy,"102_GLU, 276_ASN, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 370_GLU, 371_ILE, 425_ASN, 426_MET, 427_TRP, 428_GLN, 429_GLY, 457_ASP, 458_GLY, 459_GLY, 460_ALA, 461_ASN, 466_GLU, 467_THR, 469_ARG, 473_GLY, 476_LYS, 97_LYS",2.5
+4HJ0_A,4HJ0_Q_light+4HJ0_P_heavy,"113_ARG, 115_HIS, 119_GLU, 32_ALA, 33_GLY, 36_TYR, 37_GLN, 39_TRP, 40_GLU, 43_ARG, 47_GLN, 65_PHE, 67_MET, 68_TYR, 87_TYR, 89_PRO, 90_TRP",3.0
+4HJ0_B,4HJ0_D_light+4HJ0_C_heavy,"114_ASP, 115_HIS, 118_CYS, 119_GLU, 120_ASN, 32_ALA, 33_GLY, 35_LEU, 36_TYR, 39_TRP, 40_GLU, 47_GLN, 65_PHE, 67_MET, 68_TYR, 87_TYR",3.0
+7SYY_A,7SYY_L_light+7SYY_H_heavy,"380_SER, 384_ILE, 385_ILE, 386_HIS, 387_CYS, 388_LYS, 389_TYR, 390_SER, 391_LYS, 392_ALA, 463_MET, 481_ASN, 482_ASN, 483_SER, 498_VAL, 543_ASN",2.74
+4FQJ_A,4FQJ_L_light+4FQJ_H_heavy,"282_GLY, 283_SER, 284_LEU, 285_PRO, 287_ILE, 37_THR, 38_LYS, 39_SER, 40_TYR, 41_PHE, 52_LYS, 58_LEU, 59_ASN, 60_CYS, 61_THR, 62_ASP, 85_HIS, 86_GLU, 90_VAL",2.5
+7PHW_D,7PHW_F_light+7PHW_E_heavy,"100_THR, 120_GLU, 122_GLU, 126_ASN, 128_LYS, 132_THR, 134_HIS, 148_GLU, 94_PHE, 95_LYS, 96_GLU, 97_THR, 98_ASP, 99_LEU",2.79
+7PHW_A,7PHW_C_light+7PHW_B_heavy,"100_THR, 120_GLU, 122_GLU, 123_GLU, 126_ASN, 128_LYS, 132_THR, 134_HIS, 148_GLU, 94_PHE, 95_LYS, 96_GLU, 97_THR, 98_ASP, 99_LEU",2.79
+5VIC_E,5VIC_L_light+5VIC_H_heavy,"300_VAL, 301_MET, 302_CYS, 303_THR, 304_GLY, 305_SER, 326_TYR, 327_GLU, 328_GLY, 329_THR, 330_ASP, 336_PRO, 340_GLN, 361_LYS, 382_ALA, 383_GLY, 384_GLU, 385_LYS",3.0
+7MZF_A,7MZF_L_light+7MZF_H_heavy,"403_ARG, 408_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 478_THR, 486_PHE, 487_ASN, 489_TYR, 495_TYR, 498_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",2.49
+7SOB_D,7SOB_E_light+7SOB_F_heavy,"108_THR, 236_THR, 237_ARG, 269_TYR, 26_PRO, 270_LEU, 271_GLN, 28_TYR, 324_GLU, 325_SER, 61_ASN, 83_VAL, 85_PRO, 87_ASN, 88_ASP",2.4
+7SOB_A,7SOB_B_light+7SOB_C_heavy,"108_THR, 236_THR, 237_ARG, 269_TYR, 26_PRO, 270_LEU, 271_GLN, 28_TYR, 324_GLU, 325_SER, 61_ASN, 83_VAL, 85_PRO, 87_ASN, 88_ASP",2.4
+7SOB_K,7SOB_L_light+7SOB_M_heavy,"108_THR, 236_THR, 237_ARG, 269_TYR, 26_PRO, 270_LEU, 271_GLN, 28_TYR, 324_GLU, 325_SER, 61_ASN, 83_VAL, 85_PRO, 87_ASN, 88_ASP",2.4
+7KYL_Z,7KYL_Y_light+7KYL_X_heavy,"308_ASP, 309_LYS, 310_THR, 344_ARG, 346_VAL, 348_HIS, 351_PRO, 352_THR, 385_TYR, 388_ASP, 390_SER, 392_GLN",2.0
+7KYL_E,7KYL_L_light+7KYL_H_heavy,"308_ASP, 309_LYS, 310_THR, 344_ARG, 346_VAL, 348_HIS, 351_PRO, 352_THR, 354_ASN, 383_ILE, 385_TYR, 388_ASP, 390_SER, 391_GLN, 392_GLN",2.0
+7UZD_A,7UZD_L_light+7UZD_H_heavy,"381_GLY, 382_VAL, 383_SER, 386_LYS, 389_ASP, 390_LEU, 391_CYS, 393_THR, 430_THR, 517_LEU, 518_LEU, 519_HIS, 520_ALA, 521_PRO, 522_ALA, 525_CYS, 528_LYS, 530_SER",3.0
+7PHU_A,7PHU_C_light+7PHU_B_heavy,"378_LYS, 382_ASP, 385_ASN, 389_GLN, 392_LEU, 393_LEU, 396_ASN, 400_LYS, 491_ASN, 494_PHE, 495_HIS, 498_ILE, 499_TYR, 501_LEU, 502_GLN, 503_MET, 504_LYS, 505_PHE, 506_ASN",2.53
+7PHU_A,7PHU_E_light+7PHU_D_heavy,"193_ILE, 199_THR, 201_GLY, 202_LYS, 203_TYR, 204_ILE, 205_ALA, 207_ASP, 208_ALA, 209_PHE, 211_LYS, 212_LYS, 213_ILE, 215_GLU, 216_ALA, 335_TYR, 339_LEU",2.53
+6NFU_C,6NFU_B_light+6NFU_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.09
+8SLB_A,8SLB_L_light+8SLB_H_heavy,"100_GLU, 101_PHE, 102_PHE, 103_GLU, 104_ASN, 105_TYR, 12_LEU, 13_PRO, 14_PRO, 15_GLY, 16_THR, 17_LEU, 18_VAL, 234_SER, 54_ARG, 55_ASP, 71_ASP, 74_GLN, 75_ARG, 78_GLU, 79_PHE, 80_PHE, 81_GLY, 83_HIS, 84_PRO, 85_LEU, 86_VAL, 88_GLU, 89_ASP",2.04
+4NNP_A,4NNP_L_light+4NNP_H_heavy,"230_LYS, 231_GLN, 232_GLY, 233_THR, 234_PRO, 235_GLU, 237_MET, 238_ARG, 239_GLN, 241_ILE, 242_GLU, 262_ALA, 263_MET, 266_LEU",2.69
+4NNP_B,4NNP_Y_light+4NNP_X_heavy,"230_LYS, 231_GLN, 232_GLY, 233_THR, 234_PRO, 235_GLU, 237_MET, 238_ARG, 239_GLN, 241_ILE, 242_GLU, 262_ALA, 263_MET, 266_LEU",2.69
+4DVR_G,4DVR_L_light+4DVR_H_heavy,"120_VAL, 200_VAL, 202_THR, 203_GLN, 325_ASP, 326_ILE, 327_ARG, 419_ARG, 421_LYS, 422_GLN, 423_ILE, 432_LYS, 434_MET, 437_PRO",2.5
+1QKZ_A,1QKZ_L_light+1QKZ_H_heavy,"16_THR, 17_LEU, 18_LYS, 19_GLY, 20_GLU, 21_THR, 22_THR, 38_TYR, 40_ASN, 41_ASP, 42_ASN, 43_GLY",1.95
+7SD5_A,7SD5_L_light+7SD5_H_heavy,"369_TYR, 370_ASN, 371_SER, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 405_ASP, 408_ARG, 409_GLN, 412_PRO, 413_GLY, 414_GLN, 415_THR, 416_GLY, 427_ASP, 428_ASP, 429_PHE, 505_TYR",1.53
+7QUH_A,7QUH_L_light+7QUH_H_heavy,"103_TRP, 105_ASN, 20_ASP, 21_ARG, 25_ASP, 51_TYR, 52_PRO, 53_GLN, 54_ASP, 55_GLY, 56_TRP, 57_THR, 58_ASP, 59_SER",2.87
+1OAZ_A,1OAZ_L_light+1OAZ_H_heavy,"106_GLY, 107_ALA, 108_LEU, 109_SER, 112_GLN, 33_GLY, 34_PRO, 35_ILE, 37_GLU, 41_ARG, 43_TYR, 46_VAL, 47_GLY, 48_PRO, 87_ARG, 88_GLY, 89_ILE",2.78
+1OAZ_B,1OAZ_N_light+1OAZ_J_heavy,"105_VAL, 106_GLY, 107_ALA, 108_LEU, 109_SER, 112_GLN, 33_GLY, 35_ILE, 37_GLU, 41_ARG, 42_ARG, 43_TYR, 46_VAL, 47_GLY, 48_PRO, 87_ARG, 88_GLY, 89_ILE, 90_PRO",2.78
+7YRU_A,7YRU_L_light+7YRU_H_heavy,"102_ASN, 104_THR, 106_GLN, 107_LEU, 108_PRO, 38_GLU, 39_GLY, 59_ILE, 60_ASN, 61_ASP, 62_GLY, 63_PHE, 64_HIS, 65_VAL, 66_TYR",2.6
+8DE4_A,8DE4_C_light+8DE4_B_heavy,"236_SER, 237_CYS, 238_LYS, 239_ASN, 240_SER, 241_TRP, 242_ASN, 243_THR, 246_CYS, 247_THR, 249_TYR, 258_MET, 271_ARG, 272_HIS, 275_GLN, 277_HIS, 278_ARG",2.9
+8F38_C,8F38_I_light+8F38_F_heavy,"305_SER, 306_LEU, 307_PRO, 362_ASP, 363_GLY, 364_TRP, 381_GLN, 384_THR, 385_GLN, 388_ILE, 389_ASN, 392_THR, 396_ASN, 399_ILE, 45_HIS, 48_ASN, 49_LEU",2.64
+8F38_B,8F38_G_light+8F38_D_heavy,"305_SER, 306_LEU, 307_PRO, 362_ASP, 363_GLY, 364_TRP, 381_GLN, 384_THR, 385_GLN, 388_ILE, 389_ASN, 392_THR, 395_VAL, 396_ASN, 399_ILE, 45_HIS, 48_ASN, 49_LEU",2.64
+8F38_A,8F38_H_light+8F38_E_heavy,"305_SER, 306_LEU, 307_PRO, 362_ASP, 363_GLY, 364_TRP, 381_GLN, 384_THR, 385_GLN, 388_ILE, 389_ASN, 392_THR, 396_ASN, 399_ILE, 45_HIS, 48_ASN, 49_LEU",2.64
+7PS1_E,7PS1_B_light+7PS1_A_heavy,"403_ARG, 415_THR, 416_GLY, 417_ASN, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 459_SER, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 500_THR, 501_TYR, 502_GLY, 505_TYR",2.4
+7L8P_C,7L8P_F_light+7L8P_E_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.35
+7L8P_A,7L8P_L_light+7L8P_H_heavy,"117_GLU, 149_ASN, 154_ILE, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.35
+7UAP_A,7UAP_L_light+7UAP_H_heavy,"102_ARG, 122_ASN, 123_ALA, 124_THR, 143_VAL, 149_ASN, 150_LYS, 151_SER, 152_TRP, 154_GLU, 173_GLN, 174_PRO, 178_ASP, 180_GLU, 181_GLY, 182_LYS, 183_GLN, 97_LYS, 98_SER, 99_ASN",2.8
+7UAP_B,7UAP_N_light+7UAP_M_heavy,"102_ARG, 123_ALA, 124_THR, 143_VAL, 149_ASN, 150_LYS, 151_SER, 152_TRP, 173_GLN, 177_MET, 178_ASP, 180_GLU, 181_GLY, 182_LYS, 183_GLN, 97_LYS, 98_SER, 99_ASN",2.8
+7UED_M,7UED_L_light+7UED_H_heavy,"392_LEU, 393_GLU, 396_LYS, 400_GLU, 428_ASP, 455_SER, 458_TRP, 483_ALA, 484_PHE, 485_GLN, 486_ASN, 487_MET, 491_GLU, 495_LYS",3.0
+1EO8_A,1EO8_L_light+1EO8_H_heavy,"271_ASP, 273_PRO, 50_LYS, 59_LEU, 60_ASP, 62_ILE, 63_ASP, 74_PRO, 75_HIS, 78_VAL, 79_PHE, 82_GLU, 90_ARG, 92_LYS, 94_PHE",2.8
+7DJZ_C,7DJZ_B_light+7DJZ_A_heavy,"351_TYR, 417_LYS, 446_GLY, 449_TYR, 450_ASN, 452_LEU, 453_TYR, 455_LEU, 456_PHE, 470_THR, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER",2.4
+8U32_D,8U32_E_light+8U32_F_heavy,"126_ILE, 127_SER, 128_LEU, 130_PRO, 131_LYS, 132_ALA, 133_GLN, 134_ILE, 135_LYS, 136_GLU, 139_ARG, 29_ASP, 30_ARG, 31_PRO, 32_TRP, 66_ASN, 68_TYR, 76_THR, 78_LYS",2.51
+8U32_A,8U32_B_light+8U32_C_heavy,"126_ILE, 127_SER, 128_LEU, 130_PRO, 131_LYS, 132_ALA, 133_GLN, 134_ILE, 135_LYS, 136_GLU, 31_PRO, 32_TRP, 66_ASN, 68_TYR, 76_THR",2.51
+4G7V_S,4G7V_L_light+4G7V_H_heavy,"204_ASP, 205_GLU, 207_VAL, 208_GLN, 211_GLY, 212_ALA, 213_ASP, 214_GLY, 215_LEU, 216_GLY, 217_GLU",2.5
+5YY5_A,5YY5_L_light+5YY5_H_heavy,"505_ARG, 507_LEU, 508_SER, 514_VAL, 516_GLN, 519_ASN, 520_ALA, 522_GLN, 523_TYR, 525_PRO, 543_LYS, 545_LEU, 546_SER, 547_PRO, 548_LEU, 549_GLU, 550_GLY, 554_LEU",2.8
+5YY5_B,5YY5_D_light+5YY5_C_heavy,"505_ARG, 507_LEU, 508_SER, 514_VAL, 516_GLN, 519_ASN, 521_ASN, 522_GLN, 523_TYR, 525_PRO, 528_SER, 543_LYS, 546_SER, 547_PRO, 548_LEU, 549_GLU, 550_GLY, 552_GLY, 554_LEU",2.8
+6PHC_I,6PHC_B_light+6PHC_A_heavy,"103_THR, 135_LYS, 136_CYS, 137_SER, 138_LEU, 139_LYS, 140_CYS, 141_LEU, 16_GLN, 17_MET, 19_GLY, 1_ALA, 20_HIS, 21_LEU, 2_LYS, 33_ASN, 34_GLU, 35_GLU, 3_VAL, 4_THR, 5_VAL, 6_ASP, 7_THR, 8_VAL",2.9
+6PHC_E,6PHC_D_light+6PHC_C_heavy,"103_THR, 135_LYS, 136_CYS, 137_SER, 139_LYS, 140_CYS, 141_LEU, 16_GLN, 19_GLY, 1_ALA, 20_HIS, 21_LEU, 2_LYS, 33_ASN, 34_GLU, 35_GLU, 3_VAL, 4_THR, 5_VAL, 6_ASP, 7_THR, 8_VAL",2.9
+2DD8_S,2DD8_L_light+2DD8_H_heavy,"359_THR, 363_THR, 365_LYS, 390_LYS, 391_GLY, 392_ASP, 395_ARG, 426_ARG, 436_TYR, 482_GLY, 484_TYR, 485_THR, 486_THR, 487_THR, 488_GLY, 489_ILE, 491_TYR, 492_GLN, 494_TYR",2.3
+8HGL_E,8HGL_G_light+8HGL_F_heavy,"421_TYR, 455_LEU, 456_PHE, 473_TYR, 475_ALA, 476_GLY, 477_SER, 478_THR, 486_PHE, 487_ASN, 489_TYR, 493_GLN",2.9
+8HGL_B,8HGL_D_light+8HGL_C_heavy,"421_TYR, 455_LEU, 456_PHE, 473_TYR, 475_ALA, 476_GLY, 477_SER, 478_THR, 486_PHE, 487_ASN, 489_TYR, 493_GLN",2.9
+8HGL_A,8HGL_L_light+8HGL_H_heavy,"421_TYR, 455_LEU, 456_PHE, 473_TYR, 475_ALA, 476_GLY, 477_SER, 478_THR, 486_PHE, 487_ASN, 489_TYR, 493_GLN",2.9
+8G3M_G,8G3M_F_light+8G3M_E_heavy,"118_ARG, 119_GLU, 149_VAL, 150_ARG, 151_ASP, 152_ARG, 178_TRP, 179_SER, 199_LYS, 221_GLU, 222_ILE, 227_GLU, 246_ALA, 247_SER, 248_GLY, 292_ARG, 294_ASN, 295_TRP, 344_GLU, 345_GLY, 346_GLY, 347_HIS, 371_ARG, 406_TYR, 431_LYS",3.0
+8G3M_J,8G3M_D_light+8G3M_A_heavy,"118_ARG, 149_VAL, 150_ARG, 151_ASP, 152_ARG, 178_TRP, 179_SER, 199_LYS, 221_GLU, 222_ILE, 224_ARG, 227_GLU, 246_ALA, 247_SER, 248_GLY, 292_ARG, 294_ASN, 295_TRP, 344_GLU, 345_GLY, 346_GLY, 347_HIS, 371_ARG, 406_TYR",3.0
+8G3M_L,8G3M_I_light+8G3M_C_heavy,"118_ARG, 149_VAL, 150_ARG, 151_ASP, 152_ARG, 178_TRP, 179_SER, 199_LYS, 221_GLU, 222_ILE, 227_GLU, 246_ALA, 247_SER, 248_GLY, 292_ARG, 294_ASN, 295_TRP, 344_GLU, 345_GLY, 346_GLY, 347_HIS, 371_ARG, 406_TYR",3.0
+5GZO_A,5GZO_L_light+5GZO_H_heavy,"118_LYS, 119_PHE, 120_ALA, 233_THR, 252_ARG, 64_SER, 65_ILE, 66_SER, 67_ASP, 68_MET, 69_ALA, 84_LYS, 87_ASP, 89_GLN, 90_TYR",2.76
+5GZO_B,5GZO_D_light+5GZO_C_heavy,"118_LYS, 119_PHE, 120_ALA, 232_GLY, 233_THR, 252_ARG, 64_SER, 65_ILE, 66_SER, 67_ASP, 68_MET, 69_ALA, 84_LYS, 87_ASP, 89_GLN, 90_TYR",2.76
+6VN1_C,6VN1_N_light+6VN1_J_heavy,"115_THR, 116_LYS, 589_SER, 590_ASP, 592_ARG, 593_ILE, 594_ILE, 595_LEU, 596_GLN, 597_ASN, 617_VAL, 619_LEU, 624_THR, 655_PHE, 658_HIS, 667_TYR, 670_GLU",2.8
+6VN1_B,6VN1_M_light+6VN1_I_heavy,"115_THR, 116_LYS, 589_SER, 590_ASP, 592_ARG, 593_ILE, 594_ILE, 595_LEU, 596_GLN, 597_ASN, 617_VAL, 619_LEU, 624_THR, 655_PHE, 658_HIS, 667_TYR, 670_GLU",2.8
+6VN1_A,6VN1_L_light+6VN1_H_heavy,"115_THR, 116_LYS, 589_SER, 590_ASP, 592_ARG, 593_ILE, 594_ILE, 595_LEU, 596_GLN, 597_ASN, 617_VAL, 619_LEU, 624_THR, 655_PHE, 658_HIS, 667_TYR, 670_GLU",2.8
+7UJK_C,7UJK_F_light+7UJK_E_heavy,"149_ASN, 154_ILE, 157_GLU, 205_SER, 206_SER, 208_ARG, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.43
+7UJK_A,7UJK_L_light+7UJK_H_heavy,"117_GLU, 149_ASN, 154_ILE, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.43
+6P9H_A,6P9H_L_light+6P9H_H_heavy,"362_MET, 365_THR, 366_PHE, 387_THR, 388_ASN, 389_ASP, 390_ASP, 391_TYR, 410_LYS, 411_ASN, 412_ASN, 433_VAL, 435_VAL, 436_LYS, 437_THR, 438_ILE, 439_ASP, 440_TYR, 444_TYR",3.0
+6P9H_B,6P9H_D_light+6P9H_C_heavy,"362_MET, 363_MET, 365_THR, 366_PHE, 387_THR, 388_ASN, 390_ASP, 391_TYR, 410_LYS, 411_ASN, 412_ASN, 433_VAL, 435_VAL, 436_LYS, 437_THR, 438_ILE, 439_ASP, 440_TYR, 444_TYR",3.0
+5F3B_D,5F3B_F_light+5F3B_E_heavy,"25_GLU, 30_ASP, 31_TRP, 33_ILE, 34_ALA, 35_PRO, 36_LYS, 83_ASN, 85_LEU, 87_PHE, 91_GLU, 92_GLN, 93_ILE, 94_ILE, 95_TYR",1.76
+5F3B_C,5F3B_B_light+5F3B_A_heavy,"25_GLU, 30_ASP, 31_TRP, 33_ILE, 34_ALA, 35_PRO, 36_LYS, 83_ASN, 85_LEU, 87_PHE, 91_GLU, 92_GLN, 93_ILE, 94_ILE, 95_TYR",1.76
+4CAD_I,4CAD_G_light+4CAD_H_heavy,"107_PHE, 117_SER, 118_GLY, 129_VAL, 133_LEU, 180_ASN, 181_ASN, 183_TYR, 186_GLU, 187_ILE, 188_PHE, 189_ASN, 190_GLN, 191_SER, 194_TYR, 235_GLU, 238_ALA, 239_ILE, 240_THR, 241_GLN, 242_ASP, 31_SER, 37_SER, 40_TYR",2.5
+4CAD_C,4CAD_A_light+4CAD_B_heavy,"108_GLY, 117_SER, 118_GLY, 119_GLY, 126_PHE, 180_ASN, 181_ASN, 183_TYR, 186_GLU, 187_ILE, 188_PHE, 189_ASN, 190_GLN, 191_SER, 194_TYR, 234_GLN, 235_GLU, 238_ALA, 239_ILE, 240_THR, 241_GLN, 242_ASP, 31_SER, 37_SER, 38_GLY, 40_TYR",2.5
+4CAD_L,4CAD_J_light+4CAD_K_heavy,"107_PHE, 117_SER, 118_GLY, 133_LEU, 180_ASN, 181_ASN, 183_TYR, 186_GLU, 188_PHE, 189_ASN, 190_GLN, 191_SER, 194_TYR, 235_GLU, 238_ALA, 239_ILE, 241_GLN, 242_ASP, 31_SER, 37_SER, 38_GLY, 40_TYR",2.5
+4CAD_F,4CAD_D_light+4CAD_E_heavy,"117_SER, 118_GLY, 119_GLY, 125_ASP, 126_PHE, 180_ASN, 181_ASN, 183_TYR, 186_GLU, 187_ILE, 188_PHE, 189_ASN, 190_GLN, 191_SER, 194_TYR, 234_GLN, 235_GLU, 238_ALA, 239_ILE, 240_THR, 241_GLN, 242_ASP, 31_SER, 37_SER, 38_GLY, 40_TYR",2.5
+7X8T_D,7X8T_E_light+7X8T_F_heavy,"107_ARG, 108_VAL, 111_GLU, 112_GLN, 114_ARG, 131_SER, 133_ASP, 134_CYS, 135_ARG, 136_LYS, 137_LEU, 138_PRO, 139_ASN, 142_ASP, 144_ASN, 145_TYR, 55_LEU, 56_MET, 90_LEU",2.51
+7X8T_G,7X8T_H_light+7X8T_I_heavy,"107_ARG, 108_VAL, 111_GLU, 112_GLN, 114_ARG, 131_SER, 133_ASP, 134_CYS, 135_ARG, 136_LYS, 137_LEU, 138_PRO, 139_ASN, 142_ASP, 144_ASN, 145_TYR, 55_LEU, 56_MET",2.51
+7X8T_J,7X8T_K_light+7X8T_L_heavy,"107_ARG, 108_VAL, 111_GLU, 112_GLN, 131_SER, 133_ASP, 134_CYS, 135_ARG, 136_LYS, 137_LEU, 138_PRO, 139_ASN, 142_ASP, 144_ASN, 145_TYR, 55_LEU, 56_MET, 90_LEU",2.51
+7X8T_A,7X8T_B_light+7X8T_C_heavy,"107_ARG, 108_VAL, 111_GLU, 112_GLN, 131_SER, 133_ASP, 134_CYS, 135_ARG, 136_LYS, 137_LEU, 138_PRO, 139_ASN, 142_ASP, 144_ASN, 145_TYR, 55_LEU",2.51
+3FB5_C,3FB5_B_light+3FB5_A_heavy,"45_TYR, 49_LEU, 50_ALA, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.8
+3H42_B,3H42_L_light+3H42_H_heavy,"214_THR, 215_ARG, 216_PHE, 220_ALA, 221_SER, 222_LYS, 255_CYS, 256_GLN, 257_GLY, 258_LYS, 317_ASN, 318_PHE, 348_LEU, 349_GLY, 350_THR, 351_LEU, 367_ASP, 380_VAL, 381_SER, 382_GLN, 383_SER, 384_GLY",2.3
+4KHX_A,4KHX_L_light+4KHX_H_heavy,"49_GLN, 50_HIS, 51_LEU, 53_GLN, 54_LEU, 55_THR, 57_TRP, 58_GLY, 60_LYS, 61_GLN, 62_LEU, 63_GLN, 64_ALA, 65_ARG",2.92
+3RU8_X,3RU8_L_light+3RU8_H_heavy,"108_PRO, 110_TYR, 111_SER, 112_GLY, 113_GLY, 114_ASP, 115_PRO, 117_ILE, 118_VAL, 119_GLN, 120_GLU, 121_TRP, 122_LEU, 151_HIS, 155_GLN, 158_ALA, 159_ALA, 162_ARG, 217_ASN, 218_GLU, 220_CYS, 253_ASP, 255_CYS, 72_ASP, 75_PRO, 76_THR, 77_GLY, 78_ASP, 79_MET",2.07
+5WI9_A,5WI9_L_light+5WI9_H_heavy,"188_GLU, 235_TYR, 281_TRP, 282_HIS, 285_ASN, 289_ARG, 293_LYS, 294_GLY, 295_TRP, 335_HIS, 336_GLY, 364_ARG, 365_GLY, 367_ALA, 368_ASP, 408_ASN, 409_ASN, 410_PRO, 411_ARG, 448_ARG, 451_GLU, 453_ARG, 82_PRO, 83_LYS, 84_ASN, 86_PHE",2.7
+5WI9_B,5WI9_E_light+5WI9_F_heavy,"188_GLU, 235_TYR, 281_TRP, 282_HIS, 285_ASN, 289_ARG, 293_LYS, 294_GLY, 295_TRP, 335_HIS, 336_GLY, 364_ARG, 365_GLY, 367_ALA, 368_ASP, 408_ASN, 409_ASN, 410_PRO, 411_ARG, 447_ILE, 448_ARG, 451_GLU, 453_ARG, 82_PRO, 83_LYS, 84_ASN, 86_PHE",2.7
+4LF3_F,4LF3_D_light+4LF3_E_heavy,"108_ARG, 110_PRO, 111_ARG, 112_GLY, 113_GLN, 116_ARG, 122_GLN, 32_LEU, 36_TRP, 64_LYS, 65_TYR, 84_TYR, 85_LEU, 86_PRO",2.74
+4LF3_C,4LF3_A_light+4LF3_B_heavy,"108_ARG, 110_PRO, 111_ARG, 112_GLY, 113_GLN, 116_ARG, 122_GLN, 32_LEU, 36_TRP, 64_LYS, 65_TYR, 84_TYR, 85_LEU, 86_PRO, 87_TRP",2.74
+7RP3_A,7RP3_I_light+7RP3_H_heavy,"25_GLN, 32_TYR, 36_ILE, 38_ASP, 39_SER, 40_TYR, 41_ARG, 54_ASP, 56_LEU, 5_LYS, 67_MET, 6_LEU, 70_GLN, 71_TYR, 74_THR, 75_GLY, 7_VAL",2.0
+7M3I_R,7M3I_L_light+7M3I_H_heavy,"374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 408_ARG, 437_ASN, 503_VAL, 504_GLY, 508_TYR",2.8
+7M3I_C,7M3I_B_light+7M3I_A_heavy,"369_TYR, 370_ASN, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 408_ARG, 437_ASN, 503_VAL, 504_GLY, 508_TYR",2.8
+6XKR_P,6XKR_L_light+6XKR_H_heavy,"126_ILE, 128_LEU, 129_ALA, 130_PRO, 132_ALA, 133_GLN, 134_ILE, 136_GLU, 64_VAL, 66_ASN, 68_TYR, 71_SER, 75_GLN, 76_THR, 77_ASP, 78_LYS, 85_ASP, 87_SER, 88_GLN, 89_PRO, 90_GLY",2.59
+6OC3_F,6OC3_B_light+6OC3_A_heavy,"216_GLU, 218_ALA, 219_LYS, 220_ARG, 221_PRO, 222_LYS, 223_VAL, 224_ARG, 229_ARG, 87_PRO, 88_ASN, 93_THR, 96_PRO, 97_GLY, 98_HIS",2.85
+6OC3_E,6OC3_D_light+6OC3_C_heavy,"216_GLU, 218_ALA, 219_LYS, 220_ARG, 221_PRO, 222_LYS, 223_VAL, 224_ARG, 229_ARG, 88_ASN, 93_THR, 96_PRO, 97_GLY, 98_HIS",2.85
+6I9I_D,6I9I_L_light+6I9I_H_heavy,"400_SER, 403_PRO, 405_ILE, 406_GLY, 407_GLY, 408_HIS, 409_GLY, 410_SER, 411_LYS, 412_LYS, 424_GLU, 425_CYS, 426_THR, 427_ALA, 428_GLN, 429_TYR, 431_ASN",1.98
+6I9I_C,6I9I_B_light+6I9I_A_heavy,"400_SER, 401_GLN, 403_PRO, 405_ILE, 406_GLY, 407_GLY, 408_HIS, 409_GLY, 410_SER, 411_LYS, 412_LYS, 424_GLU, 425_CYS, 426_THR, 427_ALA, 428_GLN, 429_TYR, 431_ASN",1.98
+6I9I_C,6I9I_L_light+6I9I_A_heavy,"403_PRO, 405_ILE, 406_GLY, 407_GLY, 408_HIS, 409_GLY, 410_SER, 411_LYS, 412_LYS, 424_GLU, 425_CYS, 426_THR, 427_ALA, 428_GLN, 429_TYR, 431_ASN",1.98
+5BVP_I,5BVP_L_light+5BVP_H_heavy,"129_ASN, 19_VAL, 20_MET, 21_SER, 22_GLY, 23_PRO, 27_LYS, 32_GLN, 34_GLN, 35_ASP, 37_GLU, 38_GLN, 39_GLN, 41_VAL, 64_GLU, 65_LYS, 66_ASN, 86_ASP, 87_PRO, 88_LYS",2.2
+6WMW_B,6WMW_L_light+6WMW_H_heavy,"217_PRO, 290_GLN, 291_CYS, 292_THR, 293_CYS, 294_ARG, 295_THR, 296_ILE, 297_THR, 298_GLN, 299_SER, 301_GLU, 305_LYS, 308_GLN, 309_HIS, 312_HIS",2.91
+5WB9_G,5WB9_L_light+5WB9_H_heavy,"123_THR, 276_ASN, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 371_ILE, 431_GLY, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 461_ASN, 469_ARG, 472_GLY, 473_GLY, 474_ASN",2.4
+7TUY_R,7TUY_L_light+7TUY_H_heavy,"1036_THR, 1039_ARG, 1040_ALA, 1043_LEU, 1044_ASP, 1046_GLN, 1047_LYS, 1071_ASP, 1074_VAL, 1075_GLY, 1081_LEU, 1082_LYS, 1085_ASN",2.98
+3KS0_A,3KS0_J_light+3KS0_K_heavy,"30_ASN, 63_GLU, 64_PRO, 65_LEU, 66_HIS, 67_ALA, 68_PRO, 69_ASN, 70_VAL, 72_ASP",2.7
+3KS0_B,3KS0_L_light+3KS0_H_heavy,"30_ASN, 63_GLU, 64_PRO, 65_LEU, 66_HIS, 67_ALA, 68_PRO, 69_ASN, 70_VAL, 72_ASP, 74_TYR",2.7
+6MTN_G,6MTN_L_light+6MTN_H_heavy,"135_THR, 136_ASN, 137_ALA, 138_ILE, 321_GLY, 322_ILE, 323_ILE, 324_GLY, 325_ASP, 326_ILE, 327_ARG, 328_GLN, 330_HIS, 415_THR, 417_PRO",2.5
+7A0Y_B,7A0Y_F_light+7A0Y_E_heavy,"118_SER, 119_ASP, 120_PRO, 121_VAL, 123_ARG, 124_GLU, 277_GLN, 286_SER, 287_MET, 349_ALA, 351_GLY, 352_ASN, 353_VAL, 354_ASP, 355_PRO, 356_GLU, 357_LEU, 360_TRP, 39_HIS, 43_GLU",2.45
+7A0Y_A,7A0Y_D_light+7A0Y_C_heavy,"118_SER, 119_ASP, 120_PRO, 121_VAL, 122_THR, 123_ARG, 124_GLU, 277_GLN, 278_GLY, 287_MET, 349_ALA, 351_GLY, 352_ASN, 353_VAL, 354_ASP, 355_PRO, 356_GLU, 357_LEU, 360_TRP, 39_HIS, 43_GLU",2.45
+5DFV_B,5DFV_F_light+5DFV_E_heavy,"125_GLN, 129_GLN, 132_GLN, 134_ALA, 135_VAL, 136_VAL, 161_THR, 162_LEU, 164_ALA, 165_LEU, 167_THR, 168_SER, 171_LYS, 172_ASN, 182_ILE",2.8
+5DFV_A,5DFV_D_light+5DFV_C_heavy,"125_GLN, 129_GLN, 132_GLN, 135_VAL, 136_VAL, 144_LYS, 161_THR, 163_THR, 164_ALA, 165_LEU, 167_THR, 168_SER, 171_LYS, 172_ASN, 181_ILE, 182_ILE",2.8
+1NBY_C,1NBY_A_light+1NBY_B_heavy,"613_LYS, 614_ARG, 615_HIS, 616_GLY, 619_ASN, 620_TYR, 621_ARG, 663_TRP, 673_ARG, 675_LEU, 689_THR, 693_ASN, 696_ALA, 697_LYS, 698_ILE, 700_SER, 701_ASP, 702_GLY, 703_ASN",1.8
+1TZH_W,1TZH_A_light+1TZH_B_heavy,"45_TYR, 46_ILE, 79_GLN, 81_MET, 82_ARG, 83_ILE, 84_LYS, 85_PRO, 86_HIS, 87_GLN, 88_GLY, 89_GLN, 90_HIS, 91_ILE, 92_GLY, 93_GLU",2.6
+1TZH_V,1TZH_L_light+1TZH_H_heavy,"45_TYR, 79_GLN, 82_ARG, 83_ILE, 84_LYS, 85_PRO, 86_HIS, 87_GLN, 88_GLY, 89_GLN, 90_HIS, 91_ILE, 92_GLY",2.6
+4CNI_D,4CNI_B_light+4CNI_A_heavy,"103_ASN, 104_ARG, 106_GLU, 152_GLN, 153_ALA, 154_GLN, 155_ASN, 156_GLN, 157_TRP, 158_LEU, 159_GLN, 160_ASP, 47_SER, 48_ASN, 57_LEU",2.2
+4CNI_C,4CNI_L_light+4CNI_H_heavy,"103_ASN, 104_ARG, 106_GLU, 152_GLN, 153_ALA, 154_GLN, 155_ASN, 156_GLN, 157_TRP, 158_LEU, 159_GLN, 160_ASP, 47_SER, 48_ASN, 57_LEU",2.2
+7WTF_D,7WTF_K_light+7WTF_I_heavy,"405_ARG, 419_ASN, 423_TYR, 451_TYR, 457_LEU, 458_PHE, 477_ALA, 478_GLY, 486_ALA, 488_PHE, 489_ASN, 492_PHE, 495_ARG, 496_SER, 497_TYR, 498_SER, 503_TYR",3.0
+7WTF_B,7WTF_L_light+7WTF_H_heavy,"405_ARG, 419_ASN, 451_TYR, 455_TYR, 457_LEU, 477_ALA, 478_GLY, 486_ALA, 488_PHE, 489_ASN, 491_TYR, 492_PHE, 494_LEU, 495_ARG, 496_SER, 498_SER, 503_TYR",3.0
+7WTF_C,7WTF_J_light+7WTF_G_heavy,"419_ASN, 423_TYR, 451_TYR, 457_LEU, 458_PHE, 477_ALA, 478_GLY, 486_ALA, 488_PHE, 489_ASN, 492_PHE, 494_LEU, 495_ARG, 496_SER, 500_ARG, 503_TYR",3.0
+7X2K_E,7X2K_L_light+7X2K_H_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 503_VAL, 505_TYR",2.4
+6RLO_I,6RLO_B_light+6RLO_A_heavy,"151_ASN, 153_CYS, 157_GLY, 158_GLY, 159_VAL, 160_GLU, 161_GLN, 164_SER, 165_ASP, 167_CYS, 168_PRO, 169_LYS, 170_LYS, 171_ASP, 172_VAL, 173_LEU, 174_GLU, 175_THR, 176_PHE, 177_THR, 178_VAL",2.2
+6RLO_J,6RLO_D_light+6RLO_C_heavy,"151_ASN, 153_CYS, 157_GLY, 158_GLY, 159_VAL, 160_GLU, 161_GLN, 164_SER, 165_ASP, 167_CYS, 168_PRO, 169_LYS, 170_LYS, 171_ASP, 172_VAL, 173_LEU, 174_GLU, 175_THR, 176_PHE, 177_THR, 178_VAL",2.2
+6RLO_K,6RLO_F_light+6RLO_E_heavy,"151_ASN, 153_CYS, 157_GLY, 158_GLY, 159_VAL, 160_GLU, 161_GLN, 164_SER, 165_ASP, 167_CYS, 168_PRO, 169_LYS, 170_LYS, 171_ASP, 172_VAL, 173_LEU, 174_GLU, 175_THR, 176_PHE, 177_THR, 178_VAL",2.2
+6RLO_L,6RLO_H_light+6RLO_G_heavy,"151_ASN, 153_CYS, 157_GLY, 158_GLY, 159_VAL, 160_GLU, 161_GLN, 164_SER, 165_ASP, 167_CYS, 168_PRO, 169_LYS, 170_LYS, 171_ASP, 172_VAL, 173_LEU, 174_GLU, 175_THR, 176_PHE, 177_THR, 178_VAL",2.2
+8EUW_D,8EUW_J_light+8EUW_I_heavy,"512_ALA, 513_VAL, 514_GLY, 515_ILE, 516_GLY, 517_ALA, 518_VAL, 519_PHE, 520_LEU",2.7
+8EUW_F,8EUW_L_light+8EUW_K_heavy,"512_ALA, 513_VAL, 514_GLY, 515_ILE, 516_GLY, 517_ALA, 518_VAL, 519_PHE, 520_LEU",2.7
+8EUW_B,8EUW_H_light+8EUW_G_heavy,"512_ALA, 513_VAL, 514_GLY, 515_ILE, 516_GLY, 517_ALA, 518_VAL, 519_PHE, 520_LEU",2.7
+5EBL_C,5EBL_B_light+5EBL_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 63_PRO, 64_ARG",2.3
+8J80_A,8J80_C_light+8J80_E_heavy,"313_GLN, 314_GLN, 315_LEU, 316_GLN, 317_GLY, 318_VAL, 319_TYR, 343_ALA, 344_PRO, 345_ASP, 346_ALA, 347_ASP, 349_ARG, 350_TRP, 353_SER, 354_GLN, 356_HIS, 373_PHE, 375_ALA, 376_MET",2.68
+8J80_B,8J80_D_light+8J80_F_heavy,"313_GLN, 314_GLN, 315_LEU, 316_GLN, 317_GLY, 318_VAL, 319_TYR, 343_ALA, 344_PRO, 345_ASP, 346_ALA, 347_ASP, 349_ARG, 350_TRP, 353_SER, 354_GLN, 356_HIS, 373_PHE, 375_ALA, 376_MET",2.68
+2HG5_C,2HG5_B_light+2HG5_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 63_PRO, 64_ARG",2.75
+4NP4_A,4NP4_L_light+4NP4_H_heavy,"2009_TYR, 2018_TYR, 2021_GLU, 2031_ASN, 2032_THR, 2033_GLU, 2034_ASP, 2035_GLY, 2036_PHE, 2070_ASP, 2072_SER, 2074_THR, 2075_ALA, 2076_VAL, 2077_VAL, 2092_GLU, 2093_ASP",2.89
+4NP4_A,4NP4_M_light+4NP4_I_heavy,"1878_GLU, 1887_TYR, 1890_GLN, 1900_SER, 1901_THR, 1902_GLU, 1903_ASP, 1905_PHE, 1939_ASP, 1941_ASN, 1943_ARG, 1944_GLY, 1946_VAL, 1947_GLU, 1960_PRO, 1961_GLU",2.89
+3U7Y_G,3U7Y_L_light+3U7Y_H_heavy,"102_GLU, 122_LEU, 123_THR, 124_GLY, 276_ASN, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 371_ILE, 427_TRP, 430_THR, 431_GLY, 432_GLN, 455_THR, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_ALA, 461_ASN, 465_ASN, 466_GLU, 467_THR, 469_ARG, 474_ASN, 476_LYS, 480_ARG, 97_LYS, 98_ASN, 99_ASN",2.45
+3WKM_B,3WKM_M_light+3WKM_I_heavy,"113_GLY, 114_SER, 115_GLU, 117_PRO, 161_ARG, 162_ASP, 165_ILE, 195_VAL, 197_LYS, 198_GLY, 200_GLU, 207_VAL, 215_LYS, 216_LYS, 242_TRP, 246_VAL, 250_ARG, 276_LYS, 277_ASP, 278_PRO, 279_LYS, 280_THR, 281_GLY, 282_THR, 283_TYR, 289_PRO",2.2
+3WKM_A,3WKM_L_light+3WKM_H_heavy,"116_VAL, 117_PRO, 161_ARG, 162_ASP, 165_ILE, 195_VAL, 198_GLY, 200_GLU, 207_VAL, 215_LYS, 216_LYS, 242_TRP, 246_VAL, 250_ARG, 276_LYS, 277_ASP, 278_PRO, 279_LYS, 280_THR, 281_GLY, 282_THR, 283_TYR, 288_PHE, 289_PRO",2.2
+6SUZ_A,6SUZ_L_light+6SUZ_H_heavy,"142_GLY, 143_SER, 144_ASP, 145_TYR, 146_GLU, 147_ASP, 148_ARG, 149_TYR, 151_ARG, 152_GLU, 153_ASN, 156_ARG, 197_ASN, 199_THR, 201_THR",2.5
+7WLZ_B,7WLZ_O_light+7WLZ_P_heavy,"340_GLU, 344_ALA, 345_THR, 346_ARG, 347_PHE, 348_ALA, 351_TYR, 352_ALA, 354_ASN, 449_TYR, 450_ASN, 466_ARG, 470_THR, 483_VAL, 490_PHE",2.98
+4ENE_A,4ENE_D_light+4ENE_C_heavy,"243_LYS, 246_ASP, 248_PRO, 249_LEU, 250_ASN, 380_PRO, 381_GLN, 382_TYR, 383_HIS",2.4
+4ENE_B,4ENE_F_light+4ENE_E_heavy,"243_LYS, 244_LEU, 246_ASP, 248_PRO, 249_LEU, 250_ASN, 380_PRO, 381_GLN, 382_TYR, 383_HIS",2.4
+7RT9_Y,7RT9_B_light+7RT9_A_heavy,"1_TYR, 30_LEU, 32_THR, 33_ARG, 34_GLN, 35_ARG, 36_TYR",1.9
+7RT9_Z,7RT9_D_light+7RT9_C_heavy,"30_LEU, 32_THR, 33_ARG, 34_GLN, 35_ARG, 36_TYR",1.9
+3HI6_A,3HI6_L_light+3HI6_H_heavy,"139_SER, 140_MET, 141_SER, 142_LEU, 143_GLN, 144_PRO, 200_LYS, 201_HIS, 202_MET, 203_LEU, 204_LEU, 205_LEU, 206_THR, 239_ASP, 241_GLU, 242_ALA, 243_THR, 263_LYS, 264_HIS, 268_LYS, 269_GLU, 273_THR",2.3
+3HI6_B,3HI6_Y_light+3HI6_X_heavy,"139_SER, 140_MET, 141_SER, 142_LEU, 143_GLN, 144_PRO, 200_LYS, 201_HIS, 202_MET, 203_LEU, 204_LEU, 205_LEU, 206_THR, 239_ASP, 241_GLU, 242_ALA, 243_THR, 263_LYS, 264_HIS, 269_GLU",2.3
+6BA5_N,6BA5_C_light+6BA5_D_heavy,"330_SER, 331_GLY, 332_ARG, 368_GLN, 369_LYS, 370_GLU, 378_ASP, 380_ASN, 381_PHE, 382_LYS, 383_SER, 384_ASP, 385_LEU",1.62
+6BA5_O,6BA5_E_light+6BA5_F_heavy,"330_SER, 331_GLY, 332_ARG, 368_GLN, 369_LYS, 370_GLU, 378_ASP, 380_ASN, 381_PHE, 382_LYS, 383_SER, 384_ASP, 385_LEU",1.62
+6BA5_M,6BA5_A_light+6BA5_B_heavy,"330_SER, 331_GLY, 332_ARG, 369_LYS, 370_GLU, 378_ASP, 380_ASN, 381_PHE, 382_LYS, 383_SER, 384_ASP, 385_LEU",1.62
+6BA5_P,6BA5_G_light+6BA5_H_heavy,"330_SER, 331_GLY, 332_ARG, 368_GLN, 369_LYS, 378_ASP, 380_ASN, 381_PHE, 382_LYS, 383_SER, 384_ASP, 385_LEU",1.62
+1KB5_A,1KB5_L_light+1KB5_H_heavy,"101_ARG, 103_ALA, 1_GLN, 26_ASP, 2_GLN, 94_TYR, 95_GLN, 96_GLY, 97_GLY",2.5
+1KB5_B,1KB5_L_light+1KB5_H_heavy,"100_TRP, 31_TRP, 42_GLN, 44_GLN, 45_TRP, 50_ARG, 51_SER, 54_ASP, 56_GLU, 58_LYS, 59_SER, 60_LEU, 61_PRO, 98_PRO",2.5
+8HHY_B,8HHY_W_light+8HHY_F_heavy,"367_VAL, 369_TYR, 370_ASN, 371_SER, 372_ALA, 377_PHE, 378_LYS, 379_CYS, 408_ARG, 437_ASN, 503_VAL, 504_GLY, 508_TYR",2.77
+8HHY_A,8HHY_L_light+8HHY_H_heavy,"370_ASN, 371_SER, 372_ALA, 384_PRO, 405_ASP, 408_ARG, 409_GLN, 414_GLN",2.77
+8Y6H_A,8Y6H_B_light+8Y6H_C_heavy,"322_LEU, 324_GLY, 325_GLU, 326_TYR, 327_SER, 739_PHE, 742_ILE, 743_ASP, 744_ASP, 745_PRO, 748_LYS, 77_ASP, 78_ILE, 81_ASN, 84_ASN, 85_LEU, 88_LEU, 89_MET",2.49
+7N3C_C,7N3C_L_light+7N3C_H_heavy,"120_GLY, 123_TYR, 134_ALA, 136_GLU, 137_GLY, 138_ALA, 139_LEU, 140_ASN, 141_THR, 142_PRO, 144_ASP, 68_ARG, 69_GLY, 81_ASP, 84_ILE",1.82
+8UCD_C,8UCD_L_light+8UCD_H_heavy,"200_VAL, 201_GLN, 202_GLN, 203_ASN, 205_GLU",3.0
+7JTG_E,7JTG_B_light+7JTG_A_heavy,"422_ILE, 425_THR, 427_LEU, 439_THR, 441_LEU, 442_PHE, 443_TYR, 505_PRO, 507_TYR, 529_TRP, 612_PRO, 613_TYR",2.6
+7K9J_C,7K9J_N_light+7K9J_J_heavy,"340_GLU, 343_ASN, 344_ALA, 345_THR, 346_ARG, 440_ASN, 441_LEU, 442_ASP, 443_SER, 444_LYS, 445_VAL, 448_ASN",3.0
+7K9J_B,7K9J_M_light+7K9J_I_heavy,"340_GLU, 343_ASN, 344_ALA, 345_THR, 346_ARG, 440_ASN, 441_LEU, 442_ASP, 443_SER, 444_LYS, 445_VAL",3.0
+7K9J_A,7K9J_L_light+7K9J_H_heavy,"340_GLU, 343_ASN, 344_ALA, 345_THR, 346_ARG, 440_ASN, 441_LEU, 442_ASP, 443_SER, 444_LYS, 445_VAL, 448_ASN, 450_ASN",3.0
+5IES_C,5IES_L_light+5IES_H_heavy,"138_SER, 139_GLY, 140_GLY, 141_ASP, 144_PHE, 25_THR, 26_ARG, 27_GLN, 28_GLY, 29_GLY, 30_TYR, 37_ILE, 39_ARG, 42_GLY, 43_GLY, 44_ASP, 79_ASP, 81_ARG, 82_ASP, 83_ASN, 84_ALA, 85_LYS",2.16
+3CX5_E,3CX5_K_light+3CX5_J_heavy,"123_PRO, 124_HIS, 126_ILE, 127_GLN, 130_ASN, 131_SER, 132_VAL, 133_ASP, 134_MET, 142_THR, 143_ASP, 144_ALA, 147_VAL, 148_LYS, 149_ASP, 150_PRO, 151_GLN",1.9
+3CX5_P,3CX5_V_light+3CX5_U_heavy,"123_PRO, 124_HIS, 126_ILE, 127_GLN, 130_ASN, 131_SER, 132_VAL, 133_ASP, 134_MET, 142_THR, 143_ASP, 144_ALA, 147_VAL, 148_LYS, 149_ASP, 150_PRO, 151_GLN",1.9
+6DDR_C,6DDR_A_light+6DDR_B_heavy,"240_ARG, 242_ASP, 243_GLY, 244_VAL, 277_GLU, 278_GLN, 279_ARG, 280_PHE, 281_THR, 283_TYR, 285_GLU, 288_GLY, 289_ASN, 290_HIS, 291_SER, 292_THR, 294_PRO, 295_VAL, 296_PRO, 297_SER",1.9
+8CWK_C,8CWK_L_light+8CWK_H_heavy,"456_PHE, 474_GLN, 476_GLY, 477_SER, 478_THR, 479_PRO, 483_VAL, 485_GLY, 486_PHE, 487_ASN, 488_CYS, 489_TYR",2.37
+8CWK_C,8CWK_B_light+8CWK_A_heavy,"348_ALA, 349_SER, 351_TYR, 352_ALA, 353_TRP, 355_ARG, 356_LYS, 357_ARG, 393_THR, 394_ASN, 396_TYR, 450_ASN, 452_LEU, 464_PHE, 465_GLU, 466_ARG, 468_ILE, 470_THR, 471_GLU, 520_ALA, 521_PRO, 523_THR",2.37
+8T04_A,8T04_D_light+8T04_C_heavy,"137_MET, 140_LYS, 141_LYS, 142_GLY, 143_LEU, 144_TYR, 146_ASP, 147_LYS, 148_SER, 149_ILE, 200_LYS",2.98
+8T04_B,8T04_F_light+8T04_E_heavy,"137_MET, 140_LYS, 141_LYS, 142_GLY, 143_LEU, 144_TYR, 146_ASP, 147_LYS, 148_SER, 149_ILE, 200_LYS",2.98
+1AR1_B,1AR1_D_light+1AR1_C_heavy,"166_VAL, 167_GLY, 168_LYS, 205_SER, 206_VAL, 207_ASP, 208_GLN, 209_GLU, 235_SER, 236_GLN, 237_GLU, 240_GLU, 25_SER, 26_PRO, 29_HIS, 30_ASP",2.7
+3A6B_Y,3A6B_L_light+3A6B_H_heavy,"100_SER, 101_ASP, 102_GLY, 103_ASN, 15_HIS, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 62_TRP, 63_TRP, 73_ARG, 74_ASN, 75_LEU, 77_ASN, 89_THR, 93_ASN, 96_LYS, 97_LYS, 98_ILE",1.8
+1N8Z_C,1N8Z_A_light+1N8Z_B_heavy,"557_PRO, 558_GLU, 560_ASP, 561_GLN, 569_LYS, 570_ASP, 571_PRO, 572_PRO, 573_PHE, 591_ILE, 593_LYS, 596_ASP, 598_GLU, 600_ALA, 602_GLN, 603_PRO, 604_CYS",2.52
+6ML8_A,6ML8_L_light+6ML8_H_heavy,"108_TYR, 144_THR, 146_ARG, 147_GLY, 148_VAL, 149_THR, 158_SER, 166_TRP, 168_THR, 171_ASN, 202_GLU, 203_GLU, 205_ARG, 206_ALA, 207_LEU, 209_ARG, 239_GLN, 240_SER",2.92
+6WFW_G,6WFW_L_light+6WFW_H_heavy,"16_THR, 17_LEU, 18_LYS, 19_GLY, 20_GLU, 21_THR, 22_THR, 38_TYR, 41_ASP, 42_ASN, 43_GLY",2.09
+6WHK_A,6WHK_B_light+6WHK_C_heavy,"134_GLY, 135_VAL, 136_THR, 137_ALA, 143_GLY, 144_LYS, 145_SER, 153_TRP, 156_GLU, 158_ASN, 159_GLY, 187_ASN, 189_ARG, 190_ASP, 192_ARG, 193_ALA, 194_ILE, 196_HIS, 226_GLN, 95_TYR",2.6
+6HHC_A,6HHC_L_light+6HHC_H_heavy,"406_HIS, 410_PRO, 411_THR, 412_GLN, 413_ARG, 414_HIS, 451_GLN, 452_SER, 455_LYS, 522_ARG, 523_LYS, 524_LEU, 525_ARG, 527_LYS, 528_ILE, 529_GLN, 530_ASN",2.7
+7N8Q_G,7N8Q_L_light+7N8Q_H_heavy,"219_THR, 220_PRO, 221_ALA, 224_VAL, 244_SER, 245_VAL, 246_GLN, 492_GLU, 53_PHE, 71_THR, 72_HIS, 73_ALA, 75_VAL, 78_ASP, 79_PRO, 80_ASN, 82_GLN, 84_ILE",2.9
+7N8Q_A,7N8Q_D_light+7N8Q_C_heavy,"219_THR, 220_PRO, 221_ALA, 224_VAL, 245_VAL, 246_GLN, 53_PHE, 72_HIS, 73_ALA, 74_CYS, 75_VAL, 76_PRO, 78_ASP, 79_PRO, 80_ASN, 82_GLN, 84_ILE",2.9
+2WUB_C,2WUB_Q_light+2WUB_R_heavy,"101_HIS, 178_PRO, 179_ASN, 233_ASN, 241_ARG, 243_ARG, 59_PHE, 60_SER, 61_ARG, 87_LYS, 88_TYR, 89_ILE, 90_PRO, 91_TYR, 92_THR, 93_LEU, 94_TYR, 95_SER, 96_VAL, 97_PHE",2.9
+2WUB_A,2WUB_L_light+2WUB_H_heavy,"101_HIS, 179_ASN, 233_ASN, 236_ASP, 241_ARG, 59_PHE, 60_SER, 61_ARG, 87_LYS, 88_TYR, 89_ILE, 90_PRO, 91_TYR, 92_THR, 93_LEU, 94_TYR, 95_SER, 96_VAL, 97_PHE",2.9
+7S0B_E,7S0B_D_light+7S0B_C_heavy,"346_ARG, 348_ALA, 352_ALA, 353_TRP, 354_ASN, 355_ARG, 356_LYS, 357_ARG, 396_TYR, 457_ARG, 462_LYS, 464_PHE, 465_GLU, 466_ARG, 467_ASP, 468_ILE, 469_SER, 470_THR",2.9
+7S0B_F,7S0B_B_light+7S0B_A_heavy,"346_ARG, 348_ALA, 352_ALA, 353_TRP, 354_ASN, 355_ARG, 357_ARG, 396_TYR, 457_ARG, 462_LYS, 464_PHE, 465_GLU, 466_ARG, 467_ASP, 468_ILE, 469_SER, 470_THR, 471_GLU",2.9
+4TSB_A,4TSB_L_light+4TSB_H_heavy,"102_GLY, 103_ASN, 104_GLY, 106_ASN, 109_VAL, 111_TRP, 112_ARG, 113_ASN, 114_ARG, 116_LYS, 117_GLY, 118_THR, 21_ARG, 22_GLY, 23_TYR",1.95
+3NPS_A,3NPS_C_light+3NPS_B_heavy,"143_HIS, 145_GLN, 146_TYR, 147_GLY, 148_GLY, 175_GLN, 189_ASP, 190_SER, 191_CYS, 192_GLN, 195_SER, 215_TRP, 216_GLY, 219_GLY, 220_CYS, 226_GLY, 41_ILE, 42_CYS, 57_HIS, 60_ILE, 96_ASP, 97_PHE, 98_THR, 99_PHE",1.5
+3ZDZ_A,3ZDZ_L_light+3ZDZ_H_heavy,"117_GLU, 149_ASN, 154_ILE, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.75
+3ZDZ_C,3ZDZ_F_light+3ZDZ_E_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.75
+3BDY_V,3BDY_L_light+3BDY_H_heavy,"45_TYR, 48_LYS, 81_MET, 83_ILE, 84_LYS, 85_PRO, 86_HIS, 87_GLN, 88_GLY, 89_GLN, 90_HIS, 91_ILE, 92_GLY, 93_GLU",2.6
+1G7I_C,1G7I_A_light+1G7I_B_heavy,"102_GLY, 116_LYS, 117_GLY, 118_THR, 119_ASP, 120_VAL, 121_GLN, 124_ILE, 125_ARG, 18_ASP, 19_ASN, 22_GLY, 23_TYR, 24_SER, 27_ASN",1.8
+4YQX_M,4YQX_O_light+4YQX_N_heavy,"134_ARG, 36_GLN, 37_GLU, 40_SER, 41_ARG, 43_GLU, 44_ASN, 45_TYR, 46_ARG, 49_LYS, 52_ARG, 53_MET, 93_GLN",2.83
+4YQX_A,4YQX_L_light+4YQX_H_heavy,"130_VAL, 134_ARG, 36_GLN, 37_GLU, 40_SER, 41_ARG, 43_GLU, 44_ASN, 45_TYR, 46_ARG, 49_LYS, 52_ARG, 53_MET, 93_GLN",2.83
+7TCT_A,7TCT_L_light+7TCT_H_heavy,"117_GLU, 149_ASN, 154_ILE, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.5
+7TCT_C,7TCT_F_light+7TCT_E_heavy,"149_ASN, 154_ILE, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.5
+7S5Q_A,7S5Q_C_light+7S5Q_B_heavy,"455_LEU, 473_TYR, 475_ALA, 476_GLY, 477_SER, 478_THR, 485_GLY, 486_PHE, 487_ASN, 488_CYS, 489_TYR, 493_GLN",2.88
+7S5Q_A,7S5Q_L_light+7S5Q_H_heavy,"369_TYR, 371_SER, 372_ALA, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 408_ARG, 409_GLN, 412_PRO, 413_GLY, 414_GLN, 415_THR, 416_GLY, 427_ASP, 428_ASP, 429_PHE",2.88
+7R40_C,7R40_G_light+7R40_E_heavy,"417_LYS, 421_TYR, 455_LEU, 456_PHE, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN",2.9
+7R40_B,7R40_F_light+7R40_D_heavy,"417_LYS, 421_TYR, 455_LEU, 456_PHE, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN",2.9
+7R40_A,7R40_L_light+7R40_H_heavy,"417_LYS, 421_TYR, 455_LEU, 456_PHE, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN",2.9
+6PPG_G,6PPG_A_light+6PPG_B_heavy,"125_VAL, 127_PRO, 128_VAL, 129_ILE, 130_HIS, 75_LEU, 84_GLU, 85_ASP, 86_ILE, 87_SER, 89_ASN, 91_VAL",2.75
+6PPG_F,6PPG_L_light+6PPG_H_heavy,"125_VAL, 127_PRO, 128_VAL, 129_ILE, 75_LEU, 84_GLU, 85_ASP, 86_ILE, 87_SER, 89_ASN",2.75
+6JJP_C,6JJP_B_light+6JJP_A_heavy,"128_LEU, 129_ALA, 130_PRO, 131_LYS, 132_ALA, 59_THR, 60_SER, 61_GLU, 62_SER, 83_PRO, 84_GLU, 86_ARG, 87_SER, 88_GLN",2.9
+6JJP_F,6JJP_E_light+6JJP_D_heavy,"128_LEU, 129_ALA, 130_PRO, 131_LYS, 132_ALA, 59_THR, 60_SER, 61_GLU, 62_SER, 64_VAL, 83_PRO, 84_GLU, 86_ARG, 87_SER, 89_PRO",2.9
+7RXP_A,7RXP_L_light+7RXP_H_heavy,"310_GLU, 311_PRO, 312_SER, 313_ASP, 316_ILE, 331_TRP, 340_ASN, 342_ILE, 343_GLN, 345_ARG, 347_LYS, 348_PRO, 349_GLY, 360_TYR, 365_GLU, 366_LYS, 367_LYS, 368_ILE, 369_CYS, 370_LYS",1.76
+7STS_C,7STS_L_light+7STS_H_heavy,"104_ARG, 105_ASN, 106_PRO, 107_ALA, 108_ASN, 109_ASN, 110_ALA, 72_PRO",2.16
+7STS_D,7STS_B_light+7STS_A_heavy,"104_ARG, 105_ASN, 106_PRO, 107_ALA, 108_ASN, 109_ASN, 110_ALA",2.16
+6OTC_A,6OTC_L_light+6OTC_H_heavy,"254_GLN, 257_SER, 258_GLU, 260_GLU, 268_ARG",1.7
+5T6L_I,5T6L_L_light+5T6L_H_heavy,"118_GLY, 119_ASN, 120_TRP, 121_PHE, 124_THR, 125_ASN, 127_LEU, 128_TRP, 131_LYS, 64_LEU, 66_VAL, 79_LYS",2.1
+5T6L_G,5T6L_B_light+5T6L_A_heavy,"118_GLY, 119_ASN, 120_TRP, 121_PHE, 124_THR, 125_ASN, 127_LEU, 128_TRP, 131_LYS, 64_LEU, 66_VAL, 73_ILE, 79_LYS",2.1
+4XVS_G,4XVS_L_light+4XVS_H_heavy,"275_GLU, 276_ASN, 278_LYS, 279_ASP, 280_ASN, 281_ALA, 282_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 371_ILE, 430_VAL, 455_THR, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_SER, 461_SER, 462_THR, 470_ARG, 473_GLY, 474_GLY, 475_ASP, 477_ARG, 97_LYS",1.9
+7Z0Y_R,7Z0Y_L_light+7Z0Y_H_heavy,"439_ASN, 440_ASN, 443_SER, 444_LYS, 445_VAL, 446_GLY, 447_GLY, 448_ASN, 449_TYR, 450_ASN, 499_PRO, 500_THR, 501_ASN, 502_GLY, 506_GLN",2.95
+6Q20_A,6Q20_L_light+6Q20_H_heavy,"118_ARG, 119_GLU, 151_ASP, 152_ARG, 198_ASP, 199_ARG, 220_GLN, 221_ASN, 222_ILE, 224_ARG, 245_SER, 246_ALA, 249_ARG, 276_GLU, 277_GLU, 295_TRP, 296_LYS, 342_ASN, 346_ASN, 347_PRO, 370_SER, 371_ARG, 406_TYR, 432_GLN",2.45
+6IEB_A,6IEB_L_light+6IEB_H_heavy,"120_PRO, 121_LYS, 137_ALA, 138_SER, 141_LYS, 21_GLN, 22_GLU, 23_ASP, 24_ALA, 25_THR, 27_LYS, 30_THR",2.41
+6IEB_B,6IEB_F_light+6IEB_E_heavy,"120_PRO, 121_LYS, 137_ALA, 138_SER, 141_LYS, 21_GLN, 22_GLU, 23_ASP, 24_ALA, 25_THR, 30_THR",2.41
+7NEH_E,7NEH_L_light+7NEH_H_heavy,"403_ARG, 405_ASP, 408_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",1.77
+1HYS_B,1HYS_C_light+1HYS_D_heavy,"199_ARG, 222_GLN, 223_LYS, 224_GLU, 225_PRO, 226_PRO, 227_PHE, 229_TRP, 230_MET",3.0
+7PS2_G,7PS2_B_light+7PS2_A_heavy,"403_ARG, 408_ARG, 415_THR, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 493_GLN, 494_SER, 495_TYR, 496_GLY, 500_THR, 501_TYR, 502_GLY, 505_TYR",2.99
+7PS2_G,7PS2_L_light+7PS2_H_heavy,"339_GLY, 340_GLU, 343_ASN, 344_ALA, 345_THR, 346_ARG, 373_SER, 374_PHE, 436_TRP, 440_ASN, 441_LEU, 442_ASP, 443_SER, 444_LYS, 445_VAL, 448_ASN, 450_ASN, 451_TYR",2.99
+7AHU_F,7AHU_A_light+7AHU_B_heavy,"267_HIS, 269_GLU, 270_GLN, 274_VAL, 279_ARG, 282_THR, 283_LEU, 284_ALA, 291_ILE, 293_THR",2.6
+7AHU_C,7AHU_L_light+7AHU_H_heavy,"108_ARG, 112_PRO, 124_LEU, 125_PRO, 126_GLU, 240_LEU, 241_LYS, 244_ASP, 245_ARG, 247_MET, 248_LYS, 249_THR, 48_SER, 49_GLU, 50_PHE, 51_TYR, 87_GLU",2.6
+7AHU_D,7AHU_L_light+7AHU_H_heavy,"269_GLU, 279_ARG, 282_THR, 283_LEU, 284_ALA, 285_ASP, 291_ILE, 293_THR, 294_GLY, 295_PRO",2.6
+7AHU_E,7AHU_A_light+7AHU_B_heavy,"108_ARG, 112_PRO, 113_ILE, 124_LEU, 125_PRO, 126_GLU, 240_LEU, 241_LYS, 244_ASP, 245_ARG, 247_MET, 248_LYS, 47_LEU, 48_SER, 49_GLU",2.6
+8G3V_I,8G3V_N_light+8G3V_C_heavy,"118_ARG, 119_GLU, 149_VAL, 150_ARG, 151_ASP, 152_ARG, 178_TRP, 179_SER, 198_ASP, 199_LYS, 220_ASN, 221_ASP, 222_ILE, 227_GLU, 246_ALA, 247_THR, 248_GLY, 292_ARG, 294_ASN, 295_TRP, 344_GLU, 346_GLY, 347_HIS, 371_ARG, 406_TYR, 431_LYS",2.2
+8G3V_G,8G3V_L_light+8G3V_A_heavy,"118_ARG, 119_GLU, 149_VAL, 150_ARG, 151_ASP, 152_ARG, 178_TRP, 179_SER, 198_ASP, 199_LYS, 220_ASN, 221_ASP, 222_ILE, 227_GLU, 246_ALA, 247_THR, 248_GLY, 292_ARG, 294_ASN, 295_TRP, 344_GLU, 346_GLY, 347_HIS, 371_ARG, 406_TYR, 431_LYS",2.2
+8G3V_J,8G3V_O_light+8G3V_D_heavy,"118_ARG, 119_GLU, 149_VAL, 150_ARG, 151_ASP, 152_ARG, 178_TRP, 179_SER, 198_ASP, 199_LYS, 220_ASN, 221_ASP, 222_ILE, 227_GLU, 246_ALA, 247_THR, 248_GLY, 292_ARG, 294_ASN, 295_TRP, 344_GLU, 346_GLY, 347_HIS, 371_ARG, 406_TYR, 431_LYS",2.2
+8G3V_H,8G3V_M_light+8G3V_B_heavy,"118_ARG, 149_VAL, 150_ARG, 151_ASP, 152_ARG, 178_TRP, 179_SER, 198_ASP, 199_LYS, 220_ASN, 221_ASP, 222_ILE, 227_GLU, 246_ALA, 247_THR, 248_GLY, 292_ARG, 294_ASN, 295_TRP, 344_GLU, 346_GLY, 347_HIS, 371_ARG, 406_TYR, 431_LYS",2.2
+6ZTR_I,6ZTR_L_light+6ZTR_H_heavy,"124_PHE, 125_PRO, 151_ASP, 153_PRO, 154_PRO, 155_GLU, 156_GLY, 159_ALA, 160_VAL, 161_GLU, 162_LYS, 163_GLU, 168_LEU, 170_ASN, 171_LYS, 172_PRO",2.1
+6ZTR_J,6ZTR_B_light+6ZTR_A_heavy,"124_PHE, 125_PRO, 151_ASP, 153_PRO, 154_PRO, 155_GLU, 156_GLY, 159_ALA, 160_VAL, 161_GLU, 162_LYS, 163_GLU, 168_LEU, 170_ASN, 171_LYS",2.1
+5KVG_E,5KVG_L_light+5KVG_H_heavy,"309_THR, 310_ALA, 311_ALA, 312_PHE, 313_THR, 314_PHE, 331_GLN, 332_TYR, 333_ALA, 334_GLY, 335_THR, 336_ASP, 337_GLY, 368_SER, 370_GLU, 371_ASN, 393_GLU, 394_LYS, 395_LYS, 396_ILE, 397_THR",1.4
+8TBQ_P,8TBQ_L_light+8TBQ_H_heavy,"110_SER, 116_ARG, 147_LEU, 151_ILE, 154_HIS, 155_HIS, 156_SER, 157_GLU, 159_ARG, 69_TYR, 73_TYR, 84_SER, 86_ARG, 88_PRO, 93_THR, 94_PHE, 95_GLN, 96_ASP, 98_HIS",2.59
+8TBQ_Q,8TBQ_B_light+8TBQ_A_heavy,"147_LEU, 151_ILE, 154_HIS, 156_SER, 157_GLU, 159_ARG, 69_TYR, 73_TYR, 83_CYS, 84_SER, 86_ARG, 87_ARG, 88_PRO, 93_THR, 94_PHE, 95_GLN, 98_HIS",2.59
+8URF_A,8URF_L_light+8URF_H_heavy,"179_VAL, 180_ASN, 181_TRP, 182_VAL, 183_GLU, 184_HIS, 191_PHE, 192_SER, 193_HIS, 222_LYS, 223_PHE, 226_GLN, 227_HIS, 229_ASN, 297_ARG",1.9
+2BDN_A,2BDN_L_light+2BDN_H_heavy,"28_TYR, 30_ARG, 31_ILE, 32_THR, 34_SER, 37_PRO, 38_LYS, 39_GLU, 40_ALA, 41_VAL, 55_PRO, 56_LYS, 61_GLN, 64_MET, 65_ASP, 68_ASP, 69_LYS",2.53
+4Z7N_C,4Z7N_F_light+4Z7N_E_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.6
+4Z7N_A,4Z7N_L_light+4Z7N_H_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 217_SER, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.6
+2WUC_A,2WUC_L_light+2WUC_H_heavy,"101_HIS, 178_PRO, 179_ASN, 233_ASN, 236_ASP, 241_ARG, 59_PHE, 60_SER, 61_ARG, 87_LYS, 88_TYR, 89_ILE, 90_PRO, 91_TYR, 92_THR, 93_LEU, 94_TYR, 95_SER, 96_VAL, 97_PHE",2.7
+5L0Q_D,5L0Q_E_light+5L0Q_F_heavy,"557_ARG, 628_PRO, 639_CYS, 640_ASP, 641_VAL, 642_PHE, 644_ARG, 646_ARG",2.76
+5L0Q_A,5L0Q_B_light+5L0Q_C_heavy,"451_VAL, 557_ARG, 589_ASP, 591_LYS, 628_PRO, 638_TYR, 639_CYS, 640_ASP, 641_VAL, 642_PHE, 644_ARG, 646_ARG",2.76
+8ELQ_A,8ELQ_L_light+8ELQ_H_heavy,"403_ARG, 405_ASP, 408_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 449_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 503_VAL, 505_TYR",2.21
+7CHS_E,7CHS_L_light+7CHS_H_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 503_VAL, 505_TYR",2.4
+8DXT_R,8DXT_L_light+8DXT_H_heavy,"345_THR, 346_ARG, 347_PHE, 348_ALA, 351_TYR, 440_ASN, 441_LEU, 442_ASP, 443_SER, 444_LYS, 445_VAL, 447_GLY, 448_ASN, 449_TYR, 450_ASN, 451_TYR, 452_LEU, 470_THR, 481_ASN, 482_GLY, 483_VAL, 484_GLU, 490_PHE, 494_SER, 509_ARG",2.25
+5VJO_E,5VJO_B_light+5VJO_A_heavy,"100_SER, 101_ASP, 102_GLY, 15_LEU, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 62_TRP, 63_TRP, 73_LYS, 75_ALA, 93_ARG, 96_LYS, 97_ARG, 98_ILE",2.43
+5VJO_F,5VJO_D_light+5VJO_C_heavy,"100_SER, 101_ASP, 102_GLY, 15_LEU, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 63_TRP, 73_LYS, 75_ALA, 93_ARG, 96_LYS, 97_ARG, 98_ILE",2.43
+4HF5_A,4HF5_L_light+4HF5_H_heavy,"131_THR, 133_THR, 134_GLY, 135_GLY, 136_SER, 137_ARG, 140_ALA, 143_GLY, 144_ASN, 145_PRO, 153_TRP, 155_THR, 156_GLU, 158_GLY, 189_THR, 190_GLU, 193_THR, 194_LEU, 226_GLN",3.0
+2IH1_C,2IH1_B_light+2IH1_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 63_PRO, 64_ARG",2.4
+8YWQ_A,8YWQ_L_light+8YWQ_H_heavy,"22_SER, 23_GLU, 27_LYS, 30_ARG, 33_LEU, 34_ASP, 73_CYS, 74_PHE, 75_GLN",2.51
+1BJ1_W,1BJ1_L_light+1BJ1_H_heavy,"45_TYR, 48_LYS, 79_GLN, 80_ILE, 81_MET, 82_ARG, 83_ILE, 86_HIS, 87_GLN, 88_GLY, 89_GLN, 90_HIS, 91_ILE, 92_GLY, 93_GLU, 94_MET",2.4
+1BJ1_V,1BJ1_J_light+1BJ1_K_heavy,"45_TYR, 80_ILE, 81_MET, 82_ARG, 86_HIS, 87_GLN, 88_GLY, 89_GLN, 90_HIS, 91_ILE, 92_GLY, 93_GLU, 94_MET",2.4
+8UT3_C,8UT3_I_light+8UT3_F_heavy,"318_THR, 348_ASP, 349_GLY, 350_TRP, 367_LEU, 368_LYS, 370_THR, 371_GLN, 374_ILE, 377_ILE, 378_ASN, 381_LEU, 382_ASN, 385_ILE, 386_GLY, 387_LYS, 40_THR",2.8
+4WFE_A,4WFE_D_light+4WFE_E_heavy,"69_ARG, 70_GLU, 73_LEU, 74_ARG, 76_HIS, 77_PRO, 78_CYS, 79_VAL, 80_SER, 81_ASP, 82_GLN, 83_GLU",2.5
+4WFE_B,4WFE_F_light+4WFE_G_heavy,"69_ARG, 70_GLU, 73_LEU, 74_ARG, 76_HIS, 77_PRO, 78_CYS, 79_VAL, 80_SER, 81_ASP, 82_GLN, 83_GLU",2.5
+7DM2_A,7DM2_L_light+7DM2_H_heavy,"125_LYS, 174_GLN, 177_SER, 178_LYS, 181_PRO, 182_GLU, 190_PHE, 246_SER, 247_SER, 248_GLY, 267_SER, 268_LYS, 270_PRO, 271_ALA, 274_ALA, 275_ILE, 276_SER, 279_ASP, 280_GLY, 281_PRO, 85_TYR, 88_GLU",2.4
+1NMA_N,1NMA_L_light+1NMA_H_heavy,"327_ARG, 328_PRO, 329_ASN, 330_ASP, 331_PRO, 332_THR, 341_TYR, 342_PRO, 344_ASN, 366_ILE, 368_ILE, 369_ALA, 370_SER, 400_ASN, 401_THR, 403_TRP, 432_LYS",3.0
+6E56_D,6E56_I_light+6E56_G_heavy,"100_TYR, 101_ASP, 102_VAL, 103_PRO, 104_ASP, 105_TYR, 106_ALA, 219_SER, 220_ARG, 221_PRO, 222_TRP, 223_VAL, 224_ARG, 229_ARG, 231_SER, 269_ARG, 271_ASP, 91_SER, 94_PHE, 95_SER, 96_ASN",2.0
+6E56_A,6E56_J_light+6E56_H_heavy,"100_TYR, 101_ASP, 102_VAL, 103_PRO, 104_ASP, 105_TYR, 106_ALA, 219_SER, 220_ARG, 221_PRO, 222_TRP, 223_VAL, 229_ARG, 231_SER, 269_ARG, 271_ASP, 91_SER, 92_LYS, 94_PHE",2.0
+8TUZ_A,8TUZ_L_light+8TUZ_H_heavy,"105_ASP, 106_GLY, 107_LYS, 108_THR, 109_LEU, 124_GLU, 126_PHE, 128_GLU, 129_LYS, 130_GLY, 132_VAL, 87_LYS",2.19
+6B0A_A,6B0A_L_light+6B0A_H_heavy,"102_ASP, 104_SER, 105_ASN, 106_PRO, 107_VAL, 108_LYS, 109_THR, 111_VAL, 112_CYS, 113_SER, 120_PRO, 123_GLN, 125_GLN, 126_ASN, 127_LYS, 18_SER, 19_GLY, 87_ASN, 88_GLU, 91_GLN, 92_VAL, 93_THR",2.5
+6LZ9_B,6LZ9_L_light+6LZ9_H_heavy,"497_ASN, 498_GLY, 499_ILE, 500_PRO, 501_THR, 502_ARG, 505_ILE, 592_ASP, 611_SER, 613_SER, 615_TYR, 633_HIS, 635_TYR, 678_VAL, 680_GLU, 683_LYS, 685_ARG",2.8
+6CMI_B,6CMI_C_light+6CMI_D_heavy,"218_THR, 238_GLY, 239_SER, 240_CYS, 241_THR, 242_ARG, 458_TYR, 488_PRO, 489_GLY, 490_GLN, 504_TRP, 505_GLU, 506_GLY, 507_THR, 530_GLN, 532_ALA, 533_GLU, 534_ASN, 554_ASP, 555_ASP, 557_ASN, 558_ALA, 559_GLN, 579_GLU, 581_TYR, 588_ILE",2.72
+6FS0_A,6FS0_L_light+6FS0_H_heavy,"205_ALA, 208_ARG, 308_LYS, 309_GLN, 310_ARG, 311_GLY, 313_ASP, 317_GLU, 318_PHE, 320_HIS",2.25
+8H7L_B,8H7L_G_light+8H7L_D_heavy,"415_THR, 416_GLY, 417_ASN, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 473_TYR, 475_ALA, 486_PHE, 487_ASN, 489_TYR, 501_TYR",2.44
+8H7L_A,8H7L_I_light+8H7L_F_heavy,"415_THR, 416_GLY, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 473_TYR, 475_ALA, 486_PHE, 487_ASN, 489_TYR, 493_ARG, 501_TYR",2.44
+8H7L_C,8H7L_H_light+8H7L_E_heavy,"416_GLY, 417_ASN, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 473_TYR, 475_ALA, 486_PHE, 487_ASN, 489_TYR, 493_ARG, 501_TYR",2.44
+2NY7_G,2NY7_L_light+2NY7_H_heavy,"280_ASN, 281_ALA, 365_SER, 366_GLY, 367_GLY, 368_ASP, 369_PRO, 371_ILE, 372_VAL, 373_THR, 384_TYR, 386_ASN, 417_PRO, 419_ARG, 430_VAL, 431_GLY, 432_LYS, 455_THR, 456_ARG, 472_GLY, 473_GLY, 474_ASP, 475_MET",2.3
+4LSU_G,4LSU_L_light+4LSU_H_heavy,"122_LEU, 123_THR, 124_GLY, 257_THR, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 370_GLU, 371_ILE, 425_ASN, 426_MET, 427_TRP, 430_THR, 431_GLY, 432_GLN, 455_THR, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_ALA, 461_ASN, 465_ASN, 466_GLU, 467_THR, 469_ARG, 471_GLY, 474_ASN, 97_LYS",2.3
+6BFS_C,6BFS_L_light+6BFS_H_heavy,"114_LEU, 115_LEU, 116_VAL, 117_ILE, 118_PRO, 119_PHE, 120_ASP, 121_CYS, 122_TRP, 123_GLU, 124_PRO, 16_VAL, 20_GLN, 23_ARG, 49_LEU, 50_GLN, 83_HIS, 86_GLN, 87_HIS, 88_CYS, 89_PRO",2.0
+4KRP_A,4KRP_C_light+4KRP_D_heavy,"349_PRO, 350_VAL, 353_ARG, 382_LEU, 384_GLN, 408_GLN, 409_HIS, 411_GLN, 412_PHE, 415_ALA, 417_VAL, 418_SER, 438_ILE, 440_SER, 441_GLY, 443_LYS, 465_LYS, 466_ILE, 467_ILE, 468_SER, 469_ASN, 470_ARG, 471_GLY, 472_GLU, 473_ASN",2.82
+8YWW_A,8YWW_D_light+8YWW_E_heavy,"415_THR, 417_ASN, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_ASN, 489_TYR, 501_TYR, 505_HIS",2.7
+8YWW_B,8YWW_F_light+8YWW_G_heavy,"415_THR, 417_ASN, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_ASN, 489_TYR, 501_TYR, 505_HIS",2.7
+8HRD_T,8HRD_X_light+8HRD_W_heavy,"355_ARG, 356_LYS, 357_ARG, 396_TYR, 426_PRO, 428_ASP, 429_PHE, 430_THR, 462_LYS, 464_PHE, 465_GLU, 466_ARG, 468_ILE, 514_SER, 515_PHE, 516_GLU, 517_LEU, 518_LEU, 519_HIS",2.86
+8HRD_T,8HRD_Z_light+8HRD_Y_heavy,"369_TYR, 374_PHE, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 405_ASP, 406_GLU, 408_ARG, 409_GLN, 412_PRO, 413_GLY, 415_THR, 427_ASP, 428_ASP, 429_PHE",2.86
+8HRD_K,8HRD_M_light+8HRD_L_heavy,"355_ARG, 356_LYS, 357_ARG, 396_TYR, 426_PRO, 428_ASP, 429_PHE, 430_THR, 462_LYS, 463_PRO, 464_PHE, 465_GLU, 466_ARG, 515_PHE, 516_GLU, 518_LEU, 519_HIS",2.86
+8HRD_K,8HRD_Q_light+8HRD_N_heavy,"369_TYR, 370_ASN, 371_SER, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 382_VAL, 383_SER, 384_PRO, 385_THR, 408_ARG, 409_GLN, 412_PRO, 413_GLY, 415_THR, 427_ASP, 429_PHE",2.86
+8HRD_B,8HRD_E_light+8HRD_C_heavy,"355_ARG, 356_LYS, 357_ARG, 396_TYR, 426_PRO, 428_ASP, 429_PHE, 430_THR, 462_LYS, 464_PHE, 465_GLU, 466_ARG, 468_ILE, 514_SER, 515_PHE, 516_GLU, 517_LEU, 518_LEU, 519_HIS",2.86
+8HRD_B,8HRD_H_light+8HRD_G_heavy,"369_TYR, 371_SER, 374_PHE, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 382_VAL, 383_SER, 384_PRO, 385_THR, 408_ARG, 412_PRO, 413_GLY, 414_GLN, 415_THR, 427_ASP, 428_ASP, 429_PHE",2.86
+8HRD_A,8HRD_P_light+8HRD_O_heavy,"355_ARG, 356_LYS, 357_ARG, 396_TYR, 426_PRO, 428_ASP, 429_PHE, 430_THR, 462_LYS, 463_PRO, 464_PHE, 465_GLU, 466_ARG, 468_ILE, 514_SER, 515_PHE, 516_GLU, 517_LEU, 518_LEU, 519_HIS",2.86
+8HRD_A,8HRD_F_light+8HRD_D_heavy,"369_TYR, 371_SER, 374_PHE, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 408_ARG, 409_GLN, 412_PRO, 413_GLY, 415_THR, 427_ASP, 428_ASP, 429_PHE",2.86
+5D70_A,5D70_L_light+5D70_H_heavy,"10_THR, 119_PHE, 11_GLN, 120_ASP, 12_PRO, 13_TRP, 15_HIS, 16_VAL, 17_ASN, 20_GLN, 21_GLU, 24_ARG, 83_HIS, 86_GLN, 87_HIS",2.06
+7SHY_B,7SHY_F_light+7SHY_E_heavy,"373_THR, 374_TRP, 375_SER, 376_ARG, 377_ALA, 378_SER, 379_GLY, 380_LYS, 381_PRO, 417_GLN, 419_ARG, 421_THR, 426_PRO, 427_ARG, 428_ALA, 430_MET",3.0
+7SHY_H,7SHY_L_light+7SHY_K_heavy,"373_THR, 374_TRP, 375_SER, 376_ARG, 377_ALA, 378_SER, 379_GLY, 380_LYS, 381_PRO, 412_GLU, 413_GLY, 414_GLU, 417_GLN, 419_ARG, 427_ARG, 428_ALA, 430_MET",3.0
+7SHY_A,7SHY_D_light+7SHY_C_heavy,"373_THR, 374_TRP, 375_SER, 376_ARG, 377_ALA, 378_SER, 379_GLY, 380_LYS, 417_GLN, 419_ARG, 427_ARG, 428_ALA, 430_MET",3.0
+7SHY_G,7SHY_J_light+7SHY_I_heavy,"373_THR, 374_TRP, 375_SER, 376_ARG, 377_ALA, 378_SER, 379_GLY, 380_LYS, 381_PRO, 417_GLN, 419_ARG, 421_THR, 427_ARG, 428_ALA, 430_MET",3.0
+4YDK_G,4YDK_L_light+4YDK_H_heavy,"123_THR, 124_GLY, 280_ASN, 281_ALA, 282_LYS, 283_THR, 365_SER, 366_GLY, 367_GLY, 368_ASP, 369_LEU, 370_GLU, 371_ILE, 375_HIS, 425_ASN, 426_MET, 427_TRP, 428_GLN, 429_GLY, 430_THR, 431_GLY, 432_GLN, 455_THR, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_ALA, 469_ARG, 471_GLY, 472_GLY, 473_GLY, 474_ASN, 475_ILE, 477_ASP",2.05
+1ORS_C,1ORS_A_light+1ORS_B_heavy,"107_GLU, 111_ALA, 112_GLY, 113_LEU, 114_GLY, 115_LEU, 116_PHE, 117_ARG, 119_VAL, 123_ARG",1.9
+6SNI_X,6SNI_L_light+6SNI_H_heavy,"288_LYS, 291_HIS, 292_GLN, 295_HIS, 296_ARG, 298_PHE, 300_PHE, 302_ARG, 303_GLY, 304_ILE, 326_PHE, 327_THR, 328_ILE, 329_GLN, 332_GLN, 336_LEU, 90_TRP, 93_LEU, 94_GLN",3.0
+8TFL_B,8TFL_L_light+8TFL_H_heavy,"102_ARG, 103_SER, 104_SER, 66_THR, 67_TYR, 68_GLY, 69_TYR, 70_SER, 71_PRO, 73_VAL, 76_MET, 80_CYS, 81_ASN, 82_THR, 83_ALA, 84_ALA, 85_THR, 86_ASP, 87_ALA",2.89
+5TRU_C,5TRU_L_light+5TRU_H_heavy,"100_TYR, 101_PRO, 102_PRO, 103_PRO, 104_TYR, 105_TYR, 106_LEU, 107_GLY, 108_ILE, 33_GLU, 35_ARG, 39_LEU, 3_MET, 44_SER, 46_VAL, 48_GLU, 4_HIS, 95_LYS, 97_GLU, 99_MET",3.0
+5TRU_c,5TRU_l_light+5TRU_h_heavy,"100_TYR, 102_PRO, 103_PRO, 104_TYR, 105_TYR, 106_LEU, 107_GLY, 108_ILE, 2_ALA, 33_GLU, 35_ARG, 39_LEU, 3_MET, 45_GLN, 95_LYS, 97_GLU, 99_MET",3.0
+6XKQ_A,6XKQ_L_light+6XKQ_H_heavy,"446_GLY, 449_TYR, 453_TYR, 455_LEU, 456_PHE, 475_ALA, 476_GLY, 477_SER, 478_THR, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 495_TYR, 498_GLN, 501_ASN, 505_TYR",2.55
+8TQA_A,8TQA_L_light+8TQA_H_heavy,"128_GLU, 212_ASP, 214_THR, 216_THR, 219_LEU, 220_ASN, 221_GLY, 222_GLU, 223_GLU, 225_THR, 226_GLN, 260_ARG, 262_TYR, 264_GLU, 267_PRO, 268_GLU, 269_PRO, 271_THR",2.6
+6OCB_A,6OCB_L_light+6OCB_H_heavy,"100_TYR, 101_ASP, 216_ASN, 217_ILE, 218_GLY, 219_SER, 220_ARG, 221_PRO, 222_TRP, 223_VAL, 224_ARG, 229_ARG, 99_PRO",2.1
+4DKF_B,4DKF_M_light+4DKF_I_heavy,"158_LEU, 160_ARG, 162_LYS, 166_ASP, 167_ASN, 170_ARG, 47_TYR, 55_LYS, 61_ASN, 63_LYS, 73_ARG, 74_ILE",2.61
+4DKF_A,4DKF_L_light+4DKF_H_heavy,"158_LEU, 160_ARG, 162_LYS, 166_ASP, 167_ASN, 170_ARG, 47_TYR, 55_LYS, 61_ASN, 63_LYS, 73_ARG, 74_ILE",2.61
+6Z2M_E,6Z2M_L_light+6Z2M_H_heavy,"369_TYR, 370_ASN, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 389_ASP, 390_LEU, 392_PHE, 408_ARG, 430_THR, 515_PHE, 517_LEU",2.71
+6Z2M_A,6Z2M_C_light+6Z2M_B_heavy,"369_TYR, 370_ASN, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 390_LEU, 408_ARG, 428_ASP, 430_THR, 517_LEU, 518_LEU",2.71
+5DUM_A,5DUM_L_light+5DUM_H_heavy,"115_GLN, 116_ILE, 118_PRO, 121_SER, 123_SER, 124_ASP, 159_THR, 161_LYS, 162_ARG, 163_SER, 164_TYR, 165_ASN, 167_THR, 168_ASN, 240_ASN, 241_PHE, 242_GLU",3.0
+4XP1_A,4XP1_L_light+4XP1_H_heavy,"337_TYR, 498_TYR, 502_ARG, 505_GLU, 506_ASP, 508_ARG, 509_ASP, 511_ILE, 512_GLY, 513_PHE, 514_PRO, 598_ARG, 599_ASP, 90_HIS",2.89
+7M2H_C,7M2H_B_light+7M2H_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 63_PRO, 64_ARG",2.64
+7M2H_F,7M2H_E_light+7M2H_D_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 63_PRO, 64_ARG",2.64
+7LKF_A,7LKF_L_light+7LKF_H_heavy,"132_HIS, 134_LEU, 135_ARG, 136_ASP, 137_SER, 138_SER, 246_ARG, 250_SER, 253_SER, 254_ARG, 257_LEU, 260_PRO, 58_SER",2.9
+5NGV_A,5NGV_L_light+5NGV_H_heavy,"101_PHE, 35_ASN, 38_LEU, 39_GLU, 52_GLU, 54_ASP, 55_LYS, 60_TYR, 62_SER, 74_LYS, 77_CYS, 78_TRP, 79_LEU, 80_ASP, 81_ASP, 82_PHE, 83_ASN, 85_TYR, 94_GLU, 99_VAL",2.0
+4UUJ_C,4UUJ_B_light+4UUJ_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 63_PRO, 64_ARG",2.4
+6AZZ_D,6AZZ_E_light+6AZZ_F_heavy,"102_ASP, 105_ASN, 106_PRO, 107_VAL, 108_LYS, 109_THR, 110_GLY, 111_VAL, 122_VAL, 123_GLN, 125_GLN, 126_ASN, 78_ASP, 87_ASN, 88_GLU, 90_LYS, 91_GLN, 92_VAL",2.4
+6AZZ_A,6AZZ_B_light+6AZZ_C_heavy,"102_ASP, 105_ASN, 106_PRO, 107_VAL, 108_LYS, 109_THR, 110_GLY, 111_VAL, 122_VAL, 123_GLN, 125_GLN, 126_ASN, 76_GLY, 87_ASN, 88_GLU, 90_LYS, 91_GLN, 92_VAL",2.4
+8QH1_E,8QH1_L_light+8QH1_H_heavy,"403_ARG, 415_THR, 416_GLY, 417_ASN, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_ASN, 487_ASN, 489_TYR, 493_GLN, 500_THR, 501_TYR, 505_HIS",2.65
+7ZF8_E,7ZF8_L_light+7ZF8_H_heavy,"403_ARG, 405_ASN, 415_THR, 416_GLY, 417_ASN, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_ASN, 486_PHE, 487_ASN, 489_TYR, 493_ARG, 501_TYR, 505_HIS",2.95
+8YXI_A,8YXI_B_light+8YXI_C_heavy,"128_LYS, 137_CYS, 42_LEU, 43_ARG, 44_ASN, 45_HIS, 46_SER, 47_GLN, 83_ASP, 89_VAL, 90_VAL, 91_LYS, 92_LYS, 94_LYS, 95_GLY, 96_SER",2.4
+4OLX_G,4OLX_L_light+4OLX_H_heavy,"102_GLU, 106_GLU, 276_ASN, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 371_ILE, 429_GLY, 457_ASP, 458_GLY, 459_GLY, 460_ALA, 461_ASN, 465_ASN, 466_GLU, 467_THR, 469_ARG, 473_GLY, 474_ASN, 97_LYS",2.2
+5U5M_E,5U5M_A_light+5U5M_B_heavy,"27_LEU, 33_LYS, 34_ILE, 35_GLN, 36_THR, 37_ALA, 38_GLU, 39_PHE, 40_LYS, 49_GLU, 52_ARG, 53_TYR, 56_LEU, 60_VAL, 61_ASN",1.88
+5U5M_C,5U5M_A_light+5U5M_B_heavy,"23_ALA, 24_GLN, 27_GLY, 28_PHE, 30_GLN, 31_SER, 34_ASP, 35_ASP, 37_SER, 38_GLN, 41_ASN, 42_VAL, 45_GLU, 49_LEU",1.88
+5V7J_G,5V7J_L_light+5V7J_H_heavy,"135_THR, 136_ASN, 137_ASN, 140_ASP, 322_ILE, 324_GLY, 325_ASP, 326_ILE, 327_ARG, 328_GLN",2.91
+6RPS_A,6RPS_L_light+6RPS_H_heavy,"130_ASP, 131_ALA, 132_SER, 135_SER, 170_LYS, 171_GLY, 173_GLU, 175_PHE, 200_THR, 201_PRO, 202_PRO, 20_TYR, 234_HIS, 3_MET, 4_LYS, 58_LEU, 5_TRP, 60_THR, 62_ASN, 64_HIS, 67_LYS, 92_GLN",2.79
+6RPS_B,6RPS_M_light+6RPS_N_heavy,"130_ASP, 131_ALA, 132_SER, 135_SER, 141_LEU, 170_LYS, 171_GLY, 173_GLU, 175_PHE, 200_THR, 201_PRO, 202_PRO, 20_TYR, 234_HIS, 236_ASP, 3_MET, 4_LYS, 58_LEU, 5_TRP, 60_THR, 62_ASN, 64_HIS, 67_LYS, 92_GLN",2.79
+1JHL_A,1JHL_L_light+1JHL_H_heavy,"103_ASP, 106_ASN, 112_ARG, 113_LYS, 116_LYS, 117_GLY, 118_THR, 119_ASP, 121_ASN, 21_ARG, 23_TYR",2.4
+6B9Y_E,6B9Y_A_light+6B9Y_B_heavy,"27_LEU, 33_LYS, 34_ILE, 35_GLN, 36_THR, 37_ALA, 38_GLU, 39_PHE, 40_LYS, 49_GLU, 52_ARG, 53_TYR, 56_LEU, 57_LEU, 60_VAL",2.14
+6B9Y_C,6B9Y_A_light+6B9Y_B_heavy,"23_ALA, 24_GLN, 27_GLY, 28_PHE, 30_GLN, 31_SER, 34_ASP, 35_ASP, 37_SER, 38_GLN, 41_ASN, 42_VAL, 45_GLU, 49_LEU",2.14
+3VI4_D,3VI4_L_light+3VI4_H_heavy,"119_GLU, 120_ASP, 151_ASN, 154_ARG, 155_ARG, 156_ILE, 157_THR, 158_SER, 399_SER, 400_ILE, 77_ASN, 78_ARG, 79_SER, 80_LYS, 81_GLY, 82_THR, 83_ALA, 84_GLU, 85_LYS, 87_LYS",2.9
+3VI4_B,3VI4_E_light+3VI4_F_heavy,"119_GLU, 120_ASP, 151_ASN, 154_ARG, 155_ARG, 156_ILE, 157_THR, 158_SER, 399_SER, 400_ILE, 77_ASN, 78_ARG, 79_SER, 80_LYS, 81_GLY, 82_THR, 83_ALA, 84_GLU, 85_LYS, 87_LYS",2.9
+8BSF_A,8BSF_L_light+8BSF_H_heavy,"343_ASN, 345_THR, 346_ARG, 373_SER, 374_PHE, 436_TRP, 437_ASN, 439_ASN, 440_ASN, 441_LEU, 443_SER, 444_LYS, 445_VAL, 448_ASN, 450_ASN, 499_PRO, 500_THR",2.2
+8SIC_G,8SIC_L_light+8SIC_H_heavy,"155_GLU, 156_THR, 157_VAL, 158_PRO, 159_TYR, 160_ILE, 161_LYS, 162_TRP, 163_ASP, 164_ASN, 201_GLU, 208_ASP, 210_GLU, 21_GLU, 284_PHE, 294_GLY, 295_GLY, 296_ASP, 297_TYR, 298_CYS",2.76
+8SIC_E,8SIC_B_light+8SIC_A_heavy,"154_PRO, 155_GLU, 156_THR, 157_VAL, 158_PRO, 159_TYR, 160_ILE, 161_LYS, 162_TRP, 163_ASP, 164_ASN, 201_GLU, 208_ASP, 210_GLU, 21_GLU, 284_PHE, 296_ASP, 297_TYR, 298_CYS",2.76
+8DFH_A,8DFH_L_light+8DFH_H_heavy,"10_LYS, 12_CYS, 13_PRO, 14_GLU, 15_ASN, 16_SER, 17_GLY, 18_CYS, 31_LEU, 33_ASN, 34_TYR, 40_LYS, 41_CYS, 42_VAL, 43_GLU, 44_ASN, 45_PRO, 69_SER, 71_ARG, 87_PHE, 88_ASP",2.3
+8TFN_B,8TFN_L_light+8TFN_H_heavy,"113_GLU, 15_THR, 16_GLY, 57_GLN, 59_GLU, 61_PRO, 62_ASN, 63_GLY, 64_TYR, 65_ALA, 79_LEU, 80_SER, 81_ALA, 82_ALA, 83_PRO, 84_SER, 85_GLN",2.54
+7WVL_F,7WVL_L_light+7WVL_H_heavy,"455_LEU, 456_PHE, 475_ALA, 476_GLY, 477_SER, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 505_TYR",3.0
+6YX9_A,6YX9_L_light+6YX9_H_heavy,"0_PHE, 100_GLU, 101_GLY, 102_ARG, 103_TRP, 104_ARG, 106_ILE, 107_PRO, 110_VAL, 123_HIS, 127_PRO, 128_PRO, 129_MET, 130_PRO, 131_SER, 133_ARG",2.4
+7OX3_C,7OX3_B_light+7OX3_A_heavy,"25_ALA, 28_LEU, 29_ASP, 32_PHE, 82_GLN, 83_THR, 84_ARG, 85_TYR, 86_PRO, 87_LEU, 88_ILE, 90_SER, 91_ARG, 94_LYS",1.7
+1ZTX_E,1ZTX_L_light+1ZTX_H_heavy,"302_TYR, 306_SER, 307_LYS, 308_ALA, 309_PHE, 330_THR, 331_GLY, 332_THR, 333_ASP, 365_ALA, 366_THR, 367_ALA, 368_ASN, 389_GLY, 390_GLU, 391_GLN",2.5
+7FCQ_A,7FCQ_L_light+7FCQ_H_heavy,"369_TYR, 370_ASN, 371_SER, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 408_ARG, 412_PRO, 413_GLY, 414_GLN, 427_ASP, 428_ASP, 429_PHE",1.89
+8R1C_B,8R1C_E_light+8R1C_D_heavy,"529_ASN, 530_LEU, 531_VAL, 532_LYS, 533_ASN, 534_LYS, 551_GLU, 552_SER, 553_ASN, 577_GLN, 578_THR, 579_LEU, 580_GLU",2.2
+8R1C_C,8R1C_G_light+8R1C_F_heavy,"529_ASN, 530_LEU, 531_VAL, 532_LYS, 533_ASN, 534_LYS, 551_GLU, 552_SER, 553_ASN, 577_GLN, 578_THR, 579_LEU, 580_GLU",2.2
+8R1C_A,8R1C_L_light+8R1C_H_heavy,"529_ASN, 530_LEU, 531_VAL, 532_LYS, 533_ASN, 534_LYS, 551_GLU, 552_SER, 553_ASN, 577_GLN, 578_THR, 579_LEU, 580_GLU",2.2
+6WIR_C,6WIR_B_light+6WIR_A_heavy,"124_VAL, 125_THR, 126_PRO, 127_ILE, 128_VAL, 129_HIS, 74_LEU, 85_TYR, 86_HIS, 87_MET, 88_ASN",2.96
+5CZX_A,5CZX_L_light+5CZX_H_heavy,"1440_SER, 1463_ARG, 1466_THR, 1467_CYS, 1468_ASN, 1469_PRO, 1470_VAL, 1471_TYR, 1472_GLU, 1474_TYR, 1486_GLN, 1487_GLY, 1619_ARG, 1621_ASP",2.1
+5CZX_B,5CZX_D_light+5CZX_C_heavy,"1440_SER, 1463_ARG, 1466_THR, 1467_CYS, 1468_ASN, 1469_PRO, 1470_VAL, 1471_TYR, 1472_GLU, 1474_TYR, 1486_GLN, 1487_GLY, 1621_ASP",2.1
+7XCK_M,7XCK_B_light+7XCK_A_heavy,"334_ASN, 335_LEU, 337_PRO, 339_ASP, 340_GLU, 343_ASN, 344_ALA, 345_THR, 346_ARG, 356_LYS, 359_SER, 361_CYS, 441_LEU",2.5
+5EU7_B,5EU7_D_light+5EU7_F_heavy,"156_LYS, 157_GLU, 159_LYS, 160_LYS, 161_ILE, 162_ILE, 163_GLY, 164_GLN, 165_VAL, 166_ARG, 168_GLN, 172_LEU, 182_ILE, 186_LYS, 187_ARG, 188_LYS, 189_GLY, 190_GLY, 191_ILE, 68_LEU, 69_GLU",2.64
+5EU7_A,5EU7_C_light+5EU7_E_heavy,"156_LYS, 157_GLU, 159_LYS, 160_LYS, 161_ILE, 162_ILE, 163_GLY, 164_GLN, 165_VAL, 166_ARG, 172_LEU, 181_PHE, 182_ILE, 186_LYS, 187_ARG, 188_LYS, 189_GLY, 190_GLY, 68_LEU, 69_GLU",2.64
+6HIG_B,6HIG_L_light+6HIG_H_heavy,"136_GLU, 137_SER, 138_LEU, 139_ARG, 143_ARG, 34_PRO, 36_THR, 37_PHE, 38_SER, 39_PRO, 40_ALA, 41_LEU, 42_LEU, 43_VAL",2.2
+6VUG_B,6VUG_C_light+6VUG_D_heavy,"195_ILE, 196_GLY, 199_ARG, 220_LYS, 223_LYS, 224_GLU, 225_PRO, 226_PRO, 227_PHE, 229_TRP, 230_MET, 356_ARG, 357_MET, 358_ARG, 359_GLY, 361_HIS, 362_THR",3.0
+7UKP_C,7UKP_F_light+7UKP_E_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN",2.8
+7UKP_A,7UKP_L_light+7UKP_H_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.8
+3BT2_U,3BT2_L_light+3BT2_H_heavy,"185_GLU, 186_ASN, 187_LEU, 188_PRO, 189_GLN, 190_ASN, 191_GLY, 192_ARG, 217_GLY, 218_PRO, 220_ASN, 267_THR, 269_SER",2.5
+7PQY_E,7PQY_L_light+7PQY_H_heavy,"403_ARG, 415_THR, 416_GLY, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 484_GLU, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 501_ASN, 502_GLY, 505_TYR",3.0
+7PQY_A,7PQY_C_light+7PQY_B_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 501_ASN, 502_GLY, 505_TYR",3.0
+3DET_B,3DET_F_light+3DET_E_heavy,"243_LYS, 246_ASP, 248_PRO, 249_LEU, 250_ASN, 380_PRO, 381_GLN, 382_TYR, 383_HIS",2.8
+3DET_A,3DET_D_light+3DET_C_heavy,"243_LYS, 246_ASP, 248_PRO, 249_LEU, 250_ASN, 380_PRO, 381_GLN, 382_TYR, 383_HIS",2.8
+2OZ4_A,2OZ4_L_light+2OZ4_H_heavy,"336_GLU, 367_TYR, 372_ASP, 373_GLU, 374_ARG, 375_ASP, 377_PRO, 379_ASN, 380_TRP, 389_THR, 391_MET, 393_GLN, 394_ALA, 395_TRP, 396_GLY, 399_LEU, 414_ILE, 415_GLY",2.7
+3HMX_A,3HMX_L_light+3HMX_H_heavy,"15_TRP, 17_PRO, 18_ASP, 20_PRO, 23_MET, 40_LEU, 42_GLN, 43_SER, 45_GLU, 47_LEU, 54_THR, 55_ILE, 56_GLN, 58_LYS, 59_GLU, 61_GLY, 62_ASP, 66_TYR",3.0
+1G7H_C,1G7H_A_light+1G7H_B_heavy,"102_GLY, 103_ASN, 116_LYS, 117_GLY, 118_THR, 119_ASP, 120_VAL, 121_GLN, 124_ILE, 125_ARG, 18_ASP, 19_ASN, 22_GLY, 23_TYR, 24_SER, 27_ASN",1.85
+1RJL_C,1RJL_A_light+1RJL_B_heavy,"232_SER, 233_ASN, 250_ALA, 251_ASP, 252_SER, 253_LYS, 254_LYS, 272_ASN, 273_THR, 274_ALA, 275_GLY, 276_THR, 277_SER",2.6
+3V6O_B,3V6O_F_light+3V6O_D_heavy,"465_ARG, 466_TYR, 467_HIS, 468_ARG, 469_SER, 470_SER, 471_LEU, 480_HIS, 481_PRO, 482_ILE, 483_SER, 484_GLU, 502_PRO, 504_PHE",1.95
+3V6O_A,3V6O_E_light+3V6O_C_heavy,"465_ARG, 466_TYR, 467_HIS, 468_ARG, 469_SER, 470_SER, 471_LEU, 480_HIS, 481_PRO, 482_ILE, 484_GLU, 502_PRO, 504_PHE",1.95
+8CIN_A,8CIN_L_light+8CIN_H_heavy,"527_ASN, 528_LEU, 530_LYS, 531_ASN, 532_LYS, 549_GLU, 551_ASN, 575_GLN, 576_THR, 577_LEU, 578_GLU",2.7
+8CIN_C,8CIN_K_light+8CIN_J_heavy,"527_ASN, 528_LEU, 530_LYS, 531_ASN, 532_LYS, 549_GLU, 551_ASN, 575_GLN, 576_THR, 577_LEU, 578_GLU",2.7
+8CIN_B,8CIN_F_light+8CIN_E_heavy,"527_ASN, 528_LEU, 530_LYS, 531_ASN, 532_LYS, 549_GLU, 551_ASN, 575_GLN, 576_THR, 577_LEU, 578_GLU",2.7
+1NMC_N,1NMC_L_light+1NMC_H_heavy,"328_PRO, 329_ASN, 330_ASP, 331_PRO, 343_GLY, 344_ASN, 366_ILE, 367_SER, 368_ILE, 369_ALA, 370_SER, 400_ASN, 401_THR, 403_TRP, 432_LYS",2.5
+1NMC_A,1NMC_C_light+1NMC_B_heavy,"328_PRO, 329_ASN, 330_ASP, 331_PRO, 343_GLY, 344_ASN, 366_ILE, 367_SER, 368_ILE, 369_ALA, 370_SER, 400_ASN, 401_THR, 403_TRP, 432_LYS",2.5
+4Z7Q_C,4Z7Q_F_light+4Z7Q_E_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.7
+4Z7Q_A,4Z7Q_L_light+4Z7Q_H_heavy,"117_GLU, 149_ASN, 154_ILE, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.7
+7BBG_A,7BBG_L_light+7BBG_H_heavy,"150_ALA, 155_GLN, 158_ALA, 163_THR, 166_GLU, 167_TRP, 170_ARG, 58_GLU, 62_GLY, 65_ARG, 66_LYS, 69_ALA",2.64
+4OLZ_G,4OLZ_L_light+4OLZ_H_heavy,"102_GLU, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 370_GLU, 371_ILE, 425_ASN, 427_TRP, 429_GLY, 457_ASP, 458_GLY, 459_GLY, 460_ALA, 461_ASN, 465_ASN, 466_GLU, 467_THR, 469_ARG, 473_GLY, 97_LYS",2.1
+8TM1_A,8TM1_L_light+8TM1_H_heavy,"369_TYR, 371_SER, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 382_VAL, 383_SER, 384_PRO, 408_ARG, 412_PRO, 413_GLY, 427_ASP, 429_PHE",2.79
+8TM1_B,8TM1_L_light+8TM1_H_heavy,"446_GLY, 449_TYR, 455_LEU, 486_PHE, 487_ASN, 489_TYR, 493_GLN",2.79
+1A2Y_C,1A2Y_A_light+1A2Y_B_heavy,"102_GLY, 103_ASN, 116_LYS, 117_GLY, 118_THR, 119_ASP, 120_VAL, 121_GLN, 124_ILE, 125_ARG, 19_ASN, 22_GLY, 23_TYR, 24_SER, 27_ASN",1.5
+6VY4_A,6VY4_L_light+6VY4_H_heavy,"196_THR, 199_LYS, 200_PRO, 201_ARG, 202_LEU, 203_ILE, 205_TYR, 206_THR, 207_LEU, 210_ASN, 254_GLU, 256_LEU, 258_ARG, 260_ASP, 262_VAL, 263_PRO, 264_SER, 266_PHE, 553_GLU",2.0
+6VY4_B,6VY4_D_light+6VY4_C_heavy,"196_THR, 199_LYS, 200_PRO, 201_ARG, 202_LEU, 203_ILE, 205_TYR, 206_THR, 207_LEU, 208_PRO, 210_ASN, 254_GLU, 256_LEU, 258_ARG, 259_GLY, 260_ASP, 262_VAL, 263_PRO, 264_SER, 266_PHE, 267_MET, 584_GLY, 585_ASP",2.0
+6FLC_I,6FLC_A_light+6FLC_B_heavy,"297_MET, 298_SER, 299_TYR, 300_SER, 301_MET, 303_THR, 328_GLY, 329_ASP, 330_GLY, 331_SER, 332_PRO, 334_LYS, 361_LYS, 382_VAL, 383_GLU, 384_PRO",2.0
+6FLC_G,6FLC_L_light+6FLC_H_heavy,"297_MET, 298_SER, 299_TYR, 300_SER, 301_MET, 303_THR, 328_GLY, 329_ASP, 330_GLY, 331_SER, 332_PRO, 334_LYS, 361_LYS, 382_VAL, 383_GLU, 384_PRO",2.0
+8D1T_A,8D1T_L_light+8D1T_H_heavy,"258_ALA, 259_THR, 260_TRP, 261_GLY, 262_HIS, 263_PRO, 265_THR, 267_ASP, 268_HIS, 271_HIS, 344_GLN, 346_ASN",2.94
+4XPH_A,4XPH_L_light+4XPH_H_heavy,"337_TYR, 498_TYR, 502_ARG, 505_GLU, 506_ASP, 508_ARG, 509_ASP, 511_ILE, 512_GLY, 513_PHE, 514_PRO, 599_ASP, 90_HIS",2.9
+6MTJ_G,6MTJ_L_light+6MTJ_H_heavy,"135_THR, 136_ASN, 137_ALA, 139_THR, 322_ILE, 323_ILE, 324_GLY, 325_ASP, 326_ILE, 327_ARG, 328_GLN, 330_HIS, 415_THR, 417_PRO",2.34
+8XSI_B,8XSI_L_light+8XSI_H_heavy,"355_ARG, 357_ARG, 396_TYR, 426_PRO, 428_ASP, 462_LYS, 463_PRO, 464_PHE, 465_GLU, 466_ARG, 468_ILE, 516_GLU, 518_LEU, 519_HIS",2.1
+8IUY_A,8IUY_C_light+8IUY_B_heavy,"126_VAL, 131_ARG, 132_ARG, 133_SER, 134_GLY, 135_SER, 136_SER, 138_TYR, 248_ARG, 68_ASP, 69_GLN, 70_PHE, 71_LEU",2.9
+6UYN_A,6UYN_L_light+6UYN_H_heavy,"306_SER, 307_LEU, 308_PRO, 333_THR, 45_HIS, 47_VAL, 48_ASN, 49_LEU",2.85
+6UYN_B,6UYN_L_light+6UYN_H_heavy,"363_ASP, 364_GLY, 365_TRP, 382_LEU, 385_THR, 386_GLN, 389_ILE, 390_ASP, 393_THR, 396_VAL, 397_ASN, 400_ILE",2.85
+3LD8_A,3LD8_B_light+3LD8_C_heavy,"158_ASP, 291_SER, 319_GLU, 320_HIS, 322_GLU, 326_LEU, 53_GLN, 55_SER, 57_GLU, 58_GLU, 61_GLU, 62_ARG, 65_ARG",2.7
+1XGU_C,1XGU_A_light+1XGU_B_heavy,"100_SER, 101_ASP, 102_GLY, 103_ASN, 14_ARG, 15_HIS, 16_GLY, 18_ASP, 19_ASN, 20_TYR, 21_ARG, 62_TRP, 63_TRP, 73_ARG, 75_LEU, 77_ASN, 89_THR, 93_ASN, 96_LYS, 97_LYS",2.1
+7B0B_F,7B0B_L_light+7B0B_H_heavy,"455_LEU, 456_PHE, 475_ALA, 476_GLY, 477_SER, 478_THR, 485_GLY, 486_PHE, 487_ASN, 488_CYS, 489_TYR, 493_GLN",2.98
+4JZJ_D,4JZJ_B_light+4JZJ_A_heavy,"48_THR, 49_ASP, 51_GLU, 56_ALA, 57_ASP, 58_TYR, 59_SER, 60_MET, 61_PRO, 62_ALA, 82_THR, 84_ARG, 85_VAL, 86_ALA, 87_ASN, 89_PRO, 90_PHE, 91_SER",2.8
+4JZJ_C,4JZJ_L_light+4JZJ_H_heavy,"48_THR, 49_ASP, 51_GLU, 56_ALA, 57_ASP, 58_TYR, 59_SER, 60_MET, 61_PRO, 63_VAL, 64_ASN, 84_ARG, 85_VAL, 86_ALA, 87_ASN, 89_PRO, 90_PHE, 91_SER",2.8
+7T7B_A,7T7B_L_light+7T7B_H_heavy,"369_TYR, 370_ASN, 371_SER, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 408_ARG, 412_PRO, 413_GLY, 414_GLN, 415_THR, 416_GLY, 427_ASP, 428_ASP, 429_PHE",2.59
+1XGP_C,1XGP_A_light+1XGP_B_heavy,"100_SER, 101_ASP, 102_GLY, 14_ARG, 15_HIS, 16_GLY, 18_ASP, 19_ASN, 20_TYR, 21_ARG, 63_TRP, 73_ARG, 75_LEU, 89_THR, 93_ASN, 96_LYS, 97_LYS, 98_ILE",2.1
+6P4A_C,6P4A_L_light+6P4A_H_heavy,"100_SER, 101_ASP, 102_GLY, 103_ASN, 14_ARG, 15_HIS, 16_GLY, 20_TYR, 21_GLN, 62_TRP, 63_TRP, 73_GLU, 75_LEU, 77_ASN, 89_THR, 93_ASN, 96_LYS, 97_LYS, 98_ILE",2.2
+6MU6_G,6MU6_L_light+6MU6_H_heavy,"135_THR, 136_ASN, 137_ALA, 138_ILE, 139_THR, 322_ILE, 323_ILE, 324_GLY, 325_ASP, 326_ILE, 327_ARG, 328_GLN, 330_HIS, 415_THR, 417_PRO",2.55
+4EDX_W,4EDX_L_light+4EDX_H_heavy,"103_ARG, 109_VAL, 111_VAL, 31_ILE, 32_LYS, 33_GLY, 83_THR, 84_HIS, 86_PHE",2.5
+4EDX_V,4EDX_L_light+4EDX_H_heavy,"106_THR, 107_ALA, 108_CYS, 10_GLY, 11_GLU, 12_PHE, 13_SER, 17_SER, 19_SER, 20_VAL, 21_TRP, 24_ASP, 50_LYS, 52_TYR, 54_PHE, 56_THR, 59_ARG",2.5
+8TZW_C,8TZW_L_light+8TZW_H_heavy,"101_THR, 118_SER, 119_ASN, 120_TRP, 121_ASP, 124_THR, 125_ASN, 127_LEU, 128_TYR, 131_LYS, 134_ILE, 64_LEU, 73_ILE, 78_GLY, 79_GLU",2.66
+5TUD_D,5TUD_E_light+5TUD_F_heavy,"125_LEU, 126_VAL, 129_PRO, 130_ALA, 191_PRO, 192_ILE, 194_GLY, 195_ILE, 196_GLU, 197_THR, 198_ASP, 199_VAL, 200_ASP, 201_ASN, 204_ASN, 210_THR, 212_GLU, 213_ARG, 356_THR",3.0
+5TUD_A,5TUD_B_light+5TUD_C_heavy,"125_LEU, 126_VAL, 129_PRO, 130_ALA, 191_PRO, 192_ILE, 194_GLY, 195_ILE, 196_GLU, 197_THR, 198_ASP, 199_VAL, 200_ASP, 201_ASN, 204_ASN, 210_THR, 212_GLU, 213_ARG, 355_GLN, 356_THR, 359_GLN",3.0
+4QTI_U,4QTI_L_light+4QTI_H_heavy,"112_SER, 113_LEU, 114_GLN, 116_ARG, 118_PRO, 174_THR, 58_ARG, 89_ARG, 90_SER, 91_ARG, 92_TYR, 94_GLU",3.0
+7DF1_D,7DF1_L_light+7DF1_H_heavy,"347_GLN, 403_SER, 405_ASN, 516_GLU, 517_PRO",2.81
+7DF1_D,7DF1_K_light+7DF1_G_heavy,"135_HIS, 379_PRO, 380_GLY, 381_GLN, 392_GLU, 393_SER, 396_PRO, 397_ASP, 398_ILE, 399_PRO, 400_GLY, 401_ALA",2.81
+7DF1_C,7DF1_J_light+7DF1_F_heavy,"135_HIS, 379_PRO, 380_GLY, 381_GLN, 392_GLU, 393_SER, 396_PRO, 397_ASP, 398_ILE, 399_PRO, 400_GLY, 401_ALA",2.81
+7SO5_A,7SO5_L_light+7SO5_H_heavy,"17_PHE, 18_ARG, 19_THR, 20_GLN, 21_GLU, 22_ASP, 23_GLU, 25_VAL, 26_ALA, 29_ASP, 58_ILE, 62_THR, 63_TYR",1.8
+5VYF_F,5VYF_L_light+5VYF_H_heavy,"21_ASN, 22_GLU, 23_LEU, 24_LEU, 26_ASP, 27_LEU, 30_THR, 31_LYS, 36_GLU, 39_ARG, 43_LYS, 47_ASP",2.9
+5VYF_C,5VYF_A_light+5VYF_B_heavy,"21_ASN, 22_GLU, 23_LEU, 26_ASP, 27_LEU, 30_THR, 31_LYS, 36_GLU, 39_ARG, 43_LYS, 47_ASP",2.9
+5TFW_O,5TFW_L_light+5TFW_H_heavy,"118_GLY, 119_ASN, 120_TRP, 121_PHE, 124_THR, 125_ASN, 127_LEU, 128_TRP, 131_LYS, 64_LEU, 66_VAL, 73_ILE, 79_LYS",2.17
+3HFM_Y,3HFM_L_light+3HFM_H_heavy,"100_SER, 101_ASP, 102_GLY, 14_ARG, 15_HIS, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 63_TRP, 73_ARG, 75_LEU, 89_THR, 93_ASN, 96_LYS, 97_LYS, 98_ILE",3.0
+6O9I_C,6O9I_B_light+6O9I_A_heavy,"16_GLU, 19_LYS, 20_MET, 21_LEU, 22_GLU, 23_THR, 24_THR, 25_GLU, 26_PRO, 57_TRP, 9_ARG",2.6
+6O9I_C,6O9I_E_light+6O9I_D_heavy,"109_GLN, 39_TYR, 50_THR, 71_PHE, 80_TRP, 82_SER, 83_TRP, 84_ARG, 85_ASP, 86_HIS, 87_THR, 88_GLN",2.6
+3U30_D,3U30_E_light+3U30_F_heavy,"133_SER, 136_ASN, 137_ILE, 138_GLN, 139_LYS, 31_GLN, 32_ASP, 33_LYS, 34_GLU, 35_GLY, 36_ILE, 40_GLN, 42_ARG, 70_VAL, 71_LEU, 72_ARG, 73_LEU, 74_ARG, 75_GLY, 76_GLY, 77_MET, 8_LEU, 95_PRO, 9_THR",2.43
+3U30_A,3U30_B_light+3U30_C_heavy,"133_SER, 136_ASN, 137_ILE, 138_GLN, 139_LYS, 31_GLN, 32_ASP, 33_LYS, 34_GLU, 35_GLY, 36_ILE, 37_PRO, 40_GLN, 42_ARG, 71_LEU, 73_LEU, 74_ARG, 75_GLY, 76_GLY, 77_MET, 8_LEU, 95_PRO, 9_THR",2.43
+8DN7_C,8DN7_B_light+8DN7_A_heavy,"131_VAL, 133_MET, 136_ASP, 137_MET, 139_GLU, 142_LYS, 145_PHE, 146_PHE, 149_GLN, 150_GLU, 183_VAL, 184_SER, 185_ALA, 188_LEU, 189_GLN, 192_ARG, 210_VAL, 211_ASN, 212_PHE, 215_LEU, 216_ASN",2.0
+8DN7_E,8DN7_H_light+8DN7_D_heavy,"113_ARG, 131_VAL, 133_MET, 135_PRO, 137_MET, 139_GLU, 142_LYS, 143_ILE, 146_PHE, 147_ARG, 149_GLN, 150_GLU, 182_ARG, 183_VAL, 184_SER, 185_ALA, 188_LEU, 189_GLN, 192_ARG, 210_VAL, 211_ASN, 212_PHE, 215_LEU, 216_ASN",2.0
+4LVH_J,4LVH_L_light+4LVH_K_heavy,"111_PHE, 112_GLU, 113_ARG, 115_GLU, 118_PRO, 120_THR, 121_SER, 162_SER, 163_LYS, 164_SER, 169_LYS, 171_LYS, 253_TYR, 70_LEU, 71_SER, 73_ALA, 74_SER",2.8
+4LVH_A,4LVH_C_light+4LVH_B_heavy,"110_SER, 111_PHE, 112_GLU, 113_ARG, 114_PHE, 115_GLU, 118_PRO, 120_THR, 121_SER, 146_LYS, 163_LYS, 164_SER, 166_ILE, 169_LYS, 171_LYS, 253_TYR, 256_ALA, 70_LEU, 71_SER, 72_THR, 73_ALA",2.8
+4LVH_G,4LVH_I_light+4LVH_H_heavy,"112_GLU, 113_ARG, 114_PHE, 115_GLU, 118_PRO, 120_THR, 121_SER, 146_LYS, 162_SER, 163_LYS, 164_SER, 169_LYS, 171_LYS, 252_ARG, 253_TYR, 70_LEU, 71_SER, 72_THR, 73_ALA",2.8
+4LVH_D,4LVH_F_light+4LVH_E_heavy,"112_GLU, 113_ARG, 114_PHE, 115_GLU, 118_PRO, 120_THR, 121_SER, 122_SER, 163_LYS, 165_TYR, 166_ILE, 167_ASN, 169_LYS, 170_GLY, 173_VAL, 253_TYR, 255_PHE, 256_ALA, 70_LEU, 71_SER, 72_THR, 73_ALA",2.8
+7FJC_E,7FJC_L_light+7FJC_H_heavy,"345_THR, 346_ARG, 441_LEU, 444_LYS, 445_VAL, 446_GLY, 447_GLY, 449_TYR, 450_ASN, 470_THR, 490_PHE",2.96
+5W6G_A,5W6G_L_light+5W6G_H_heavy,"119_GLU, 120_ILE, 121_PHE, 122_PRO, 125_SER, 126_SER, 128_PRO, 129_ASN, 165_SER, 166_LYS, 167_SER, 168_TYR, 169_ALA, 170_ASN, 171_ASN, 173_GLU, 240_GLY, 242_THR",2.79
+6VZI_G,6VZI_L_light+6VZI_H_heavy,"321_GLY, 322_ILE, 324_GLY, 325_ASP, 326_ILE, 327_ARG, 328_GLN, 330_HIS, 415_THR, 417_PRO",2.72
+3DVG_Y,3DVG_A_light+3DVG_B_heavy,"34_GLU, 35_GLY, 36_ILE, 37_PRO, 39_ASP, 40_GLN, 71_LEU, 72_ARG, 73_LEU, 74_ARG, 75_GLY, 76_GLY, 8_LEU, 9_THR",2.6
+3DVG_X,3DVG_A_light+3DVG_B_heavy,"18_GLU, 19_PRO, 20_SER, 55_THR, 56_LEU, 57_SER, 58_ASP, 60_ASN, 61_ILE, 62_GLN",2.6
+1RZJ_G,1RZJ_L_light+1RZJ_H_heavy,"119_CYS, 120_VAL, 122_LEU, 202_THR, 203_GLN, 419_ARG, 421_LYS, 422_GLN, 423_ILE, 434_MET, 437_PRO",2.2
+8SI9_B,8SI9_I_light+8SI9_J_heavy,"170_GLU, 171_TRP, 172_THR, 173_ARG, 174_GLU, 175_PRO, 176_ALA, 177_ARG, 196_GLN, 197_THR, 198_VAL, 199_ASP, 200_SER, 201_GLY, 202_ILE, 42_LYS, 71_LYS",2.98
+8SI9_D,8SI9_L_light+8SI9_K_heavy,"124_ASP, 164_ARG, 170_GLU, 171_TRP, 172_THR, 173_ARG, 174_GLU, 175_PRO, 176_ALA, 177_ARG, 196_GLN, 197_THR, 198_VAL, 199_ASP, 200_SER, 201_GLY, 202_ILE, 42_LYS, 71_LYS",2.98
+7CR5_A,7CR5_L_light+7CR5_H_heavy,"159_LEU, 160_GLN, 161_LEU, 162_PRO, 163_GLN, 164_GLY, 165_THR, 166_THR, 167_LEU, 168_PRO, 169_LYS, 170_GLY, 171_PHE, 56_LEU, 57_THR, 59_HIS, 65_LYS, 66_PHE, 67_PRO, 70_GLN",2.08
+5MEV_A,5MEV_L_light+5MEV_H_heavy,"174_LEU, 205_ALA, 208_ARG, 307_VAL, 308_LYS, 309_GLN, 310_ARG, 311_GLY, 313_ASP, 316_VAL, 317_GLU, 320_HIS, 322_GLU",2.94
+5VPL_A,5VPL_C_light+5VPL_D_heavy,"14_GLU, 156_GLY, 157_ARG, 158_THR, 159_ILE, 16_ASP, 181_THR, 182_GLN, 186_TYR, 18_ARG, 199_ASP, 19_SER, 202_TYR, 204_TYR, 21_ARG",1.9
+5FCU_G,5FCU_L_light+5FCU_H_heavy,"107_ASP, 217_TYR, 220_PRO, 221_ALA, 246_GLN, 247_CYS, 51_THR, 52_LEU, 53_PHE, 54_CYS, 55_ALA, 72_HIS, 73_ALA, 74_CYS, 75_VAL, 76_PRO, 77_THR, 78_ASP, 79_PRO, 80_ASN",1.85
+7TB8_A,7TB8_E_light+7TB8_D_heavy,"345_THR, 346_ARG, 440_ASN, 442_ASP, 443_SER, 444_LYS, 445_VAL, 447_GLY, 448_ASN, 449_TYR, 490_PHE, 494_SER",2.83
+7TB8_A,7TB8_L_light+7TB8_H_heavy,"417_LYS, 421_TYR, 455_LEU, 456_PHE, 458_LYS, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 478_THR, 480_CYS, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 488_CYS, 489_TYR, 490_PHE, 492_LEU, 493_GLN",2.83
+7TB8_B,7TB8_G_light+7TB8_F_heavy,"345_THR, 346_ARG, 441_LEU, 444_LYS, 445_VAL, 446_GLY, 447_GLY, 448_ASN, 449_TYR, 452_LEU, 489_TYR, 493_GLN, 494_SER",2.83
+7TB8_C,7TB8_K_light+7TB8_J_heavy,"444_LYS, 445_VAL, 446_GLY, 447_GLY, 449_TYR, 492_LEU, 493_GLN, 494_SER",2.83
+5LDN_A,5LDN_L_light+5LDN_H_heavy,"177_ASN, 179_THR, 180_LYS, 181_GLU, 190_GLN, 191_THR, 192_PRO, 194_TYR, 277_ASP",2.7
+4LSS_G,4LSS_L_light+4LSS_H_heavy,"122_LEU, 124_GLY, 276_ASN, 278_SER, 279_ASP, 280_ASN, 281_ALA, 282_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 371_ILE, 430_VAL, 431_GLY, 432_GLN, 455_THR, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_ASN, 461_THR, 465_ASN, 466_GLU, 467_THR, 469_ARG, 476_ARG, 97_LYS",2.59
+4YXL_A,4YXL_L_light+4YXL_H_heavy,"138_MET, 140_HIS, 141_PHE, 142_GLY, 143_ASN, 144_ASP, 145_TRP, 146_GLU, 147_ASP, 148_ARG, 204_LYS, 208_ARG",2.6
+4YUE_C,4YUE_L_light+4YUE_H_heavy,"105_ARG, 117_THR, 119_GLU, 122_PHE, 55_THR, 56_PHE, 57_LYS, 59_TYR, 71_GLN, 75_ASP, 76_GLU, 77_LEU, 78_GLY, 79_PRO, 82_HIS",2.19
+8HN7_F,8HN7_C_light+8HN7_D_heavy,"449_TYR, 450_ASN, 452_LEU, 470_THR, 482_GLY, 483_VAL, 484_GLU, 485_GLY, 490_PHE",3.0
+8HN7_E,8HN7_A_light+8HN7_B_heavy,"346_ARG, 449_TYR, 450_ASN, 452_LEU, 470_THR, 480_CYS, 482_GLY, 483_VAL, 484_GLU, 485_GLY, 490_PHE",3.0
+3S37_X,3S37_L_light+3S37_H_heavy,"220_GLY, 221_TYR, 222_ARG, 251_GLU, 255_GLY, 256_ILE, 257_ASP, 259_ASN, 261_GLU, 308_ALA, 310_SER, 311_SER, 312_GLY, 313_LEU, 314_MET, 315_THR, 316_LYS, 317_LYS",2.7
+6CW3_F,6CW3_D_light+6CW3_C_heavy,"102_PHE, 103_ARG, 104_VAL, 167_ASP, 168_LYS, 195_HIS, 198_ASP, 199_TYR, 201_ARG, 202_ASN, 203_THR, 204_SER, 205_ASN, 257_ARG, 258_ILE, 259_ARG, 261_LEU, 262_ASP, 266_ILE, 269_LEU",1.98
+6CW3_H,6CW3_B_light+6CW3_A_heavy,"102_PHE, 103_ARG, 104_VAL, 148_MET, 167_ASP, 195_HIS, 198_ASP, 199_TYR, 201_ARG, 202_ASN, 203_THR, 204_SER, 205_ASN, 257_ARG, 258_ILE, 259_ARG, 261_LEU, 262_ASP, 265_LYS, 266_ILE, 269_LEU",1.98
+8G40_I,8G40_N_light+8G40_C_heavy,"118_ARG, 149_VAL, 150_ARG, 151_ASP, 152_ARG, 178_TRP, 179_SER, 198_ASP, 199_LYS, 220_ASN, 221_ASP, 222_ILE, 224_ARG, 227_GLU, 292_ARG, 294_ASN, 295_TRP, 296_LYS, 326_PRO, 344_GLU, 346_GLY, 347_HIS, 371_ARG, 406_TYR, 431_LYS",2.8
+8G40_G,8G40_L_light+8G40_A_heavy,"118_ARG, 149_VAL, 150_ARG, 151_ASP, 152_ARG, 178_TRP, 179_SER, 198_ASP, 199_LYS, 220_ASN, 221_ASP, 222_ILE, 224_ARG, 227_GLU, 292_ARG, 294_ASN, 295_TRP, 296_LYS, 326_PRO, 344_GLU, 346_GLY, 347_HIS, 371_ARG, 406_TYR, 431_LYS",2.8
+8G40_H,8G40_M_light+8G40_B_heavy,"118_ARG, 149_VAL, 150_ARG, 151_ASP, 152_ARG, 178_TRP, 179_SER, 198_ASP, 199_LYS, 221_ASP, 222_ILE, 224_ARG, 227_GLU, 292_ARG, 294_ASN, 295_TRP, 326_PRO, 344_GLU, 346_GLY, 347_HIS, 371_ARG, 406_TYR, 431_LYS",2.8
+8GV7_B,8GV7_D_light+8GV7_C_heavy,"153_LYS, 16_GLY, 18_ILE, 19_ASP, 20_GLY, 21_TRP, 26_HIS, 35_ALA, 38_LYS, 41_THR, 42_GLN, 45_VAL, 46_ASP, 48_ILE, 49_THR",2.6
+8JLX_A,8JLX_L_light+8JLX_H_heavy,"1163_ALA, 1190_ASN, 1191_TRP, 1194_ASN, 1195_PRO, 1196_THR, 1197_TRP, 1198_CYS, 1199_TRP, 1200_GLY, 1201_VAL, 1363_GLY, 1365_TRP",3.0
+8DF5_S,8DF5_D_light+8DF5_C_heavy,"334_ASN, 335_LEU, 337_PRO, 339_GLY, 340_GLU, 343_ASN, 344_ALA, 345_THR, 346_ARG, 356_LYS, 359_SER, 361_CYS, 440_ASN, 441_LEU",2.7
+8DF5_S,8DF5_N_light+8DF5_M_heavy,"369_TYR, 370_ASN, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 412_PRO, 414_GLN, 427_ASP, 428_ASP, 429_PHE, 517_LEU",2.7
+8DF5_R,8DF5_B_light+8DF5_A_heavy,"334_ASN, 335_LEU, 337_PRO, 339_GLY, 340_GLU, 343_ASN, 344_ALA, 345_THR, 346_ARG, 356_LYS, 357_ARG, 359_SER, 361_CYS, 441_LEU",2.7
+8DF5_R,8DF5_L_light+8DF5_H_heavy,"369_TYR, 370_ASN, 374_PHE, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 390_LEU, 412_PRO, 414_GLN, 427_ASP, 428_ASP, 429_PHE, 517_LEU, 528_LYS",2.7
+7R8L_E,7R8L_L_light+7R8L_H_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 500_THR, 501_ASN, 502_GLY, 504_GLY, 505_TYR",2.6
+7R8L_E,7R8L_D_light+7R8L_C_heavy,"369_TYR, 370_ASN, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 386_LYS, 389_ASP, 390_LEU, 428_ASP, 430_THR, 517_LEU",2.6
+3WFE_C,3WFE_L_light+3WFE_H_heavy,"101_ILE, 105_ASN, 106_VAL, 107_PRO, 109_ARG, 94_PHE, 97_ALA, 98_TRP",2.49
+3WFE_B,3WFE_L_light+3WFE_H_heavy,"421_GLY, 422_ALA, 423_ALA, 424_MET, 425_THR, 427_MET, 428_ALA, 431_ASP",2.49
+4YPG_D,4YPG_L_light+4YPG_H_heavy,"-1_THR, 0_SER, 100_ILE, 101_GLN, 106_THR, 109_PRO, 111_MET, 113_GLU, 116_ILE, 120_ARG, 2_ASP, 53_ILE, 54_PRO, 57_HIS, 58_GLU, 61_GLN, 64_PHE, 65_ASN, 86_THR, 89_TYR, 90_GLN, 92_LEU, 93_ASN, 96_GLU, 97_ALA, 99_VAL",3.0
+4YPG_C,4YPG_A_light+4YPG_B_heavy,"-1_THR, 0_SER, 100_ILE, 101_GLN, 111_MET, 113_GLU, 116_ILE, 120_ARG, 2_ASP, 53_ILE, 54_PRO, 57_HIS, 58_GLU, 61_GLN, 64_PHE, 65_ASN, 68_SER, 86_THR, 89_TYR, 90_GLN, 92_LEU, 93_ASN, 96_GLU, 97_ALA, 99_VAL",3.0
+6QB6_A,6QB6_L_light+6QB6_H_heavy,"205_ALA, 208_ARG, 308_LYS, 310_ARG, 311_GLY, 313_ASP, 317_GLU, 318_PHE, 320_HIS, 322_GLU",2.24
+7ZXK_C,7ZXK_L_light+7ZXK_H_heavy,"138_TRP, 142_LEU, 143_ASP, 146_ASP, 147_LEU, 149_ARG, 150_HIS, 152_ARG, 153_PHE, 156_LEU, 157_ALA, 159_GLY, 160_PHE, 161_ASN, 162_LEU, 164_GLU, 31_ARG, 33_PRO, 37_GLN, 38_LEU, 42_GLU, 46_GLU, 49_VAL, 50_SER, 53_LEU, 56_LYS",2.2
+7ZXK_A,7ZXK_I_light+7ZXK_J_heavy,"142_LEU, 143_ASP, 146_ASP, 147_LEU, 149_ARG, 150_HIS, 152_ARG, 153_PHE, 156_LEU, 157_ALA, 159_GLY, 160_PHE, 161_ASN, 162_LEU, 164_GLU, 37_GLN, 38_LEU, 42_GLU, 46_GLU, 49_VAL, 50_SER, 53_LEU",2.2
+8OXX_A,8OXX_C_light+8OXX_B_heavy,"242_THR, 243_GLY, 244_GLY, 245_ARG, 258_LYS, 259_ASN, 261_LYS, 262_LYS, 263_SER, 264_GLY, 266_SER, 267_PRO, 268_VAL, 269_ARG, 270_TYR",2.5
+7JMP_A,7JMP_L_light+7JMP_H_heavy,"446_GLY, 449_TYR, 456_PHE, 475_ALA, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN",1.71
+3KR3_D,3KR3_L_light+3KR3_H_heavy,"10_GLY, 11_GLY, 12_GLU, 14_VAL, 15_ASP, 17_LEU, 26_PHE, 34_ARG, 35_VAL, 40_ARG, 43_VAL, 44_GLU, 45_GLU, 47_CYS, 48_PHE, 49_ARG, 59_TYR, 7_THR, 9_CYS",2.2
+7DUO_B,7DUO_L_light+7DUO_H_heavy,"158_ASP, 159_VAL, 161_HIS, 192_GLN, 193_THR, 195_GLU, 197_TRP, 228_GLN, 229_PHE, 230_SER, 231_CYS, 232_LYS, 76_ASN, 77_LYS, 91_PHE, 95_GLN, 97_ASP, 98_MET",2.81
+3GB7_C,3GB7_B_light+3GB7_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 63_PRO, 64_ARG",2.85
+7X2H_F,7X2H_D_light+7X2H_C_heavy,"342_PHE, 343_ASN, 344_ALA, 345_THR, 371_SER, 372_ALA, 373_SER, 374_PHE, 375_SER, 436_TRP, 437_ASN, 438_SER, 439_ASN, 440_ASN, 441_LEU, 443_SER, 444_LYS, 499_PRO, 500_THR, 501_ASN, 503_VAL, 506_GLN, 508_TYR",2.1
+7X2H_E,7X2H_B_light+7X2H_A_heavy,"342_PHE, 343_ASN, 345_THR, 371_SER, 372_ALA, 373_SER, 374_PHE, 375_SER, 436_TRP, 437_ASN, 438_SER, 439_ASN, 440_ASN, 441_LEU, 443_SER, 445_VAL, 499_PRO, 500_THR, 501_ASN, 503_VAL, 506_GLN, 508_TYR",2.1
+3IGA_C,3IGA_B_light+3IGA_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 63_PRO, 64_ARG",2.75
+8W85_D,8W85_B_light+8W85_A_heavy,"1060_TYR, 1066_ASP, 1077_ARG, 10_PRO, 2_GLN, 3_PRO, 5_GLN, 6_PRO, 8_GLN, 9_LEU",2.77
+8W85_G,8W85_F_light+8W85_E_heavy,"16_TYR, 21_PRO, 23_GLY, 39_LEU, 40_GLY, 42_LYS, 55_ARG, 57_ASP, 59_GLN, 60_PHE, 62_LEU, 63_THR, 64_ASN, 65_ILE, 66_ALA, 67_VAL, 70_HIS",2.77
+8W85_H,8W85_F_light+8W85_E_heavy,"0_ILE, 1060_TYR, 1066_ASP, 2_GLN, 3_PRO, 5_GLN, 6_PRO, 8_GLN, 9_LEU",2.77
+8W85_C,8W85_B_light+8W85_A_heavy,"16_TYR, 21_PRO, 39_LEU, 40_GLY, 42_LYS, 55_ARG, 57_ASP, 59_GLN, 60_PHE, 62_LEU, 63_THR, 64_ASN, 65_ILE, 66_ALA, 67_VAL, 69_LYS, 70_HIS",2.77
+7NX6_E,7NX6_L_light+7NX6_H_heavy,"366_SER, 369_TYR, 370_ASN, 374_PHE, 377_PHE, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 388_ASN, 390_LEU, 392_PHE, 412_PRO, 427_ASP, 428_ASP, 429_PHE, 517_LEU",2.25
+7NX6_E,7NX6_B_light+7NX6_A_heavy,"403_ARG, 405_ASP, 408_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",2.25
+8FR6_F,8FR6_K_light+8FR6_J_heavy,"512_ALA, 513_VAL, 514_GLY, 515_ILE, 516_GLY, 517_ALA, 518_VAL, 519_PHE, 524_GLY, 528_SER, 532_ALA, 535_MET",2.5
+8FR6_A,8FR6_E_light+8FR6_D_heavy,"512_ALA, 513_VAL, 514_GLY, 515_ILE, 516_GLY, 517_ALA, 518_VAL, 519_PHE, 524_GLY, 528_SER, 532_ALA, 535_MET",2.5
+8FR6_B,8FR6_L_light+8FR6_H_heavy,"512_ALA, 513_VAL, 514_GLY, 515_ILE, 516_GLY, 517_ALA, 518_VAL, 519_PHE, 524_GLY, 528_SER, 532_ALA, 535_MET",2.5
+5Y11_C,5Y11_B_light+5Y11_A_heavy,"251_SER, 252_SER, 253_SER, 256_PHE, 275_LYS, 276_THR, 277_SER, 278_GLY, 284_MET, 286_PHE, 287_CYS, 288_LYS, 289_VAL, 290_ALA, 291_GLY, 292_CYS, 293_GLU, 294_HIS",2.1
+1OTT_A,1OTT_D_light+1OTT_C_heavy,"243_LYS, 246_ASP, 248_PRO, 249_LEU, 250_ASN, 380_PRO, 381_GLN, 382_TYR, 383_HIS",3.0
+1OTT_B,1OTT_F_light+1OTT_E_heavy,"243_LYS, 246_ASP, 248_PRO, 249_LEU, 250_ASN, 380_PRO, 381_GLN, 382_TYR, 383_HIS",3.0
+8A96_C,8A96_J_light+8A96_K_heavy,"351_TYR, 449_TYR, 452_LEU, 455_LEU, 470_THR, 472_ILE, 478_THR, 481_ASN, 482_GLY, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 488_CYS, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER",2.7
+1OTS_B,1OTS_F_light+1OTS_E_heavy,"243_LYS, 246_ASP, 248_PRO, 249_LEU, 250_ASN, 380_PRO, 381_GLN, 382_TYR, 383_HIS",2.51
+9DWE_A,9DWE_N_light+9DWE_J_heavy,"298_SER, 299_MET, 300_PRO, 325_THR, 357_VAL, 358_ASP, 359_GLY, 360_TRP, 375_ALA, 377_LYS, 380_THR, 381_GLN, 384_ILE, 385_ASP, 387_VAL, 388_THR, 38_HIS, 395_ILE, 40_GLN, 41_ASP",2.8
+9DWE_C,9DWE_L_light+9DWE_H_heavy,"298_SER, 299_MET, 300_PRO, 325_THR, 357_VAL, 358_ASP, 359_GLY, 360_TRP, 375_ALA, 377_LYS, 380_THR, 381_GLN, 384_ILE, 385_ASP, 387_VAL, 388_THR, 38_HIS, 395_ILE, 40_GLN, 41_ASP",2.8
+9DWE_B,9DWE_M_light+9DWE_I_heavy,"298_SER, 299_MET, 300_PRO, 325_THR, 357_VAL, 358_ASP, 359_GLY, 360_TRP, 375_ALA, 377_LYS, 380_THR, 381_GLN, 384_ILE, 385_ASP, 387_VAL, 388_THR, 38_HIS, 395_ILE, 40_GLN, 41_ASP",2.8
+5C7X_B,5C7X_N_light+5C7X_M_heavy,"114_LEU, 115_LEU, 116_VAL, 117_ILE, 119_PHE, 11_GLN, 120_ASP, 123_GLU, 12_PRO, 13_TRP, 15_HIS, 16_VAL, 17_ASN, 19_ILE, 20_GLN, 21_GLU, 23_ARG, 24_ARG, 85_LYS, 86_GLN, 87_HIS, 88_CYS",2.95
+5C7X_A,5C7X_L_light+5C7X_H_heavy,"112_ASP, 114_LEU, 115_LEU, 116_VAL, 117_ILE, 119_PHE, 11_GLN, 120_ASP, 123_GLU, 12_PRO, 13_TRP, 15_HIS, 16_VAL, 17_ASN, 19_ILE, 20_GLN, 21_GLU, 23_ARG, 24_ARG, 83_HIS, 86_GLN, 87_HIS, 89_PRO",2.95
+7S8H_A,7S8H_C_light+7S8H_B_heavy,"226_THR, 227_TRP, 228_GLU, 229_ASP, 230_HIS, 231_CYS, 232_GLN, 235_ARG, 236_PRO, 74_ASN, 76_GLU, 88_LYS, 96_MET",2.7
+7S8H_a,7S8H_L_light+7S8H_H_heavy,"356_ARG, 361_ILE, 362_PRO, 387_LEU, 389_SER, 392_SER, 394_LEU, 395_ASN, 397_THR, 398_HIS, 399_PHE, 400_SER, 401_ASP, 402_ASP, 404_GLU",2.7
+4HJG_H,4HJG_A_light+4HJG_B_heavy,"23_ALA, 24_GLN, 27_GLY, 28_PHE, 30_GLN, 31_SER, 34_ASP, 35_ASP, 37_SER, 38_GLN, 41_ASN, 42_VAL, 45_GLU, 49_LEU",2.0
+4HJG_E,4HJG_A_light+4HJG_B_heavy,"27_LEU, 33_LYS, 34_ILE, 35_GLN, 36_THR, 37_ALA, 38_GLU, 39_PHE, 40_LYS, 49_GLU, 52_ARG, 53_TYR, 56_LEU, 57_LEU",2.0
+5WT9_G,5WT9_L_light+5WT9_H_heavy,"128_LEU, 129_ALA, 130_PRO, 131_LYS, 132_ALA, 25_LEU, 26_ASP, 27_SER, 28_PRO, 29_ASP, 30_ARG, 31_PRO, 59_THR, 60_SER",2.4
+7Y0V_R,7Y0V_L_light+7Y0V_H_heavy,"339_ASP, 340_GLU, 342_PHE, 343_ASN, 344_ALA, 345_THR, 346_ARG, 371_LEU, 374_PHE, 436_TRP, 440_LYS, 441_LEU, 442_ASP, 444_LYS, 451_TYR, 509_ARG",2.48
+4LIQ_E,4LIQ_L_light+4LIQ_H_heavy,"314_THR, 315_VAL, 316_GLY, 317_GLU, 318_GLY, 319_LEU, 320_ASN, 367_SER, 369_PRO, 370_ARG, 372_LYS, 373_PRO, 375_GLU, 401_PRO, 402_PRO, 439_ASP, 493_SER",2.6
+7JMW_A,7JMW_L_light+7JMW_H_heavy,"369_TYR, 371_SER, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 408_ARG, 412_PRO, 413_GLY, 414_GLN, 415_THR, 416_GLY, 427_ASP, 428_ASP, 429_PHE",2.89
+8SIQ_A,8SIQ_L_light+8SIQ_H_heavy,"369_TYR, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 382_VAL, 383_SER, 384_PRO, 403_ARG, 405_ASP, 406_GLU, 408_ARG, 409_GLN, 412_PRO, 414_GLN, 415_THR, 416_GLY, 420_ASP, 505_TYR",2.5
+8SIQ_A,8SIQ_N_light+8SIQ_M_heavy,"343_ASN, 344_ALA, 345_THR, 346_ARG, 436_TRP, 437_ASN, 440_ASN, 441_LEU, 444_LYS, 445_VAL, 448_ASN, 450_ASN, 509_ARG",2.5
+7UBR_C,7UBR_F_light+7UBR_E_heavy,"149_ASN, 154_ILE, 157_GLU, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.05
+7UBR_A,7UBR_L_light+7UBR_H_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.05
+7KFY_A,7KFY_L_light+7KFY_H_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 498_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",2.16
+6K65_A,6K65_L_light+6K65_H_heavy,"24_GLN, 45_GLU, 48_ALA, 49_LEU, 51_HIS, 52_LEU, 53_GLN, 54_ASN, 55_GLU, 56_VAL, 58_ARG, 59_LEU, 60_LYS",1.65
+8DFI_A,8DFI_L_light+8DFI_H_heavy,"11_GLN, 12_CYS, 13_PRO, 14_GLU, 15_ASN, 31_LEU, 32_LEU, 33_ASN, 34_TYR, 37_GLU, 40_LYS, 41_CYS, 42_VAL, 43_GLU, 44_ASN, 45_PRO, 6_GLN, 73_LYS, 87_PHE, 88_ASP",1.9
+7CHC_R,7CHC_L_light+7CHC_H_heavy,"403_ARG, 405_ASP, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 478_THR, 486_PHE, 487_ASN, 489_TYR, 501_ASN, 505_TYR",2.71
+7CHC_R,7CHC_B_light+7CHC_A_heavy,"346_ARG, 446_GLY, 449_TYR, 450_ASN, 452_LEU, 472_ILE, 481_ASN, 482_GLY, 483_VAL, 484_GLU, 490_PHE",2.71
+5UTZ_D,5UTZ_L_light+5UTZ_H_heavy,"16_HIS, 19_LEU, 20_ASP, 23_MET, 76_LYS, 77_ASN, 78_PHE, 79_HIS, 80_LEU, 81_ARG, 84_ASP, 85_LEU, 88_ASN",2.75
+5UTZ_E,5UTZ_G_light+5UTZ_F_heavy,"16_HIS, 19_LEU, 20_ASP, 23_MET, 76_LYS, 77_ASN, 78_PHE, 79_HIS, 80_LEU, 81_ARG, 84_ASP, 85_LEU, 88_ASN",2.75
+5UTZ_A,5UTZ_C_light+5UTZ_B_heavy,"16_HIS, 19_LEU, 20_ASP, 23_MET, 76_LYS, 77_ASN, 78_PHE, 79_HIS, 81_ARG, 84_ASP, 85_LEU, 88_ASN",2.75
+7KZB_C,7KZB_L_light+7KZB_H_heavy,"368_LEU, 369_TYR, 370_ASN, 371_SER, 372_ALA, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 383_SER, 384_PRO, 385_THR, 408_ARG, 427_ASP, 428_ASP, 429_PHE",2.83
+7KZB_C,7KZB_B_light+7KZB_A_heavy,"405_ASP, 417_LYS, 455_LEU, 456_PHE, 475_ALA, 476_GLY, 477_SER, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR",2.83
+7N0U_C,7N0U_L_light+7N0U_H_heavy,"10_THR, 11_SER, 12_VAL, 141_GLU, 142_THR, 145_ARG, 146_ALA, 148_GLU, 149_SER, 150_TYR, 152_LEU, 153_ALA, 154_HIS, 31_PRO, 32_LYS, 33_VAL, 35_PRO, 36_GLN",3.0
+7Q6C_A,7Q6C_L_light+7Q6C_H_heavy,"841_ARG, 842_GLN, 845_TRP, 872_LYS, 873_CYS, 874_SER, 875_ALA, 876_SER, 877_THR, 879_LYS, 881_VAL, 882_CYS, 883_LEU, 884_LEU, 885_PRO, 886_PRO, 887_GLN, 888_CYS, 889_PHE, 890_LYS, 891_GLY, 907_LYS, 910_ASN, 913_GLU, 919_CYS",2.29
+8DYX_I,8DYX_L_light+8DYX_H_heavy,"142_ALA, 143_ASN, 144_PRO, 145_ASN, 146_ALA, 147_ASN, 148_PRO, 149_ASN, 150_ALA",3.0
+8DYX_I,8DYX_B_light+8DYX_A_heavy,"103_ASP, 104_PRO, 105_ASN, 106_ALA, 107_ASN, 108_PRO, 109_ASN, 110_VAL",3.0
+8DYX_I,8DYX_D_light+8DYX_C_heavy,"110_VAL, 111_ASP, 112_PRO, 113_ASN, 114_ALA, 115_ASN, 116_PRO, 117_ASN, 118_VAL",3.0
+8DYX_I,8DYX_F_light+8DYX_E_heavy,"118_VAL, 119_ASP, 120_PRO, 121_ASN, 122_ALA, 123_ASN, 124_PRO, 125_ASN, 126_VAL",3.0
+8DYX_I,8DYX_N_light+8DYX_M_heavy,"126_VAL, 127_ASP, 128_PRO, 129_ASN, 130_ALA, 131_ASN, 132_PRO, 133_ASN, 134_ALA",3.0
+8DYX_I,8DYX_P_light+8DYX_O_heavy,"134_ALA, 135_ASN, 136_PRO, 137_ASN, 138_ALA, 139_ASN, 140_PRO, 141_ASN, 142_ALA",3.0
+8DYX_I,8DYX_R_light+8DYX_Q_heavy,"150_ALA, 151_ASN, 152_PRO, 153_ASN, 154_ALA, 155_ASN, 156_PRO, 157_ASN, 158_ALA",3.0
+8DYX_I,8DYX_T_light+8DYX_S_heavy,"158_ALA, 159_ASN, 160_PRO, 161_ASN, 162_ALA, 163_ASN, 164_PRO, 165_ASN, 166_ALA",3.0
+8DYX_I,8DYX_V_light+8DYX_U_heavy,"167_ASN, 168_PRO, 169_ASN, 170_ALA, 171_ASN, 172_PRO, 173_ASN, 174_ALA",3.0
+8DYX_I,8DYX_X_light+8DYX_W_heavy,"174_ALA, 175_ASN, 176_PRO, 177_ASN, 179_ASN, 180_PRO, 181_ASN, 182_ALA, 183_ASN",3.0
+8DYX_I,8DYX_Z_light+8DYX_Y_heavy,"183_ASN, 184_PRO, 185_ASN, 186_ALA, 187_ASN, 188_PRO, 189_ASN, 190_ALA",3.0
+1DQJ_C,1DQJ_A_light+1DQJ_B_heavy,"100_SER, 101_ASP, 102_GLY, 103_ASN, 14_ARG, 15_HIS, 16_GLY, 18_ASP, 19_ASN, 20_TYR, 21_ARG, 62_TRP, 63_TRP, 73_ARG, 75_LEU, 77_ASN, 89_THR, 93_ASN, 96_LYS, 97_LYS, 98_ILE",2.0
+8Y0R_2,8Y0R_L_light+8Y0R_H_heavy,"131_GLU, 137_LYS, 189_THR, 195_SER, 196_GLN, 198_LYS, 65_HIS, 66_LEU, 68_ASP, 70_THR, 71_THR, 72_ASP, 74_PRO, 75_PHE, 76_GLY, 77_HIS",2.52
+4LMQ_D,4LMQ_L_light+4LMQ_H_heavy,"15_GLU, 16_SER, 17_HIS, 18_VAL, 19_ALA, 20_ARG, 21_ALA, 22_ASN, 42_LEU, 43_LYS, 44_ASN, 45_ASN, 46_ASN, 47_ARG, 49_VAL, 57_TRP",2.77
+4LMQ_F,4LMQ_I_light+4LMQ_E_heavy,"15_GLU, 17_HIS, 18_VAL, 19_ALA, 20_ARG, 21_ALA, 22_ASN, 42_LEU, 43_LYS, 44_ASN, 45_ASN, 46_ASN, 47_ARG, 49_VAL, 57_TRP",2.77
+7FCP_A,7FCP_L_light+7FCP_H_heavy,"369_TYR, 371_SER, 372_ALA, 374_PHE, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 408_ARG, 412_PRO, 413_GLY, 414_GLN, 415_THR, 427_ASP, 428_ASP, 429_PHE",2.4
+7FCP_A,7FCP_D_light+7FCP_B_heavy,"403_ARG, 417_LYS, 446_GLY, 453_TYR, 455_LEU, 456_PHE, 473_TYR, 475_ALA, 476_GLY, 477_SER, 478_THR, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 493_GLN, 494_SER, 495_TYR, 496_GLY, 498_GLN, 505_TYR",2.4
+8D9Z_D,8D9Z_A_light+8D9Z_B_heavy,"24_MET, 27_ASP, 28_ASN, 29_PHE, 32_MET, 33_ARG, 34_GLY, 35_LYS, 36_ARG, 37_VAL, 65_PHE, 7_PRO, 8_ASP, 9_VAL",1.8
+6XP6_D,6XP6_I_light+6XP6_J_heavy,"39_LYS, 55_ASP, 57_GLN, 58_PHE, 60_LEU, 61_THR, 62_ASN, 63_ILE, 64_ALA, 65_VAL, 67_LYS, 68_HIS",2.4
+6XP6_B,6XP6_G_light+6XP6_H_heavy,"60_TYR, 64_GLN, 66_ASP, 70_ARG, 77_ARG",2.4
+6XP6_A,6XP6_G_light+6XP6_H_heavy,"39_LYS, 55_ASP, 57_GLN, 58_PHE, 60_LEU, 61_THR, 62_ASN, 63_ILE, 64_ALA, 65_VAL, 67_LYS, 68_HIS",2.4
+6XP6_E,6XP6_I_light+6XP6_J_heavy,"60_TYR, 64_GLN, 66_ASP, 70_ARG, 77_ARG",2.4
+7WNB_R,7WNB_L_light+7WNB_H_heavy,"403_ARG, 406_GLU, 409_GLN, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",2.18
+1JPS_T,1JPS_L_light+1JPS_H_heavy,"152_ILE, 154_THR, 156_TYR, 164_GLY, 165_LYS, 166_LYS, 167_THR, 168_ALA, 169_LYS, 170_THR, 171_ASN, 190_GLN, 192_VAL, 194_PRO, 197_THR, 198_VAL, 199_ASN, 200_ARG, 201_LYS, 203_THR, 204_ASP",1.85
+5TZU_C,5TZU_L_light+5TZU_H_heavy,"100_GLU, 101_LEU, 102_THR, 103_ARG, 104_GLU, 29_GLU, 31_GLN, 32_ASN, 34_THR, 35_GLU, 36_VAL, 37_TYR, 39_LYS, 46_ASP, 51_ASP, 53_ALA, 54_LEU, 97_GLU, 99_THR",2.1
+8PXO_A,8PXO_E_light+8PXO_F_heavy,"598_THR, 599_GLY, 600_ASN, 601_ASN, 602_PRO, 603_CYS, 604_ASN, 605_TYR, 612_GLU, 615_VAL, 621_LEU",3.0
+8PXO_B,8PXO_C_light+8PXO_D_heavy,"598_THR, 599_GLY, 600_ASN, 601_ASN, 602_PRO, 603_CYS, 604_ASN, 605_TYR, 612_GLU, 615_VAL, 621_LEU",3.0
+8V52_B,8V52_C_light+8V52_D_heavy,"325_ARG, 331_LYS, 334_HIS, 335_GLU, 387_THR, 389_LEU, 391_TYR, 392_VAL, 393_GLY, 394_ARG, 396_PRO, 398_VAL",2.5
+8V52_A,8V52_E_light+8V52_F_heavy,"331_LYS, 334_HIS, 335_GLU, 387_THR, 388_ILE, 389_LEU, 391_TYR, 392_VAL, 393_GLY, 394_ARG, 396_PRO, 398_VAL",2.5
+3SDY_B,3SDY_L_light+3SDY_H_heavy,"146_ASN, 150_GLU, 153_ARG, 15_GLU, 16_GLY, 17_MET, 18_ILE, 19_ASP, 25_ARG, 26_HIS, 30_GLU, 31_GLY, 32_THR, 33_GLY, 34_GLN, 35_ALA, 36_ALA, 38_LEU",2.85
+7JN5_F,7JN5_L_light+7JN5_H_heavy,"356_TYR, 357_ASN, 362_SER, 363_THR, 364_PHE, 365_LYS, 366_CYS, 367_TYR, 368_GLY, 369_VAL, 370_SER, 371_ALA, 372_THR, 373_LYS, 376_ASP, 377_LEU, 395_ARG, 415_ASP, 417_MET",2.71
+9B2W_D,9B2W_F_light+9B2W_E_heavy,"120_VAL, 121_ASN, 122_SER, 123_ASP, 124_LEU, 30_ARG, 32_THR, 33_HIS, 35_VAL, 36_GLY, 38_LYS, 427_HIS, 428_TYR, 429_ASN, 430_LYS, 432_TYR, 455_GLU, 459_SER, 460_CYS, 47_ARG, 48_CYS, 49_THR, 56_MET, 90_LEU",2.51
+9B2W_A,9B2W_C_light+9B2W_B_heavy,"120_VAL, 121_ASN, 122_SER, 123_ASP, 124_LEU, 31_ILE, 32_THR, 33_HIS, 35_VAL, 36_GLY, 427_HIS, 428_TYR, 429_ASN, 430_LYS, 432_TYR, 44_ASP, 459_SER, 460_CYS, 47_ARG, 48_CYS, 49_THR, 50_SER, 56_MET, 90_LEU",2.51
+7ZOZ_A,7ZOZ_L_light+7ZOZ_H_heavy,"139_ARG, 141_PHE, 151_HIS, 154_TYR, 157_ARG, 158_HIS, 159_GLY, 43_ARG, 44_TRP, 50_PRO, 85_GLU, 86_PRO, 87_TYR, 88_ALA",2.1
+8GY5_P,8GY5_L_light+8GY5_H_heavy,"126_ILE, 128_LEU, 129_ALA, 130_PRO, 131_LYS, 132_ALA, 133_GLN, 60_SER, 61_GLU, 62_SER, 64_VAL, 83_PRO, 84_GLU, 86_ARG",1.98
+8GY5_Q,8GY5_B_light+8GY5_A_heavy,"126_ILE, 128_LEU, 129_ALA, 130_PRO, 131_LYS, 132_ALA, 133_GLN, 60_SER, 61_GLU, 62_SER, 83_PRO, 84_GLU",1.98
+1G9N_G,1G9N_L_light+1G9N_H_heavy,"119_CYS, 120_VAL, 122_LEU, 200_VAL, 202_THR, 203_GLN, 419_ARG, 421_LYS, 422_GLN, 423_ILE, 434_MET, 437_PRO",2.9
+3VI3_D,3VI3_L_light+3VI3_H_heavy,"119_GLU, 120_ASP, 151_ASN, 154_ARG, 155_ARG, 156_ILE, 157_THR, 158_SER, 399_SER, 400_ILE, 77_ASN, 78_ARG, 79_SER, 80_LYS, 81_GLY, 82_THR, 83_ALA, 84_GLU, 85_LYS, 87_LYS",2.9
+3VI3_B,3VI3_E_light+3VI3_F_heavy,"119_GLU, 120_ASP, 151_ASN, 154_ARG, 155_ARG, 156_ILE, 157_THR, 158_SER, 399_SER, 400_ILE, 77_ASN, 78_ARG, 79_SER, 80_LYS, 81_GLY, 82_THR, 83_ALA, 84_GLU, 85_LYS",2.9
+6XCJ_G,6XCJ_L_light+6XCJ_H_heavy,"236_THR, 274_SER, 275_GLU, 276_ASN, 278_THR, 279_ASN, 280_ASN, 281_VAL, 282_LYS, 283_THR, 364_SER, 365_GLY, 366_GLY, 367_ASP, 370_ILE, 454_THR, 455_ARG, 456_ASP, 457_GLY, 458_GLY, 459_LYS, 466_ARG, 469_GLY, 470_GLY, 471_ASN, 473_LYS, 477_ARG, 94_ASN, 95_MET, 96_TRP, 97_LYS",2.8
+5U8R_A,5U8R_L_light+5U8R_H_heavy,"241_PHE, 251_PHE, 253_ALA, 254_ASN, 255_ILE, 256_LEU, 257_SER, 258_ALA, 259_GLU, 263_SER, 265_GLY, 26_GLU, 286_SER, 287_GLN, 289_MET, 327_LYS, 381_GLU, 4_GLY, 5_PRO",3.0
+7V5K_A,7V5K_D_light+7V5K_E_heavy,"101_PHE, 132_ILE, 134_PRO, 135_SER, 35_ILE, 36_GLN, 38_THR, 39_PHE, 91_HIS, 93_THR, 96_THR, 98_GLN, 99_LYS",2.8
+7V5K_B,7V5K_H_light+7V5K_I_heavy,"101_PHE, 134_PRO, 135_SER, 304_GLN, 34_ASP, 35_ILE, 36_GLN, 38_THR, 39_PHE, 91_HIS, 93_THR, 96_THR, 98_GLN, 99_LYS",2.8
+7V5K_C,7V5K_F_light+7V5K_G_heavy,"101_PHE, 132_ILE, 135_SER, 307_ARG, 32_GLU, 34_ASP, 35_ILE, 36_GLN, 38_THR, 39_PHE, 91_HIS, 93_THR, 96_THR, 98_GLN, 99_LYS",2.8
+5IKC_N,5IKC_L_light+5IKC_H_heavy,"132_PHE, 134_LYS, 135_LYS, 136_VAL, 60_ASN, 62_ASP, 63_ARG, 64_TYR, 65_PHE, 93_ASP, 94_ASN, 95_ILE, 96_ASN",2.06
+5IKC_M,5IKC_A_light+5IKC_B_heavy,"130_ASP, 132_PHE, 134_LYS, 135_LYS, 136_VAL, 60_ASN, 62_ASP, 63_ARG, 64_TYR, 93_ASP, 94_ASN, 95_ILE, 96_ASN",2.06
+8RP8_D,8RP8_L_light+8RP8_H_heavy,"100_GLU, 101_LEU, 102_THR, 104_GLU, 106_GLU, 34_THR, 35_GLU, 37_TYR, 41_LYS, 51_ASP, 53_ALA, 54_LEU, 99_THR",2.0
+8RP8_C,8RP8_B_light+8RP8_A_heavy,"100_GLU, 101_LEU, 102_THR, 104_GLU, 106_GLU, 34_THR, 35_GLU, 37_TYR, 41_LYS, 51_ASP, 53_ALA, 54_LEU, 99_THR",2.0
+8H3N_C,8H3N_I_light+8H3N_H_heavy,"345_THR, 346_ARG, 440_LYS, 441_LEU, 442_ASP, 443_SER, 444_LYS, 445_VAL, 448_ASN, 450_ASN, 451_TYR",2.73
+8H3N_A,8H3N_E_light+8H3N_D_heavy,"345_THR, 346_ARG, 439_ASN, 440_LYS, 441_LEU, 442_ASP, 443_SER, 444_LYS, 445_VAL, 448_ASN, 450_ASN, 451_TYR, 499_PRO",2.73
+5W2B_A,5W2B_L_light+5W2B_H_heavy,"649_GLU, 652_TYR, 653_HIS, 656_LEU, 657_ARG, 658_THR, 659_GLN, 660_GLY, 661_PRO, 665_ILE, 668_TYR, 676_VAL, 678_PHE, 679_SER, 680_THR, 681_ASP, 682_ASP, 728_ARG, 735_LEU, 736_GLN, 739_GLN",2.25
+4DQO_C,4DQO_L_light+4DQO_H_heavy,"158_SER, 160_ASN, 163_THR, 166_LYS, 167_ASP, 168_ARG, 169_LYS, 170_GLN, 171_LYS, 172_VAL, 173_ASN",2.44
+1NL0_G,1NL0_L_light+1NL0_H_heavy,"10_VAL, 11_GLN, 5_LYS, 6_LEU, 9_PHE",2.2
+8HP9_A,8HP9_L_light+8HP9_H_heavy,"361_THR, 362_ARG, 453_ASN, 455_ASN, 456_LYS, 457_LEU, 460_LYS, 461_VAL, 462_GLY, 463_GLY, 465_TYR, 466_ASN, 514_ARG, 515_PRO, 516_THR",2.75
+8HP9_A,8HP9_N_light+8HP9_M_heavy,"421_ASN, 431_THR, 436_ASP, 437_TYR, 469_TYR, 471_LEU, 472_PHE, 473_ARG, 474_LYS, 489_TYR, 491_ALA, 492_GLY, 493_ASN, 494_LYS, 502_PHE, 503_ASN, 505_TYR, 509_ARG, 517_TYR, 518_GLY, 521_HIS",2.75
+8HP9_B,8HP9_Q_light+8HP9_P_heavy,"453_ASN, 455_ASN, 456_LYS, 457_LEU, 459_SER, 460_LYS, 461_VAL, 462_GLY, 463_GLY, 465_TYR, 466_ASN, 514_ARG, 515_PRO, 516_THR",2.75
+8HP9_B,8HP9_F_light+8HP9_E_heavy,"421_ASN, 431_THR, 432_GLY, 433_ASN, 436_ASP, 437_TYR, 469_TYR, 471_LEU, 472_PHE, 473_ARG, 474_LYS, 476_ASN, 489_TYR, 490_GLN, 491_ALA, 492_GLY, 493_ASN, 500_ALA, 501_GLY, 502_PHE, 503_ASN, 505_TYR, 506_PHE, 509_ARG, 517_TYR, 520_GLY",2.75
+6Z7Z_F,6Z7Z_C_light+6Z7Z_D_heavy,"25_PHE, 26_TYR, 27_THR, 28_PRO, 29_LYS, 30_ALA",2.4
+6Z7Z_H,6Z7Z_A_light+6Z7Z_B_heavy,"25_PHE, 26_TYR, 27_THR, 28_PRO, 29_LYS, 30_ALA",2.4
+8F6O_C,8F6O_B_light+8F6O_A_heavy,"114_ARG, 119_PHE, 120_GLN, 121_SER, 122_ASN, 123_LEU, 124_ILE, 125_GLU, 126_SER, 127_HIS, 128_SER, 35_ASP, 37_GLN, 38_LEU, 39_TYR, 40_ILE, 41_LYS, 42_ARG, 74_LEU, 76_GLY, 77_THR, 78_THR",2.31
+7U9V_C,7U9V_F_light+7U9V_E_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.25
+7U9V_A,7U9V_L_light+7U9V_H_heavy,"117_GLU, 149_ASN, 154_ILE, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.25
+8VGN_I,8VGN_B_light+8VGN_A_heavy,"160_PRO, 161_TYR, 166_ASN, 168_GLU, 169_PRO, 170_ALA, 171_ASN, 172_PRO, 173_SER, 174_GLU, 175_LYS, 76_ILE",2.5
+8VGN_G,8VGN_L_light+8VGN_H_heavy,"160_PRO, 161_TYR, 166_ASN, 168_GLU, 169_PRO, 170_ALA, 171_ASN, 172_PRO, 173_SER, 174_GLU, 175_LYS, 76_ILE",2.5
+8V5Q_G,8V5Q_L_light+8V5Q_H_heavy,"148_GLU, 149_ASN, 150_HIS, 151_PRO, 152_PHE, 153_THR, 155_ARG, 232_TRP, 242_ASP, 245_GLU, 246_LEU, 247_ASP, 250_GLU, 257_LYS, 258_VAL, 259_LEU, 260_ARG, 262_GLU",1.9
+8TRS_D,8TRS_G_light+8TRS_A_heavy,"277_SER, 278_PRO, 279_GLY, 280_PHE, 281_PHE, 289_PRO, 291_LEU, 292_GLU, 293_CYS, 294_PRO, 295_GLU, 296_HIS, 297_THR, 298_LEU, 299_PRO, 310_GLU, 313_PHE, 322_SER",1.9
+2NY0_A,2NY0_C_light+2NY0_D_heavy,"119_CYS, 120_VAL, 122_LEU, 202_THR, 203_GLN, 419_ARG, 421_LYS, 422_GLN, 423_ILE, 434_MET, 437_PRO",2.2
+4HT1_T,4HT1_L_light+4HT1_H_heavy,"126_GLY, 130_ARG, 132_ARG, 134_LEU, 135_PRO, 136_TRP, 31_VAL, 32_ASP, 55_ILE, 57_GLU, 59_ILE, 81_TYR, 87_LEU, 89_ASP, 90_GLY, 91_VAL, 92_LEU, 97_LEU",2.5
+7S4S_A,7S4S_L_light+7S4S_H_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 498_GLN, 500_THR, 502_GLY, 505_TYR",2.05
+6BFQ_G,6BFQ_D_light+6BFQ_C_heavy,"100_ILE, 101_ILE, 102_THR, 105_SER, 108_GLU, 109_ASN, 112_ASP, 37_ASN, 38_GLU, 39_THR, 41_GLU, 44_SER, 45_GLU, 46_MET, 96_CYS, 97_ALA, 98_THR, 99_GLN",2.6
+6BFQ_I,6BFQ_B_light+6BFQ_A_heavy,"100_ILE, 101_ILE, 102_THR, 105_SER, 108_GLU, 109_ASN, 112_ASP, 37_ASN, 38_GLU, 39_THR, 40_VAL, 41_GLU, 44_SER, 45_GLU, 46_MET, 96_CYS, 97_ALA, 98_THR, 99_GLN",2.6
+6BFQ_J,6BFQ_F_light+6BFQ_E_heavy,"100_ILE, 101_ILE, 102_THR, 105_SER, 108_GLU, 109_ASN, 112_ASP, 37_ASN, 38_GLU, 39_THR, 40_VAL, 41_GLU, 44_SER, 45_GLU, 46_MET, 96_CYS, 97_ALA, 98_THR, 99_GLN",2.6
+6BFQ_K,6BFQ_L_light+6BFQ_H_heavy,"100_ILE, 101_ILE, 102_THR, 104_GLU, 105_SER, 108_GLU, 109_ASN, 112_ASP, 37_ASN, 38_GLU, 39_THR, 41_GLU, 44_SER, 45_GLU, 46_MET, 96_CYS, 97_ALA, 98_THR, 99_GLN",2.6
+7XW6_R,7XW6_L_light+7XW6_Y_heavy,"104_THR, 105_HIS, 107_GLU, 109_ARG, 110_ASN, 129_LYS, 130_PHE, 134_PHE, 151_ASP, 152_ILE, 153_PHE, 155_ILE, 157_GLU, 159_THR, 183_LYS, 185_TYR, 206_TYR, 208_ASN, 209_LYS, 234_SER, 235_GLN, 253_ILE, 255_ARG, 256_ASN, 274_ARG, 276_ASP, 34_GLU, 35_GLU, 38_ARG, 58_LYS, 79_SER, 80_ARG, 82_TYR, 85_ILE",2.78
+6Q0L_A,6Q0L_L_light+6Q0L_H_heavy,"135_VAL, 136_THR, 137_ALA, 145_SER, 153_TRP, 155_THR, 156_GLU, 158_ASN, 159_GLY, 160_LEU, 183_HIS, 185_PRO, 186_SER, 187_ASN, 190_ASP, 192_ARG, 193_ALA, 194_ILE, 196_HIS, 222_LYS, 225_GLY, 226_GLN, 227_GLU, 228_GLY, 95_TYR",2.5
+6S8J_E,6S8J_U_light+6S8J_Y_heavy,"114_LYS, 115_LYS, 116_PRO, 117_ASP, 118_GLY, 119_SER, 144_THR, 146_PRO, 148_ALA, 223_THR, 224_GLY, 227_THR, 231_GLU",2.91
+6S8J_C,6S8J_O_light+6S8J_P_heavy,"114_LYS, 115_LYS, 116_PRO, 117_ASP, 118_GLY, 119_SER, 144_THR, 146_PRO, 148_ALA, 223_THR, 224_GLY, 227_THR, 231_GLU",2.91
+6S8J_A,6S8J_L_light+6S8J_H_heavy,"114_LYS, 116_PRO, 117_ASP, 118_GLY, 119_SER, 144_THR, 146_PRO, 148_ALA, 223_THR, 224_GLY, 227_THR, 231_GLU",2.91
+4M5Z_A,4M5Z_L_light+4M5Z_H_heavy,"133_ASN, 135_VAL, 136_THR, 137_ALA, 143_GLY, 144_ALA, 145_LYS, 153_TRP, 189_ALA, 190_ASP, 192_GLN, 193_SER, 194_LEU, 222_LYS, 225_ASP, 226_GLN",2.25
+4I3R_G,4I3R_L_light+4I3R_H_heavy,"252_LYS, 254_VAL, 256_SER, 275_GLU, 276_ASP, 278_SER, 279_ASP, 280_ASN, 281_ALA, 282_LYS, 283_ASN, 365_SER, 366_GLY, 367_GLY, 368_ASP, 371_ILE, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_SER, 461_GLY, 462_SER, 469_ARG, 472_GLY, 473_GLY, 474_ASP",3.0
+5VPG_A,5VPG_C_light+5VPG_D_heavy,"13_GLU, 155_GLY, 156_ARG, 157_THR, 158_ILE, 15_ASP, 17_ARG, 180_ALA, 181_GLN, 185_TYR, 18_GLN, 198_ASP, 203_TYR, 20_ARG",1.95
+6DFI_E,6DFI_L_light+6DFI_H_heavy,"303_VAL, 304_SER, 305_TYR, 306_SER, 307_LEU, 309_THR, 310_ALA, 334_GLY, 335_THR, 336_ASP, 337_GLY, 338_PRO, 368_SER, 369_THR, 370_GLU, 391_VAL, 392_GLY",2.48
+4F3F_C,4F3F_A_light+4F3F_B_heavy,"11_LYS, 18_GLU, 19_SER, 21_ILE, 22_PHE, 24_LYS, 25_LYS, 26_TRP, 27_GLU, 45_ASN, 46_ALA, 47_ILE, 48_PRO, 49_PHE, 50_THR, 51_TYR, 52_GLU",2.65
+6U38_B,6U38_L_light+6U38_H_heavy,"153_SER, 194_ARG, 195_GLU, 238_ASP, 239_ALA, 369_ILE, 374_ASP, 375_CYS, 376_SER, 377_THR, 378_CYS, 379_PHE",2.73
+8A1E_A,8A1E_E_light+8A1E_D_heavy,"212_GLU, 333_ARG, 334_THR, 336_ASN, 337_GLU, 340_PRO, 341_SER, 342_LYS, 344_CYS, 346_ARG, 348_GLY, 349_GLY, 351_CYS, 370_ASN, 380_LEU, 382_GLN",2.83
+8A1E_A,8A1E_C_light+8A1E_B_heavy,"189_CYS, 192_PHE, 194_ASN, 196_ARG, 198_LYS, 199_ARG, 202_LYS, 203_GLY, 228_CYS, 229_GLY, 230_VAL, 231_LEU, 242_ALA, 243_MET, 244_GLN, 245_THR, 36_THR, 37_ASN, 38_LEU, 39_SER, 40_GLY, 41_PHE, 42_SER, 44_MET",2.83
+5VLP_A,5VLP_L_light+5VLP_H_heavy,"535_SER, 536_VAL, 537_HIS, 538_THR, 539_ALA, 540_PRO, 541_PRO, 542_ALA, 543_GLU, 544_ALA, 546_MET, 547_GLY, 548_THR, 549_ARG, 550_VAL, 551_HIS, 553_HIS, 554_GLN, 557_HIS, 567_GLU, 568_VAL",2.9
+7WN8_A,7WN8_L_light+7WN8_H_heavy,"115_GLU, 117_THR, 119_LEU, 120_THR, 121_ARG, 122_GLU, 19_GLN, 52_THR, 53_GLU, 54_VAL, 55_TYR, 57_LYS, 64_ASP, 69_ASP, 71_ALA, 72_LEU",2.8
+7WN8_C,7WN8_Q_light+7WN8_P_heavy,"115_GLU, 117_THR, 119_LEU, 120_THR, 121_ARG, 122_GLU, 19_GLN, 52_THR, 53_GLU, 54_VAL, 55_TYR, 57_LYS, 69_ASP, 71_ALA, 72_LEU",2.8
+2VDL_A,2VDL_L_light+2VDL_H_heavy,"117_GLU, 149_ASN, 154_ILE, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.75
+6WXL_D,6WXL_E_light+6WXL_F_heavy,"18_ILE, 19_ASP, 20_GLY, 38_TYR, 39_LYS, 41_THR, 42_GLN, 45_ILE, 46_ASP, 49_THR, 52_LEU, 53_ASN, 55_LEU, 56_ILE",2.76
+6WXL_I,6WXL_J_light+6WXL_K_heavy,"18_ILE, 19_ASP, 20_GLY, 38_TYR, 39_LYS, 41_THR, 42_GLN, 45_ILE, 46_ASP, 49_THR, 52_LEU, 53_ASN, 55_LEU, 56_ILE",2.76
+6WXL_G,6WXL_J_light+6WXL_K_heavy,"276_ALA, 278_GLU, 291_ASN, 292_LEU, 40_THR, 41_GLU",2.76
+6WXL_C,6WXL_E_light+6WXL_F_heavy,"276_ALA, 278_GLU, 291_ASN, 292_LEU, 40_THR, 41_GLU",2.76
+6WXL_B,6WXL_L_light+6WXL_H_heavy,"18_ILE, 19_ASP, 20_GLY, 38_TYR, 39_LYS, 41_THR, 42_GLN, 45_ILE, 46_ASP, 49_THR, 52_LEU, 53_ASN, 55_LEU, 56_ILE",2.76
+6WXL_A,6WXL_L_light+6WXL_H_heavy,"276_ALA, 278_GLU, 291_ASN, 292_LEU, 40_THR, 41_GLU",2.76
+4DW2_U,4DW2_L_light+4DW2_H_heavy,"144_GLU, 145_GLN, 146_SER, 147_THR, 148_ASP, 149_TYR, 150_LEU, 154_GLN, 155_LEU, 156_LYS, 157_MET, 158_THR, 159_VAL, 185_ASP, 186_TRP, 21_PHE, 23_THR, 26_ASN, 27_GLN",2.97
+4QWW_B,4QWW_C_light+4QWW_D_heavy,"272_GLN, 276_ASP, 341_LYS, 342_ASP, 431_LEU, 68_TYR, 71_VAL, 72_ASP, 73_THR, 74_SER, 75_TYR, 76_PRO, 77_GLY, 78_PHE, 79_GLN, 82_GLU, 85_ASN, 86_PRO, 90_MET",2.7
+4QWW_A,4QWW_E_light+4QWW_F_heavy,"272_GLN, 276_ASP, 341_LYS, 342_ASP, 431_LEU, 68_TYR, 70_MET, 71_VAL, 72_ASP, 73_THR, 74_SER, 75_TYR, 76_PRO, 77_GLY, 78_PHE, 79_GLN, 82_GLU, 85_ASN, 86_PRO, 90_MET",2.7
+6UYM_E,6UYM_L_light+6UYM_H_heavy,"427_LEU, 428_ASN, 429_CYS, 431_GLU, 434_ASN, 436_GLY, 438_LEU, 439_ALA, 442_PHE, 443_TYR, 529_TRP",2.85
+6UYM_F,6UYM_B_light+6UYM_A_heavy,"427_LEU, 428_ASN, 429_CYS, 430_ASN, 431_GLU, 434_ASN, 436_GLY, 438_LEU, 439_ALA, 441_LEU, 442_PHE, 443_TYR, 529_TRP",2.85
+6B3S_I,6B3S_K_light+6B3S_J_heavy,"349_PRO, 384_GLN, 409_HIS, 412_PHE, 417_VAL, 418_SER, 438_ILE, 440_SER, 441_GLY, 443_LYS, 464_THR, 465_LYS, 466_ILE, 467_ILE, 468_ARG, 469_ASN, 473_ASN",2.8
+6B3S_B,6B3S_D_light+6B3S_C_heavy,"348_LEU, 349_PRO, 384_GLN, 409_HIS, 412_PHE, 417_VAL, 418_SER, 438_ILE, 440_SER, 441_GLY, 443_LYS, 464_THR, 465_LYS, 467_ILE, 468_ARG, 469_ASN, 473_ASN",2.8
+6B3S_A,6B3S_L_light+6B3S_H_heavy,"348_LEU, 349_PRO, 384_GLN, 409_HIS, 412_PHE, 415_ALA, 417_VAL, 418_SER, 438_ILE, 440_SER, 441_GLY, 443_LYS, 464_THR, 465_LYS, 466_ILE, 467_ILE, 468_ARG, 469_ASN, 473_ASN",2.8
+6B3S_E,6B3S_G_light+6B3S_F_heavy,"348_LEU, 349_PRO, 384_GLN, 409_HIS, 412_PHE, 415_ALA, 417_VAL, 418_SER, 438_ILE, 440_SER, 441_GLY, 443_LYS, 464_THR, 465_LYS, 466_ILE, 467_ILE, 468_ARG, 469_ASN, 473_ASN",2.8
+5VPH_A,5VPH_C_light+5VPH_D_heavy,"13_GLU, 155_GLY, 156_ARG, 157_THR, 158_ILE, 15_ASP, 17_ARG, 180_ALA, 181_GLN, 185_TYR, 187_ILE, 18_GLN, 198_ASP, 203_TYR, 20_ARG",2.5
+6BY2_C,6BY2_B_light+6BY2_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.35
+6BAE_E,6BAE_A_light+6BAE_B_heavy,"33_LYS, 34_ILE, 35_GLN, 36_THR, 37_ALA, 38_GLU, 39_PHE, 40_LYS, 49_GLU, 52_ARG, 53_TYR, 56_LEU, 57_LEU, 60_VAL, 61_ASN",2.14
+6BAE_C,6BAE_A_light+6BAE_B_heavy,"23_ALA, 24_GLN, 27_GLY, 28_PHE, 30_GLN, 31_SER, 34_ASP, 35_ASP, 37_SER, 38_GLN, 41_ASN, 42_VAL, 45_GLU, 49_LEU",2.14
+7CZT_A,7CZT_L_light+7CZT_H_heavy,"403_ARG, 408_ARG, 409_GLN, 414_GLN, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 446_GLY, 449_TYR, 456_PHE, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 496_GLY, 498_GLN, 501_ASN, 505_TYR",2.7
+7CZT_C,7CZT_M_light+7CZT_I_heavy,"403_ARG, 408_ARG, 409_GLN, 414_GLN, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 446_GLY, 449_TYR, 456_PHE, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 496_GLY, 498_GLN, 501_ASN, 505_TYR",2.7
+2H9G_S,2H9G_L_light+2H9G_H_heavy,"34_ILE, 35_SER, 36_GLU, 37_ASP, 38_GLY, 53_HIS, 56_ASP, 57_LEU, 58_LEU, 59_PHE, 61_LEU, 62_ARG, 64_THR",2.32
+2H9G_R,2H9G_A_light+2H9G_B_heavy,"34_ILE, 35_SER, 36_GLU, 38_GLY, 53_HIS, 56_ASP, 57_LEU, 58_LEU, 59_PHE, 61_LEU, 62_ARG, 97_PRO, 98_GLU, 99_MET",2.32
+7TP4_Z,7TP4_L_light+7TP4_H_heavy,"373_SER, 374_PHE, 375_SER, 403_ARG, 404_GLY, 405_ASP, 408_ARG, 439_ASN, 498_GLN, 499_PRO, 500_THR, 501_ASN, 502_GLY, 503_VAL, 504_GLY, 505_TYR, 506_GLN, 508_TYR",1.95
+6XC3_C,6XC3_L_light+6XC3_H_heavy,"369_TYR, 370_ASN, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 390_LEU, 517_LEU",2.7
+6XC3_C,6XC3_A_light+6XC3_B_heavy,"403_ARG, 405_ASP, 408_ARG, 409_GLN, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 495_TYR, 496_GLY, 498_GLN, 501_ASN, 502_GLY, 505_TYR",2.7
+8ELO_A,8ELO_L_light+8ELO_H_heavy,"403_ARG, 405_ASP, 408_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",2.72
+1J1P_Y,1J1P_L_light+1J1P_H_heavy,"100_SER, 101_ASP, 102_GLY, 103_ASN, 14_ARG, 15_HIS, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 62_TRP, 63_TRP, 73_ARG, 75_LEU, 77_ASN, 89_THR, 93_ASN, 96_LYS, 97_LYS, 98_ILE",1.8
+7XIK_A,7XIK_L_light+7XIK_H_heavy,"403_ARG, 406_GLU, 408_ARG, 409_GLN, 415_THR, 416_GLY, 417_ASN, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_ASN, 478_LYS, 486_PHE, 487_ASN, 489_TYR, 493_ARG, 501_TYR, 502_GLY, 505_HIS",2.89
+4G6M_A,4G6M_L_light+4G6M_H_heavy,"115_ALA, 116_GLN, 117_PHE, 118_PRO, 72_VAL, 73_LEU, 74_LYS, 75_ASP, 81_GLN, 83_GLU, 84_SER, 86_ASP, 89_ASN, 90_TYR, 92_LYS, 94_LYS, 96_GLU, 97_LYS, 98_ARG",1.81
+9DH2_T,9DH2_H_light+9DH2_G_heavy,"140_LYS, 141_GLU, 147_LYS, 159_HIS, 163_ASN, 164_GLY, 166_TRP, 173_ILE, 174_LEU, 175_SER, 176_PRO, 177_ASN, 180_THR, 181_ILE, 183_GLU, 195_SER, 200_ILE",2.98
+9DH2_R,9DH2_D_light+9DH2_A_heavy,"140_LYS, 141_GLU, 147_LYS, 159_HIS, 163_ASN, 164_GLY, 166_TRP, 173_ILE, 174_LEU, 175_SER, 176_PRO, 177_ASN, 180_THR, 181_ILE, 183_GLU, 195_SER, 200_ILE",2.98
+9DH2_M,9DH2_L_light+9DH2_K_heavy,"140_LYS, 141_GLU, 147_LYS, 159_HIS, 164_GLY, 165_SER, 166_TRP, 174_LEU, 175_SER, 176_PRO, 177_ASN, 180_THR, 181_ILE, 183_GLU, 195_SER, 200_ILE",2.98
+9DH2_S,9DH2_Q_light+9DH2_P_heavy,"140_LYS, 141_GLU, 147_LYS, 159_HIS, 164_GLY, 165_SER, 166_TRP, 174_LEU, 175_SER, 176_PRO, 177_ASN, 180_THR, 181_ILE, 183_GLU, 186_LYS, 195_SER, 200_ILE",2.98
+1NDG_C,1NDG_A_light+1NDG_B_heavy,"614_ARG, 615_HIS, 616_GLY, 619_ASN, 620_TYR, 621_ARG, 662_TRP, 663_TRP, 672_SER, 673_ARG, 674_ASN, 675_LEU, 677_ASN, 689_THR, 693_ASN, 696_LYS, 697_LYS, 698_ILE, 700_SER, 701_ASP, 702_GLY",1.9
+8E8Y_1,8E8Y_L_light+8E8Y_H_heavy,"100_ARG, 101_ALA, 103_ARG, 105_PHE, 106_SER, 107_VAL, 108_TRP, 114_ASP, 166_ILE, 168_GLY, 228_LEU, 234_LEU, 239_SER, 282_PRO, 87_VAL, 88_ALA, 89_ILE, 90_ILE",2.5
+7CDI_E,7CDI_L_light+7CDI_H_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 459_SER, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 502_GLY, 505_TYR",2.96
+8BSE_A,8BSE_L_light+8BSE_H_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 501_ASN, 502_GLY, 505_TYR",1.9
+5DHV_N,5DHV_B_light+5DHV_A_heavy,"11_ASP, 14_LYS, 15_ALA, 18_LEU, 21_PHE, 22_LEU, 48_ARG, 51_GLN, 55_ILE, 58_ARG, 59_ILE, 62_THR, 63_TYR",2.3
+5DHV_M,5DHV_L_light+5DHV_H_heavy,"11_ASP, 14_LYS, 15_ALA, 18_LEU, 21_PHE, 22_LEU, 48_ARG, 51_GLN, 55_ILE, 58_ARG, 59_ILE, 62_THR, 63_TYR",2.3
+8VGO_I,8VGO_B_light+8VGO_A_heavy,"150_GLU, 160_PRO, 161_TYR, 166_ASN, 168_GLU, 169_PRO, 170_ALA, 171_ASN, 172_PRO, 173_SER, 174_GLU, 175_LYS, 76_ILE",2.6
+8VGO_G,8VGO_L_light+8VGO_H_heavy,"160_PRO, 161_TYR, 166_ASN, 168_GLU, 169_PRO, 170_ALA, 171_ASN, 172_PRO, 173_SER, 174_GLU, 175_LYS, 76_ILE",2.6
+8D9Y_L,8D9Y_G_light+8D9Y_H_heavy,"28_ASP, 29_GLY, 30_HIS, 34_ARG, 35_GLY, 36_LYS, 37_ARG, 38_VAL, 6_THR, 7_PRO, 9_VAL",2.2
+8D9Y_K,8D9Y_E_light+8D9Y_F_heavy,"28_ASP, 29_GLY, 30_HIS, 34_ARG, 35_GLY, 36_LYS, 37_ARG, 38_VAL, 67_PHE, 7_PRO, 8_SER, 9_VAL",2.2
+8D9Y_J,8D9Y_C_light+8D9Y_D_heavy,"10_ARG, 28_ASP, 29_GLY, 30_HIS, 34_ARG, 35_GLY, 36_LYS, 37_ARG, 38_VAL, 67_PHE, 7_PRO, 8_SER, 9_VAL",2.2
+8D9Y_I,8D9Y_A_light+8D9Y_B_heavy,"10_ARG, 28_ASP, 29_GLY, 30_HIS, 34_ARG, 35_GLY, 36_LYS, 37_ARG, 38_VAL, 67_PHE, 6_THR, 7_PRO, 8_SER",2.2
+3ZDX_A,3ZDX_L_light+3ZDX_H_heavy,"117_GLU, 149_ASN, 154_ILE, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.45
+3ZDX_C,3ZDX_F_light+3ZDX_E_heavy,"117_GLU, 148_GLY, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.45
+2VDR_A,2VDR_L_light+2VDR_H_heavy,"149_ASN, 154_ILE, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.4
+7NX9_E,7NX9_L_light+7NX9_H_heavy,"366_SER, 369_TYR, 370_ASN, 377_PHE, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 388_ASN, 390_LEU, 392_PHE, 412_PRO, 427_ASP, 428_ASP, 429_PHE, 517_LEU, 527_LYS",2.4
+7NX9_E,7NX9_B_light+7NX9_A_heavy,"403_ARG, 408_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 501_TYR, 505_TYR",2.4
+5XXY_A,5XXY_L_light+5XXY_H_heavy,"111_VAL, 112_TYR, 113_ARG, 115_MET, 117_SER, 118_TYR, 119_GLY, 120_GLY, 121_ALA, 122_ASP, 123_TYR, 125_ARG, 18_ALA, 19_PHE, 49_ASP, 51_ALA, 52_ALA, 54_ILE, 56_TYR, 58_GLU, 59_MET, 60_GLU, 61_ASP, 62_LYS, 63_ASN, 66_GLN, 68_VAL, 69_HIS",2.9
+7MZH_A,7MZH_C_light+7MZH_B_heavy,"449_TYR, 455_LEU, 456_PHE, 475_ALA, 482_GLY, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN",2.1
+7MZH_E,7MZH_L_light+7MZH_H_heavy,"449_TYR, 455_LEU, 456_PHE, 475_ALA, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN",2.1
+8D7E_C,8D7E_G_light+8D7E_F_heavy,"111_GLU, 140_THR, 142_ASN, 144_THR, 146_LEU, 178_LYS, 180_SER, 182_SER, 184_SER, 185_ASN, 188_GLY, 189_HIS, 190_ASN, 191_ALA, 192_THR, 193_ALA",2.93
+8D7E_C,8D7E_E_light+8D7E_D_heavy,"24_ARG, 25_HIS, 26_SER, 27_PRO, 28_GLN, 45_PRO, 47_GLY, 48_THR, 49_ALA, 50_ASN, 51_TRP, 52_ASP, 53_ALA, 73_GLN, 96_HIS, 98_ARG",2.93
+3LEV_A,3LEV_L_light+3LEV_H_heavy,"107_LEU, 108_GLU, 109_LEU, 110_ASP, 111_LYS, 112_TRP, 113_ALA, 116_GLY, 244_TYR",2.5
+7ST5_F,7ST5_l_light+7ST5_h_heavy,"104_PHE, 31_ILE, 32_PRO, 33_VAL, 34_GLY, 35_PRO, 52_GLN, 53_LYS, 66_LEU, 67_THR, 68_LYS, 69_ARG, 95_ARG, 96_LYS, 98_SER",2.2
+7ST5_A,7ST5_L_light+7ST5_H_heavy,"104_PHE, 31_ILE, 32_PRO, 33_VAL, 34_GLY, 35_PRO, 52_GLN, 53_LYS, 66_LEU, 67_THR, 68_LYS, 69_ARG, 95_ARG, 96_LYS, 98_SER",2.2
+8GAV_A,8GAV_L_light+8GAV_H_heavy,"213_ASP, 214_SER, 218_TRP, 236_THR, 251_ASP, 253_LYS, 258_GLU, 261_LYS, 262_ILE, 263_VAL, 264_HIS, 265_THR, 267_LYS, 273_GLN, 309_ASP, 310_HIS, 312_ILE",2.7
+4JPW_G,4JPW_L_light+4JPW_H_heavy,"122_LEU, 124_GLY, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 370_GLU, 371_ILE, 425_ASN, 427_TRP, 429_GLY, 431_GLY, 432_GLN, 455_THR, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_ALA, 462_ASN, 469_ARG, 473_GLY, 474_ASN, 476_LYS, 480_ARG",2.9
+8KEP_A,8KEP_L_light+8KEP_H_heavy,"455_LEU, 456_PHE, 473_TYR, 475_ALA, 478_LYS, 479_PRO, 480_CYS, 481_ASN, 483_VAL, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 493_ARG",2.93
+6WIX_G,6WIX_L_light+6WIX_H_heavy,"134_PHE, 135_ASN, 136_PRO, 322_ILE, 323_ILE, 324_GLY, 325_ASP, 326_ILE, 327_ARG, 328_GLN, 330_HIS, 415_THR, 417_PRO",2.67
+4YXK_A,4YXK_L_light+4YXK_H_heavy,"139_HIS, 140_PHE, 141_GLY, 142_ASN, 143_ASP, 144_TYR, 145_GLU, 146_ASP, 207_ARG, 211_GLN",2.81
+8T7A_b,8T7A_G_light+8T7A_F_heavy,"385_ASP, 386_ILE, 387_PHE, 388_ASN, 389_PRO, 476_ASN, 477_PHE, 478_TYR, 479_ASP, 496_ASN, 499_ILE, 503_LEU, 506_ILE",2.8
+8T7A_c,8T7A_I_light+8T7A_H_heavy,"385_ASP, 386_ILE, 387_PHE, 388_ASN, 389_PRO, 476_ASN, 477_PHE, 478_TYR, 479_ASP, 496_ASN, 499_ILE, 503_LEU, 506_ILE",2.8
+8T7A_a,8T7A_E_light+8T7A_D_heavy,"385_ASP, 386_ILE, 387_PHE, 388_ASN, 389_PRO, 476_ASN, 477_PHE, 478_TYR, 479_ASP, 496_ASN, 499_ILE, 503_LEU, 506_ILE",2.8
+7UK9_C,7UK9_F_light+7UK9_E_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 206_SER, 208_ARG, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN",2.6
+7UK9_A,7UK9_L_light+7UK9_H_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.6
+6DFJ_E,6DFJ_L_light+6DFJ_H_heavy,"298_SER, 299_TYR, 300_VAL, 301_MET, 303_THR, 304_GLY, 326_TYR, 328_GLY, 329_THR, 330_ASP, 331_ALA, 332_PRO, 361_LYS, 362_GLU, 382_ALA, 383_GLY, 384_GLU, 385_LYS",2.07
+7KQE_A,7KQE_L_light+7KQE_H_heavy,"369_TYR, 370_ASN, 371_SER, 372_ALA, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 382_VAL, 383_SER, 384_PRO, 385_THR, 408_ARG, 414_GLN",2.88
+7KQE_B,7KQE_N_light+7KQE_I_heavy,"369_TYR, 370_ASN, 371_SER, 372_ALA, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 408_ARG, 414_GLN",2.88
+7KQE_C,7KQE_M_light+7KQE_J_heavy,"369_TYR, 370_ASN, 371_SER, 372_ALA, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 382_VAL, 383_SER, 384_PRO, 385_THR, 408_ARG, 414_GLN",2.88
+3SE8_G,3SE8_L_light+3SE8_H_heavy,"124_GLY, 198_GLY, 199_SER, 276_ASN, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 360_ILE, 365_SER, 366_GLY, 367_GLY, 368_ASP, 370_GLU, 371_ILE, 425_ASN, 426_MET, 427_TRP, 429_GLY, 431_GLY, 455_THR, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_ALA, 461_ASN, 462_ASN, 463_THR, 465_ASN, 466_GLU, 467_THR, 469_ARG, 472_GLY, 473_GLY, 474_ASN, 475_ILE, 97_LYS",1.9
+4M8Q_C,4M8Q_L_light+4M8Q_H_heavy,"100_ALA, 119_ASN, 120_TRP, 121_PHE, 122_ASP, 123_ILE, 124_THR, 125_ASN, 127_LEU, 128_TRP, 60_PHE, 75_HIS, 78_SER",2.89
+4M8Q_S,4M8Q_B_light+4M8Q_A_heavy,"100_ALA, 101_GLY, 119_ASN, 120_TRP, 121_PHE, 123_ILE, 124_THR, 125_ASN, 127_LEU, 128_TRP, 60_PHE, 61_PRO, 62_THR, 75_HIS",2.89
+6BIT_G,6BIT_L_light+6BIT_J_heavy,"100_ASP, 101_ASP, 35_PRO, 48_LEU, 52_GLN, 53_LYS, 54_GLU, 55_GLY, 56_HIS, 58_PRO, 67_THR, 96_LYS, 97_GLY, 99_PRO",2.19
+6BIT_H,6BIT_K_light+6BIT_I_heavy,"100_ASP, 101_ASP, 35_PRO, 39_PHE, 47_GLU, 48_LEU, 52_GLN, 53_LYS, 54_GLU, 55_GLY, 56_HIS, 58_PRO, 67_THR, 96_LYS, 97_GLY, 99_PRO",2.19
+7EC5_C,7EC5_F_light+7EC5_E_heavy,"240_HIS, 241_ALA, 243_GLU, 244_ALA, 247_GLN",2.89
+7EC5_B,7EC5_F_light+7EC5_E_heavy,"134_ALA, 135_HIS, 136_ASN, 137_THR, 139_THR, 140_SER, 142_GLY, 144_ASP, 156_ASN, 157_HIS",2.89
+7EC5_A,7EC5_F_light+7EC5_E_heavy,"204_GLU, 206_ASN, 207_GLY, 208_LEU, 263_ALA, 85_ARG",2.89
+5TE6_G,5TE6_L_light+5TE6_H_heavy,"122_LEU, 275_GLU, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 283_THR, 365_SER, 366_GLY, 367_GLY, 368_ASP, 370_GLU, 371_ILE, 425_ASN, 426_MET, 427_TRP, 429_GLY, 430_THR, 431_GLY, 432_GLN, 455_THR, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_ALA, 461_ASN, 462_ASN, 469_ARG, 473_GLY, 97_LYS",2.4
+6W0J_C,6W0J_B_light+6W0J_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.5
+6WZJ_E,6WZJ_B_light+6WZJ_A_heavy,"10_GLN, 11_CYS, 12_LEU, 13_GLN, 14_THR, 15_LEU, 34_HIS, 35_CYS, 36_ALA, 37_GLN, 41_ILE, 48_GLN, 49_LYS, 50_ALA, 51_CYS, 7_LEU, 8_ARG, 9_CYS",2.37
+6WZJ_F,6WZJ_D_light+6WZJ_C_heavy,"10_GLN, 12_LEU, 13_GLN, 14_THR, 15_LEU, 34_HIS, 35_CYS, 36_ALA, 37_GLN, 41_ILE, 49_LYS, 50_ALA, 51_CYS, 7_LEU, 8_ARG, 9_CYS",2.37
+5E8D_A,5E8D_L_light+5E8D_H_heavy,"10_MET, 11_ASN, 12_GLY, 13_TYR, 21_TYR, 23_VAL, 24_ASP, 26_SER, 27_GLN, 28_ASN, 39_VAL, 40_ARG, 42_GLU, 5_LYS, 6_CYS, 7_SER, 8_SER, 9_ASP",2.5
+4LEO_C,4LEO_B_light+4LEO_A_heavy,"101_MET, 102_LEU, 104_TYR, 106_THR, 129_TYR, 132_LYS, 145_ARG, 153_ALA, 154_GLU, 155_ILE, 156_VAL, 158_LYS, 159_ASP, 162_ARG, 72_MET, 93_ASP, 96_PHE, 99_PHE",2.64
+7MZM_A,7MZM_L_light+7MZM_H_heavy,"340_GLU, 341_VAL, 344_ALA, 346_ARG, 347_PHE, 348_ALA, 349_SER, 351_TYR, 352_ALA, 353_TRP, 354_ASN, 355_ARG, 356_LYS, 357_ARG, 444_LYS, 447_GLY, 448_ASN, 449_TYR, 450_ASN, 452_LEU, 466_ARG, 468_ILE, 470_THR, 471_GLU, 484_GLU, 490_PHE",2.3
+5F3H_K,5F3H_H_light+5F3H_G_heavy,"25_GLU, 30_ASP, 31_TRP, 33_ILE, 34_ALA, 35_PRO, 36_LYS, 83_ASN, 85_LEU, 87_PHE, 91_GLU, 92_GLN, 93_ILE, 94_ILE, 95_TYR",2.7
+5F3H_J,5F3H_B_light+5F3H_A_heavy,"25_GLU, 30_ASP, 31_TRP, 33_ILE, 34_ALA, 35_PRO, 36_LYS, 83_ASN, 85_LEU, 87_PHE, 91_GLU, 92_GLN, 93_ILE, 94_ILE, 95_TYR",2.7
+5F3H_I,5F3H_D_light+5F3H_C_heavy,"25_GLU, 30_ASP, 31_TRP, 33_ILE, 34_ALA, 35_PRO, 36_LYS, 83_ASN, 85_LEU, 87_PHE, 91_GLU, 92_GLN, 93_ILE, 94_ILE, 95_TYR",2.7
+5F3H_L,5F3H_F_light+5F3H_E_heavy,"30_ASP, 31_TRP, 33_ILE, 34_ALA, 35_PRO, 36_LYS, 83_ASN, 85_LEU, 87_PHE, 91_GLU, 92_GLN, 93_ILE, 94_ILE, 95_TYR",2.7
+2VXQ_A,2VXQ_L_light+2VXQ_H_heavy,"32_GLU, 34_ARG, 39_ASP, 40_GLU, 41_TRP, 43_ALA, 65_ASN, 67_ARG, 68_PHE, 75_LYS, 76_ASN, 77_VAL, 78_PHE, 79_ASP, 80_ASP",1.9
+1TQC_A,1TQC_C_light+1TQC_B_heavy,"127_GLY, 128_LEU, 152_TYR, 156_ASN, 158_TYR, 159_ARG, 185_ILE, 188_LYS, 189_GLN, 191_THR, 192_VAL, 193_THR, 194_THR, 195_THR, 196_THR, 197_LYS, 198_GLY, 199_GLU, 200_ASN",2.8
+8XSE_A,8XSE_L_light+8XSE_H_heavy,"345_THR, 346_ARG, 351_TYR, 440_ASN, 441_LEU, 443_SER, 444_LYS, 445_VAL, 447_GLY, 448_ASN, 449_TYR, 450_ASN",2.5
+8XSE_A,8XSE_N_light+8XSE_M_heavy,"403_ARG, 405_ASP, 408_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 446_GLY, 449_TYR, 453_TYR, 455_LEU, 456_PHE, 485_GLY, 486_PHE, 493_GLN, 494_SER, 495_TYR, 496_GLY, 497_PHE, 498_GLN, 501_ASN, 505_TYR",2.5
+7R6X_R,7R6X_L_light+7R6X_H_heavy,"369_TYR, 375_SER, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 390_LEU, 391_CYS, 392_PHE, 412_PRO, 427_ASP, 428_ASP, 429_PHE, 517_LEU, 528_LYS",2.95
+7R6X_R,7R6X_B_light+7R6X_A_heavy,"331_ASN, 332_ILE, 333_THR, 334_ASN, 335_LEU, 337_PRO, 339_GLY, 340_GLU, 343_ASN, 344_ALA, 345_THR, 346_ARG, 356_LYS, 357_ARG, 359_SER, 361_CYS, 441_LEU",2.95
+7R6X_R,7R6X_D_light+7R6X_C_heavy,"455_LEU, 473_TYR, 475_ALA, 476_GLY, 477_SER, 478_THR, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 488_CYS, 489_TYR, 493_GLN",2.95
+8V2E_C,8V2E_B_light+8V2E_A_heavy,"100_PHE, 103_THR, 104_ASN, 106_LEU, 107_ASN, 110_LYS, 113_ILE, 43_LEU, 44_GLN, 79_TRP, 80_TYR, 97_ARG, 98_ASN, 99_TRP",2.62
+8V2E_F,8V2E_E_light+8V2E_D_heavy,"100_PHE, 103_THR, 104_ASN, 106_LEU, 107_ASN, 109_ILE, 110_LYS, 113_ILE, 43_LEU, 44_GLN, 80_TYR, 97_ARG, 98_ASN, 99_TRP",2.62
+7BEM_E,7BEM_L_light+7BEM_H_heavy,"403_ARG, 406_GLU, 409_GLN, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",2.52
+8GAT_A,8GAT_N_light+8GAT_M_heavy,"118_ARG, 119_GLU, 151_ASP, 152_ARG, 178_TRP, 197_ASN, 198_ASP, 199_ASN, 220_LYS, 221_ASN, 222_ILE, 224_ARG, 227_GLU, 247_SER, 277_GLU, 295_TRP, 296_LYS, 346_GLY, 347_HIS, 371_ARG, 406_TYR, 432_GLU",3.0
+8F5I_A,8F5I_Y_light+8F5I_X_heavy,"37_GLU, 38_LEU, 41_LEU, 56_PRO, 57_LEU, 58_PHE, 59_LYS, 61_GLU, 62_LEU, 63_ASP, 65_TYR, 66_PHE, 68_SER",1.9
+8F5I_B,8F5I_L_light+8F5I_H_heavy,"37_GLU, 41_LEU, 54_SER, 57_LEU, 58_PHE, 59_LYS, 61_GLU, 62_LEU, 63_ASP, 65_TYR, 66_PHE, 68_SER",1.9
+7Q0G_E,7Q0G_L_light+7Q0G_H_heavy,"403_ARG, 415_THR, 416_GLY, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 459_SER, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 502_GLY, 505_TYR",1.82
+7Q0G_E,7Q0G_B_light+7Q0G_A_heavy,"333_THR, 334_ASN, 335_LEU, 336_CYS, 337_PRO, 338_PHE, 339_GLY, 340_GLU, 342_PHE, 343_ASN, 345_THR, 346_ARG, 362_VAL, 363_ALA, 364_ASP, 365_TYR, 367_VAL, 368_LEU, 527_LYS, 528_LYS",1.82
+6V8Z_A,6V8Z_E_light+6V8Z_D_heavy,"141_ASN, 323_ILE, 324_GLY, 325_ASP, 326_ILE, 327_ARG, 328_GLN, 415_THR",2.9
+6V8Z_A,6V8Z_F_light+6V8Z_C_heavy,"198_THR, 278_THR, 279_ASN, 280_ASN, 281_ALA, 365_SER, 367_GLY, 368_ASP, 370_GLU, 371_VAL, 425_ASN, 427_TRP, 428_GLN, 430_ILE, 457_ASP, 458_GLY, 459_GLY, 460_SER, 461_THR, 462_ASN, 465_THR, 466_GLU, 469_ARG, 473_GLY, 97_LYS",2.9
+6V8Z_G,6V8Z_K_light+6V8Z_J_heavy,"141_ASN, 323_ILE, 324_GLY, 325_ASP, 326_ILE, 327_ARG, 328_GLN, 415_THR",2.9
+6V8Z_G,6V8Z_L_light+6V8Z_I_heavy,"198_THR, 278_THR, 279_ASN, 280_ASN, 281_ALA, 365_SER, 367_GLY, 368_ASP, 370_GLU, 371_VAL, 425_ASN, 427_TRP, 428_GLN, 430_ILE, 457_ASP, 458_GLY, 459_GLY, 460_SER, 461_THR, 462_ASN, 465_THR, 466_GLU, 469_ARG, 473_GLY, 97_LYS",2.9
+6V8Z_M,6V8Z_Q_light+6V8Z_P_heavy,"141_ASN, 323_ILE, 324_GLY, 325_ASP, 326_ILE, 327_ARG, 328_GLN, 415_THR",2.9
+6V8Z_M,6V8Z_R_light+6V8Z_O_heavy,"198_THR, 278_THR, 279_ASN, 280_ASN, 281_ALA, 365_SER, 367_GLY, 368_ASP, 370_GLU, 371_VAL, 425_ASN, 427_TRP, 428_GLN, 430_ILE, 457_ASP, 458_GLY, 459_GLY, 460_SER, 461_THR, 462_ASN, 465_THR, 466_GLU, 469_ARG, 473_GLY, 97_LYS",2.9
+8QH0_A,8QH0_L_light+8QH0_H_heavy,"415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 449_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",1.87
+7N3D_C,7N3D_L_light+7N3D_H_heavy,"113_LEU, 114_GLY, 119_ALA, 120_GLY, 123_TYR, 134_ALA, 135_THR, 136_GLU, 137_GLY, 138_ALA, 139_LEU, 140_ASN, 141_THR, 142_PRO, 144_ASP, 68_ARG, 69_GLY, 84_ILE",1.53
+4HLZ_D,4HLZ_J_light+4HLZ_I_heavy,"18_VAL, 19_ASP, 20_GLY, 21_TRP, 38_LYS, 39_GLU, 41_THR, 42_GLN, 45_PHE, 46_ASP, 56_ILE",2.9
+4HLZ_B,4HLZ_H_light+4HLZ_G_heavy,"18_VAL, 19_ASP, 20_GLY, 21_TRP, 38_LYS, 39_GLU, 41_THR, 42_GLN, 45_PHE, 46_ASP, 52_VAL, 56_ILE",2.9
+4HLZ_F,4HLZ_L_light+4HLZ_K_heavy,"18_VAL, 19_ASP, 20_GLY, 21_TRP, 38_LYS, 41_THR, 42_GLN, 45_PHE, 56_ILE",2.9
+4HLZ_C,4HLZ_J_light+4HLZ_I_heavy,"18_HIS, 291_THR, 292_LEU, 318_THR, 38_HIS, 39_ALA, 40_LYS",2.9
+4HLZ_A,4HLZ_H_light+4HLZ_G_heavy,"291_THR, 292_LEU, 293_PRO, 318_THR, 38_HIS, 40_LYS",2.9
+7UXL_R,7UXL_E_light+7UXL_F_heavy,"312_ASP, 314_LEU, 318_LEU, 319_VAL, 320_ASP, 321_ASP, 322_SER, 323_ALA, 324_HIS, 325_ILE, 326_SER, 327_CYS, 328_ASN, 362_GLU, 363_GLU, 364_LEU",2.86
+7UXL_R,7UXL_B_light+7UXL_A_heavy,"347_ASP, 348_ILE, 349_ILE, 350_PRO, 351_ASP, 354_PHE, 355_GLN, 357_TYR, 365_GLU, 367_SER, 369_ILE, 371_TYR, 413_LYS, 415_ASP, 416_LYS",2.86
+5A3I_E,5A3I_H_light+5A3I_G_heavy,"113_LYS, 114_ILE, 115_GLN, 116_ILE, 118_PRO, 120_SER, 121_SER, 123_SER, 137_TYR, 138_GLN, 140_LYS, 162_ARG, 163_SER, 164_TYR, 165_ASN, 167_THR, 252_TYR, 71_ILE, 72_ASN",2.89
+5A3I_A,5A3I_D_light+5A3I_C_heavy,"113_LYS, 114_ILE, 115_GLN, 116_ILE, 118_PRO, 120_SER, 121_SER, 137_TYR, 145_ARG, 162_ARG, 163_SER, 164_TYR, 165_ASN, 252_TYR, 71_ILE, 72_ASN",2.89
+3JWD_B,3JWD_O_light+3JWD_P_heavy,"119_CYS, 120_VAL, 122_LEU, 200_VAL, 202_THR, 203_GLN, 204_ALA, 205_CYS, 369_PRO, 419_ARG, 421_LYS, 422_GLN, 423_ILE, 434_MET, 435_TYR, 436_ALA, 437_PRO",2.61
+3JWD_A,3JWD_L_light+3JWD_H_heavy,"119_CYS, 122_LEU, 200_VAL, 202_THR, 204_ALA, 369_PRO, 419_ARG, 421_LYS, 422_GLN, 423_ILE, 432_LYS, 434_MET, 435_TYR, 436_ALA, 437_PRO",2.61
+7COE_D,7COE_C_light+7COE_B_heavy,"502_LYS, 504_SER, 505_ARG, 506_LEU, 509_ASP, 510_ASP, 511_ARG, 512_THR, 513_GLU, 514_VAL, 515_PRO, 540_TYR, 553_TRP, 555_VAL",2.05
+7COE_A,7COE_L_light+7COE_H_heavy,"466_GLN, 467_PHE, 502_LYS, 504_SER, 505_ARG, 506_LEU, 509_ASP, 510_ASP, 511_ARG, 512_THR, 513_GLU, 514_VAL, 515_PRO, 517_LEU, 540_TYR, 542_ARG, 553_TRP, 555_VAL",2.05
+4D3C_A,4D3C_L_light+4D3C_H_heavy,"156_ILE, 157_PRO, 159_GLU, 160_HIS, 161_SER, 166_SER, 181_ARG, 190_PHE, 194_PRO, 197_ARG, 198_TYR",2.62
+4FFY_A,4FFY_L_light+4FFY_H_heavy,"300_VAL, 301_MET, 334_LYS, 336_PRO, 337_PHE, 338_SER, 339_THR, 340_GLN, 342_GLU, 343_LYS, 344_GLY, 345_ALA, 346_THR, 347_GLN, 349_GLY, 350_ARG, 351_LEU, 370_GLU, 371_PRO, 372_PRO, 379_VAL, 382_ALA, 383_GLY, 384_GLU, 386_ALA",2.5
+7UE0_A,7UE0_L_light+7UE0_H_heavy,"117_GLU, 149_ASN, 154_ILE, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.74
+7UE0_C,7UE0_F_light+7UE0_E_heavy,"149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.74
+4G7Y_S,4G7Y_L_light+4G7Y_H_heavy,"204_ASP, 205_GLU, 207_VAL, 208_GLN, 211_GLY, 212_ALA, 213_ASP, 214_GLY, 215_LEU, 216_GLY, 217_GLU",2.8
+6GKU_A,6GKU_L_light+6GKU_H_heavy,"113_ASP, 114_HIS, 115_ARG, 116_ILE, 117_LYS, 119_GLU, 120_ALA, 43_GLU, 49_ASP, 68_GLU, 69_TYR, 73_LYS, 98_ASP",1.91
+5MES_A,5MES_L_light+5MES_H_heavy,"174_LEU, 205_ALA, 208_ARG, 308_LYS, 309_GLN, 310_ARG, 311_GLY, 313_ASP, 317_GLU, 318_PHE, 320_HIS, 322_GLU",2.24
+7ZLI_A,7ZLI_L_light+7ZLI_H_heavy,"338_ASN, 339_LEU, 340_LYS, 341_PHE, 344_ALA, 439_LEU, 440_TRP, 442_ASN, 444_GLU",2.99
+3RKD_B,3RKD_C_light+3RKD_D_heavy,"476_THR, 477_ALA, 479_GLU, 484_THR, 485_TYR, 496_ASP, 497_SER, 498_VAL, 499_THR, 508_GLN, 510_VAL, 512_ARG, 513_SER, 514_LEU, 515_ASP, 534_LYS, 573_ASN, 574_ALA, 575_ALA, 576_GLY, 577_HIS, 578_ARG, 592_PRO",1.9
+3RKD_A,3RKD_L_light+3RKD_H_heavy,"476_THR, 477_ALA, 479_GLU, 484_THR, 485_TYR, 496_ASP, 497_SER, 498_VAL, 499_THR, 510_VAL, 512_ARG, 513_SER, 514_LEU, 515_ASP, 534_LYS, 573_ASN, 574_ALA, 575_ALA, 576_GLY, 577_HIS, 578_ARG",1.9
+3LIZ_A,3LIZ_L_light+3LIZ_H_heavy,"199_ILE, 200_GLY, 201_ASP, 232_ASN, 233_GLU, 234_ALA, 235_ILE, 236_GLY, 237_CYS, 248_ASP, 250_SER, 251_LYS, 253_PRO, 254_SER, 256_PRO, 257_ASP, 270_SER, 272_GLN",1.8
+6DDV_C,6DDV_A_light+6DDV_B_heavy,"226_ARG, 233_ARG, 254_GLY, 255_ASP, 256_VAL, 257_LEU, 258_PRO, 259_ASP, 260_GLY, 261_ASN, 265_GLN, 266_THR, 267_TRP",2.05
+3U9P_C,3U9P_M_light+3U9P_K_heavy,"100_GLN, 101_SER, 103_ASN, 120_ARG, 121_LYS, 122_THR, 3_PRO, 4_ALA, 5_PRO, 6_SER, 7_LEU, 83_SER, 84_ARG, 87_GLN, 89_THR, 8_LEU, 9_THR",2.8
+3U9P_D,3U9P_L_light+3U9P_H_heavy,"100_GLN, 101_SER, 103_ASN, 120_ARG, 121_LYS, 122_THR, 127_GLN, 3_PRO, 4_ALA, 5_PRO, 6_SER, 7_LEU, 83_SER, 84_ARG, 87_GLN, 8_LEU, 9_THR",2.8
+1RZK_G,1RZK_L_light+1RZK_H_heavy,"119_CYS, 120_VAL, 122_LEU, 200_VAL, 202_THR, 203_GLN, 419_ARG, 421_LYS, 422_GLN, 423_ILE, 434_MET, 437_PRO",2.9
+5W4L_A,5W4L_I_light+5W4L_F_heavy,"103_GLN, 106_GLU, 107_ASP, 110_SER, 114_GLN, 217_TYR, 51_THR, 52_LEU, 53_PHE, 58_ALA, 60_ALA, 69_TRP, 71_THR, 72_HIS, 73_ALA, 74_CYS, 75_VAL, 76_PRO, 77_THR, 78_ASP, 79_PRO",2.92
+5W4L_A,5W4L_C_light+5W4L_B_heavy,"224_VAL, 244_SER, 246_GLN, 34_LEU, 35_TRP, 36_VAL, 37_THR, 38_VAL, 39_TYR, 40_TYR, 43_PRO, 82_GLN, 84_ILE, 86_LEU, 87_GLU",2.92
+5W4L_G,5W4L_L_light+5W4L_H_heavy,"103_GLN, 106_GLU, 107_ASP, 114_GLN, 217_TYR, 51_THR, 52_LEU, 53_PHE, 58_ALA, 69_TRP, 71_THR, 72_HIS, 73_ALA, 74_CYS, 75_VAL, 76_PRO, 77_THR, 78_ASP, 79_PRO, 80_ASN",2.92
+5W4L_G,5W4L_E_light+5W4L_D_heavy,"224_VAL, 244_SER, 32_ASP, 34_LEU, 35_TRP, 36_VAL, 37_THR, 38_VAL, 39_TYR, 40_TYR, 43_PRO, 491_ILE, 82_GLN, 84_ILE, 86_LEU, 87_GLU",2.92
+8S9G_E,8S9G_L_light+8S9G_H_heavy,"334_ASN, 335_LEU, 337_PRO, 339_HIS, 340_GLU, 343_ASN, 344_ALA, 345_THR, 361_CYS, 441_LEU",3.0
+5H35_C,5H35_B_light+5H35_A_heavy,"107_LEU, 110_SER, 111_LEU, 113_TYR, 114_SER, 115_TYR, 164_TYR, 168_THR, 169_PRO, 172_HIS, 173_HIS, 176_LEU, 1_MET, 2_TYR, 55_ILE, 57_PRO, 59_GLN, 5_LEU, 62_LYS, 6_GLU, 9_ASN",2.64
+5H35_D,5H35_I_light+5H35_H_heavy,"107_LEU, 110_SER, 111_LEU, 113_TYR, 114_SER, 115_TYR, 164_TYR, 168_THR, 169_PRO, 172_HIS, 173_HIS, 176_LEU, 1_MET, 2_TYR, 55_ILE, 57_PRO, 59_GLN, 5_LEU, 62_LYS, 6_GLU, 9_ASN",2.64
+5H35_E,5H35_G_light+5H35_F_heavy,"107_LEU, 110_SER, 111_LEU, 113_TYR, 114_SER, 115_TYR, 164_TYR, 168_THR, 169_PRO, 172_HIS, 173_HIS, 176_LEU, 1_MET, 2_TYR, 55_ILE, 57_PRO, 59_GLN, 5_LEU, 62_LYS, 6_GLU, 9_ASN",2.64
+4DTG_K,4DTG_L_light+4DTG_H_heavy,"10_GLU, 11_GLU, 12_ASP, 13_PRO, 17_ARG, 19_TYR, 21_THR, 23_TYR, 24_PHE, 26_ASN, 28_GLN, 31_GLN, 32_CYS, 33_GLU, 34_ARG, 36_LYS, 50_LEU",1.8
+3SKJ_E,3SKJ_L_light+3SKJ_H_heavy,"129_SER, 132_PHE, 133_GLU, 135_ARG, 164_CYS, 165_VAL, 166_ALA, 33_ASN, 42_MET, 44_SER, 46_CYS, 48_VAL, 49_MET, 79_ARG, 83_SER, 84_PHE, 85_PRO",2.5
+3SKJ_F,3SKJ_M_light+3SKJ_I_heavy,"129_SER, 132_PHE, 133_GLU, 135_ARG, 164_CYS, 165_VAL, 166_ALA, 32_GLN, 33_ASN, 34_ILE, 35_MET, 42_MET, 44_SER, 46_CYS, 48_VAL, 49_MET, 79_ARG, 83_SER, 84_PHE, 85_PRO",2.5
+8ULJ_B,8ULJ_L_light+8ULJ_H_heavy,"166_LYS, 169_SER, 170_ALA, 172_LEU, 173_SER, 174_THR, 175_ASN, 176_LYS, 177_ALA, 178_VAL, 191_LYS, 192_VAL, 193_LEU, 194_ASP, 197_ASN, 201_LYS, 263_ASP",3.0
+7TP3_Z,7TP3_L_light+7TP3_H_heavy,"372_ALA, 374_PHE, 404_GLY, 405_ASP, 437_ASN, 439_ASN, 440_ASN, 445_VAL, 498_GLN, 499_PRO, 500_THR, 501_ASN, 502_GLY, 503_VAL, 504_GLY, 505_TYR, 506_GLN, 508_TYR",2.33
+9FJK_A,9FJK_L_light+9FJK_H_heavy,"122_THR, 123_ASN, 162_THR, 163_PHE, 164_GLU, 165_TYR, 166_VAL, 167_SER, 168_GLN, 169_PRO, 170_PHE, 172_MET, 222_PRO, 223_LEU, 224_VAL, 225_ASP, 227_PRO",2.84
+9FJK_B,9FJK_L_light+9FJK_H_heavy,"352_ARG, 357_ASN, 556_PHE, 557_LEU, 558_PRO, 577_GLN, 578_THR, 579_LEU, 580_GLU",2.84
+1UJ3_C,1UJ3_A_light+1UJ3_B_heavy,"722_LYS, 749_LYS, 752_ILE, 754_THR, 756_TYR, 765_LYS, 766_LYS, 767_THR, 768_ALA, 769_LYS, 771_ASN, 790_GLN, 792_VAL, 794_PRO, 796_ARG, 797_THR, 799_ASN, 800_ARG, 801_LYS, 804_ASP",2.1
+7RP0_C,7RP0_B_light+7RP0_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.48
+5VCO_F,5VCO_A_light+5VCO_B_heavy,"12_ALA, 13_GLU, 155_GLY, 156_ARG, 157_THR, 15_ASP, 178_SER, 179_ASN, 17_ARG, 180_ALA, 181_GLN, 182_GLY, 184_ASP, 185_TYR, 187_ILE, 18_GLN, 198_ASP, 199_ASN, 203_TYR, 20_ARG",2.74
+5VCO_E,5VCO_C_light+5VCO_D_heavy,"12_ALA, 13_GLU, 156_ARG, 157_THR, 158_ILE, 15_ASP, 178_SER, 179_ASN, 17_ARG, 180_ALA, 181_GLN, 182_GLY, 185_TYR, 187_ILE, 18_GLN, 198_ASP, 199_ASN, 203_TYR, 20_ARG",2.74
+7LXW_A,7LXW_L_light+7LXW_H_heavy,"140_PHE, 142_GLY, 143_VAL, 144_TYR, 145_TYR, 146_HIS, 147_LYS, 148_ASN, 152_TRP, 154_GLU, 156_GLU, 158_ARG, 16_VAL, 17_ASN, 244_LEU, 245_HIS, 246_ARG, 249_LEU, 251_PRO",2.8
+7QNX_E,7QNX_L_light+7QNX_H_heavy,"366_SER, 369_TYR, 370_ASN, 374_PHE, 375_SER, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 389_ASP, 412_PRO, 414_GLN, 427_ASP, 428_ASP, 429_PHE, 517_LEU",2.92
+7QNX_E,7QNX_B_light+7QNX_A_heavy,"405_ASP, 408_ARG, 439_ASN, 440_ASN, 443_SER, 445_VAL, 498_GLN, 499_PRO, 500_THR, 501_ASN, 502_GLY, 503_VAL, 504_GLY, 505_TYR",2.92
+1KIR_C,1KIR_A_light+1KIR_B_heavy,"102_GLY, 103_ASN, 116_LYS, 117_GLY, 118_THR, 119_ASP, 120_VAL, 121_GLN, 125_ARG, 129_LEU, 19_ASN, 22_GLY, 23_TYR, 24_SER, 27_ASN",2.0
+3SQO_A,3SQO_L_light+3SQO_H_heavy,"153_SER, 155_PRO, 194_ARG, 195_GLU, 197_GLU, 237_ARG, 238_ASP, 369_ILE, 374_ASP, 375_CYS, 377_THR, 378_CYS, 379_PHE, 381_SER",2.7
+5Y2L_A,5Y2L_J_light+5Y2L_I_heavy,"278_ILE, 280_GLU, 289_PRO, 290_ASN, 291_ASP, 318_THR, 40_THR",2.9
+5Y2L_B,5Y2L_J_light+5Y2L_I_heavy,"18_ILE, 19_ASP, 20_GLY, 21_TRP, 38_LEU, 41_THR, 42_GLN, 45_ILE, 46_ASP, 48_ILE, 49_ASN, 52_LEU, 53_ASN, 56_ILE",2.9
+7XDA_A,7XDA_C_light+7XDA_B_heavy,"385_ARG, 390_ARG, 397_THR, 398_GLY, 399_LYS, 403_TYR, 431_TYR, 435_TYR, 437_LEU, 438_PHE, 439_ARG, 440_LYS, 441_SER, 442_ASN, 455_TYR, 456_GLN, 457_ALA, 458_GLY, 459_SER, 466_GLU, 467_GLY, 468_PHE, 469_ASN, 471_TYR, 472_PHE, 475_GLN, 476_SER, 480_GLN, 487_TYR",2.98
+7XDA_A,7XDA_E_light+7XDA_D_heavy,"327_THR, 328_ARG, 422_ASN, 423_LEU, 424_ASP, 425_SER, 426_LYS, 427_VAL, 430_ASN, 481_PRO, 482_THR",2.98
+5TH9_C,5TH9_N_light+5TH9_J_heavy,"108_GLN, 109_THR, 110_PHE, 111_GLU, 124_TRP, 126_GLN, 127_ASN, 160_TYR, 161_SER, 162_ARG, 167_VAL, 169_GLN, 174_GLU, 176_GLY, 177_ASP, 178_GLY, 179_TYR, 196_PRO, 197_GLY, 198_ILE, 199_GLN, 201_ASP, 203_HIS",3.0
+5TH9_B,5TH9_M_light+5TH9_I_heavy,"108_GLN, 109_THR, 110_PHE, 111_GLU, 124_TRP, 126_GLN, 127_ASN, 160_TYR, 161_SER, 162_ARG, 163_ASP, 167_VAL, 169_GLN, 174_GLU, 176_GLY, 177_ASP, 178_GLY, 179_TYR, 196_PRO, 197_GLY, 198_ILE, 199_GLN, 201_ASP",3.0
+5TH9_A,5TH9_L_light+5TH9_H_heavy,"108_GLN, 109_THR, 110_PHE, 111_GLU, 124_TRP, 126_GLN, 130_GLU, 160_TYR, 161_SER, 162_ARG, 163_ASP, 167_VAL, 169_GLN, 174_GLU, 176_GLY, 177_ASP, 178_GLY, 179_TYR, 196_PRO, 197_GLY, 198_ILE, 199_GLN, 201_ASP",3.0
+2B2X_B,2B2X_M_light+2B2X_I_heavy,"156_SER, 157_ASN, 158_SER, 160_TYR, 192_GLU, 218_GLN, 219_ARG, 220_GLY, 221_GLY, 222_ARG, 223_GLN, 224_THR, 259_GLU, 260_SER, 261_HIS, 263_ASN, 264_TYR",2.2
+2B2X_A,2B2X_L_light+2B2X_H_heavy,"156_SER, 157_ASN, 158_SER, 160_TYR, 192_GLU, 218_GLN, 219_ARG, 220_GLY, 221_GLY, 222_ARG, 223_GLN, 224_THR, 259_GLU, 260_SER, 261_HIS, 263_ASN, 264_TYR, 294_LEU",2.2
+6CYF_Q,6CYF_T_light+6CYF_U_heavy,"158_ALA, 159_ARG, 160_GLN, 161_GLY, 163_ARG, 227_GLU, 229_PRO, 230_PHE, 231_GLU, 232_LYS, 233_ASP",2.78
+6CYF_I,6CYF_L_light+6CYF_M_heavy,"158_ALA, 159_ARG, 160_GLN, 161_GLY, 163_ARG, 226_ASP, 227_GLU, 229_PRO, 230_PHE, 231_GLU, 232_LYS, 233_ASP",2.78
+6CYF_A,6CYF_C_light+6CYF_D_heavy,"158_ALA, 159_ARG, 160_GLN, 161_GLY, 163_ARG, 165_ASP, 226_ASP, 227_GLU, 229_PRO, 230_PHE, 231_GLU, 232_LYS, 233_ASP",2.78
+6CYF_B,6CYF_E_light+6CYF_F_heavy,"158_ALA, 159_ARG, 160_GLN, 161_GLY, 163_ARG, 227_GLU, 229_PRO, 230_PHE, 231_GLU, 232_LYS, 233_ASP",2.78
+4KI5_M,4KI5_D_light+4KI5_C_heavy,"2181_GLU, 2182_SER, 2183_LYS, 2184_ALA, 2186_SER, 2187_ASP, 2209_ARG, 2211_HIS, 2213_GLN, 2214_GLY, 2215_ARG",2.47
+4KI5_M,4KI5_F_light+4KI5_E_heavy,"2194_SER, 2222_GLN, 2223_VAL, 2224_ASN, 2225_ASN, 2226_PRO, 2227_LYS, 2228_GLU, 2229_TRP, 2261_LEU, 2263_SER, 2271_TRP, 2273_LEU, 2275_PHE, 2279_LYS, 2280_VAL, 2282_VAL, 2307_ARG, 2309_HIS, 2311_GLN",2.47
+7LY3_B,7LY3_E_light+7LY3_F_heavy,"144_TYR, 145_TYR, 146_HIS, 147_LYS, 148_ASN, 155_SER, 158_ARG, 16_VAL, 17_ASN, 246_ARG, 249_LEU, 250_THR, 251_PRO, 252_GLY, 253_ASP, 77_LYS",3.0
+7LY3_A,7LY3_C_light+7LY3_D_heavy,"144_TYR, 145_TYR, 146_HIS, 147_LYS, 148_ASN, 155_SER, 158_ARG, 16_VAL, 17_ASN, 20_THR, 246_ARG, 249_LEU, 250_THR, 251_PRO, 252_GLY, 253_ASP",3.0
+8C0Y_A,8C0Y_D_light+8C0Y_B_heavy,"403_ARG, 405_ASP, 415_THR, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_ASN, 486_PHE, 487_ASN, 489_TYR, 493_ARG, 496_SER, 501_TYR, 505_HIS",2.68
+4NZT_M,4NZT_L_light+4NZT_H_heavy,"103_ASN, 106_SER, 107_GLU, 110_THR, 114_LYS, 119_PRO, 144_TYR, 158_TYR, 160_SER, 162_GLU, 163_TYR, 177_ASN, 179_THR, 180_LEU, 181_MET, 187_TYR, 188_ASP, 191_PHE, 192_ARG, 193_GLN, 338_TYR, 340_LEU, 341_PHE, 384_ARG, 389_TYR, 390_PHE, 391_ALA, 392_GLY, 393_GLY, 394_TYR, 396_ASP, 426_GLU, 427_GLU, 429_TYR, 438_ARG, 439_VAL, 440_ASN, 442_ASN, 444_TYR, 447_ALA, 449_ILE, 99_ARG",2.5
+6Q0E_A,6Q0E_L_light+6Q0E_H_heavy,"134_GLY, 135_VAL, 136_THR, 137_ALA, 145_SER, 153_TRP, 155_THR, 156_GLU, 158_ASN, 159_GLY, 183_HIS, 184_HIS, 185_PRO, 186_SER, 187_ASN, 189_ARG, 190_ASP, 193_ALA, 194_ILE, 196_HIS, 222_LYS, 225_GLY, 226_GLN, 227_GLU, 228_GLY, 95_TYR",2.15
+5HDB_A,5HDB_L_light+5HDB_H_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.7
+5HDB_C,5HDB_F_light+5HDB_E_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.7
+4M1G_B,4M1G_L_light+4M1G_H_heavy,"115_ASP, 116_TYR, 117_GLN, 118_MET, 121_ASP, 150_ASP, 173_GLN, 174_GLU, 175_VAL, 176_ARG, 177_LYS",1.6
+4M1G_A,4M1G_L_light+4M1G_H_heavy,"116_TYR, 149_GLU, 150_ASP, 160_THR, 161_LYS, 162_THR, 163_THR, 168_ASP, 169_SER, 170_ASP, 171_VAL, 172_SER, 175_VAL",1.6
+6YXD_A,6YXD_L_light+6YXD_H_heavy,"0_PHE, 100_GLU, 101_GLY, 102_ARG, 103_TRP, 104_ARG, 106_ILE, 107_PRO, 110_VAL, 123_HIS, 127_PRO, 128_PRO, 129_MET, 130_PRO, 131_SER, 133_ARG",2.9
+1EGJ_A,1EGJ_L_light+1EGJ_H_heavy,"362_LYS, 363_MET, 364_ARG, 365_TYR, 366_GLU, 367_HIS, 395_HIS, 416_THR, 418_ARG, 419_THR, 421_TYR",2.8
+4TSA_A,4TSA_L_light+4TSA_H_heavy,"102_GLY, 103_ASN, 104_GLY, 106_ASN, 109_VAL, 111_TRP, 112_ARG, 113_ASN, 116_LYS, 117_GLY, 21_ARG, 22_GLY, 23_TYR",2.27
+6YE3_I,6YE3_H_light+6YE3_G_heavy,"52_GLN, 55_GLU, 56_GLU, 58_LEU, 59_LYS, 62_GLU, 75_LEU, 76_ARG, 78_ARG, 79_ASP, 81_ILE, 82_SER, 83_ASN, 85_ASN, 86_VAL, 89_LEU, 96_THR, 98_PHE, 99_MET",2.89
+6YE3_F,6YE3_E_light+6YE3_D_heavy,"52_GLN, 55_GLU, 56_GLU, 58_LEU, 59_LYS, 62_GLU, 75_LEU, 78_ARG, 79_ASP, 81_ILE, 82_SER, 83_ASN, 85_ASN, 86_VAL, 89_LEU, 96_THR, 98_PHE, 99_MET",2.89
+6YE3_C,6YE3_B_light+6YE3_A_heavy,"52_GLN, 55_GLU, 56_GLU, 58_LEU, 59_LYS, 62_GLU, 75_LEU, 76_ARG, 78_ARG, 79_ASP, 81_ILE, 82_SER, 83_ASN, 85_ASN, 86_VAL, 89_LEU, 90_GLU, 96_THR, 98_PHE, 99_MET",2.89
+5W08_B,5W08_J_light+5W08_I_heavy,"131_THR, 133_ASN, 134_GLY, 135_THR, 136_SER, 145_ASN, 98_TYR",2.6
+5W08_D,5W08_R_light+5W08_Q_heavy,"131_THR, 133_ASN, 134_GLY, 135_THR, 136_SER, 145_ASN, 153_TRP, 155_THR, 156_HIS, 157_LEU, 158_ASN, 159_PHE, 160_LYS, 186_VAL, 190_ASP, 193_PHE, 194_LEU, 226_ILE, 227_PRO, 228_SER, 98_TYR",2.6
+5W08_E,5W08_P_light+5W08_O_heavy,"131_THR, 133_ASN, 134_GLY, 135_THR, 136_SER, 145_ASN, 153_TRP, 155_THR, 156_HIS, 157_LEU, 158_ASN, 159_PHE, 185_PRO, 186_VAL, 190_ASP, 193_PHE, 194_LEU, 227_PRO, 228_SER, 98_TYR",2.6
+5W08_F,5W08_N_light+5W08_M_heavy,"131_THR, 133_ASN, 134_GLY, 135_THR, 136_SER, 145_ASN, 153_TRP, 155_THR, 156_HIS, 157_LEU, 158_ASN, 159_PHE, 183_HIS, 185_PRO, 186_VAL, 190_ASP, 193_PHE, 194_LEU, 227_PRO, 228_SER, 98_TYR",2.6
+5W08_A,5W08_H_light+5W08_G_heavy,"131_THR, 133_ASN, 134_GLY, 135_THR, 136_SER, 145_ASN, 153_TRP, 155_THR, 156_HIS, 157_LEU, 158_ASN, 159_PHE, 160_LYS, 183_HIS, 185_PRO, 186_VAL, 190_ASP, 193_PHE, 194_LEU, 226_ILE, 227_PRO, 228_SER, 98_TYR",2.6
+5W08_C,5W08_L_light+5W08_K_heavy,"131_THR, 133_ASN, 134_GLY, 135_THR, 136_SER, 145_ASN, 153_TRP, 155_THR, 156_HIS, 157_LEU, 158_ASN, 159_PHE, 160_LYS, 185_PRO, 186_VAL, 190_ASP, 193_PHE, 194_LEU, 227_PRO, 228_SER, 98_TYR",2.6
+7MJL_A,7MJL_L_light+7MJL_H_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 501_TYR, 505_TYR",2.95
+7E3O_R,7E3O_L_light+7E3O_H_heavy,"351_TYR, 444_LYS, 445_VAL, 446_GLY, 449_TYR, 450_ASN, 452_LEU, 468_ILE, 470_THR, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER, 495_TYR",2.51
+6NFV_C,6NFV_B_light+6NFV_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 63_PRO, 64_ARG",2.13
+2XWT_C,2XWT_B_light+2XWT_A_heavy,"104_THR, 105_HIS, 107_GLU, 109_ARG, 110_ASN, 129_LYS, 130_PHE, 134_PHE, 151_ASP, 152_ILE, 153_PHE, 155_ILE, 157_GLU, 181_THR, 183_LYS, 203_ASP, 36_ASP, 38_ARG, 42_LYS, 55_GLN, 58_LYS, 60_ILE, 61_GLU, 80_ARG, 82_TYR, 84_SER, 85_ILE",1.9
+6BF4_A,6BF4_C_light+6BF4_B_heavy,"262_ASN, 290_LYS, 291_SER, 292_VAL, 293_GLU, 334_ASP, 337_GLU, 446_LEU, 448_ASN",2.38
+6NM6_G,6NM6_L_light+6NM6_H_heavy,"135_THR, 322_ILE, 323_ILE, 324_GLY, 325_ASP, 326_ILE, 327_ARG, 328_GLN, 330_HIS, 415_THR, 417_PRO",2.74
+6NM6_G,6NM6_V_light+6NM6_U_heavy,"198_THR, 275_GLU, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 283_ASN, 365_SER, 366_GLY, 367_GLY, 368_ASP, 370_GLU, 371_VAL, 428_GLN, 430_ILE, 455_THR, 456_ARG, 457_ASP, 458_GLY, 469_ARG, 473_GLY, 474_ASP, 475_MET",2.74
+2I60_G,2I60_L_light+2I60_H_heavy,"119_CYS, 120_VAL, 122_LEU, 202_THR, 203_GLN, 419_ARG, 420_ILE, 421_LYS, 422_GLN, 423_ILE, 434_MET, 437_PRO",2.4
+2I60_P,2I60_Q_light+2I60_R_heavy,"119_CYS, 120_VAL, 122_LEU, 200_VAL, 202_THR, 203_GLN, 204_ALA, 205_CYS, 419_ARG, 420_ILE, 421_LYS, 422_GLN, 423_ILE, 434_MET, 437_PRO",2.4
+5HDQ_A,5HDQ_L_light+5HDQ_H_heavy,"234_HIS, 243_LYS, 250_SER, 251_GLU, 252_GLU, 253_THR, 254_LYS, 255_LYS, 256_ASP, 257_ILE, 258_PHE, 259_GLY, 260_GLU, 272_LYS",1.83
+8ACF_A,8ACF_L_light+8ACF_H_heavy,"101_THR, 102_PRO, 103_ARG, 104_LEU, 105_GLY, 106_SER, 108_PRO, 112_ASN, 114_SER, 116_GLU, 128_VAL, 159_GLY, 161_VAL, 178_SER, 179_SER, 56_TYR, 85_LYS, 88_ARG, 90_PRO, 91_ALA, 92_PRO, 93_VAL, 99_ILE",1.8
+8VSJ_A,8VSJ_L_light+8VSJ_H_heavy,"142_LEU, 167_GLY, 168_LEU, 169_ASP, 172_LEU, 174_LYS, 78_THR, 79_PRO, 81_THR, 82_ASN, 83_VAL, 84_PRO, 85_PRO, 86_GLU, 87_VAL",2.28
+8TS0_A,8TS0_L_light+8TS0_H_heavy,"155_PRO, 156_VAL, 157_ASN, 167_TRP, 171_SER, 173_LYS, 174_ALA, 176_ALA, 177_ASP, 180_ASN, 181_TYR, 184_LEU, 185_GLU, 186_ASP, 275_TRP, 279_THR, 281_LEU",1.7
+6IEA_A,6IEA_L_light+6IEA_H_heavy,"120_PRO, 121_LYS, 138_SER, 139_LEU, 141_LYS, 18_GLY, 20_THR, 21_GLN, 22_GLU, 23_ASP, 24_ALA, 25_THR, 27_LYS, 28_PRO",2.0
+8VGM_A,8VGM_F_light+8VGM_E_heavy,"1657_GLU, 1658_ARG, 1659_PHE, 1660_PRO, 1661_GLU, 1662_TRP, 1665_THR, 1668_GLU, 1689_PRO, 1692_GLU, 1693_VAL",2.6
+8VGM_C,8VGM_L_light+8VGM_H_heavy,"1657_GLU, 1658_ARG, 1659_PHE, 1660_PRO, 1661_GLU, 1662_TRP, 1665_THR, 1668_GLU, 1689_PRO, 1692_GLU, 1693_VAL",2.6
+9IVK_A,9IVK_L_light+9IVK_H_heavy,"117_LYS, 121_THR, 124_ARG, 125_ALA, 128_LEU, 129_ASP, 131_GLN, 132_LYS, 156_ASP, 159_VAL, 160_GLY, 163_ASP, 166_LEU, 167_LYS, 170_ASN",2.74
+1E6J_P,1E6J_L_light+1E6J_H_heavy,"187_GLU, 204_ALA, 205_LEU, 206_GLY, 207_PRO, 208_ALA, 209_ALA, 210_THR, 212_GLU, 213_GLU, 216_THR, 217_ALA",3.0
+6MTO_T,6MTO_L_light+6MTO_H_heavy,"100_ALA, 101_GLY, 119_ASN, 120_TRP, 121_PHE, 122_ASP, 123_ILE, 124_THR, 125_LYS, 128_TRP, 166_PHE, 56_ARG, 60_PHE, 75_HIS, 76_THR",2.63
+1QFU_A,1QFU_L_light+1QFU_H_heavy,"143_PRO, 271_ASP, 272_ALA, 273_PRO, 274_ILE, 48_THR, 49_GLY, 50_LYS, 59_LEU, 60_ASP, 62_ILE, 63_ASP, 74_PRO, 75_HIS, 78_VAL, 79_PHE, 90_ARG, 92_LYS, 94_PHE",2.8
+4PS4_A,4PS4_L_light+4PS4_H_heavy,"104_LYS, 105_LYS, 107_PHE, 108_ARG, 109_GLU, 110_GLY, 11_ARG, 14_ILE, 15_GLU, 7_SER",2.8
+8JYR_A,8JYR_L_light+8JYR_H_heavy,"109_GLU, 110_ASP, 111_ASN, 112_TYR, 149_LEU, 150_LYS, 154_LEU, 156_GLN, 175_LYS, 176_ASN, 177_ASN, 178_GLN, 179_LEU, 180_ALA, 181_LEU, 182_THR, 183_LEU",1.69
+2JEL_P,2JEL_L_light+2JEL_H_heavy,"1_MET, 2_PHE, 34_THR, 3_GLN, 41_SER, 4_GLN, 64_SER, 66_GLU, 67_GLY, 68_GLU, 70_GLU, 71_GLN, 72_LYS, 75_GLU, 76_HIS",2.5
+6H3T_B,6H3T_M_light+6H3T_I_heavy,"573_ARG, 574_SER, 576_THR, 580_CYS, 581_ASN, 582_THR, 583_ASN, 584_PRO, 585_HIS, 590_ARG, 593_GLU, 610_SER, 611_ILE, 612_TYR, 614_ASP, 615_GLY, 616_HIS, 618_ASP, 619_ARG",2.84
+6H3T_A,6H3T_L_light+6H3T_H_heavy,"573_ARG, 576_THR, 580_CYS, 581_ASN, 582_THR, 583_ASN, 584_PRO, 585_HIS, 589_CYS, 590_ARG, 593_GLU, 610_SER, 611_ILE, 612_TYR, 614_ASP, 615_GLY, 616_HIS, 618_ASP, 619_ARG",2.84
+7TMZ_C,7TMZ_F_light+7TMZ_E_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.2
+7TMZ_A,7TMZ_L_light+7TMZ_H_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.2
+8SGO_D,8SGO_L_light+8SGO_K_heavy,"124_ASP, 164_ARG, 170_GLU, 171_TRP, 172_THR, 173_ARG, 174_GLU, 175_PRO, 176_ALA, 177_ARG, 196_GLN, 197_THR, 198_VAL, 199_ASP, 200_SER, 201_GLY, 202_ILE, 42_LYS, 71_LYS",2.65
+8SGO_B,8SGO_I_light+8SGO_J_heavy,"170_GLU, 171_TRP, 172_THR, 173_ARG, 174_GLU, 175_PRO, 176_ALA, 177_ARG, 196_GLN, 197_THR, 198_VAL, 199_ASP, 200_SER, 201_GLY, 202_ILE, 42_LYS, 71_LYS",2.65
+2VDN_A,2VDN_L_light+2VDN_H_heavy,"149_ASN, 154_ILE, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.9
+6C6Z_B,6C6Z_L_light+6C6Z_H_heavy,"502_LYS, 506_PHE, 510_ASP, 511_ARG, 513_GLU, 535_TRP, 536_GLU, 537_ASP, 538_GLY, 539_ASP, 540_TYR, 541_TYR, 542_ARG, 553_TRP, 555_VAL, 557_SER",2.1
+6C6Z_A,6C6Z_D_light+6C6Z_C_heavy,"502_LYS, 506_PHE, 510_ASP, 511_ARG, 513_GLU, 535_TRP, 536_GLU, 537_ASP, 538_GLY, 539_ASP, 540_TYR, 541_TYR, 542_ARG, 553_TRP, 555_VAL",2.1
+7UVH_C,7UVH_B_light+7UVH_A_heavy,"580_ASP, 581_LYS, 582_TYR, 583_ALA, 584_SER, 585_GLN, 594_ASP, 595_PHE, 596_THR, 597_ASP, 598_GLN, 600_LYS, 601_PRO, 602_THR, 609_LYS, 611_CYS, 612_GLU, 614_LYS, 632_VAL, 716_LYS",2.59
+6K0Y_C,6K0Y_B_light+6K0Y_A_heavy,"126_ILE, 128_LEU, 129_ALA, 130_PRO, 131_LYS, 132_ALA, 134_ILE, 61_GLU, 62_SER, 64_VAL, 83_PRO, 85_ASP, 86_ARG",1.7
+1NBZ_C,1NBZ_A_light+1NBZ_B_heavy,"615_HIS, 616_GLY, 618_ASP, 619_ASN, 620_TYR, 621_ARG, 663_TRP, 673_ARG, 675_LEU, 677_ASN, 689_THR, 693_ASN, 696_LYS, 697_ALA, 698_ILE, 700_SER, 701_ASP, 702_GLY, 703_ASN",1.85
+7BNV_A,7BNV_L_light+7BNV_H_heavy,"353_TRP, 355_ARG, 396_TYR, 457_ARG, 462_LYS, 463_PRO, 464_PHE, 465_GLU, 466_ARG, 467_ASP, 468_ILE, 516_GLU, 518_LEU",2.35
+7L0N_S,7L0N_D_light+7L0N_C_heavy,"331_ASN, 333_THR, 334_ASN, 335_LEU, 337_PRO, 339_GLY, 340_GLU, 343_ASN, 344_ALA, 345_THR, 346_ARG, 356_LYS, 357_ARG, 359_SER, 361_CYS, 440_ASN, 441_LEU, 509_ARG",2.78
+7L0N_S,7L0N_N_light+7L0N_M_heavy,"369_TYR, 370_ASN, 375_SER, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 390_LEU, 392_PHE, 412_PRO, 413_GLY, 427_ASP, 428_ASP, 429_PHE, 517_LEU",2.78
+7L0N_R,7L0N_B_light+7L0N_A_heavy,"331_ASN, 332_ILE, 333_THR, 334_ASN, 335_LEU, 337_PRO, 339_GLY, 340_GLU, 343_ASN, 344_ALA, 345_THR, 346_ARG, 356_LYS, 357_ARG, 359_SER, 361_CYS, 441_LEU",2.78
+7L0N_R,7L0N_L_light+7L0N_H_heavy,"369_TYR, 370_ASN, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 388_ASN, 390_LEU, 412_PRO, 427_ASP, 428_ASP, 429_PHE, 517_LEU",2.78
+4U6V_B,4U6V_M_light+4U6V_K_heavy,"173_ASN, 179_TRP, 181_PRO, 182_TYR, 183_ASP, 185_ASP, 186_SER, 187_TRP, 188_ASN, 189_PRO, 190_VAL, 191_TYR, 200_ARG, 263_THR, 264_ASN, 266_LYS",2.56
+4U6V_A,4U6V_L_light+4U6V_H_heavy,"173_ASN, 179_TRP, 181_PRO, 182_TYR, 183_ASP, 185_ASP, 186_SER, 187_TRP, 188_ASN, 189_PRO, 190_VAL, 191_TYR, 200_ARG, 263_THR, 264_ASN, 266_LYS",2.56
+6MG7_G,6MG7_L_light+6MG7_H_heavy,"219_THR, 220_PRO, 221_ALA, 244_SER, 246_GLN, 491_ILE, 495_GLY, 53_PHE, 75_VAL, 76_PRO, 78_ASP, 82_GLN, 84_ILE, 85_HIS, 87_GLU",2.91
+1IC5_Y,1IC5_L_light+1IC5_H_heavy,"100_SER, 101_ASP, 102_GLY, 15_HIS, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 63_TRP, 73_ARG, 75_LEU, 89_THR, 93_ASN, 96_LYS, 97_LYS, 98_ILE",2.3
+4HWB_A,4HWB_L_light+4HWB_H_heavy,"202_PRO, 203_ASP, 226_ASN, 229_SER, 230_ARG, 231_CYS, 232_LEU, 233_PHE, 235_GLU, 248_VAL, 250_TYR, 251_VAL, 252_GLN, 253_GLU, 289_LYS, 290_THR, 291_ASN, 292_LYS, 293_LEU, 294_CYS, 295_TYR, 296_GLU",2.61
+6APB_C,6APB_L_light+6APB_H_heavy,"312_PRO, 314_TRP, 31_GLU, 33_TYR, 344_ASP, 35_SER, 377_SER, 378_GLU, 380_ASN, 381_LEU, 383_ASN, 384_VAL, 390_LYS, 42_LYS",3.0
+3GBN_A,3GBN_L_light+3GBN_H_heavy,"291_SER, 292_LEU, 38_HIS, 40_VAL, 41_ASN, 42_LEU",2.2
+3GBN_B,3GBN_L_light+3GBN_H_heavy,"19_ASP, 20_GLY, 21_TRP, 38_GLN, 41_THR, 42_GLN, 45_ILE, 46_ASP, 49_THR, 52_VAL, 53_ASN, 56_ILE",2.2
+7WGT_A,7WGT_L_light+7WGT_H_heavy,"337_TYR, 498_TYR, 502_ARG, 505_GLU, 506_ASP, 508_ARG, 509_ASP, 510_MET, 511_ILE, 512_GLY, 513_PHE, 514_PRO, 599_ASP, 90_HIS",2.75
+4OII_B,4OII_M_light+4OII_I_heavy,"232_TRP, 235_GLY, 236_ILE, 237_LEU, 239_SER, 240_ASP, 253_ASN, 256_ARG, 257_ARG, 258_PRO, 260_TYR, 261_LYS, 262_THR, 264_ASN, 289_GLU, 293_HIS, 294_ARG, 314_ARG, 315_SER, 351_ASN",3.0
+4OII_A,4OII_L_light+4OII_H_heavy,"232_TRP, 235_GLY, 236_ILE, 237_LEU, 239_SER, 240_ASP, 253_ASN, 256_ARG, 257_ARG, 258_PRO, 260_TYR, 261_LYS, 262_THR, 264_ASN, 265_GLN, 289_GLU, 293_HIS, 294_ARG, 314_ARG, 315_SER, 351_ASN",3.0
+8Y6A_B,8Y6A_D_light+8Y6A_C_heavy,"334_ASN, 335_LEU, 337_PRO, 339_HIS, 340_GLU, 341_VAL, 343_ASN, 344_ALA, 345_THR, 346_ARG, 356_THR, 358_ILE, 359_SER, 440_LYS, 441_LEU",2.72
+6J15_C,6J15_B_light+6J15_A_heavy,"126_ILE, 127_SER, 128_LEU, 129_ALA, 130_PRO, 131_LYS, 132_ALA, 133_GLN, 134_ILE, 135_LYS, 32_TRP, 59_THR, 62_SER, 64_VAL",2.6
+6J15_D,6J15_L_light+6J15_H_heavy,"126_ILE, 127_SER, 128_LEU, 129_ALA, 130_PRO, 131_LYS, 132_ALA, 133_GLN, 134_ILE, 30_ILE, 32_TRP, 59_THR, 62_SER, 64_VAL",2.6
+6CW2_D,6CW2_B_light+6CW2_A_heavy,"168_LYS, 170_GLU, 198_ASP, 201_ARG, 202_ASN, 203_THR, 204_SER, 205_ASN, 207_LYS, 254_MET, 256_PRO, 257_ARG, 258_ILE, 259_ARG, 262_ASP, 265_LYS, 266_ILE, 269_LEU, 77_PHE, 78_GLU, 79_PHE, 80_ASP",2.67
+6CW2_C,6CW2_B_light+6CW2_A_heavy,"110_GLU, 112_LYS, 113_TYR, 114_TYR, 115_PRO, 116_ILE",2.67
+8G3P_D,8G3P_K_light+8G3P_G_heavy,"118_ARG, 149_VAL, 150_ARG, 151_ASP, 152_ARG, 178_TRP, 179_SER, 198_ASP, 199_LYS, 221_ASP, 222_ILE, 224_ARG, 227_GLU, 292_ARG, 294_ASN, 295_TRP, 296_LYS, 326_PRO, 344_GLU, 345_GLY, 346_GLY, 347_HIS, 371_ARG, 406_TYR, 431_LYS",2.5
+8G3P_E,8G3P_L_light+8G3P_H_heavy,"118_ARG, 149_VAL, 150_ARG, 151_ASP, 152_ARG, 178_TRP, 179_SER, 198_ASP, 199_LYS, 221_ASP, 222_ILE, 224_ARG, 227_GLU, 292_ARG, 294_ASN, 295_TRP, 296_LYS, 326_PRO, 344_GLU, 345_GLY, 346_GLY, 347_HIS, 371_ARG, 406_TYR, 431_LYS",2.5
+8G3P_A,8G3P_J_light+8G3P_F_heavy,"118_ARG, 149_VAL, 150_ARG, 151_ASP, 152_ARG, 178_TRP, 179_SER, 198_ASP, 199_LYS, 221_ASP, 222_ILE, 224_ARG, 227_GLU, 292_ARG, 294_ASN, 295_TRP, 296_LYS, 326_PRO, 344_GLU, 345_GLY, 346_GLY, 347_HIS, 371_ARG, 406_TYR, 431_LYS",2.5
+8G3P_B,8G3P_I_light+8G3P_C_heavy,"118_ARG, 149_VAL, 150_ARG, 151_ASP, 152_ARG, 178_TRP, 179_SER, 198_ASP, 199_LYS, 221_ASP, 222_ILE, 224_ARG, 227_GLU, 292_ARG, 294_ASN, 295_TRP, 296_LYS, 326_PRO, 344_GLU, 345_GLY, 346_GLY, 347_HIS, 371_ARG, 406_TYR, 431_LYS",2.5
+5OB5_A,5OB5_L_light+5OB5_H_heavy,"12_LEU, 13_GLN, 32_GLY, 33_PRO, 34_HIS, 35_CYS, 36_ALA, 37_GLN, 7_LEU, 8_ARG, 9_CYS",1.65
+1OAK_A,1OAK_L_light+1OAK_H_heavy,"627_PRO, 628_GLN, 629_ARG, 631_SER, 632_ARG, 633_ASN, 634_PHE, 635_VAL, 636_ARG, 658_ASN, 660_LYS, 661_GLN, 664_LEU",2.2
+7XCO_C,7XCO_P_light+7XCO_O_heavy,"334_ASN, 335_LEU, 337_PRO, 339_ASP, 340_GLU, 343_ASN, 344_ALA, 345_THR, 354_ASN, 356_LYS, 361_CYS, 441_LEU",2.5
+7XCO_B,7XCO_N_light+7XCO_M_heavy,"334_ASN, 335_LEU, 337_PRO, 339_ASP, 340_GLU, 341_VAL, 343_ASN, 344_ALA, 345_THR, 354_ASN, 356_LYS, 359_SER, 441_LEU",2.5
+7XCO_A,7XCO_L_light+7XCO_H_heavy,"334_ASN, 335_LEU, 337_PRO, 339_ASP, 340_GLU, 341_VAL, 343_ASN, 344_ALA, 345_THR, 354_ASN, 356_LYS, 359_SER, 360_ASN, 361_CYS, 441_LEU",2.5
+6A67_B,6A67_D_light+6A67_C_heavy,"126_GLU, 128_SER, 129_LEU, 131_VAL, 132_SER, 133_SER, 139_ARG, 140_LYS, 141_SER, 149_TRP, 151_ILE, 152_LYS, 153_LYS, 154_ASN, 155_SER, 186_GLU, 189_LYS, 190_LEU",2.33
+6A67_A,6A67_L_light+6A67_H_heavy,"126_GLU, 128_SER, 129_LEU, 131_VAL, 132_SER, 133_SER, 139_ARG, 140_LYS, 141_SER, 149_TRP, 151_ILE, 152_LYS, 153_LYS, 154_ASN, 155_SER, 185_ALA, 186_GLU, 189_LYS, 190_LEU, 218_LYS",2.33
+6S5A_D,6S5A_L_light+6S5A_H_heavy,"236_GLY, 237_GLY, 238_PRO, 267_SER, 268_HIS, 269_GLU, 270_ASP, 271_PRO, 272_GLU, 322_LYS, 325_ASN, 326_LYS, 327_ALA, 328_LEU, 329_GLY, 330_ALA, 331_PRO",1.72
+6S5A_A,6S5A_L_light+6S5A_H_heavy,"232_PRO, 233_GLU, 234_ALA, 235_ALA, 236_GLY, 269_GLU",1.72
+7PI7_D,7PI7_F_light+7PI7_E_heavy,"124_PHE, 125_SER, 160_LEU, 184_PRO, 185_TYR, 187_PHE, 188_LYS, 189_ASP, 219_TYR, 220_LYS, 221_LEU, 222_GLY, 224_GLN, 244_GLY, 245_ASP, 246_ASN, 247_ILE, 248_ASN, 252_ASN, 274_LEU",2.72
+7PI7_A,7PI7_C_light+7PI7_B_heavy,"124_PHE, 125_SER, 160_LEU, 184_PRO, 185_TYR, 187_PHE, 188_LYS, 189_ASP, 219_TYR, 220_LYS, 221_LEU, 222_GLY, 223_VAL, 244_GLY, 245_ASP, 246_ASN, 247_ILE, 248_ASN, 250_VAL, 252_ASN",2.72
+5MO9_X,5MO9_L_light+5MO9_H_heavy,"316_GLN, 318_PHE, 320_ASN, 321_GLY, 322_ALA, 323_ILE, 325_ASN, 358_ASP, 360_THR, 362_ILE, 364_LYS, 369_LYS, 370_ASP, 371_GLU, 372_LYS, 373_GLN",2.59
+8TLM_C,8TLM_B_light+8TLM_A_heavy,"103_THR, 173_GLN, 174_VAL, 175_ALA, 176_SER, 177_GLU, 178_ASP, 179_GLY, 181_LEU, 187_TYR, 188_ASN, 189_GLN, 190_GLN, 98_GLN",2.9
+7LJB_B,7LJB_F_light+7LJB_G_heavy,"70_GLU, 73_LEU, 74_ARG, 76_HIS, 77_PRO, 78_CYS, 79_VAL, 80_SER, 81_ASP, 82_GLN, 83_GLU",2.97
+7LJB_A,7LJB_D_light+7LJB_E_heavy,"69_ARG, 70_GLU, 73_LEU, 74_ARG, 76_HIS, 77_PRO, 78_CYS, 79_VAL, 80_SER, 81_ASP, 82_GLN, 83_GLU",2.97
+3R1G_B,3R1G_L_light+3R1G_H_heavy,"314_SER, 316_GLU, 317_LYS, 318_PHE, 319_PRO, 327_GLN, 328_LEU, 329_VAL, 330_CYS, 331_TRP, 332_GLN, 333_ALA, 335_THR, 337_PRO, 340_ILE, 375_THR, 378_ASP, 380_CYS, 426_PHE",2.8
+8VQD_A,8VQD_B_light+8VQD_C_heavy,"105_THR, 106_GLN, 258_PHE, 267_HIS, 274_PHE, 27_THR, 290_THR, 291_PHE, 292_GLY, 303_TYR, 306_THR, 307_ASP, 308_VAL, 310_PHE, 311_CYS, 312_THR, 313_LEU, 314_VAL, 316_PRO, 317_LEU, 318_HIS, 333_LYS, 334_CYS, 335_SER, 336_LYS, 337_PRO, 343_TYR, 348_GLU, 349_HIS, 351_ARG, 352_GLU, 434_ARG, 436_LEU, 439_GLY, 57_GLN, 81_GLN",2.61
+8KER_C,8KER_H_light+8KER_I_heavy,"366_SER, 369_TYR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 383_SER, 384_PRO, 385_THR, 386_LYS, 390_LEU, 412_PRO, 414_GLN, 427_ASP, 428_ASP, 429_PHE",2.95
+8KER_B,8KER_F_light+8KER_G_heavy,"366_SER, 369_TYR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 383_SER, 384_PRO, 385_THR, 386_LYS, 390_LEU, 412_PRO, 413_GLY, 414_GLN, 427_ASP, 428_ASP, 429_PHE, 531_THR",2.95
+8KER_A,8KER_D_light+8KER_E_heavy,"366_SER, 369_TYR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 383_SER, 384_PRO, 385_THR, 386_LYS, 390_LEU, 412_PRO, 414_GLN, 427_ASP, 428_ASP, 429_PHE",2.95
+8TQ8_C,8TQ8_G_light+8TQ8_F_heavy,"104_GLU, 106_ASP, 107_GLY, 108_ARG, 109_LEU, 110_LEU, 128_GLU, 129_ASP, 130_LEU, 131_LYS, 132_THR, 157_ARG, 161_GLU, 162_GLY, 165_VAL, 169_ARG",2.69
+8TQ8_A,8TQ8_L_light+8TQ8_H_heavy,"104_GLU, 106_ASP, 107_GLY, 108_ARG, 109_LEU, 110_LEU, 111_ARG, 128_GLU, 129_ASP, 131_LYS, 132_THR, 161_GLU, 169_ARG",2.69
+6MUF_G,6MUF_L_light+6MUF_H_heavy,"322_ILE, 323_ILE, 324_GLY, 325_ASN, 326_ILE, 327_ARG, 328_GLN, 330_HIS, 415_THR, 417_GLN",2.91
+9GWT_P,9GWT_L_light+9GWT_H_heavy,"111_PRO, 112_ASP, 113_GLN, 114_ASN, 115_SER, 136_THR, 137_PRO, 138_ASP, 141_PHE, 37_ASN, 38_TYR, 40_LYS, 41_VAL, 63_PRO, 64_ILE, 65_ALA, 66_LEU, 67_ARG, 68_ASN, 69_LEU",2.89
+6JBT_F,6JBT_L_light+6JBT_H_heavy,"107_ILE, 108_SER, 109_LEU, 110_ALA, 111_PRO, 112_LYS, 113_ALA, 114_GLN, 115_ILE, 12_PRO, 13_TRP, 40_THR, 43_SER, 45_VAL, 47_ASN",2.47
+8SDF_Z,8SDF_L_light+8SDF_H_heavy,"355_ARG, 356_LYS, 357_ARG, 394_ASN, 396_TYR, 426_PRO, 428_ASP, 462_LYS, 463_PRO, 464_PHE, 465_GLU, 466_ARG, 468_ILE, 469_SER, 470_THR, 471_GLU, 514_SER, 516_GLU, 517_LEU, 518_LEU",1.79
+8SDF_A,8SDF_C_light+8SDF_B_heavy,"355_ARG, 356_LYS, 357_ARG, 394_ASN, 396_TYR, 426_PRO, 428_ASP, 462_LYS, 463_PRO, 464_PHE, 465_GLU, 466_ARG, 468_ILE, 469_SER, 470_THR, 471_GLU, 514_SER, 516_GLU, 518_LEU",1.79
+7UVF_B,7UVF_Y_light+7UVF_X_heavy,"113_PRO, 114_ALA, 115_LYS, 116_PRO, 13_ASN, 14_LEU, 15_GLY, 40_ARG, 83_ASP, 85_ARG, 86_ARG, 87_GLU, 89_GLU",2.6
+7UVF_A,7UVF_L_light+7UVF_H_heavy,"112_LEU, 113_PRO, 114_ALA, 115_LYS, 116_PRO, 117_THR, 13_ASN, 14_LEU, 15_GLY, 85_ARG, 86_ARG, 87_GLU",2.6
+2DWE_C,2DWE_B_light+2DWE_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.5
+8Y0Q_2,8Y0Q_L_light+8Y0Q_H_heavy,"134_LYS, 189_VAL, 195_PRO, 196_GLN, 198_LYS, 65_HIS, 66_LEU, 67_PHE, 68_ASP, 70_GLY, 71_THR, 72_ASN, 74_SER, 75_PHE, 76_GLY, 77_ARG, 79_HIS",2.44
+4OGY_A,4OGY_L_light+4OGY_H_heavy,"410_VAL, 412_LEU, 414_ALA, 415_GLN, 416_ARG, 418_LEU, 419_CYS, 434_HIS, 435_CYS, 437_ASP, 438_GLY, 475_TYR, 477_VAL, 478_SER, 479_GLU, 480_GLY, 483_ASP, 527_GLU, 528_LYS, 554_ASP, 555_TYR, 572_ASP, 573_ALA, 574_CYS, 575_LYS, 578_SER, 597_SER, 598_TRP, 599_GLY, 600_GLU, 601_GLY, 602_CYS, 604_ARG, 609_GLY",2.1
+4OGY_B,4OGY_N_light+4OGY_M_heavy,"434_HIS, 475_TYR, 478_SER, 479_GLU, 480_GLY, 483_ASP, 527_GLU, 528_LYS, 553_GLN, 554_ASP, 555_TYR, 572_ASP, 573_ALA, 574_CYS, 575_LYS, 597_SER, 598_TRP, 599_GLY, 600_GLU, 601_GLY, 602_CYS, 604_ARG, 609_GLY",2.1
+1NCA_N,1NCA_L_light+1NCA_H_heavy,"326_PRO, 327_ARG, 328_PRO, 329_ASN, 343_GLY, 344_ASN, 345_ASN, 347_ASN, 366_ILE, 367_SER, 368_ILE, 369_ALA, 370_SER, 372_SER, 399_LEU, 400_ASN, 401_THR, 402_ASP, 403_TRP, 431_PRO, 432_LYS",2.5
+4LVO_A,4LVO_B_light+4LVO_C_heavy,"363_VAL, 364_MET, 365_SER, 387_GLU, 390_LEU, 391_LYS, 393_LEU, 394_HIS, 395_GLY, 396_ARG, 397_LYS, 401_ASP, 408_ASP, 410_ILE, 454_LYS",2.26
+8SID_D,8SID_L_light+8SID_K_heavy,"124_ASP, 164_ARG, 170_GLU, 171_TRP, 172_THR, 173_ARG, 174_GLU, 175_PRO, 176_ALA, 177_ARG, 196_GLN, 197_THR, 198_VAL, 199_ASP, 200_SER, 201_GLY, 202_ILE, 42_LYS, 71_LYS",2.71
+8SID_B,8SID_I_light+8SID_J_heavy,"170_GLU, 171_TRP, 172_THR, 173_ARG, 174_GLU, 175_PRO, 176_ALA, 177_ARG, 196_GLN, 197_THR, 198_VAL, 199_ASP, 200_SER, 201_GLY, 202_ILE, 42_LYS, 71_LYS",2.71
+7UL0_A,7UL0_L_light+7UL0_H_heavy,"475_ALA, 477_SER, 478_THR, 479_PRO, 480_CYS, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 488_CYS, 489_TYR",2.49
+2DQD_Y,2DQD_L_light+2DQD_H_heavy,"100_SER, 101_ASP, 102_GLY, 103_ASN, 14_ARG, 15_HIS, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 62_TRP, 63_TRP, 73_ARG, 74_ASN, 75_LEU, 77_ASN, 89_THR, 93_ASN, 96_LYS, 97_LYS, 98_ILE",1.8
+1SY6_A,1SY6_L_light+1SY6_H_heavy,"139_GLY, 140_SER, 141_GLU, 153_GLY, 155_GLU, 186_ARG, 187_GLY, 188_SER, 189_LYS, 190_PRO, 192_ASP",2.1
+2VXT_I,2VXT_L_light+2VXT_H_heavy,"140_ARG, 143_PRO, 144_GLY, 145_HIS, 148_LYS, 152_GLU, 157_GLU, 160_PHE, 176_LYS, 177_GLU, 178_ASP, 179_GLU, 180_LEU, 181_GLY, 182_ASP, 183_ARG, 186_MET, 51_LEU, 93_PRO",1.49
+1BGX_T,1BGX_L_light+1BGX_H_heavy,"313_ARG, 314_LYS, 315_GLU, 352_LEU, 382_PRO, 383_SER, 385_THR, 386_THR, 388_GLU, 392_ARG, 397_GLU, 398_TRP, 399_THR, 400_GLU, 460_SER, 464_ALA, 467_ILE, 471_GLU, 475_PHE, 481_PRO, 482_PHE, 483_ASN, 484_LEU, 485_ASN, 486_SER, 506_THR, 507_GLU, 511_LYS, 512_ARG, 515_SER, 516_ALA, 532_ILE, 535_TYR, 536_ARG, 539_THR, 541_LEU, 542_LYS, 543_SER, 544_THR, 545_TYR, 546_ILE, 547_ASP, 548_PRO, 565_ASN, 568_ALA, 569_THR, 570_ALA, 578_ASP, 580_ASN, 583_ASN, 728_ARG, 732_ASP",2.3
+2J88_A,2J88_L_light+2J88_H_heavy,"138_ARG, 140_GLU, 142_PRO, 143_PHE, 144_TRP, 145_ASP, 146_ASP, 147_GLN, 148_ARG, 149_VAL",2.6
+6XE1_E,6XE1_L_light+6XE1_H_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 502_GLY, 505_TYR",2.75
+6B0G_E,6B0G_C_light+6B0G_D_heavy,"115_ASN, 13_PHE, 148_LYS, 149_ALA, 150_VAL, 151_ASP, 152_GLY, 153_ILE, 155_LYS, 157_ASP, 17_MET, 22_GLU, 24_LYS, 25_CYS, 26_GLU, 27_ASN, 40_LYS, 81_ASN, 82_ASN, 83_VAL, 95_GLY, 98_LYS",1.9
+4TSC_A,4TSC_L_light+4TSC_H_heavy,"102_GLY, 103_ASN, 104_GLY, 106_ASN, 109_VAL, 111_TRP, 112_ARG, 113_ASN, 114_ARG, 116_LYS, 117_GLY, 118_THR, 21_ARG, 22_GLY, 23_TYR",1.92
+6TYB_G,6TYB_L_light+6TYB_H_heavy,"232_ASN, 234_SER, 236_PHE, 238_PRO, 240_SER, 241_LYS, 274_TRP, 350_VAL, 351_LYS, 352_HIS, 353_PRO, 354_ARG, 355_TYR, 356_THR, 88_ASN, 90_THR",2.3
+2NLJ_C,2NLJ_A_light+2NLJ_B_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.52
+8VZO_A,8VZO_D_light+8VZO_B_heavy,"142_SER, 143_ALA, 144_PHE, 147_ASP, 148_TRP, 151_MET, 154_MET, 348_TYR, 353_LEU, 356_ARG, 359_ILE, 360_LEU, 361_HIS, 363_PRO, 420_ASN, 422_ASN, 474_ILE, 477_THR, 478_ILE, 481_GLY, 482_GLN, 485_ASP, 486_ASN, 487_HIS",2.49
+3FB6_C,3FB6_B_light+3FB6_A_heavy,"45_TYR, 49_LEU, 50_ALA, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 63_PRO, 64_ARG",3.0
+7RAH_E,7RAH_A_light+7RAH_B_heavy,"1227_TYR, 1232_GLU, 1234_ASP, 1235_LEU, 1236_SER, 1237_ARG, 1238_GLU, 1239_TRP, 1241_ARG, 1244_SER, 1249_ASP, 1251_TYR, 1253_ASN, 1268_VAL, 1270_ILE, 1272_ASP, 1274_GLN, 1288_ARG",2.6
+7RAH_E,7RAH_C_light+7RAH_D_heavy,"1074_TRP, 1075_SER, 1097_GLU, 1100_GLU, 1101_ARG, 1117_GLU, 1121_ALA, 1122_LEU, 1123_ASN, 1124_LEU, 1125_PHE, 1127_VAL, 1129_HIS",2.6
+5W3M_B,5W3M_G_light+5W3M_E_heavy,"202_THR, 203_GLY, 204_GLN, 206_TYR, 58_THR, 59_HIS, 60_THR, 74_ASN, 75_ARG, 76_GLN, 77_ASN, 78_GLU",2.26
+4BZ2_A,4BZ2_L_light+4BZ2_H_heavy,"308_ILE, 309_ASP, 310_LYS, 311_GLU, 312_MET, 313_ALA, 314_GLU, 315_THR, 316_GLN, 323_LYS, 344_LYS, 364_VAL, 377_TYR, 390_HIS, 391_TRP, 392_PHE, 393_ARG, 394_LYS, 396_HIS",2.03
+3BN9_A,3BN9_E_light+3BN9_F_heavy,"146_TYR, 147_GLY, 173_PRO, 174_GLN, 175_GLN, 190_SER, 191_CYS, 192_GLN, 215_TRP, 216_GLY, 217_ASP, 219_GLY, 220_CYS, 221_ALA, 57_HIS, 60_ILE, 95_ASN, 96_ASP, 97_PHE, 98_THR, 99_PHE",2.17
+3BN9_B,3BN9_C_light+3BN9_D_heavy,"146_TYR, 147_GLY, 173_PRO, 174_GLN, 175_GLN, 190_SER, 191_CYS, 192_GLN, 195_SER, 215_TRP, 216_GLY, 217_ASP, 219_GLY, 220_CYS, 221_ALA, 57_HIS, 60_ILE, 95_ASN, 96_ASP, 97_PHE, 98_THR, 99_PHE",2.17
+7YCK_A,7YCK_D_light+7YCK_C_heavy,"368_LEU, 369_TYR, 370_ASN, 371_SER, 372_ALA, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 383_SER, 384_PRO, 385_THR, 387_LEU, 388_ASN, 528_LYS",2.6
+7YCK_B,7YCK_F_light+7YCK_E_heavy,"368_LEU, 369_TYR, 370_ASN, 371_SER, 372_ALA, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 382_VAL, 383_SER, 384_PRO, 385_THR, 388_ASN, 528_LYS",2.6
+7YAD_E,7YAD_D_light+7YAD_C_heavy,"334_ASN, 335_LEU, 337_PRO, 339_ASP, 340_GLU, 343_ASN, 344_ALA, 345_THR, 346_ARG, 356_LYS, 357_ARG, 361_CYS, 441_LEU",2.66
+7YAD_M,7YAD_B_light+7YAD_A_heavy,"334_ASN, 335_LEU, 337_PRO, 339_ASP, 340_GLU, 343_ASN, 344_ALA, 345_THR, 346_ARG, 354_ASN, 356_LYS, 359_SER, 361_CYS, 441_LEU",2.66
+4JB9_G,4JB9_L_light+4JB9_H_heavy,"123_THR, 124_GLY, 198_GLY, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 370_GLU, 371_ILE, 425_ASN, 426_MET, 427_TRP, 429_GLY, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_ALA, 461_ASN, 465_ASN, 466_GLU, 467_THR, 469_ARG, 473_GLY, 94_ASN, 97_LYS",2.6
+7LUE_A,7LUE_L_light+7LUE_H_heavy,"172_LEU, 173_SER, 174_THR, 175_ASN, 176_LYS, 178_VAL, 180_SER, 186_SER, 188_LEU, 191_LYS, 192_VAL, 193_LEU, 194_ASP, 197_ASN, 198_TYR, 202_GLN, 262_ASN, 263_ASP, 265_PRO, 271_LYS, 53_TYR",2.9
+7LUE_B,7LUE_M_light+7LUE_I_heavy,"172_LEU, 173_SER, 174_THR, 175_ASN, 176_LYS, 178_VAL, 180_SER, 186_SER, 188_LEU, 191_LYS, 192_VAL, 193_LEU, 194_ASP, 197_ASN, 198_TYR, 202_GLN, 262_ASN, 263_ASP, 265_PRO, 271_LYS, 53_TYR",2.9
+7LUE_C,7LUE_N_light+7LUE_J_heavy,"172_LEU, 173_SER, 174_THR, 175_ASN, 176_LYS, 178_VAL, 180_SER, 186_SER, 188_LEU, 191_LYS, 192_VAL, 193_LEU, 194_ASP, 197_ASN, 198_TYR, 202_GLN, 262_ASN, 263_ASP, 265_PRO, 271_LYS, 53_TYR",2.9
+4LSR_G,4LSR_L_light+4LSR_H_heavy,"276_ASN, 278_SER, 279_ASP, 280_ASN, 281_ALA, 354_ASN, 357_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 371_ILE, 432_GLN, 455_THR, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_ASN, 461_THR, 463_ASN, 465_SER, 466_ARG, 469_GLU, 476_GLY",2.28
+8SXP_C,8SXP_L_light+8SXP_H_heavy,"28_ASP, 29_GLY, 30_PHE, 34_ARG, 35_GLY, 36_LYS, 37_ARG, 38_ILE, 66_PHE, 6_THR, 7_PRO, 8_ASP, 9_ALA",2.9
+2XRA_A,2XRA_L_light+2XRA_H_heavy,"1643_HIS, 4570_VAL, 4573_ILE, 4574_LYS, 4577_GLN, 564_HIS, 565_LEU, 567_GLN, 568_LEU, 569_THR, 571_TRP, 572_GLY, 574_LYS, 575_GLN, 578_ALA, 579_ARG",2.3
+4A6Y_A,4A6Y_L_light+4A6Y_H_heavy,"131_ASN, 160_GLN, 186_ARG, 189_GLU, 190_ARG",2.9
+8C3V_X,8C3V_B_light+8C3V_A_heavy,"345_THR, 346_ARG, 351_TYR, 352_ALA, 440_ASN, 441_LEU, 443_SER, 444_LYS, 445_VAL, 446_GLY, 448_ASN, 449_TYR, 450_ASN, 452_ARG, 468_ILE, 472_ILE, 484_GLU, 490_PHE, 492_LEU, 493_GLN, 494_SER, 499_PRO",2.74
+8C3V_Y,8C3V_D_light+8C3V_C_heavy,"345_THR, 346_ARG, 351_TYR, 352_ALA, 440_ASN, 441_LEU, 443_SER, 444_LYS, 445_VAL, 446_GLY, 448_ASN, 449_TYR, 450_ASN, 452_ARG, 472_ILE, 484_GLU, 490_PHE, 493_GLN, 494_SER, 499_PRO",2.74
+8C3V_R,8C3V_L_light+8C3V_H_heavy,"345_THR, 346_ARG, 351_TYR, 352_ALA, 440_ASN, 441_LEU, 443_SER, 444_LYS, 445_VAL, 446_GLY, 449_TYR, 450_ASN, 452_ARG, 468_ILE, 472_ILE, 482_GLY, 484_GLU, 490_PHE, 493_GLN, 494_SER, 499_PRO",2.74
+5CBA_E,5CBA_B_light+5CBA_A_heavy,"18_PHE, 21_ARG, 22_ARG, 24_ILE, 25_ASP, 26_ARG, 27_ILE, 28_GLN, 46_LYS, 48_LYS, 59_TRP, 62_ARG, 63_MET, 66_VAL",2.5
+5CBA_F,5CBA_D_light+5CBA_C_heavy,"18_PHE, 21_ARG, 22_ARG, 24_ILE, 25_ASP, 26_ARG, 27_ILE, 28_GLN, 46_LYS, 59_TRP, 62_ARG, 63_MET, 66_VAL, 67_LEU",2.5
+6HXW_A,6HXW_L_light+6HXW_H_heavy,"103_HIS, 129_GLU, 133_LYS, 134_GLU, 135_ALA, 136_LYS, 137_PHE, 138_PRO, 153_GLN, 155_SER, 156_GLY, 157_LEU, 159_LEU, 162_LYS, 163_VAL, 164_LEU, 165_PRO, 170_VAL, 210_LEU, 323_ASP, 326_LYS, 327_TRP",2.78
+6HXW_B,6HXW_D_light+6HXW_C_heavy,"103_HIS, 129_GLU, 133_LYS, 134_GLU, 135_ALA, 136_LYS, 137_PHE, 138_PRO, 153_GLN, 155_SER, 156_GLY, 157_LEU, 162_LYS, 163_VAL, 164_LEU, 165_PRO, 170_VAL, 210_LEU, 323_ASP, 326_LYS, 327_TRP",2.78
+8W83_D,8W83_B_light+8W83_A_heavy,"1060_TYR, 1064_GLN, 1066_ASP, 1067_ILE, 1070_ARG, 1071_LYS, 1074_ALA, 1077_ARG, 1078_VAL, 2_PHE, 3_PRO, 5_PRO, 6_GLU, 7_LEU, 8_PRO",2.82
+8W83_H,8W83_F_light+8W83_E_heavy,"1060_TYR, 1064_GLN, 1066_ASP, 1070_ARG, 1071_LYS, 1074_ALA, 1077_ARG, 1078_VAL, 10_PRO, 2_PHE, 3_PRO, 5_PRO, 6_GLU, 7_LEU, 8_PRO",2.82
+8W83_L,8W83_J_light+8W83_I_heavy,"1064_GLN, 1066_ASP, 1067_ILE, 1070_ARG, 1071_LYS, 1074_ALA, 1077_ARG, 1078_VAL, 2_PHE, 3_PRO, 5_PRO, 6_GLU, 7_LEU, 8_PRO",2.82
+8W83_O,8W83_N_light+8W83_M_heavy,"55_ARG, 57_ASP, 59_GLN, 60_PHE, 63_THR, 64_ASN, 66_ALA, 67_VAL",2.82
+8W83_G,8W83_F_light+8W83_E_heavy,"57_ASP, 59_GLN, 60_PHE, 63_THR, 64_ASN, 66_ALA, 67_VAL",2.82
+8W83_P,8W83_N_light+8W83_M_heavy,"1060_TYR, 1064_GLN, 1066_ASP, 1067_ILE, 1070_ARG, 1071_LYS, 1074_ALA, 1077_ARG, 1078_VAL, 2_PHE, 3_PRO, 4_GLN, 5_PRO, 6_GLU, 7_LEU, 8_PRO",2.82
+8W83_C,8W83_B_light+8W83_A_heavy,"57_ASP, 59_GLN, 60_PHE, 63_THR, 66_ALA, 67_VAL",2.82
+8W83_K,8W83_J_light+8W83_I_heavy,"57_ASP, 59_GLN, 60_PHE, 62_LEU, 63_THR, 66_ALA, 67_VAL, 69_LYS",2.82
+2YBR_I,2YBR_H_light+2YBR_G_heavy,"14_TYR, 15_GLU, 16_CYS, 17_LEU, 18_LYS, 19_LEU, 22_ASN, 24_TYR, 27_ARG, 42_TYR, 43_ALA, 44_PHE, 45_ALA, 66_SER, 7_ASP",2.55
+2YBR_F,2YBR_E_light+2YBR_D_heavy,"14_TYR, 15_GLU, 16_CYS, 17_LEU, 18_LYS, 19_LEU, 22_ASN, 24_TYR, 27_ARG, 42_TYR, 43_ALA, 44_PHE, 45_ALA, 7_ASP",2.55
+2YBR_C,2YBR_B_light+2YBR_A_heavy,"14_TYR, 15_GLU, 16_CYS, 17_LEU, 18_LYS, 19_LEU, 22_ASN, 24_TYR, 27_ARG, 42_TYR, 43_ALA, 44_PHE, 45_ALA, 7_ASP",2.55
+7WPH_A,7WPH_D_light+7WPH_E_heavy,"345_THR, 346_ARG, 440_ASN, 441_LEU, 443_SER, 444_LYS, 445_VAL, 446_GLY, 447_GLY, 448_ASN, 449_TYR, 450_ASN",2.89
+7WPH_B,7WPH_L_light+7WPH_H_heavy,"345_THR, 346_ARG, 440_ASN, 441_LEU, 443_SER, 444_LYS, 445_VAL, 446_GLY, 447_GLY, 448_ASN, 449_TYR, 450_ASN",2.89
+6E63_P,6E63_L_light+6E63_H_heavy,"321_ASP, 322_SER, 345_PRO, 346_GLY, 347_ASP, 349_ILE, 350_PRO, 351_ASP, 354_PHE, 355_GLN, 369_ILE, 371_TYR, 392_LYS, 394_LYS, 411_ILE, 413_LYS, 414_LYS, 415_ASP, 416_LYS",2.6
+6E63_A,6E63_C_light+6E63_B_heavy,"345_PRO, 346_GLY, 347_ASP, 349_ILE, 350_PRO, 351_ASP, 354_PHE, 355_GLN, 369_ILE, 371_TYR, 392_LYS, 394_LYS, 413_LYS, 414_LYS, 415_ASP, 416_LYS",2.6
+7TRH_G,7TRH_B_light+7TRH_H_heavy,"133_ASP, 135_VAL, 137_ALA, 140_PRO, 142_ALA, 143_GLY, 144_ALA, 145_LYS, 153_TRP, 155_VAL, 183_HIS, 190_ASP, 193_SER, 194_LEU, 226_ARG, 98_TYR",3.0
+4OGX_A,4OGX_L_light+4OGX_H_heavy,"412_LEU, 416_ARG, 418_LEU, 419_CYS, 434_HIS, 435_CYS, 437_ASP, 438_GLY, 439_LEU, 475_TYR, 477_VAL, 478_SER, 479_GLU, 480_GLY, 483_ASP, 527_GLU, 528_LYS, 554_ASP, 555_TYR, 572_ASP, 573_ALA, 574_CYS, 575_LYS, 578_SER, 597_SER, 598_TRP, 599_GLY, 600_GLU, 601_GLY, 602_CYS, 604_ARG, 609_GLY",2.4
+7RFB_D,7RFB_L_light+7RFB_H_heavy,"420_TRP, 422_ILE, 425_THR, 427_LEU, 429_CYS, 438_LEU, 439_ALA, 440_ALA, 441_LEU, 442_PHE, 443_TYR, 444_THR, 445_HIS, 446_LYS, 447_PHE, 507_TYR, 529_TRP, 612_PRO, 613_TYR",2.7
+7RFB_C,7RFB_B_light+7RFB_A_heavy,"420_TRP, 422_ILE, 425_THR, 427_LEU, 429_CYS, 438_LEU, 439_ALA, 440_ALA, 441_LEU, 442_PHE, 443_TYR, 444_THR, 445_HIS, 446_LYS, 447_PHE, 507_TYR, 612_PRO, 613_TYR",2.7
+6OGX_G,6OGX_D_light+6OGX_C_heavy,"114_GLN, 115_PRO, 116_LEU, 117_ASP, 118_SER, 119_TYR, 124_ASP, 125_CYS, 126_ALA, 127_PRO, 128_CYS, 129_PRO, 130_PRO, 132_HIS, 140_ALA, 141_CYS, 142_LYS, 143_PRO, 91_SER",2.77
+6OGX_G,6OGX_L_light+6OGX_H_heavy,"68_GLY, 69_PRO, 70_GLY, 71_PHE, 83_PRO, 86_TRP, 87_CYS, 88_ASN, 89_LEU, 90_ARG, 95_ARG, 96_LYS, 97_GLN, 98_LEU",2.77
+7F7E_E,7F7E_L_light+7F7E_C_heavy,"343_ASN, 345_THR, 346_ARG, 439_ASN, 440_ASN, 441_LEU, 442_ASP, 443_SER, 444_LYS, 445_VAL, 448_ASN, 451_TYR, 499_PRO, 500_THR, 509_ARG",2.49
+7MDJ_C,7MDJ_B_light+7MDJ_A_heavy,"45_TYR, 48_VAL, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 63_PRO, 64_ARG",2.75
+4YE4_G,4YE4_L_light+4YE4_H_heavy,"278_THR, 280_ASN, 281_ALA, 282_LYS, 283_THR, 362_GLN, 365_SER, 366_GLY, 455_THR, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_ASN, 461_ASN, 462_ASN, 463_LYS, 471_ARG, 473_GLY, 474_GLY",2.72
+7CE2_A,7CE2_B_light+7CE2_Z_heavy,"1219_ASN, 1220_ASN, 1221_LEU, 1222_ASP, 1270_THR, 1271_HIS, 1272_ASN, 1285_ILE, 1288_ASN, 1289_TRP, 1292_ASN, 1293_HIS, 1296_ASP, 1297_LYS",2.01
+8OXW_A,8OXW_C_light+8OXW_B_heavy,"242_THR, 243_GLY, 244_GLY, 245_ARG, 255_GLU, 258_LYS, 259_ASN, 261_LYS, 262_LYS, 263_SER, 264_GLY, 266_SER, 267_PRO, 268_VAL, 269_ARG, 270_TYR, 639_LEU, 640_GLY, 641_ASN, 642_LEU, 643_LYS, 644_ILE, 645_ASP",1.7
+7L7R_G,7L7R_A_light+7L7R_B_heavy,"1145_SER, 1147_VAL, 1148_PHE, 1149_GLU, 1225_LYS, 1227_GLU, 1229_ILE, 1276_PRO, 1277_GLU, 1307_LEU, 1308_GLN, 1321_TYR, 1408_THR, 1410_GLN",2.1
+7L7R_G,7L7R_C_light+7L7R_D_heavy,"1165_CYS, 1166_THR, 1187_HIS, 1190_ASN, 1191_TRP, 1194_ASN, 1197_TRP, 1198_CYS, 1199_TRP, 1200_GLY, 1201_VAL, 1202_GLY, 1203_THR",2.1
+1YQV_Y,1YQV_L_light+1YQV_H_heavy,"41_GLN, 43_THR, 44_ASN, 45_ARG, 46_ASN, 47_THR, 48_ASP, 49_GLY, 53_TYR, 67_GLY, 68_ARG, 69_THR, 70_PRO, 84_LEU",1.7
+3X3F_A,3X3F_L_light+3X3F_H_heavy,"102_ASP, 109_ASP, 110_LEU, 111_LEU, 112_PHE, 113_CYS, 115_ARG, 87_ILE, 89_GLU, 90_ASP, 91_GLY, 92_ARG",2.1
+4LQF_A,4LQF_L_light+4LQF_H_heavy,"116_TYR, 118_MET, 119_PHE, 155_ASP, 156_GLY, 157_ASN, 170_ASP, 172_SER, 173_GLN, 174_GLU, 175_VAL",2.3
+8FDO_C,8FDO_B_light+8FDO_A_heavy,"100_LYS, 104_ALA, 105_LYS, 107_LEU, 109_LYS, 10_ASP, 12_LEU, 13_PHE, 16_ALA, 17_GLN, 20_LYS, 3_ASP, 5_ARG, 6_SER, 8_TRP, 96_LYS, 99_ARG, 9_GLU",2.2
+3MJ9_A,3MJ9_L_light+3MJ9_H_heavy,"146_LYS, 148_VAL, 149_THR, 150_LYS, 169_TYR, 170_ASP, 171_SER, 172_ASN, 173_MET, 174_ARG, 175_SER, 176_GLY, 177_LYS, 178_PHE, 190_LEU, 191_THR, 193_ASP, 194_ILE, 195_SER, 196_ARG, 28_GLN, 29_ARG, 31_GLU, 79_HIS",2.95
+7MRZ_C,7MRZ_Y_light+7MRZ_X_heavy,"107_ASN, 108_PHE, 109_CYS, 111_GLU, 112_ARG, 113_PHE, 114_THR, 26_THR, 28_GLU, 30_ILE, 32_TYR, 43_GLN, 44_SER, 45_GLY, 46_LEU, 48_ARG, 68_GLY, 70_ILE, 72_LEU, 75_LYS, 88_GLN, 89_GLU",3.0
+4UV7_A,4UV7_L_light+4UV7_H_heavy,"353_ARG, 355_ASP, 356_SER, 357_PHE, 358_THR, 359_HIS, 360_THR, 361_PRO, 362_PRO",2.1
+8W86_D,8W86_B_light+8W86_A_heavy,"-3_GLU, 0_GLN, 1060_TYR, 1066_ASP, 1067_ILE, 1070_ARG, 10_PRO, 11_GLN, 2_ILE, 3_PRO, 5_GLN, 6_PRO, 7_GLN, 8_PRO, 9_TYR",2.24
+8W86_H,8W86_F_light+8W86_E_heavy,"0_GLN, 1060_TYR, 1066_ASP, 1067_ILE, 1070_ARG, 10_PRO, 2_ILE, 3_PRO, 5_GLN, 6_PRO, 7_GLN, 8_PRO, 9_TYR",2.24
+8W86_C,8W86_B_light+8W86_A_heavy,"16_TYR, 21_PRO, 22_SER, 23_GLY, 39_LEU, 40_GLY, 42_LYS, 57_ASP, 59_GLN, 60_PHE, 62_LEU, 63_THR, 64_ASN, 66_ALA, 67_VAL, 69_LYS, 70_HIS, 73_ASN",2.24
+8W86_G,8W86_F_light+8W86_E_heavy,"16_TYR, 21_PRO, 40_GLY, 42_LYS, 55_ARG, 57_ASP, 59_GLN, 60_PHE, 62_LEU, 63_THR, 64_ASN, 66_ALA, 67_VAL, 69_LYS, 70_HIS",2.24
+7KMH_C,7KMH_B_light+7KMH_A_heavy,"403_ARG, 405_ASP, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 459_SER, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 501_ASN, 502_GLY, 505_TYR",1.72
+7OR9_E,7OR9_B_light+7OR9_A_heavy,"403_ARG, 405_ASP, 408_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",2.34
+7OR9_E,7OR9_L_light+7OR9_H_heavy,"345_THR, 346_ARG, 351_TYR, 440_ASN, 441_LEU, 442_ASP, 443_SER, 444_LYS, 445_VAL, 446_GLY, 447_GLY, 448_ASN, 449_TYR, 450_ASN, 452_LEU, 470_THR, 490_PHE, 499_PRO",2.34
+8FGX_C,8FGX_A_light+8FGX_B_heavy,"391_ILE, 392_VAL, 393_GLY, 396_ASN, 397_SER, 398_SER, 399_TRP, 400_GLY, 401_GLU, 402_TRP, 403_PRO, 404_TRP, 497_GLU, 498_PHE, 516_ASN, 518_TRP, 537_LYS, 539_ASN, 585_LYS, 587_ASN, 588_GLY, 590_TRP",2.62
+7TTX_A,7TTX_L_light+7TTX_H_heavy,"369_TYR, 371_SER, 372_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 405_ASP, 408_ARG, 409_GLN, 412_PRO, 413_GLY, 414_GLN, 415_THR, 416_GLY, 417_LYS, 427_ASP, 428_ASP, 429_PHE",2.8
+8THN_C,8THN_B_light+8THN_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.9
+3SO3_A,3SO3_B_light+3SO3_C_heavy,"146_TYR, 173_PRO, 174_GLN, 175_GLN, 176_ILE, 177_THR, 178_PRO, 179_ARG, 190_SER, 191_CYS, 192_GLN, 215_TRP, 216_GLY, 217_ASP, 219_GLY, 220_CYS, 226_GLY, 60_ILE, 95_ASN, 96_ASP, 97_PHE, 98_THR, 99_PHE",2.1
+7ZJL_B,7ZJL_i_light+7ZJL_j_heavy,"346_ARG, 439_ASN, 440_ASN, 441_LEU, 443_SER, 444_LYS, 445_VAL, 446_GLY, 447_GLY, 448_ASN, 449_TYR, 450_ASN, 498_GLN, 499_PRO, 500_THR, 501_ASN, 502_GLY, 506_GLN",2.6
+7ZJL_A,7ZJL_g_light+7ZJL_h_heavy,"439_ASN, 440_ASN, 441_LEU, 443_SER, 444_LYS, 445_VAL, 446_GLY, 447_GLY, 448_ASN, 449_TYR, 450_ASN, 498_GLN, 499_PRO, 500_THR, 501_ASN, 506_GLN",2.6
+7ZJL_C,7ZJL_k_light+7ZJL_l_heavy,"439_ASN, 440_ASN, 441_LEU, 443_SER, 444_LYS, 445_VAL, 446_GLY, 447_GLY, 448_ASN, 449_TYR, 450_ASN, 499_PRO, 500_THR, 506_GLN",2.6
+7BBJ_B,7BBJ_N_light+7BBJ_M_heavy,"138_PRO, 151_ALA, 152_SER, 155_SER, 156_GLY, 157_LEU, 159_LEU, 160_PRO, 161_TYR, 162_LYS, 163_VAL, 167_GLY, 168_ASP, 169_GLU, 170_VAL, 203_GLU, 206_LYS, 209_THR, 210_LEU, 211_ASN",2.72
+7BBJ_A,7BBJ_L_light+7BBJ_H_heavy,"138_PRO, 139_ILE, 151_ALA, 152_SER, 155_SER, 156_GLY, 157_LEU, 159_LEU, 160_PRO, 161_TYR, 162_LYS, 163_VAL, 167_GLY, 168_ASP, 169_GLU, 170_VAL, 203_GLU, 206_LYS, 209_THR, 210_LEU, 211_ASN",2.72
+1P84_E,1P84_K_light+1P84_J_heavy,"123_PRO, 124_HIS, 126_ILE, 127_GLN, 130_ASN, 131_SER, 132_VAL, 133_ASP, 134_MET, 142_THR, 143_ASP, 144_ALA, 147_VAL, 148_LYS, 149_ASP, 150_PRO, 151_GLN",2.5
+8VGJ_D,8VGJ_L_light+8VGJ_K_heavy,"107_GLU, 109_GLU, 110_GLU, 36_HIS, 38_TRP, 50_GLN, 60_GLY, 62_ALA, 65_ARG, 81_GLN, 82_LEU, 83_LEU, 84_PRO, 85_VAL, 86_SER",2.5
+8VGJ_A,8VGJ_G_light+8VGJ_H_heavy,"107_GLU, 109_GLU, 110_GLU, 36_HIS, 38_TRP, 50_GLN, 60_GLY, 62_ALA, 65_ARG, 81_GLN, 82_LEU, 83_LEU, 84_PRO, 85_VAL, 86_SER",2.5
+8VGJ_C,8VGJ_E_light+8VGJ_F_heavy,"107_GLU, 109_GLU, 110_GLU, 36_HIS, 38_TRP, 50_GLN, 60_GLY, 62_ALA, 65_ARG, 81_GLN, 82_LEU, 83_LEU, 84_PRO, 85_VAL, 86_SER",2.5
+8VGJ_B,8VGJ_I_light+8VGJ_J_heavy,"107_GLU, 109_GLU, 110_GLU, 36_HIS, 38_TRP, 50_GLN, 60_GLY, 62_ALA, 65_ARG, 81_GLN, 82_LEU, 83_LEU, 84_PRO, 85_VAL, 86_SER",2.5
+7A0W_B,7A0W_F_light+7A0W_E_heavy,"118_SER, 119_ASP, 120_PRO, 121_VAL, 124_GLU, 277_GLN, 286_SER, 287_MET, 349_ALA, 351_GLY, 352_ASN, 353_VAL, 354_ASP, 355_PRO, 356_GLU, 357_LEU, 360_TRP, 39_HIS, 43_GLU",2.04
+7A0W_A,7A0W_D_light+7A0W_C_heavy,"118_SER, 119_ASP, 120_PRO, 121_VAL, 122_THR, 124_GLU, 277_GLN, 278_GLY, 286_SER, 287_MET, 349_ALA, 351_GLY, 352_ASN, 353_VAL, 354_ASP, 355_PRO, 356_GLU, 357_LEU, 360_TRP, 39_HIS, 43_GLU",2.04
+6MI2_C,6MI2_B_light+6MI2_A_heavy,"100_SER, 101_MET, 102_CYS, 103_GLU, 112_LEU, 114_LYS, 128_GLN, 130_ARG, 131_GLY, 132_ILE, 134_ARG, 135_PRO, 66_ARG, 72_PHE, 95_LEU, 96_GLY, 97_ALA",2.72
+6MI2_F,6MI2_E_light+6MI2_D_heavy,"100_SER, 101_MET, 102_CYS, 103_GLU, 113_THR, 114_LYS, 125_PHE, 128_GLN, 130_ARG, 131_GLY, 132_ILE, 134_ARG, 135_PRO, 95_LEU, 96_GLY, 97_ALA",2.72
+5GGV_Y,5GGV_L_light+5GGV_H_heavy,"103_PRO, 104_TYR, 105_TYR, 106_LEU, 107_GLY, 108_ILE, 1_LYS, 28_PRO, 2_ALA, 39_LEU, 3_MET, 41_GLN, 44_SER, 45_GLN, 46_VAL, 91_LEU, 93_ILE, 95_LYS, 97_GLU, 99_MET",2.0
+5Y9J_A,5Y9J_L_light+5Y9J_H_heavy,"161_GLY, 162_SER, 163_TYR, 205_THR, 206_TYR, 208_MET, 209_GLY, 215_LYS, 221_GLY, 222_ASP, 224_LEU, 225_SER, 226_LEU, 231_ARG, 233_ILE, 264_PRO, 265_ARG, 266_GLU",2.05
+7TLY_I,7TLY_B_light+7TLY_A_heavy,"334_ASN, 335_LEU, 337_PRO, 339_ASP, 340_GLU, 341_VAL, 343_ASN, 344_ALA, 345_THR, 346_ARG, 356_LYS, 441_LEU",3.0
+5EN2_C,5EN2_B_light+5EN2_A_heavy,"109_LYS, 111_SER, 112_PHE, 113_ASP, 114_ASP, 115_ILE, 116_ALA, 117_VAL, 119_LEU, 122_TYR, 169_LYS, 170_THR, 171_GLU, 210_GLY, 211_LYS, 212_LEU, 216_LYS, 218_GLN, 219_PRO, 220_THR, 98_HIS",1.82
+8DZ3_I,8DZ3_L_light+8DZ3_H_heavy,"25_ASN, 26_PRO, 27_ASN, 28_ALA, 29_ASN, 30_PRO, 31_ASN, 32_ALA, 33_ASN, 34_PRO, 35_ASN",2.7
+8DZ3_I,8DZ3_B_light+8DZ3_A_heavy,"10_PRO, 11_ASN, 2_PRO, 3_ASN, 4_ALA, 5_ASN, 6_PRO, 7_ASN, 8_ALA, 9_ASN",2.7
+8DZ3_I,8DZ3_D_light+8DZ3_C_heavy,"10_PRO, 11_ASN, 12_ALA, 13_ASN, 14_PRO, 15_ASN, 16_ALA, 17_ASN, 18_PRO, 19_ASN, 8_ALA",2.7
+8DZ3_I,8DZ3_F_light+8DZ3_E_heavy,"16_ALA, 18_PRO, 19_ASN, 20_ALA, 21_ASN, 22_PRO, 23_ASN, 24_ALA, 25_ASN, 26_PRO",2.7
+8DZ3_I,8DZ3_N_light+8DZ3_M_heavy,"33_ASN, 34_PRO, 35_ASN, 36_ALA, 37_ASN, 38_PRO, 39_ASN, 40_ALA, 41_ASN, 42_PRO, 43_ASN",2.7
+8DZ3_I,8DZ3_P_light+8DZ3_O_heavy,"40_ALA, 41_ASN, 42_PRO, 43_ASN, 44_ALA, 45_ASN, 46_PRO, 47_ASN, 48_ALA, 49_ASN, 50_PRO, 51_ASN",2.7
+8DZ3_I,8DZ3_R_light+8DZ3_Q_heavy,"48_ALA, 49_ASN, 50_PRO, 51_ASN, 52_ALA, 53_ASN, 54_PRO, 55_ASN, 56_ALA",2.7
+8VVK_B,8VVK_C_light+8VVK_D_heavy,"383_ARG, 385_VAL, 386_ASP, 387_THR, 388_PRO, 389_GLY, 390_PRO, 391_LYS, 487_ILE, 488_LYS, 489_GLY, 490_ARG, 491_GLU, 500_ARG, 502_TYR, 503_LYS",2.61
+8VVK_A,8VVK_L_light+8VVK_H_heavy,"383_ARG, 385_VAL, 386_ASP, 387_THR, 388_PRO, 389_GLY, 390_PRO, 391_LYS, 487_ILE, 488_LYS, 489_GLY, 490_ARG, 491_GLU, 493_ARG, 500_ARG, 502_TYR, 503_LYS",2.61
+4D9R_A,4D9R_L_light+4D9R_H_heavy,"129_ARG, 132_PRO, 164_ASP, 165_ARG, 166_ALA, 167_THR, 169_ASN, 170_ARG, 173_ASP, 174_GLY, 178_GLU, 223_ARG",2.42
+4D9R_B,4D9R_D_light+4D9R_E_heavy,"129_ARG, 132_PRO, 164_ASP, 165_ARG, 166_ALA, 167_THR, 169_ASN, 170_ARG, 173_ASP, 174_GLY, 178_GLU, 223_ARG",2.42
+5TE4_G,5TE4_L_light+5TE4_H_heavy,"278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 355_LYS, 365_ALA, 366_GLY, 367_GLY, 368_ASP, 370_GLU, 371_ILE, 425_ARG, 426_MET, 427_TRP, 430_VAL, 456_ARG, 457_ASP, 458_GLY, 459_VAL, 460_ASN, 461_ASP, 462_THR, 469_ARG, 473_GLY, 474_ASP, 476_ARG",2.75
+6GV4_B,6GV4_L_light+6GV4_H_heavy,"135_THR, 136_GLU, 137_ASP, 138_ASN, 140_LYS, 141_THR, 184_ARG, 85_ASN, 87_ASP",2.8
+7TYV_A,7TYV_E_light+7TYV_D_heavy,"100_GLU, 226_THR, 227_TRP, 228_GLU, 229_ASP, 230_HIS, 232_GLN, 76_GLU, 88_LYS",2.8
+7TYV_B,7TYV_K_light+7TYV_G_heavy,"100_GLU, 226_THR, 227_TRP, 228_GLU, 229_ASP, 230_HIS, 232_GLN, 76_GLU, 88_LYS",2.8
+7TYV_C,7TYV_L_light+7TYV_H_heavy,"226_THR, 227_TRP, 228_GLU, 229_ASP, 230_HIS, 232_GLN, 76_GLU, 88_LYS",2.8
+7K9Z_E,7K9Z_L_light+7K9Z_H_heavy,"346_ARG, 351_TYR, 355_ARG, 357_ARG, 450_ASN, 452_LEU, 465_GLU, 466_ARG, 468_ILE, 469_SER, 470_THR, 471_GLU, 482_GLY, 484_GLU, 490_PHE, 492_LEU, 493_GLN, 494_SER",2.95
+7K9Z_E,7K9Z_A_light+7K9Z_B_heavy,"456_PHE, 474_GLN, 475_ALA, 476_GLY, 477_SER, 478_THR, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN",2.95
+7E5O_A,7E5O_L_light+7E5O_H_heavy,"403_ARG, 405_ASP, 406_GLU, 408_ARG, 409_GLN, 415_THR, 416_GLY, 417_LYS, 420_ASP, 449_TYR, 453_TYR, 455_LEU, 456_PHE, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 495_TYR, 496_GLY, 498_GLN, 501_ASN, 502_GLY, 503_VAL, 504_GLY, 505_TYR",2.8
+5B71_F,5B71_C_light+5B71_D_heavy,"107_VAL, 108_SER, 109_LYS, 110_HIS, 47_THR, 48_GLU, 49_ALA, 50_PHE, 51_ASP, 52_ALA, 53_THR, 65_SER, 70_HIS, 72_HIS, 74_SER, 76_GLU",2.11
+5B71_E,5B71_A_light+5B71_B_heavy,"107_VAL, 108_SER, 109_LYS, 110_HIS, 47_THR, 48_GLU, 49_ALA, 50_PHE, 51_ASP, 52_ALA, 53_THR, 57_LYS, 70_HIS, 71_VAL, 72_HIS, 74_SER, 76_GLU",2.11
+3ZDY_C,3ZDY_F_light+3ZDY_E_heavy,"117_GLU, 149_ASN, 153_ARG, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.45
+3ZDY_A,3ZDY_L_light+3ZDY_H_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 217_SER, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.45
+8HPV_A,8HPV_L_light+8HPV_H_heavy,"437_ASN, 439_ASN, 440_ASN, 441_LEU, 444_LYS, 445_VAL, 446_GLY, 447_GLY, 498_GLN, 499_PRO, 500_THR, 506_GLN",2.65
+8HPV_B,8HPV_F_light+8HPV_E_heavy,"403_ARG, 415_THR, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 478_THR, 487_ASN, 489_TYR, 493_GLN, 505_TYR",2.65
+8HPV_B,8HPV_Q_light+8HPV_P_heavy,"346_ARG, 437_ASN, 439_ASN, 440_ASN, 441_LEU, 444_LYS, 445_VAL, 446_GLY, 448_ASN, 498_GLN, 499_PRO, 500_THR",2.65
+8HPV_C,8HPV_S_light+8HPV_R_heavy,"403_ARG, 408_ARG, 415_THR, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 476_GLY, 477_SER, 478_THR, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 493_GLN, 501_ASN, 505_TYR",2.65
+8HPV_C,8HPV_J_light+8HPV_I_heavy,"346_ARG, 437_ASN, 439_ASN, 440_ASN, 441_LEU, 444_LYS, 445_VAL, 446_GLY, 498_GLN, 499_PRO, 500_THR, 506_GLN",2.65
+6SV2_A,6SV2_L_light+6SV2_H_heavy,"142_GLY, 143_SER, 144_ASP, 145_TYR, 146_GLU, 147_ASP, 148_ARG, 149_TYR, 151_ARG, 152_GLU, 153_ASN, 156_ARG, 197_ASN, 199_THR, 201_THR",2.3
+8CWV_A,8CWV_L_light+8CWV_H_heavy,"403_ARG, 405_ASP, 408_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",2.51
+8GYE_D,8GYE_F_light+8GYE_E_heavy,"25_GLN, 26_ASP, 30_ASN, 41_ARG, 60_ARG",2.3
+8GYE_A,8GYE_F_light+8GYE_E_heavy,"40_ASN, 49_PRO, 52_SER, 58_GLY, 59_GLN, 60_ARG, 61_THR, 62_CYS, 63_ASP, 64_ILE, 83_ASN",2.3
+7UVS_F,7UVS_E_light+7UVS_D_heavy,"619_LEU, 645_LYS, 646_SER, 647_PRO, 648_TYR, 664_SER, 668_TYR, 669_GLY, 670_LEU, 671_LEU, 672_ILE, 673_SER, 674_PRO, 675_THR, 680_GLU, 681_ASN, 682_ASN, 684_LYS, 691_THR, 692_LEU, 693_PRO, 694_PRO",2.06
+7UVS_C,7UVS_B_light+7UVS_A_heavy,"555_ASP, 556_GLU, 557_LEU, 558_ASP, 619_LEU, 645_LYS, 646_SER, 647_PRO, 648_TYR, 664_SER, 667_ILE, 668_TYR, 669_GLY, 670_LEU, 671_LEU, 672_ILE, 673_SER, 674_PRO, 675_THR, 680_GLU, 681_ASN, 682_ASN, 684_LYS, 693_PRO, 694_PRO",2.06
+2VDK_A,2VDK_L_light+2VDK_H_heavy,"149_ASN, 154_ILE, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.8
+1LK3_B,1LK3_M_light+1LK3_I_heavy,"125_LYS, 128_PHE, 129_ASN, 130_LYS, 131_LEU, 132_GLN, 133_GLU, 134_LYS, 136_ILE, 137_TYR, 39_MET, 71_PHE, 74_GLU, 75_GLU, 79_GLN, 82_ASN, 83_GLN",1.91
+1LK3_A,1LK3_L_light+1LK3_H_heavy,"125_LYS, 128_PHE, 129_ASN, 130_LYS, 131_LEU, 132_GLN, 133_GLU, 134_LYS, 136_ILE, 137_TYR, 39_MET, 71_PHE, 74_GLU, 75_GLU, 78_PRO, 79_GLN, 82_ASN, 83_GLN",1.91
+5FB8_C,5FB8_A_light+5FB8_B_heavy,"102_ILE, 38_MET, 39_SER, 40_ALA, 41_GLY, 42_LEU, 43_GLY, 44_PHE, 45_SER, 46_LEU, 51_GLY, 53_LEU, 63_ARG, 65_PHE, 66_LYS, 94_THR, 95_ARG, 96_PHE, 97_GLU, 98_ALA, 99_TRP",2.07
+2NR6_A,2NR6_C_light+2NR6_D_heavy,"100_ASP, 130_ALA, 131_LEU, 132_LYS, 60_LYS, 61_PRO, 65_LYS, 66_TYR, 67_ILE, 68_SER, 69_GLY, 70_ASN, 71_VAL, 83_ARG, 86_GLU, 98_GLN",2.81
+2NR6_B,2NR6_E_light+2NR6_F_heavy,"100_ASP, 130_ALA, 131_LEU, 132_LYS, 60_LYS, 61_PRO, 65_LYS, 66_TYR, 67_ILE, 68_SER, 69_GLY, 70_ASN, 83_ARG, 86_GLU, 98_GLN",2.81
+4YDL_G,4YDL_L_light+4YDL_H_heavy,"124_GLY, 276_ASN, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 371_ILE, 428_GLN, 429_GLY, 430_THR, 455_THR, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_ALA, 461_ASN, 465_ASN, 466_GLU, 467_THR, 469_ARG, 473_GLY",1.8
+4YDL_A,4YDL_C_light+4YDL_B_heavy,"124_GLY, 276_ASN, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 371_ILE, 428_GLN, 430_THR, 455_THR, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_ALA, 461_ASN, 463_THR, 465_ASN, 466_GLU, 467_THR, 469_ARG, 473_GLY",1.8
+6M0Z_A,6M0Z_L_light+6M0Z_H_heavy,"337_TYR, 338_HIS, 498_TYR, 502_ARG, 505_GLU, 506_ASP, 508_ARG, 509_ASP, 511_ILE, 512_GLY, 513_PHE, 514_PRO, 598_ARG, 599_ASP, 90_HIS",2.88
+3GRW_A,3GRW_L_light+3GRW_H_heavy,"155_ARG, 158_ARG, 159_MET, 161_LYS, 162_LYS, 163_LEU, 164_LEU, 166_VAL, 167_PRO, 168_ALA, 169_ALA, 170_ASN, 171_THR, 172_VAL, 173_ARG, 175_ARG, 177_PRO, 205_LYS, 207_ARG, 210_GLN, 214_VAL, 216_GLU, 241_TYR, 248_ARG, 278_TYR, 279_SER, 280_ASP, 281_ALA, 282_GLN, 283_PRO, 314_SER, 315_GLU, 316_SER, 317_VAL, 318_GLU",2.1
+5VKH_C,5VKH_B_light+5VKH_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.25
+1NCD_N,1NCD_L_light+1NCD_H_heavy,"326_PRO, 327_ARG, 328_PRO, 329_ASN, 343_GLY, 344_ASN, 347_ASN, 366_ILE, 367_SER, 368_ILE, 369_ALA, 370_SER, 372_SER, 399_LEU, 400_ASN, 401_THR, 403_TRP, 431_PRO, 432_LYS",2.9
+7CHY_I,7CHY_L_light+7CHY_H_heavy,"130_GLN, 145_LEU, 146_HIS, 147_LEU, 148_GLN, 151_ASP, 154_GLN, 242_GLN, 243_ALA, 244_GLU, 245_ASN, 246_MET, 247_PRO, 256_GLY, 257_GLN",2.65
+5X8M_A,5X8M_C_light+5X8M_B_heavy,"111_VAL, 112_TYR, 113_ARG, 115_MET, 122_ASP, 123_TYR, 124_LYS, 125_ARG, 23_VAL, 26_ASP, 56_TYR, 58_GLU, 59_MET, 60_GLU, 61_ASP",2.66
+6H2Y_D,6H2Y_L_light+6H2Y_H_heavy,"100_SER, 102_GLU, 119_GLU, 120_LYS, 130_LEU, 132_ASN, 134_ARG, 166_ASP, 167_ASP, 168_PRO, 169_ASN, 191_LYS, 53_SER, 54_ALA, 55_GLN, 56_GLY, 58_GLU, 79_LYS, 82_ARG, 84_ASP, 85_PHE, 86_VAL",2.65
+8VGI_D,8VGI_L_light+8VGI_K_heavy,"107_GLU, 109_GLU, 110_GLU, 36_HIS, 38_TRP, 50_GLN, 60_GLY, 61_LEU, 62_ALA, 65_ARG, 81_GLN, 82_LEU, 83_LEU, 84_PRO, 85_VAL, 86_SER",2.7
+8VGI_A,8VGI_G_light+8VGI_H_heavy,"107_GLU, 109_GLU, 110_GLU, 36_HIS, 38_TRP, 50_GLN, 60_GLY, 61_LEU, 62_ALA, 65_ARG, 81_GLN, 82_LEU, 83_LEU, 84_PRO, 85_VAL, 86_SER",2.7
+8VGI_C,8VGI_E_light+8VGI_F_heavy,"107_GLU, 109_GLU, 110_GLU, 36_HIS, 38_TRP, 50_GLN, 60_GLY, 61_LEU, 62_ALA, 65_ARG, 81_GLN, 82_LEU, 83_LEU, 84_PRO, 85_VAL, 86_SER",2.7
+8VGI_B,8VGI_I_light+8VGI_J_heavy,"107_GLU, 109_GLU, 110_GLU, 36_HIS, 38_TRP, 50_GLN, 60_GLY, 62_ALA, 65_ARG, 81_GLN, 82_LEU, 83_LEU, 84_PRO, 85_VAL, 86_SER",2.7
+7NXA_E,7NXA_L_light+7NXA_H_heavy,"366_SER, 369_TYR, 370_ASN, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 388_ASN, 390_LEU, 392_PHE, 412_PRO, 427_ASP, 428_ASP, 429_PHE, 517_LEU",2.5
+7NXA_E,7NXA_B_light+7NXA_A_heavy,"403_ARG, 405_ASP, 408_ARG, 415_THR, 416_GLY, 417_ASN, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 459_SER, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 500_THR, 501_TYR, 502_GLY, 505_TYR",2.5
+5LQB_A,5LQB_L_light+5LQB_H_heavy,"50_ASN, 52_LYS, 53_ASN, 54_PRO, 55_LYS, 57_THR, 58_ARG, 61_THR, 62_PHE, 63_LYS, 91_ASN, 92_LEU, 93_ALA, 94_GLN, 96_LYS, 97_ASN",1.95
+1R0A_B,1R0A_L_light+1R0A_H_heavy,"199_ARG, 221_HIS, 222_GLN, 223_LYS, 224_GLU, 225_PRO, 226_PRO, 227_PHE, 229_TRP, 230_MET, 358_ARG",2.8
+4H8W_G,4H8W_L_light+4H8W_H_heavy,"103_GLN, 106_GLU, 107_ASP, 114_GLN, 51_THR, 52_LEU, 53_PHE, 58_ALA, 60_ALA, 69_TRP, 71_THR, 72_HIS, 73_ALA, 74_CYS, 75_VAL, 76_PRO, 77_THR, 78_ASP, 79_PRO",1.85
+8DPM_F,8DPM_J_light+8DPM_I_heavy,"114_LYS, 115_LYS, 116_PRO, 117_ASP, 118_GLY, 119_SER, 120_GLU, 144_THR, 145_GLY, 146_PRO, 221_GLN, 223_THR, 224_GLY, 227_THR, 229_GLU",3.0
+8DPM_K,8DPM_O_light+8DPM_N_heavy,"114_LYS, 115_LYS, 116_PRO, 117_ASP, 118_GLY, 119_SER, 120_GLU, 144_THR, 145_GLY, 146_PRO, 221_GLN, 223_THR, 224_GLY, 227_THR, 229_GLU",3.0
+8DPM_A,8DPM_E_light+8DPM_D_heavy,"114_LYS, 115_LYS, 116_PRO, 117_ASP, 118_GLY, 119_SER, 120_GLU, 144_THR, 145_GLY, 146_PRO, 221_GLN, 223_THR, 224_GLY, 227_THR, 229_GLU",3.0
+5TQ0_A,5TQ0_L_light+5TQ0_H_heavy,"36_LYS, 39_GLU, 40_GLN, 43_ARG, 63_THR, 64_SER, 65_VAL, 66_THR, 68_ARG, 69_PRO, 73_GLN, 76_LEU, 77_SER, 80_GLU, 81_ASP",2.7
+8IUZ_A,8IUZ_C_light+8IUZ_B_heavy,"124_ASN, 131_ARG, 132_ARG, 133_SER, 134_GLY, 135_SER, 136_SER, 139_ALA, 142_LYS, 248_ARG, 68_ASP, 69_GLN, 71_LEU, 72_GLU",3.0
+4F2M_E,4F2M_B_light+4F2M_A_heavy,"524_LYS, 525_ARG, 526_SER, 527_GLY, 528_TYR, 529_GLY, 530_GLN, 532_ILE, 571_TRP, 630_ARG, 631_THR, 632_ASN",3.0
+4F2M_F,4F2M_D_light+4F2M_C_heavy,"524_LYS, 525_ARG, 526_SER, 527_GLY, 528_TYR, 529_GLY, 530_GLN, 532_ILE, 571_TRP, 631_THR, 632_ASN",3.0
+8XI6_C,8XI6_I_light+8XI6_H_heavy,"323_THR, 324_GLU, 328_ARG, 530_SER, 532_ASN, 533_LEU, 534_VAL, 535_LYS, 536_ASN, 537_LYS, 553_THR, 554_GLU, 580_GLN, 581_THR, 625_HIS, 628_GLN",2.3
+8XI6_B,8XI6_G_light+8XI6_F_heavy,"323_THR, 324_GLU, 328_ARG, 530_SER, 532_ASN, 533_LEU, 534_VAL, 535_LYS, 536_ASN, 537_LYS, 553_THR, 554_GLU, 580_GLN, 581_THR, 625_HIS, 628_GLN",2.3
+8XI6_A,8XI6_E_light+8XI6_D_heavy,"323_THR, 324_GLU, 328_ARG, 530_SER, 532_ASN, 533_LEU, 534_VAL, 535_LYS, 536_ASN, 537_LYS, 553_THR, 554_GLU, 580_GLN, 581_THR, 625_HIS, 628_GLN",2.3
+9AU1_R,9AU1_N_light+9AU1_M_heavy,"334_ASN, 335_LEU, 337_PRO, 339_HIS, 340_GLU, 343_ASN, 344_ALA, 345_THR, 356_LYS, 357_ARG, 359_SER, 360_ASN, 361_CYS, 441_LEU",2.41
+9AU1_R,9AU1_L_light+9AU1_H_heavy,"403_ARG, 405_ASN, 409_GLN, 415_THR, 416_GLY, 417_ASN, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 459_SER, 460_LYS, 473_TYR, 475_ALA, 476_GLY, 477_ASN, 489_TYR, 493_GLN, 505_HIS",2.41
+7XJF_C,7XJF_B_light+7XJF_A_heavy,"101_LEU, 102_THR, 103_ARG, 1_GLN, 34_THR, 35_GLU, 36_VAL, 37_TYR, 51_ASP, 53_ALA, 54_LEU, 56_LYS, 99_THR",2.6
+4I3S_G,4I3S_L_light+4I3S_H_heavy,"252_LYS, 275_GLU, 276_ASP, 278_SER, 279_ASP, 280_ASN, 281_ALA, 282_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 371_ILE, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_SER, 462_SER, 469_ARG, 472_GLY, 473_GLY",2.85
+6PZF_B,6PZF_E_light+6PZF_F_heavy,"296_GLN, 328_PRO, 329_ASN, 330_ASP, 331_PRO, 333_ASN, 334_ILE, 336_LYS, 341_TYR, 342_PRO, 387_ARG",2.8
+6PZF_A,6PZF_L_light+6PZF_H_heavy,"296_GLN, 328_PRO, 329_ASN, 330_ASP, 331_PRO, 333_ASN, 334_ILE, 336_LYS, 341_TYR, 342_PRO, 387_ARG",2.8
+7WSL_D,7WSL_L_light+7WSL_H_heavy,"126_ILE, 128_LEU, 129_ALA, 130_PRO, 131_LYS, 132_ALA, 134_ILE, 59_THR, 60_SER, 61_GLU, 62_SER, 64_VAL, 66_ASN, 81_ALA, 83_PRO, 84_GLU, 85_ASP, 86_ARG, 87_SER",1.53
+7KQL_T,7KQL_L_light+7KQL_H_heavy,"29_PRO, 30_VAL, 31_CYS, 38_PRO, 39_VAL, 40_PHE, 41_GLU, 42_CYS, 43_GLY, 44_ASN, 45_VAL, 46_VAL, 48_ARG, 50_ASP, 90_ARG, 91_ILE, 92_GLN, 95_GLY, 96_ILE, 97_MET, 99_ASP",1.49
+7XH8_A,7XH8_C_light+7XH8_B_heavy,"420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_ASN, 478_LYS, 486_PHE, 487_ASN, 488_CYS, 489_TYR, 493_ARG",2.99
+8VGL_C,8VGL_L_light+8VGL_H_heavy,"1657_GLU, 1658_ARG, 1659_PHE, 1660_PRO, 1661_GLU, 1662_TRP, 1665_THR, 1668_GLU, 1689_PRO, 1692_GLU, 1693_VAL",2.6
+8VGL_A,8VGL_F_light+8VGL_E_heavy,"1657_GLU, 1658_ARG, 1659_PHE, 1660_PRO, 1661_GLU, 1662_TRP, 1665_THR, 1668_GLU, 1689_PRO, 1692_GLU, 1693_VAL",2.6
+6PZE_A,6PZE_L_light+6PZE_H_heavy,"118_ARG, 146_ASN, 149_ILE, 150_HIS, 151_ASP, 152_ARG, 198_ASN, 199_ASN, 222_ILE, 224_ARG, 246_ALA, 276_GLU, 277_GLU, 292_ARG, 294_ASN, 346_ASN, 347_ASN, 371_ARG, 406_TYR, 430_ARG, 431_PRO, 432_LYS, 434_ASP, 437_TRP",2.3
+3EJZ_A,3EJZ_D_light+3EJZ_C_heavy,"243_LYS, 246_ASP, 248_PRO, 249_LEU, 250_ASN, 380_PRO, 381_GLN, 382_TYR, 383_HIS",2.9
+3EJZ_B,3EJZ_F_light+3EJZ_E_heavy,"243_LYS, 246_ASP, 248_PRO, 249_LEU, 250_ASN, 380_PRO, 381_GLN, 382_TYR, 383_HIS",2.9
+7QNY_E,7QNY_B_light+7QNY_A_heavy,"340_GLU, 344_ALA, 345_THR, 346_ARG, 347_PHE, 348_ALA, 351_TYR, 352_ALA, 354_ASN, 356_LYS, 449_TYR, 450_ASN, 452_LEU, 466_ARG, 468_ILE, 470_THR, 483_VAL, 490_PHE",2.84
+7QNY_E,7QNY_L_light+7QNY_H_heavy,"403_ARG, 405_ASP, 409_GLN, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 495_TYR, 501_ASN, 502_GLY, 505_TYR",2.84
+4IJ3_A,4IJ3_B_light+4IJ3_C_heavy,"114_ASN, 115_ILE, 116_PRO, 117_GLY, 118_PHE, 119_PRO, 120_THR, 122_ARG, 132_SER, 133_GLY, 134_ALA, 135_VAL, 137_PRO, 139_ALA, 149_ARG, 69_TRP, 70_CYS, 71_GLY, 72_HIS, 76_PHE",2.7
+7KQB_C,7KQB_J_light+7KQB_K_heavy,"449_TYR, 452_LEU, 455_LEU, 456_PHE, 470_THR, 471_GLU, 472_ILE, 481_ASN, 482_GLY, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 488_CYS, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER",2.42
+5BO1_A,5BO1_M_light+5BO1_I_heavy,"190_TYR, 201_ARG, 202_PRO, 203_ARG, 204_ASP, 205_ASP, 206_PHE, 207_PHE, 209_HIS, 211_ALA, 219_THR, 221_MET, 222_GLU, 224_TRP, 246_LYS, 247_LEU, 250_ASP, 252_ARG",2.56
+5BO1_B,5BO1_L_light+5BO1_H_heavy,"190_TYR, 201_ARG, 202_PRO, 203_ARG, 204_ASP, 205_ASP, 206_PHE, 207_PHE, 209_HIS, 211_ALA, 221_MET, 222_GLU, 224_TRP, 246_LYS, 247_LEU, 250_ASP, 252_ARG",2.56
+4AL8_C,4AL8_L_light+4AL8_H_heavy,"310_LYS, 313_ALA, 314_GLU, 315_THR, 316_GLN, 317_HIS, 321_LEU, 352_ILE, 354_ALA, 364_PRO, 368_GLU",1.66
+8TTW_I,8TTW_R_light+8TTW_Q_heavy,"138_ILE, 322_ILE, 323_ILE, 324_GLY, 325_ASP, 327_ARG, 328_GLN, 329_ALA, 330_HIS, 415_THR, 417_PRO",2.96
+8TTW_A,8TTW_N_light+8TTW_M_heavy,"138_ILE, 322_ILE, 323_ILE, 324_GLY, 325_ASP, 327_ARG, 328_GLN, 329_ALA, 330_HIS, 415_THR, 417_PRO",2.96
+8TTW_E,8TTW_P_light+8TTW_O_heavy,"138_ILE, 322_ILE, 323_ILE, 324_GLY, 325_ASP, 327_ARG, 328_GLN, 329_ALA, 330_HIS, 415_THR, 417_PRO",2.96
+7MDP_A,7MDP_I_light+7MDP_H_heavy,"32_TYR, 36_ILE, 38_ASP, 39_SER, 40_TYR, 41_ARG, 54_ASP, 56_LEU, 5_LYS, 67_MET, 6_LEU, 70_GLN, 71_TYR, 74_THR, 75_GLY, 7_VAL",1.96
+8RPB_P,8RPB_L_light+8RPB_H_heavy,"115_MET, 117_SER, 119_GLY, 120_GLY, 121_ALA, 122_ASP, 123_TYR, 18_ALA, 19_PHE, 49_ASP, 51_ALA, 52_ALA, 53_LEU, 54_ILE, 56_TYR, 58_GLU, 61_ASP, 63_ASN, 66_GLN, 68_VAL, 69_HIS",2.79
+6MEI_C,6MEI_L_light+6MEI_H_heavy,"417_ASN, 418_GLY, 419_SER, 420_TRP, 427_LEU, 428_ASN, 429_CYS, 430_ASN, 431_ASP, 434_HIS, 435_THR, 436_GLY, 438_LEU, 442_PHE, 443_TYR, 446_LYS, 447_PHE, 448_ASN, 529_TRP, 531_GLU",2.9
+8W0V_C,8W0V_L_light+8W0V_H_heavy,"427_LEU, 429_CYS, 430_ASN, 431_ASP, 434_HIS, 435_THR, 436_GLY, 437_PHE, 438_LEU, 440_ALA, 442_PHE, 443_TYR, 446_LYS, 447_PHE, 448_ASN, 529_TRP, 531_GLU, 619_PRO",2.59
+7SGM_A,7SGM_L_light+7SGM_H_heavy,"129_GLU, 130_ALA, 131_SER, 132_SER, 141_ALA, 142_GLU, 143_LYS, 144_GLY, 146_TYR, 178_CYS, 217_PRO, 218_CYS, 220_GLN, 245_SER, 246_GLN, 247_VAL, 248_SER, 249_HIS, 250_GLY",2.0
+7SGM_C,7SGM_Y_light+7SGM_X_heavy,"129_GLU, 130_ALA, 131_SER, 132_SER, 141_ALA, 142_GLU, 143_LYS, 144_GLY, 146_TYR, 178_CYS, 217_PRO, 218_CYS, 220_GLN, 245_SER, 246_GLN, 247_VAL, 248_SER, 249_HIS, 250_GLY",2.0
+7SGM_B,7SGM_M_light+7SGM_K_heavy,"129_GLU, 130_ALA, 131_SER, 132_SER, 141_ALA, 142_GLU, 143_LYS, 144_GLY, 146_TYR, 178_CYS, 217_PRO, 218_CYS, 220_GLN, 245_SER, 246_GLN, 247_VAL, 248_SER, 249_HIS, 250_GLY",2.0
+2J5L_A,2J5L_B_light+2J5L_C_heavy,"485_LYS, 486_ASP, 490_CYS, 491_PRO, 492_CYS, 493_ASP, 494_PRO, 495_GLU, 508_LYS, 509_CYS, 510_VAL, 511_GLU, 512_ARG",2.9
+7NXB_E,7NXB_L_light+7NXB_H_heavy,"366_SER, 369_TYR, 370_ASN, 374_PHE, 375_SER, 377_PHE, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 388_ASN, 390_LEU, 412_PRO, 427_ASP, 428_ASP, 429_PHE, 517_LEU",2.67
+7NXB_E,7NXB_B_light+7NXB_A_heavy,"403_ARG, 405_ASP, 408_ARG, 415_THR, 416_GLY, 417_THR, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 500_THR, 501_TYR, 502_GLY, 505_TYR",2.67
+8SAK_B,8SAK_L_light+8SAK_H_heavy,"509_LEU, 513_ASN, 514_SER, 517_GLU, 533_PHE, 541_ASP, 542_GLY, 543_ASP, 544_TYR, 545_PHE, 546_THR, 547_ARG, 548_GLN",3.0
+2QQK_A,2QQK_L_light+2QQK_H_heavy,"107_ASN, 133_ALA, 136_SER, 138_ARG, 41_THR, 46_PRO, 47_GLN, 73_ASN, 74_PRO, 75_HIS, 76_PHE, 77_GLU",2.75
+4LOU_A,4LOU_D_light+4LOU_C_heavy,"243_LYS, 246_ASP, 248_PRO, 249_LEU, 250_ASN, 380_PRO, 381_GLN, 382_TYR, 383_HIS",2.98
+4LOU_B,4LOU_F_light+4LOU_E_heavy,"246_ASP, 248_PRO, 249_LEU, 250_ASN, 380_PRO, 381_GLN, 382_TYR, 383_HIS",2.98
+1R3J_C,1R3J_A_light+1R3J_B_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",1.9
+7UKO_A,7UKO_L_light+7UKO_H_heavy,"117_GLU, 149_ASN, 154_ILE, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.6
+7UKO_C,7UKO_F_light+7UKO_E_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.6
+1XGR_C,1XGR_A_light+1XGR_B_heavy,"100_SER, 101_ASP, 102_GLY, 15_HIS, 16_GLY, 18_ASP, 19_ASN, 20_TYR, 21_ARG, 63_TRP, 73_ARG, 75_LEU, 89_THR, 93_ASN, 96_LYS, 97_LYS",2.1
+5D71_A,5D71_L_light+5D71_H_heavy,"10_THR, 119_PHE, 120_ASP, 12_PRO, 13_TRP, 15_HIS, 16_VAL, 17_ASN, 20_GLN, 21_GLU, 24_ARG, 83_HIS, 86_GLN, 87_HIS",2.25
+6WDS_C,6WDS_L_light+6WDS_H_heavy,"209_THR, 58_THR, 59_GLU, 60_SER, 62_VAL, 63_GLY, 64_MET, 65_GLU, 70_ASP",2.9
+6WDS_B,6WDS_L_light+6WDS_H_heavy,"216_PRO, 219_THR, 73_THR, 74_GLY, 75_SER, 76_THR",2.9
+7RP2_A,7RP2_I_light+7RP2_H_heavy,"25_GLN, 32_TYR, 36_ILE, 38_ASP, 39_SER, 40_TYR, 41_ARG, 54_ASP, 56_LEU, 5_LYS, 65_SER, 67_MET, 6_LEU, 71_TYR, 74_THR, 75_GLY, 7_VAL",2.2
+1IC7_Y,1IC7_L_light+1IC7_H_heavy,"100_SER, 101_ASP, 102_GLY, 103_ASN, 15_HIS, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 63_TRP, 73_ARG, 75_LEU, 89_THR, 93_ASN, 96_LYS, 97_LYS, 98_ILE",2.1
+6KS0_A,6KS0_L_light+6KS0_H_heavy,"0_SER, 112_HIS, 116_PRO, 117_PRO, 118_MET, 119_PRO, 120_SER, 122_ARG, 199_LYS, 89_GLU, 90_GLY, 91_ARG, 92_TRP, 93_ARG, 94_VAL, 95_ILE, 96_PRO",2.79
+1N6Q_B,1N6Q_L_light+1N6Q_H_heavy,"199_ARG, 222_GLN, 223_LYS, 224_GLU, 225_PRO, 226_PRO, 227_PHE, 229_TRP, 230_MET, 358_ARG",3.0
+2VDQ_A,2VDQ_L_light+2VDQ_H_heavy,"117_GLU, 149_ASN, 154_ILE, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.59
+6BLH_G,6BLH_L_light+6BLH_H_heavy,"164_HIS, 165_PHE, 166_GLU, 167_VAL, 168_PHE, 169_ASN, 170_PHE, 174_SER, 175_ILE, 189_ILE, 190_PRO, 191_ASN, 192_LYS, 193_LYS, 194_PRO, 195_GLY, 196_LYS",2.0
+7U5B_I,7U5B_L_light+7U5B_H_heavy,"132_ALA, 164_SER, 165_GLN, 166_LYS, 167_ARG, 169_GLU, 170_ASP, 171_ALA, 172_TYR, 173_PRO, 174_ARG, 176_ILE, 177_ASP, 178_ASP",2.37
+7U5B_J,7U5B_B_light+7U5B_A_heavy,"132_ALA, 164_SER, 165_GLN, 166_LYS, 167_ARG, 169_GLU, 170_ASP, 171_ALA, 172_TYR, 173_PRO, 174_ARG, 176_ILE, 177_ASP, 178_ASP, 224_ARG",2.37
+7NX7_E,7NX7_L_light+7NX7_H_heavy,"366_SER, 369_TYR, 370_ASN, 374_PHE, 375_SER, 377_PHE, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 388_ASN, 390_LEU, 412_PRO, 427_ASP, 428_ASP, 429_PHE, 517_LEU",2.3
+7NX7_E,7NX7_B_light+7NX7_A_heavy,"403_ARG, 405_ASP, 408_ARG, 415_THR, 416_GLY, 417_ASN, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",2.3
+1EZV_E,1EZV_Y_light+1EZV_X_heavy,"123_PRO, 124_HIS, 126_ILE, 127_GLN, 130_ASN, 131_SER, 132_VAL, 133_ASP, 134_MET, 142_THR, 143_ASP, 144_ALA, 147_VAL, 148_LYS, 149_ASP, 150_PRO, 151_GLN",2.3
+4AEI_C,4AEI_N_light+4AEI_J_heavy,"10_VAL, 11_ASN, 12_CYS, 13_THR, 15_PHE, 41_PRO, 42_TYR, 56_ARG, 59_GLY, 60_PRO, 61_GLY, 62_ARG, 63_CYS, 64_HIS, 9_ASP",2.3
+4AEI_A,4AEI_L_light+4AEI_H_heavy,"10_VAL, 11_ASN, 12_CYS, 13_THR, 15_PHE, 41_PRO, 42_TYR, 56_ARG, 59_GLY, 60_PRO, 61_GLY, 62_ARG, 63_CYS, 64_HIS, 9_ASP",2.3
+4AEI_B,4AEI_M_light+4AEI_I_heavy,"10_VAL, 11_ASN, 12_CYS, 13_THR, 15_PHE, 41_PRO, 42_TYR, 56_ARG, 59_GLY, 60_PRO, 61_GLY, 62_ARG, 63_CYS, 64_HIS, 9_ASP",2.3
+4YX2_A,4YX2_L_light+4YX2_H_heavy,"151_HIS, 152_PHE, 153_GLY, 154_SER, 155_ASP, 156_TYR, 157_GLU, 158_ASP, 215_LYS, 219_ARG, 223_GLN",2.19
+6HGA_B,6HGA_L_light+6HGA_H_heavy,"423_LYS, 425_SER, 430_ARG, 431_LEU, 433_GLU, 434_TYR, 466_LYS, 467_ARG, 468_GLU, 469_PHE, 470_ARG, 471_HIS",2.6
+7SU1_C,7SU1_L_light+7SU1_H_heavy,"103_PRO, 104_TYR, 105_TYR, 106_LEU, 108_ILE, 2_ALA, 33_GLU, 35_ARG, 39_LEU, 3_MET, 46_VAL, 48_GLU, 95_LYS, 99_MET",2.53
+7E72_E,7E72_B_light+7E72_A_heavy,"643_GLU, 644_ASN, 645_ILE, 646_LYS, 647_ILE, 650_ILE, 677_GLN, 705_GLU, 706_THR, 707_ALA, 727_HIS, 728_GLU, 729_LEU, 730_VAL, 732_LEU, 733_PRO",2.09
+7E72_F,7E72_D_light+7E72_C_heavy,"643_GLU, 644_ASN, 645_ILE, 646_LYS, 647_ILE, 648_SER, 649_ASN, 650_ILE, 677_GLN, 705_GLU, 706_THR, 707_ALA, 727_HIS, 728_GLU, 729_LEU, 730_VAL, 732_LEU, 733_PRO",2.09
+2Q8B_A,2Q8B_L_light+2Q8B_H_heavy,"186_THR, 187_GLU, 188_PRO, 189_LEU, 190_MET, 193_MET, 194_THR, 196_ASP, 197_GLU, 200_HIS, 201_PHE, 202_TYR, 203_LYS, 204_ASP, 205_ASN, 208_VAL, 223_ASN, 224_MET, 225_ILE, 228_ASN, 230_LYS",2.3
+4ZPT_S,4ZPT_L_light+4ZPT_H_heavy,"398_ASN, 400_LYS, 495_LEU, 496_LYS, 527_VAL, 528_SER, 529_ILE, 530_VAL, 531_PRO, 532_SER, 535_TRP, 536_GLU, 539_ASP, 540_TYR, 543_LYS",2.59
+4ZPT_R,4ZPT_B_light+4ZPT_A_heavy,"398_ASN, 400_LYS, 496_LYS, 527_VAL, 528_SER, 529_ILE, 530_VAL, 531_PRO, 532_SER, 535_TRP, 536_GLU, 539_ASP, 540_TYR, 542_ARG, 543_LYS, 549_GLU",2.59
+8EZ7_A,8EZ7_L_light+8EZ7_H_heavy,"267_PRO, 269_SER, 270_GLY, 273_GLN, 283_ARG, 288_ARG, 296_LYS, 304_ASP, 309_ASP, 310_TYR, 311_SER, 312_ILE, 313_VAL, 314_SER, 318_CYS, 337_CYS, 338_LEU, 342_ASN, 383_TRP, 384_SER, 386_PRO",2.6
+1IGC_A,1IGC_L_light+1IGC_H_heavy,"15_LYS, 16_THR, 17_LEU, 18_LYS, 19_GLY, 20_GLU, 21_THR, 22_THR, 38_TYR, 40_ASN, 41_ASP, 42_ASN, 43_GLY",2.6
+8W84_D,8W84_B_light+8W84_A_heavy,"-2_LEU, 0_TYR, 1060_TYR, 1064_GLN, 1066_ASP, 1067_ILE, 1070_ARG, 1071_LYS, 1074_ALA, 1077_ARG, 2_GLN, 3_PRO, 4_GLU, 5_LEU, 6_PRO, 7_TYR, 8_PRO",2.11
+8W84_C,8W84_B_light+8W84_A_heavy,"42_LYS, 55_ARG, 57_ASP, 59_GLN, 60_PHE, 62_LEU, 63_THR, 66_ALA, 67_VAL, 69_LYS",2.11
+6UYF_E,6UYF_L_light+6UYF_H_heavy,"422_ILE, 427_LEU, 428_ASN, 429_CYS, 430_ASN, 431_ASP, 434_GLN, 435_THR, 438_ILE, 439_THR, 441_LEU, 442_PHE, 443_TYR, 500_ARG, 529_TRP, 531_GLU, 613_TYR",2.06
+8TP7_C,8TP7_L_light+8TP7_H_heavy,"126_ARG, 127_TRP, 128_THR, 129_GLN, 130_HIS, 157_LYS, 160_ASN, 161_TYR, 162_PRO, 163_VAL, 164_ALA, 165_LYS, 166_GLY, 167_SER, 196_GLN, 197_ASN, 246_GLU, 248_THR",2.8
+8TP7_B,8TP7_G_light+8TP7_E_heavy,"126_ARG, 127_TRP, 128_THR, 129_GLN, 130_HIS, 157_LYS, 160_ASN, 161_TYR, 162_PRO, 163_VAL, 164_ALA, 165_LYS, 166_GLY, 167_SER, 196_GLN, 197_ASN, 246_GLU, 248_THR",2.8
+8TP7_A,8TP7_F_light+8TP7_D_heavy,"126_ARG, 127_TRP, 128_THR, 129_GLN, 130_HIS, 157_LYS, 160_ASN, 161_TYR, 162_PRO, 163_VAL, 164_ALA, 165_LYS, 166_GLY, 167_SER, 196_GLN, 197_ASN, 246_GLU, 248_THR",2.8
+4QHU_D,4QHU_A_light+4QHU_B_heavy,"101_ARG, 102_GLU, 104_PRO, 110_PHE, 26_LEU, 29_HIS, 55_ARG, 57_GLU, 59_PRO, 60_GLU, 61_ARG, 62_TYR, 64_SER, 65_VAL, 67_TRP, 99_LEU",2.2
+4QHU_C,4QHU_L_light+4QHU_H_heavy,"100_ARG, 101_ARG, 102_GLU, 103_PRO, 104_PRO, 110_PHE, 26_LEU, 27_ASN, 28_ILE, 30_ASN, 35_THR, 36_ASN, 39_ARG, 55_ARG, 57_GLU, 59_PRO, 60_GLU, 61_ARG, 62_TYR, 64_SER, 65_VAL, 67_TRP, 99_LEU",2.2
+2XQY_E,2XQY_K_light+2XQY_J_heavy,"332_VAL, 333_ALA, 334_PRO, 335_ALA, 337_GLN, 350_PRO, 351_ARG, 353_ASP, 354_LEU, 356_ARG, 357_ALA, 358_THR, 361_GLU, 362_ALA, 365_ARG, 370_LYS, 371_PRO, 372_PHE, 373_ASP",2.05
+2XQY_A,2XQY_L_light+2XQY_G_heavy,"332_VAL, 333_ALA, 334_PRO, 335_ALA, 337_GLN, 350_PRO, 351_ARG, 353_ASP, 354_LEU, 356_ARG, 357_ALA, 358_THR, 361_GLU, 362_ALA, 365_ARG, 370_LYS, 371_PRO, 372_PHE, 373_ASP",2.05
+7MZI_A,7MZI_L_light+7MZI_H_heavy,"403_ARG, 405_ASP, 416_GLY, 417_LYS, 421_TYR, 455_LEU, 456_PHE, 458_LYS, 473_TYR, 475_ALA, 476_GLY, 477_SER, 478_THR, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 505_TYR",1.85
+6ION_A,6ION_L_light+6ION_H_heavy,"135_SER, 137_HIS, 139_TYR, 165_GLN, 37_ALA, 39_GLU, 40_THR, 41_ILE, 62_ARG, 63_GLY, 64_LEU, 65_ASP, 68_GLY, 70_LEU, 72_PHE, 74_GLN",2.75
+3OR7_C,3OR7_B_light+3OR7_A_heavy,"45_TYR, 49_LEU, 50_ALA, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.3
+7JX3_R,7JX3_B_light+7JX3_A_heavy,"334_ASN, 335_LEU, 337_PRO, 339_GLY, 340_GLU, 343_ASN, 344_ALA, 345_THR, 346_ARG, 356_LYS, 357_ARG, 359_SER, 361_CYS, 441_LEU",2.65
+7JX3_R,7JX3_D_light+7JX3_C_heavy,"403_ARG, 445_VAL, 446_GLY, 449_TYR, 453_TYR, 455_LEU, 456_PHE, 487_ASN, 489_TYR, 493_GLN, 495_TYR, 496_GLY, 498_GLN, 499_PRO, 500_THR, 501_ASN, 502_GLY, 505_TYR",2.65
+7JX3_R,7JX3_L_light+7JX3_H_heavy,"369_TYR, 370_ASN, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 388_ASN, 390_LEU, 392_PHE, 412_PRO, 414_GLN, 427_ASP, 428_ASP, 429_PHE, 430_THR, 517_LEU",2.65
+4PY8_A,4PY8_J_light+4PY8_I_heavy,"297_ASN, 298_SER, 299_SER, 300_LEU, 301_PRO, 326_THR, 38_HIS, 40_VAL, 41_ASN, 42_LEU",2.91
+4PY8_B,4PY8_J_light+4PY8_I_heavy,"18_ILE, 19_ASP, 20_GLY, 21_TRP, 38_GLN, 41_THR, 42_GLN, 45_ILE, 49_THR, 52_VAL, 53_ASN, 56_ILE",2.91
+6HX4_A,6HX4_M_light+6HX4_I_heavy,"209_HIS, 224_LEU, 229_ILE, 230_GLN, 231_HIS, 238_TRP, 256_ASP, 282_ARG, 283_SER, 285_SER, 358_MET, 359_SER, 360_ILE, 361_PRO, 362_PRO, 363_GLU, 365_LYS",2.95
+6HX4_B,6HX4_L_light+6HX4_H_heavy,"209_HIS, 224_LEU, 229_ILE, 230_GLN, 231_HIS, 238_TRP, 256_ASP, 257_GLU, 282_ARG, 283_SER, 285_SER, 287_HIS, 358_MET, 359_SER, 360_ILE, 361_PRO, 362_PRO, 363_GLU, 365_LYS",2.95
+1QLE_B,1QLE_L_light+1QLE_H_heavy,"166_VAL, 167_GLY, 205_SER, 206_VAL, 207_ASP, 208_GLN, 209_GLU, 235_SER, 236_GLN, 237_GLU, 238_LYS, 240_GLU, 25_SER, 26_PRO, 29_HIS, 30_ASP",3.0
+8TV3_A,8TV3_L_light+8TV3_H_heavy,"102_PHE, 105_ASP, 107_LYS, 108_THR, 109_LEU, 112_LYS, 126_PHE, 128_GLU, 129_LYS, 130_GLY, 131_GLU, 132_VAL, 87_LYS",2.31
+8B7H_A,8B7H_L_light+8B7H_H_heavy,"126_LYS, 127_LYS, 128_PHE, 129_THR, 130_THR, 148_ARG, 153_LYS, 154_GLN",1.95
+1TPX_A,1TPX_C_light+1TPX_B_heavy,"127_GLY, 128_LEU, 156_ASN, 158_TYR, 159_ARG, 185_ILE, 188_LYS, 189_GLN, 191_THR, 192_VAL, 193_THR, 194_THR, 195_THR, 196_THR, 197_LYS, 198_GLY, 199_GLU, 200_ASN",2.56
+6U8C_A,6U8C_L_light+6U8C_H_heavy,"16_THR, 17_LEU, 18_SER, 19_GLY, 20_TYR, 21_THR, 22_THR, 38_TYR, 40_TYR, 41_VAL, 42_HIS",2.61
+6U8C_B,6U8C_C_light+6U8C_D_heavy,"16_THR, 17_LEU, 18_SER, 19_GLY, 20_TYR, 21_THR, 22_THR, 38_TYR, 40_TYR, 41_VAL, 42_HIS",2.61
+7CHP_E,7CHP_L_light+7CHP_H_heavy,"403_ARG, 405_ASP, 406_GLU, 408_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 449_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",2.36
+2DQJ_Y,2DQJ_L_light+2DQJ_H_heavy,"100_SER, 101_ASP, 102_GLY, 103_ASN, 15_HIS, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 62_TRP, 63_TRP, 73_ARG, 75_LEU, 77_ASN, 89_THR, 93_ASN, 96_LYS, 97_LYS, 98_ILE",1.8
+7CJF_C,7CJF_B_light+7CJF_A_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",2.11
+5SX5_M,5SX5_K_light+5SX5_J_heavy,"348_LEU, 349_PRO, 353_ARG, 384_GLN, 408_GLN, 409_HIS, 417_VAL, 418_SER, 420_ASP, 438_ILE, 440_SER, 441_GLY, 443_LYS, 464_THR, 465_LYS, 466_ILE, 467_ILE, 468_ARG, 469_ASN, 471_GLY",2.5
+5SX5_N,5SX5_L_light+5SX5_H_heavy,"348_LEU, 349_PRO, 384_GLN, 409_HIS, 417_VAL, 418_SER, 420_ASP, 440_SER, 441_GLY, 443_LYS, 464_THR, 465_LYS, 466_ILE, 467_ILE, 468_ARG, 469_ASN, 471_GLY, 473_ASN",2.5
+5W3E_B,5W3E_G_light+5W3E_E_heavy,"202_THR, 203_GLY, 204_GLN, 206_TYR, 58_THR, 59_HIS, 60_THR, 74_ASN, 75_ARG, 76_GLN, 77_ASN, 78_GLU",2.53
+3NIF_A,3NIF_L_light+3NIF_H_heavy,"149_ASN, 154_ILE, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.4
+3NIF_C,3NIF_F_light+3NIF_E_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.4
+3O0R_C,3O0R_L_light+3O0R_H_heavy,"101_ILE, 104_LEU, 105_ASN, 106_VAL, 107_PRO, 109_ARG, 94_PHE, 97_ALA, 98_TRP",2.7
+3O0R_B,3O0R_L_light+3O0R_H_heavy,"421_GLY, 422_ALA, 423_ALA, 424_MET, 425_THR, 427_MET, 428_ALA, 431_ASP, 432_GLN",2.7
+7JMO_A,7JMO_L_light+7JMO_H_heavy,"403_ARG, 405_ASP, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 495_TYR, 496_GLY, 500_THR, 501_ASN, 502_GLY, 505_TYR",2.36
+8G3Z_D,8G3Z_F_light+8G3Z_E_heavy,"116_ARG, 117_GLU, 147_ARG, 148_GLU, 149_ASP, 150_ARG, 177_TRP, 178_SER, 197_ASP, 198_SER, 220_ASN, 221_ILE, 226_GLU, 245_ALA, 246_SER, 292_ARG, 294_ASN, 295_ARG, 345_SER, 346_GLY, 374_ARG, 408_TRP, 409_TYR, 435_LYS",2.3
+8G3Z_B,8G3Z_K_light+8G3Z_J_heavy,"116_ARG, 117_GLU, 147_ARG, 148_GLU, 149_ASP, 150_ARG, 177_TRP, 178_SER, 197_ASP, 198_SER, 219_LYS, 220_ASN, 221_ILE, 223_ARG, 226_GLU, 245_ALA, 246_SER, 292_ARG, 294_ASN, 295_ARG, 343_LYS, 345_SER, 346_GLY, 374_ARG, 408_TRP, 409_TYR, 435_LYS",2.3
+8G3Z_A,8G3Z_I_light+8G3Z_G_heavy,"116_ARG, 117_GLU, 147_ARG, 148_GLU, 149_ASP, 150_ARG, 177_TRP, 178_SER, 197_ASP, 198_SER, 220_ASN, 221_ILE, 226_GLU, 245_ALA, 246_SER, 292_ARG, 294_ASN, 295_ARG, 343_LYS, 345_SER, 346_GLY, 374_ARG, 408_TRP, 409_TYR, 435_LYS",2.3
+8G3Z_C,8G3Z_L_light+8G3Z_H_heavy,"116_ARG, 117_GLU, 147_ARG, 148_GLU, 149_ASP, 150_ARG, 177_TRP, 178_SER, 197_ASP, 198_SER, 220_ASN, 221_ILE, 226_GLU, 245_ALA, 246_SER, 276_GLU, 292_ARG, 294_ASN, 295_ARG, 343_LYS, 345_SER, 346_GLY, 374_ARG, 408_TRP, 409_TYR, 435_LYS",2.3
+4OM0_G,4OM0_L_light+4OM0_H_heavy,"102_GLU, 276_ASN, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 370_GLU, 371_ILE, 425_ASN, 426_MET, 427_TRP, 429_GLY, 457_ASP, 458_GLY, 459_GLY, 460_ALA, 461_ASN, 463_THR, 465_ASN, 466_GLU, 467_THR, 469_ARG, 473_GLY, 476_LYS, 97_LYS, 99_ASN",2.29
+6W03_G,6W03_L_light+6W03_H_heavy,"322_ILE, 323_ILE, 324_GLY, 325_ASP, 326_ILE, 327_ARG, 328_GLN, 330_HIS, 415_THR",2.4
+5DUR_C,5DUR_L_light+5DUR_H_heavy,"110_HIS, 112_GLU, 113_LYS, 114_ILE, 115_GLN, 119_LYS, 137_TYR, 138_GLN, 145_ARG, 167_THR, 168_ASN, 169_GLN, 170_GLU, 252_TYR, 255_LYS, 257_VAL, 258_LYS, 68_ASP, 69_GLU, 71_ILE, 72_ASN, 74_PRO",2.82
+5DUR_A,5DUR_D_light+5DUR_B_heavy,"110_HIS, 112_GLU, 113_LYS, 115_GLN, 119_LYS, 137_TYR, 138_GLN, 145_ARG, 167_THR, 168_ASN, 169_GLN, 252_TYR, 255_LYS, 257_VAL, 258_LYS, 68_ASP, 69_GLU, 71_ILE, 72_ASN",2.82
+3HAE_K,3HAE_G_light+3HAE_I_heavy,"43_GLY, 44_GLU, 77_GLU, 81_ARG, 89_GLN, 90_PRO, 92_ILE",2.9
+3HAE_J,3HAE_N_light+3HAE_O_heavy,"155_GLN, 65_ARG, 66_LYS, 68_LYS, 69_ALA, 72_GLN, 73_THR",2.9
+3HAE_D,3HAE_G_light+3HAE_I_heavy,"150_ALA, 155_GLN, 159_TYR, 163_THR, 167_TRP, 65_ARG, 66_LYS, 69_ALA, 72_GLN, 73_THR, 76_VAL, 80_THR",2.9
+3HAE_A,3HAE_L_light+3HAE_H_heavy,"150_ALA, 155_GLN, 163_THR, 167_TRP, 61_ASP, 65_ARG, 66_LYS, 68_LYS, 69_ALA, 72_GLN, 73_THR, 75_ARG, 80_THR",2.9
+3HAE_P,3HAE_S_light+3HAE_T_heavy,"146_LYS, 150_ALA, 155_GLN, 65_ARG, 66_LYS, 68_LYS, 69_ALA, 72_GLN, 73_THR, 76_VAL, 80_THR",2.9
+6O3A_E,6O3A_A_light+6O3A_B_heavy,"134_LEU, 137_LYS, 138_PHE, 139_GLY, 140_PHE, 141_GLN, 81_LEU, 84_HIS, 85_GLN, 87_TYR, 88_PRO, 89_LEU, 91_LYS",2.1
+6OSH_K,6OSH_L_light+6OSH_H_heavy,"343_GLN, 344_HIS, 345_PRO, 346_HIS, 347_SER, 348_HIS, 349_HIS, 350_LEU, 351_SER, 353_THR, 354_ASP, 355_PHE, 368_PRO, 371_GLN, 376_TRP, 378_PHE, 382_LYS, 385_ARG, 386_MET",1.12
+7E9B_C,7E9B_L_light+7E9B_H_heavy,"126_ILE, 128_LEU, 130_PRO, 131_LYS, 132_ALA, 134_ILE, 60_SER, 61_GLU, 62_SER, 64_VAL, 66_ASN, 81_ALA, 83_PRO, 84_GLU, 85_ASP, 86_ARG, 87_SER, 88_GLN",1.78
+7UJE_C,7UJE_F_light+7UJE_E_heavy,"117_GLU, 149_ASN, 154_ILE, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.5
+7UJE_A,7UJE_L_light+7UJE_H_heavy,"149_ASN, 154_ILE, 157_GLU, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.5
+3L5X_A,3L5X_L_light+3L5X_H_heavy,"101_LEU, 104_LYS, 105_LYS, 107_PHE, 108_ARG, 109_GLU, 110_GLY, 11_ARG, 14_ILE, 15_GLU",1.9
+6PA0_C,6PA0_B_light+6PA0_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.05
+7LR4_D,7LR4_L_light+7LR4_H_heavy,"508_THR, 522_GLU, 524_VAL, 526_ILE, 528_HIS, 562_ALA, 564_VAL, 573_ASN, 574_TYR, 575_ALA, 576_PHE, 577_TYR, 579_ILE",2.1
+7LR4_C,7LR4_B_light+7LR4_A_heavy,"508_THR, 512_ASP, 522_GLU, 524_VAL, 526_ILE, 528_HIS, 564_VAL, 565_ARG, 573_ASN, 574_TYR, 575_ALA, 576_PHE, 577_TYR, 579_ILE",2.1
+8J8R_B,8J8R_L_light+8J8R_H_heavy,"174_PHE, 211_HIS, 212_GLY, 214_PRO, 277_PRO, 278_PHE, 279_LEU, 280_ALA, 283_ARG, 298_ASP, 299_THR, 300_ASN, 301_LEU, 346_HIS, 8_ARG",2.9
+8J8R_C,8J8R_N_light+8J8R_M_heavy,"174_PHE, 211_HIS, 212_GLY, 214_PRO, 276_THR, 277_PRO, 278_PHE, 279_LEU, 280_ALA, 283_ARG, 298_ASP, 299_THR, 300_ASN, 301_LEU, 346_HIS, 8_ARG",2.9
+8J8R_A,8J8R_E_light+8J8R_D_heavy,"211_HIS, 212_GLY, 214_PRO, 276_THR, 278_PHE, 279_LEU, 280_ALA, 283_ARG, 298_ASP, 299_THR, 300_ASN, 301_LEU, 346_HIS, 347_PRO, 8_ARG",2.9
+3P0Y_A,3P0Y_L_light+3P0Y_H_heavy,"409_HIS, 411_GLN, 412_PHE, 417_VAL, 436_ASP, 438_ILE, 440_SER, 453_TRP, 454_LYS, 457_PHE, 460_SER, 461_GLY, 462_GLN, 463_LYS, 464_THR, 465_LYS, 467_ILE",1.8
+7N3I_C,7N3I_L_light+7N3I_H_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 495_TYR, 500_THR, 501_ASN, 502_GLY, 503_VAL, 505_TYR",2.03
+6MU7_G,6MU7_L_light+6MU7_H_heavy,"135_THR, 136_ASN, 138_ILE, 139_THR, 322_ILE, 323_ILE, 324_GLY, 325_ASP, 326_ILE, 327_ARG, 328_GLN, 330_HIS, 417_PRO",2.5
+7TD8_C,7TD8_F_light+7TD8_E_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.6
+7TD8_A,7TD8_L_light+7TD8_H_heavy,"117_GLU, 149_ASN, 154_ILE, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.6
+5KJR_G,5KJR_L_light+5KJR_H_heavy,"103_GLN, 106_GLU, 107_ASP, 217_TYR, 221_ALA, 51_THR, 52_LEU, 53_PHE, 54_CYS, 71_THR, 72_HIS, 73_ALA, 74_CYS, 75_VAL, 76_PRO, 77_THR, 78_ASP",2.98
+6A77_A,6A77_L_light+6A77_H_heavy,"13_GLN, 23_ASP, 24_GLY, 25_THR, 27_VAL, 29_SER, 57_LYS, 58_GLN, 59_LEU, 60_GLU, 61_ASN, 63_VAL, 65_GLN, 67_ARG, 68_TYR",2.0
+8H07_C,8H07_J_light+8H07_E_heavy,"449_TYR, 456_PHE, 476_GLY, 477_ASN, 483_VAL, 484_ALA, 485_GLY, 486_VAL, 487_ASN, 489_TYR, 490_PHE, 493_GLN",2.9
+8H07_C,8H07_O_light+8H07_N_heavy,"342_PHE, 343_ASN, 344_ALA, 345_THR, 368_LEU, 371_PHE, 374_PHE, 375_PHE, 436_TRP, 437_ASN, 439_ASN, 440_LYS, 441_LEU, 443_SER, 445_VAL, 499_PRO, 503_VAL, 506_GLN, 508_TYR",2.9
+8H07_B,8H07_H_light+8H07_G_heavy,"449_TYR, 456_PHE, 476_GLY, 477_ASN, 483_VAL, 484_ALA, 485_GLY, 486_VAL, 487_ASN, 489_TYR, 490_PHE, 493_GLN",2.9
+8H07_B,8H07_K_light+8H07_I_heavy,"342_PHE, 343_ASN, 344_ALA, 345_THR, 368_LEU, 371_PHE, 374_PHE, 375_PHE, 436_TRP, 437_ASN, 439_ASN, 440_LYS, 441_LEU, 443_SER, 445_VAL, 499_PRO, 503_VAL, 506_GLN, 508_TYR",2.9
+8H07_A,8H07_P_light+8H07_M_heavy,"449_TYR, 456_PHE, 476_GLY, 477_ASN, 483_VAL, 484_ALA, 485_GLY, 486_VAL, 487_ASN, 489_TYR, 490_PHE, 493_GLN",2.9
+8H07_A,8H07_R_light+8H07_Q_heavy,"342_PHE, 343_ASN, 344_ALA, 345_THR, 368_LEU, 371_PHE, 374_PHE, 375_PHE, 436_TRP, 437_ASN, 439_ASN, 440_LYS, 441_LEU, 443_SER, 445_VAL, 499_PRO, 503_VAL, 506_GLN, 508_TYR",2.9
+7UDG_C,7UDG_F_light+7UDG_E_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN",2.8
+7UDG_A,7UDG_L_light+7UDG_H_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.8
+1CZ8_W,1CZ8_L_light+1CZ8_H_heavy,"45_TYR, 48_LYS, 79_GLN, 80_ILE, 81_MET, 82_ARG, 83_ILE, 86_HIS, 87_GLN, 88_GLY, 89_GLN, 90_HIS, 91_ILE, 92_GLY, 93_GLU, 94_MET",2.4
+1CZ8_V,1CZ8_X_light+1CZ8_Y_heavy,"45_TYR, 48_LYS, 79_GLN, 80_ILE, 81_MET, 82_ARG, 83_ILE, 86_HIS, 87_GLN, 88_GLY, 89_GLN, 90_HIS, 91_ILE, 92_GLY, 93_GLU, 94_MET",2.4
+8YX1_C,8YX1_B_light+8YX1_A_heavy,"12_LEU, 13_ILE, 27_LEU, 30_ASP, 47_PHE, 49_ASP, 50_THR, 53_ARG, 54_GLU, 55_THR, 56_HIS, 57_CYS, 58_HIS, 59_GLN, 61_LYS, 80_ASP",2.7
+8YX1_R,8YX1_L_light+8YX1_H_heavy,"12_LEU, 13_ILE, 27_LEU, 30_ASP, 47_PHE, 49_ASP, 50_THR, 53_ARG, 54_GLU, 55_THR, 56_HIS, 57_CYS, 58_HIS, 59_GLN, 80_ASP, 97_GLU",2.7
+8TQ7_A,8TQ7_L_light+8TQ7_H_heavy,"195_PRO, 196_GLU, 197_GLY, 198_ASP, 219_LEU, 220_ASN, 222_GLU, 226_GLN, 227_GLU, 248_VAL, 249_VAL, 250_PRO, 253_LYS, 256_LYS, 257_TYR",2.8
+8TQ7_C,8TQ7_G_light+8TQ7_F_heavy,"195_PRO, 196_GLU, 197_GLY, 198_ASP, 219_LEU, 220_ASN, 222_GLU, 226_GLN, 227_GLU, 247_VAL, 248_VAL, 249_VAL, 250_PRO, 253_LYS, 256_LYS, 257_TYR",2.8
+2FD6_U,2FD6_L_light+2FD6_H_heavy,"185_GLU, 186_ASN, 187_LEU, 188_PRO, 189_GLN, 190_ASN, 191_GLY, 192_ARG, 217_GLY, 218_PRO, 220_ASN, 267_THR, 269_SER",1.9
+7MUB_C,7MUB_B_light+7MUB_A_heavy,"45_TYR, 48_VAL, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 63_PRO, 64_ARG",3.0
+5HBV_B,5HBV_C_light+5HBV_D_heavy,"0_LYS, 10_LYS, 112_TYR, 11_LEU, 13_GLU, 14_ASP, 15_TYR, 2_GLU, 3_HIS, 64_ASN, 66_LYS, 67_TRP, 68_ASN, 69_PRO, 6_ARG, 70_ASP, 71_ASP, 72_TYR, 73_GLY",2.7
+2W9E_A,2W9E_L_light+2W9E_H_heavy,"142_GLY, 143_SER, 144_ASP, 145_TYR, 147_ASP, 148_ARG, 149_TYR, 151_ARG, 152_GLU, 153_ASN, 155_HIS, 156_ARG, 197_ASN, 199_THR, 204_LYS",2.9
+7T87_A,7T87_L_light+7T87_H_heavy,"169_LYS, 246_GLN, 248_VAL, 252_LYS, 254_SER, 257_GLU, 278_ASP, 279_LYS, 280_LYS, 281_GLU, 283_LYS, 303_LEU, 304_ASN, 305_ASP, 66_ARG, 79_ARG, 91_VAL, 92_ASP, 93_ASP",3.0
+3U4E_G,3U4E_L_light+3U4E_H_heavy,"158_SER, 160_ASN, 162_THR, 164_GLU, 165_LEU, 166_ARG, 167_ASP, 168_LYS, 169_LYS, 170_GLN, 171_LYS, 172_ALA, 173_TYR, 223_ASP, 242_GLU",2.19
+3U4E_J,3U4E_B_light+3U4E_A_heavy,"158_SER, 160_ASN, 162_THR, 164_GLU, 165_LEU, 166_ARG, 167_ASP, 168_LYS, 169_LYS, 170_GLN, 171_LYS, 172_ALA, 173_TYR, 223_ASP, 242_GLU",2.19
+2EIZ_C,2EIZ_A_light+2EIZ_B_heavy,"100_SER, 101_ASP, 102_GLY, 14_ARG, 15_HIS, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 62_TRP, 63_TRP, 75_LEU, 77_ASN, 89_THR, 93_ASN, 96_LYS, 97_LYS, 98_ILE",1.9
+2P7T_C,2P7T_B_light+2P7T_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.05
+5XKU_A,5XKU_B_light+5XKU_C_heavy,"135_ALA, 136_THR, 137_SER, 140_ARG, 144_GLY, 145_SER, 153_TRP, 158_THR, 183_HIS, 185_SER, 186_VAL, 190_GLU, 193_LYS, 194_LEU, 219_ALA, 220_ARG, 221_PRO, 222_GLN, 225_GLY, 226_LEU, 228_GLY, 98_TYR",1.78
+4XNY_G,4XNY_L_light+4XNY_H_heavy,"102_GLU, 105_HIS, 106_THR, 109_ILE, 276_ASN, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 283_THR, 357_LYS, 365_THR, 366_GLY, 367_GLY, 368_ASP, 371_ILE, 426_MET, 427_TRP, 428_GLN, 429_ARG, 430_VAL, 455_THR, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_ASN, 461_THR, 462_ASN, 463_SER, 465_ARG, 466_GLU, 469_ARG, 476_ARG, 97_LYS",2.3
+4EDW_V,4EDW_L_light+4EDW_H_heavy,"106_THR, 107_ALA, 108_CYS, 10_GLY, 11_GLU, 12_PHE, 13_SER, 17_SER, 19_SER, 20_VAL, 21_TRP, 23_GLY, 52_TYR, 54_PHE, 56_THR, 8_HIS, 9_ARG",2.48
+7LJA_A,7LJA_D_light+7LJA_E_heavy,"69_ARG, 70_GLU, 73_LEU, 74_ARG, 76_HIS, 77_PRO, 78_CYS, 79_VAL, 80_SER, 81_ASP, 82_GLN, 83_GLU",2.77
+7LJA_B,7LJA_F_light+7LJA_G_heavy,"70_GLU, 73_LEU, 74_ARG, 76_HIS, 77_PRO, 78_CYS, 79_VAL, 80_SER, 81_ASP, 82_GLN, 83_GLU",2.77
+7ORA_R,7ORA_B_light+7ORA_A_heavy,"331_HIS, 353_TRP, 354_ASN, 355_ARG, 356_LYS, 357_ARG, 359_SER, 360_ASN, 396_TYR, 426_PRO, 428_ASP, 462_LYS, 463_PRO, 464_PHE, 465_GLU, 466_ARG, 468_ILE, 516_GLU, 518_LEU, 519_HIS, 520_ALA, 523_THR",2.6
+7ORA_R,7ORA_G_light+7ORA_F_heavy,"421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 473_TYR, 475_ALA, 476_GLY, 477_SER, 478_LYS, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN",2.6
+7ORA_C,7ORA_E_light+7ORA_D_heavy,"331_HIS, 353_TRP, 354_ASN, 355_ARG, 356_LYS, 357_ARG, 359_SER, 360_ASN, 396_TYR, 426_PRO, 428_ASP, 462_LYS, 463_PRO, 464_PHE, 465_GLU, 466_ARG, 468_ILE, 516_GLU, 518_LEU, 519_HIS, 520_ALA, 523_THR",2.6
+7ORA_C,7ORA_L_light+7ORA_H_heavy,"455_LEU, 458_LYS, 473_TYR, 475_ALA, 476_GLY, 477_SER, 478_LYS, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN",2.6
+7UFH_C,7UFH_F_light+7UFH_E_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 206_SER, 208_ARG, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",3.0
+7UFH_A,7UFH_L_light+7UFH_H_heavy,"117_GLU, 149_ASN, 154_ILE, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",3.0
+7LFA_A,7LFA_D_light+7LFA_B_heavy,"123_ILE, 124_MET, 125_LYS, 126_ASP, 127_LYS, 128_ASN, 130_HIS, 132_LYS, 133_GLY, 134_GLN, 137_ARG",1.86
+7LFA_C,7LFA_L_light+7LFA_H_heavy,"123_ILE, 124_MET, 125_LYS, 126_ASP, 127_LYS, 128_ASN, 130_HIS, 132_LYS, 133_GLY, 134_GLN, 137_ARG",1.86
+6NMR_I,6NMR_G_light+6NMR_F_heavy,"31_ILE, 32_PRO, 33_VAL, 36_ILE, 52_GLN, 53_LYS, 62_THR, 64_SER, 65_ASP, 66_LEU, 67_THR, 68_LYS, 69_ARG, 70_ASN, 71_ASN, 74_PHE, 76_ILE, 95_ARG, 96_LYS",2.42
+6NMR_E,6NMR_B_light+6NMR_A_heavy,"29_SER, 30_LEU, 31_ILE, 32_PRO, 33_VAL, 36_ILE, 38_TRP, 50_TYR, 52_GLN, 53_LYS, 62_THR, 64_SER, 67_THR, 68_LYS, 69_ARG, 70_ASN, 71_ASN, 74_PHE, 76_ILE, 96_LYS",2.42
+6NMR_M,6NMR_K_light+6NMR_J_heavy,"29_SER, 30_LEU, 31_ILE, 32_PRO, 33_VAL, 36_ILE, 52_GLN, 53_LYS, 62_THR, 64_SER, 65_ASP, 67_THR, 68_LYS, 69_ARG, 70_ASN, 71_ASN, 74_PHE, 96_LYS",2.42
+6NMR_S,6NMR_L_light+6NMR_H_heavy,"30_LEU, 31_ILE, 32_PRO, 33_VAL, 36_ILE, 38_TRP, 50_TYR, 52_GLN, 53_LYS, 54_GLU, 62_THR, 64_SER, 67_THR, 68_LYS, 69_ARG, 70_ASN, 71_ASN, 73_ASP, 74_PHE, 75_SER, 76_ILE, 95_ARG, 96_LYS",2.42
+7XS8_E,7XS8_L_light+7XS8_A_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",2.8
+6U3I_A,6U3I_L_light+6U3I_H_heavy,"535_SER, 536_VAL, 537_HIS, 538_THR, 539_ALA, 540_PRO, 541_PRO, 542_ALA, 543_GLU, 544_ALA, 545_SER, 546_MET, 547_GLY, 548_THR, 549_ARG, 550_VAL, 551_HIS, 553_HIS, 554_GLN, 555_GLN, 557_HIS, 567_GLU, 568_VAL, 569_GLU, 580_ARG, 593_GLU, 595_SER",2.9
+5CBE_F,5CBE_D_light+5CBE_C_heavy,"18_PHE, 20_PRO, 21_ARG, 22_ARG, 23_PHE, 24_ILE, 25_ASP, 26_ARG, 27_ILE, 28_GLN, 46_LYS, 47_ASN, 59_TRP, 62_ARG, 63_MET, 66_VAL, 67_LEU, 69_LYS, 70_ARG",2.4
+5CBE_E,5CBE_B_light+5CBE_A_heavy,"18_PHE, 19_ILE, 20_PRO, 21_ARG, 22_ARG, 23_PHE, 24_ILE, 25_ASP, 26_ARG, 27_ILE, 28_GLN, 46_LYS, 47_ASN, 59_TRP, 62_ARG, 63_MET",2.4
+7WHB_B,7WHB_K_light+7WHB_J_heavy,"445_VAL, 446_GLY, 449_TYR, 450_ASN, 452_LEU, 470_THR, 472_ILE, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER, 500_THR, 505_TYR",2.67
+7WHB_A,7WHB_E_light+7WHB_D_heavy,"445_VAL, 446_GLY, 449_TYR, 450_ASN, 452_LEU, 470_THR, 484_GLU, 485_GLY, 486_PHE, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER, 500_THR, 505_TYR",2.67
+7WHB_C,7WHB_H_light+7WHB_G_heavy,"445_VAL, 446_GLY, 449_TYR, 450_ASN, 452_LEU, 470_THR, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER, 500_THR, 505_TYR",2.67
+6P4B_D,6P4B_A_light+6P4B_B_heavy,"100_SER, 101_ASP, 102_GLY, 15_HIS, 16_GLY, 19_ASN, 20_TYR, 21_GLN, 62_TRP, 63_TRP, 74_ASN, 76_SER, 77_ASN, 89_THR, 93_ASN, 96_LYS, 97_LYS",1.9
+6P4B_C,6P4B_L_light+6P4B_H_heavy,"100_SER, 101_ASP, 102_GLY, 15_HIS, 16_GLY, 19_ASN, 20_TYR, 21_GLN, 62_TRP, 63_TRP, 73_GLU, 74_ASN, 76_SER, 77_ASN, 89_THR, 93_ASN, 96_LYS, 97_LYS",1.9
+7TXT_S,7TXT_D_light+7TXT_C_heavy,"194_GLN, 199_SER, 200_CYS, 201_LYS, 202_ASN, 203_SER, 204_TRP, 205_ASN, 206_THR, 207_GLY, 208_ASN, 209_CYS, 210_THR, 211_ASN, 214_SER, 218_ILE, 234_ARG, 235_HIS, 238_GLN, 240_HIS, 241_ARG",3.0
+3NIG_A,3NIG_L_light+3NIG_H_heavy,"149_ASN, 154_ILE, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.25
+3NIG_C,3NIG_F_light+3NIG_E_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.25
+3L5W_I,3L5W_L_light+3L5W_H_heavy,"101_LEU, 104_LYS, 105_LYS, 107_PHE, 108_ARG, 109_GLU, 110_GLY, 11_ARG, 14_ILE, 15_GLU",2.0
+3L5W_J,3L5W_A_light+3L5W_B_heavy,"101_LEU, 104_LYS, 105_LYS, 107_PHE, 108_ARG, 109_GLU, 110_GLY, 11_ARG, 14_ILE, 15_GLU",2.0
+4OLU_G,4OLU_L_light+4OLU_H_heavy,"102_GLU, 276_ASN, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 371_ILE, 457_ASP, 458_GLY, 459_GLY, 460_ALA, 461_ASN, 463_THR, 465_ASN, 466_GLU, 467_THR, 469_ARG, 476_LYS, 97_LYS",2.2
+4FFV_A,4FFV_L_light+4FFV_H_heavy,"116_TYR, 117_ASN, 133_TYR, 138_ARG, 139_GLN, 140_LEU, 145_LYS, 241_ASP, 89_GLU, 91_SER, 94_GLU, 95_ILE, 96_PHE, 97_GLY, 98_ASP, 99_SER",2.4
+4FFV_B,4FFV_C_light+4FFV_D_heavy,"116_TYR, 117_ASN, 138_ARG, 139_GLN, 140_LEU, 145_LYS, 241_ASP, 89_GLU, 91_SER, 94_GLU, 95_ILE, 96_PHE, 97_GLY, 98_ASP, 99_SER",2.4
+7KF1_F,7KF1_E_light+7KF1_D_heavy,"48_LYS, 49_PRO, 81_MET, 83_ILE, 86_HIS, 87_GLN, 88_GLY, 89_GLN, 90_HIS, 91_ILE, 92_GLY, 93_GLU",2.45
+7KF1_J,7KF1_I_light+7KF1_G_heavy,"48_LYS, 49_PRO, 81_MET, 86_HIS, 87_GLN, 88_GLY, 89_GLN, 90_HIS, 91_ILE, 92_GLY, 93_GLU",2.45
+7KF1_C,7KF1_B_light+7KF1_A_heavy,"48_LYS, 49_PRO, 81_MET, 83_ILE, 86_HIS, 87_GLN, 88_GLY, 89_GLN, 90_HIS, 91_ILE, 92_GLY, 93_GLU",2.45
+7KF1_V,7KF1_L_light+7KF1_H_heavy,"48_LYS, 49_PRO, 81_MET, 83_ILE, 84_LYS, 86_HIS, 87_GLN, 88_GLY, 89_GLN, 90_HIS, 91_ILE, 92_GLY, 93_GLU",2.45
+8K3C_B,8K3C_D_light+8K3C_C_heavy,"214_GLY, 237_ILE, 238_GLY, 239_SER, 240_CYS, 241_SER, 242_ARG, 243_GLY, 244_VAL, 305_LEU, 458_PHE, 488_PRO, 489_GLY, 490_GLN, 504_TRP, 506_GLY, 507_VAL, 530_GLN, 531_THR, 532_ALA, 557_ASN, 559_GLN, 581_TYR, 588_ILE",2.88
+6E4X_B,6E4X_Y_light+6E4X_Z_heavy,"100_TYR, 101_ASP, 105_TYR, 216_ASN, 218_GLY, 219_PHE, 220_ARG, 221_PRO, 222_ARG, 223_ILE, 224_ARG, 229_ARG, 269_ARG, 65_THR, 90_ARG, 91_SER, 93_ALA, 94_TYR, 95_SER",2.25
+2NXY_A,2NXY_C_light+2NXY_D_heavy,"119_CYS, 120_VAL, 122_LEU, 200_VAL, 202_THR, 203_GLN, 419_ARG, 421_LYS, 422_GLN, 423_ILE, 434_MET, 437_PRO",2.0
+4RGM_A,4RGM_B_light+4RGM_C_heavy,"110_ARG, 177_PHE, 178_ASN, 18_THR, 19_GLY, 207_LYS, 20_LEU, 22_GLU, 23_ASN, 26_VAL, 29_ASP, 30_ASP, 31_ASN, 58_LEU, 59_GLY, 60_ASN, 88_ASN, 90_TYR",2.69
+4RGM_S,4RGM_L_light+4RGM_H_heavy,"110_ARG, 177_PHE, 178_ASN, 18_THR, 19_GLY, 20_LEU, 22_GLU, 23_ASN, 26_VAL, 29_ASP, 30_ASP, 31_ASN, 58_LEU, 59_GLY, 60_ASN, 88_ASN, 90_TYR",2.69
+7N4J_A,7N4J_L_light+7N4J_H_heavy,"444_LYS, 446_GLY, 449_TYR, 450_ASN, 452_LEU, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER, 495_TYR, 496_GLY, 498_GLN, 505_TYR",2.21
+6VY6_A,6VY6_L_light+6VY6_H_heavy,"242_ARG, 389_TYR, 401_VAL, 402_ASN, 458_TYR, 488_PRO, 489_GLY, 490_GLN, 491_SER, 492_GLN, 494_PRO, 497_ASN, 501_GLU, 504_TRP, 505_GLU, 506_GLY, 507_THR, 528_SER, 529_ASN, 530_GLN, 531_THR, 532_ALA, 533_GLU, 555_ASP, 556_THR, 557_ASN, 559_GLN, 581_TYR, 583_THR",2.6
+3G04_C,3G04_A_light+3G04_B_heavy,"105_HIS, 107_GLU, 109_ARG, 110_ASN, 129_LYS, 130_PHE, 151_ASP, 152_ILE, 153_PHE, 155_ILE, 157_GLU, 183_LYS, 185_TYR, 208_ASN, 209_LYS, 235_GLN, 255_ARG, 256_ASN, 38_ARG, 56_THR, 58_LYS, 80_ARG, 82_TYR",2.55
+5GMQ_A,5GMQ_C_light+5GMQ_B_heavy,"506_LEU, 510_ASP, 529_ILE, 531_PRO, 535_TRP, 536_GLU, 539_ASP, 540_TYR, 541_TYR, 542_ARG, 543_LYS, 544_GLN, 553_TRP",2.7
+6XSN_C,6XSN_L_light+6XSN_H_heavy,"133_GLN, 155_LYS, 183_PRO, 184_LEU, 185_GLU, 186_GLU, 190_LYS",2.87
+5KVF_E,5KVF_L_light+5KVF_H_heavy,"307_LEU, 340_LYS, 342_PRO, 343_ALA, 344_GLN, 347_VAL, 348_ASP, 350_GLN, 351_THR, 352_LEU, 353_THR, 354_PRO, 355_VAL, 358_LEU, 391_VAL",1.4
+7LXX_A,7LXX_L_light+7LXX_H_heavy,"144_TYR, 16_VAL, 17_ASN, 19_THR, 246_ARG, 247_SER, 248_TYR, 250_THR, 251_PRO, 252_GLY, 253_ASP, 254_SER, 255_SER, 256_SER, 257_GLY",3.0
+4ALA_C,4ALA_L_light+4ALA_H_heavy,"310_LYS, 313_SER, 314_GLU, 315_THR, 316_GLN, 317_HIS, 321_LEU, 352_ILE, 354_ALA, 368_GLU",1.84
+5X2P_A,5X2P_L_light+5X2P_H_heavy,"194_GLY, 197_LEU, 198_ASN, 200_ASN, 201_TRP, 202_ARG, 203_TRP, 225_ILE, 226_GLU, 227_ASP, 228_SER, 229_GLU, 230_ILE, 255_ASN, 257_LYS, 453_SER, 454_ILE, 455_HIS, 456_LEU, 460_LYS",2.61
+5X2P_C,5X2P_K_light+5X2P_J_heavy,"194_GLY, 197_LEU, 198_ASN, 200_ASN, 201_TRP, 202_ARG, 203_TRP, 225_ILE, 226_GLU, 227_ASP, 228_SER, 229_GLU, 230_ILE, 255_ASN, 453_SER, 454_ILE, 455_HIS, 456_LEU, 460_LYS",2.61
+5LCV_A,5LCV_L_light+5LCV_H_heavy,"101_TRP, 102_GLY, 103_ASN, 104_GLY, 251_LYS, 252_ARG, 69_ALA, 70_SER, 71_ASP, 72_SER, 73_ARG, 82_LEU, 99_ARG",2.64
+4WV1_F,4WV1_D_light+4WV1_E_heavy,"154_THR, 155_ASN, 158_LYS, 161_LYS, 163_LEU, 164_HIS, 167_PRO, 168_ALA, 169_ALA, 170_ASN, 171_THR, 172_VAL, 173_LYS, 175_ARG, 177_PRO, 205_LYS, 207_ARG, 210_HIS, 214_ILE, 216_GLU, 241_TYR, 248_ARG",2.36
+4WV1_C,4WV1_A_light+4WV1_B_heavy,"154_THR, 155_ASN, 158_LYS, 159_MET, 161_LYS, 163_LEU, 164_HIS, 167_PRO, 168_ALA, 169_ALA, 170_ASN, 171_THR, 172_VAL, 173_LYS, 175_ARG, 177_PRO, 205_LYS, 207_ARG, 210_HIS, 214_ILE, 216_GLU, 248_ARG",2.36
+1BVK_F,1BVK_D_light+1BVK_E_heavy,"102_GLY, 116_LYS, 117_GLY, 118_THR, 119_ASP, 120_VAL, 121_GLN, 124_ILE, 125_ARG, 18_ASP, 19_ASN, 22_GLY, 23_TYR, 24_SER, 27_ASN",2.7
+1BVK_C,1BVK_A_light+1BVK_B_heavy,"102_GLY, 103_ASN, 116_LYS, 117_GLY, 118_THR, 119_ASP, 120_VAL, 121_GLN, 124_ILE, 125_ARG, 129_LEU, 18_ASP, 19_ASN, 22_GLY, 23_TYR, 24_SER, 27_ASN",2.7
+5X2M_A,5X2M_L_light+5X2M_H_heavy,"194_GLY, 197_LEU, 198_ASN, 200_ASN, 201_TRP, 202_ARG, 203_TRP, 225_ILE, 226_GLU, 227_ASP, 228_SER, 229_GLU, 230_ILE, 255_ASN, 453_SER, 454_ILE, 455_HIS, 456_LEU, 460_LYS",2.21
+5X2M_C,5X2M_K_light+5X2M_J_heavy,"194_GLY, 197_LEU, 198_ASN, 200_ASN, 201_TRP, 202_ARG, 203_TRP, 225_ILE, 226_GLU, 227_ASP, 228_SER, 229_GLU, 230_ILE, 255_ASN, 453_SER, 454_ILE, 455_HIS, 456_LEU, 460_LYS",2.21
+8E8S_2,8E8S_L_light+8E8S_H_heavy,"137_THR, 138_THR, 139_HIS, 141_PHE, 171_ARG",2.73
+8E8S_1,8E8S_L_light+8E8S_H_heavy,"102_SER, 103_LYS, 105_PHE, 106_SER, 107_VAL, 108_TRP, 109_LYS, 114_ASP, 168_GLY, 226_ASP, 227_SER, 228_LEU, 234_LEU, 282_PRO, 87_VAL, 88_ALA, 89_ILE, 91_GLU",2.73
+6W0F_C,6W0F_B_light+6W0F_A_heavy,"45_TYR, 49_LEU, 50_ALA, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.4
+4G6J_A,4G6J_L_light+4G6J_H_heavy,"129_ASN, 19_VAL, 20_MET, 21_SER, 22_GLY, 23_PRO, 25_GLU, 27_LYS, 29_LEU, 31_LEU, 34_GLN, 35_ASP, 37_GLU, 38_GLN, 39_GLN, 64_GLU, 65_LYS, 66_ASN, 86_ASP, 87_PRO, 88_LYS",2.03
+4UU9_D,4UU9_L_light+4UU9_H_heavy,"14_LYS, 15_HIS, 16_SER, 17_VAL, 18_VAL, 20_LYS, 21_CYS, 24_ASP, 27_CYS, 32_GLU, 37_ARG, 40_ARG, 41_ILE, 42_SER, 43_LEU, 44_GLY, 45_PRO, 46_ARG, 47_CYS, 51_PHE",2.12
+4UU9_C,4UU9_B_light+4UU9_A_heavy,"14_LYS, 15_HIS, 16_SER, 17_VAL, 18_VAL, 20_LYS, 24_ASP, 27_CYS, 37_ARG, 40_ARG, 41_ILE, 42_SER, 43_LEU, 44_GLY, 45_PRO, 46_ARG, 47_CYS, 51_PHE",2.12
+3S35_X,3S35_L_light+3S35_H_heavy,"238_VAL, 241_LYS, 243_VAL, 266_LYS, 267_HIS, 270_LYS, 271_LYS, 272_LEU, 275_ARG, 293_THR, 294_ILE, 295_ASP, 298_THR, 299_ARG, 326_GLU, 327_LYS, 328_PRO, 329_PHE",2.2
+3GBM_C,3GBM_M_light+3GBM_I_heavy,"291_SER, 292_MET, 293_PRO, 318_THR, 38_HIS, 40_GLN, 41_ASP, 42_ILE",2.7
+3GBM_D,3GBM_M_light+3GBM_I_heavy,"19_ASP, 20_GLY, 21_TRP, 38_LYS, 41_THR, 42_GLN, 45_ILE, 46_ASP, 48_VAL, 49_THR, 52_VAL, 53_ASN",2.7
+3GBM_B,3GBM_L_light+3GBM_H_heavy,"19_ASP, 20_GLY, 21_TRP, 38_LYS, 41_THR, 42_GLN, 45_ILE, 46_ASP, 49_THR, 52_VAL, 53_ASN",2.7
+3GBM_A,3GBM_L_light+3GBM_H_heavy,"291_SER, 292_MET, 293_PRO, 318_THR, 38_HIS, 40_GLN, 41_ASP, 42_ILE",2.7
+3B9K_F,3B9K_C_light+3B9K_D_heavy,"22_VAL, 23_LYS, 24_SER, 25_ILE, 26_SER, 27_LYS, 28_LEU, 51_TRP, 52_SER, 53_SER, 55_LYS, 56_GLY, 57_VAL, 71_LEU, 72_GLU, 73_SER, 74_SER, 75_ASP, 76_SER, 77_ARG, 78_ARG",2.7
+3B9K_B,3B9K_L_light+3B9K_H_heavy,"22_VAL, 23_LYS, 24_SER, 25_ILE, 26_SER, 27_LYS, 28_LEU, 51_TRP, 53_SER, 54_SER, 57_VAL, 71_LEU, 72_GLU, 73_SER, 74_SER, 75_ASP, 76_SER, 77_ARG, 78_ARG",2.7
+1YY9_A,1YY9_C_light+1YY9_D_heavy,"349_PRO, 353_ARG, 382_LEU, 384_GLN, 408_GLN, 409_HIS, 411_GLN, 412_PHE, 417_VAL, 418_SER, 438_ILE, 440_SER, 441_GLY, 443_LYS, 465_LYS, 467_ILE, 468_SER, 469_ASN, 471_GLY, 473_ASN",2.61
+7UZ6_C,7UZ6_Q_light+7UZ6_P_heavy,"337_PRO, 340_GLU, 343_ASN, 344_ALA, 345_THR, 346_ARG, 356_LYS, 440_ASN, 441_LEU, 442_ASP, 443_SER, 444_LYS, 445_VAL, 448_ASN, 450_ASN, 499_PRO, 509_ARG",2.8
+7UZ6_B,7UZ6_N_light+7UZ6_M_heavy,"337_PRO, 340_GLU, 343_ASN, 344_ALA, 345_THR, 346_ARG, 356_LYS, 440_ASN, 441_LEU, 442_ASP, 443_SER, 444_LYS, 445_VAL, 448_ASN, 450_ASN, 499_PRO",2.8
+7UZ6_A,7UZ6_L_light+7UZ6_H_heavy,"340_GLU, 343_ASN, 344_ALA, 345_THR, 346_ARG, 356_LYS, 440_ASN, 441_LEU, 442_ASP, 443_SER, 444_LYS, 445_VAL, 448_ASN, 450_ASN, 451_TYR, 499_PRO, 509_ARG",2.8
+6BKC_E,6BKC_L_light+6BKC_H_heavy,"422_ILE, 427_LEU, 428_ASN, 429_CYS, 430_ASN, 431_ASP, 434_GLN, 438_ILE, 439_THR, 441_LEU, 442_PHE, 500_ARG, 529_TRP, 531_GLU, 613_TYR",2.6
+5D72_A,5D72_L_light+5D72_H_heavy,"112_ASP, 114_LEU, 115_LEU, 116_VAL, 117_ILE, 119_PHE, 120_ASP, 121_CYS, 123_GLU, 124_PRO, 12_PRO, 13_TRP, 15_HIS, 16_VAL, 17_ASN, 19_ILE, 20_GLN, 21_GLU, 23_ARG, 24_ARG, 83_HIS, 86_GLN, 87_HIS, 89_PRO",2.6
+5D72_B,5D72_N_light+5D72_M_heavy,"112_ASP, 114_LEU, 115_LEU, 116_VAL, 117_ILE, 119_PHE, 11_GLN, 120_ASP, 123_GLU, 12_PRO, 13_TRP, 15_HIS, 16_VAL, 17_ASN, 20_GLN, 21_GLU, 23_ARG, 24_ARG, 46_MET, 83_HIS, 85_LYS, 86_GLN, 87_HIS, 88_CYS, 90_PRO",2.6
+8JA5_D,8JA5_C_light+8JA5_B_heavy,"213_SER, 214_GLY, 237_ILE, 238_GLY, 239_SER, 240_CYS, 241_SER, 242_ARG, 243_GLY, 305_LEU, 401_ILE, 402_ARG, 403_PRO, 490_GLN, 491_SER, 492_GLN, 502_ILE, 503_CYS, 504_TRP, 557_ASN, 559_GLN, 581_TYR, 585_ASP, 586_ASN, 588_ILE",2.79
+8JA5_A,8JA5_L_light+8JA5_H_heavy,"212_GLN, 213_SER, 214_GLY, 237_ILE, 238_GLY, 239_SER, 240_CYS, 241_SER, 242_ARG, 243_GLY, 305_LEU, 401_ILE, 402_ARG, 403_PRO, 490_GLN, 491_SER, 492_GLN, 502_ILE, 503_CYS, 504_TRP, 557_ASN, 558_ALA, 559_GLN, 581_TYR, 585_ASP, 586_ASN",2.79
+7XEG_B,7XEG_F_light+7XEG_E_heavy,"403_ARG, 405_ASP, 406_GLU, 415_THR, 417_ASN, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 501_TYR, 502_GLY, 505_TYR",2.69
+7XEG_A,7XEG_D_light+7XEG_C_heavy,"403_ARG, 405_ASP, 406_GLU, 415_THR, 416_GLY, 417_ASN, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 501_TYR, 502_GLY, 505_TYR",2.69
+4FFW_B,4FFW_C_light+4FFW_D_heavy,"116_TYR, 117_ASN, 133_TYR, 138_ARG, 139_GLN, 140_LEU, 145_LYS, 241_ASP, 89_GLU, 90_ASN, 91_SER, 94_GLU, 95_ILE, 96_PHE, 97_GLY, 98_ASP, 99_SER",2.9
+4FFW_A,4FFW_L_light+4FFW_H_heavy,"116_TYR, 117_ASN, 133_TYR, 138_ARG, 139_GLN, 140_LEU, 145_LYS, 241_ASP, 72_ASN, 89_GLU, 90_ASN, 91_SER, 94_GLU, 95_ILE, 96_PHE, 97_GLY, 98_ASP, 99_SER",2.9
+3LDB_A,3LDB_B_light+3LDB_C_heavy,"158_ASP, 291_SER, 319_GLU, 320_HIS, 322_GLU, 323_LEU, 53_GLN, 55_SER, 57_GLU, 58_GLU, 61_GLU, 62_ARG, 65_ARG",2.7
+8WO4_E,8WO4_C_light+8WO4_D_heavy,"101_PRO, 102_GLN, 146_GLU, 173_GLU, 174_ASP, 175_TYR, 176_SER, 177_LEU, 178_GLU, 224_GLU, 227_LYS, 245_LYS, 352_ALA, 99_ARG",2.8
+8WO4_F,8WO4_A_light+8WO4_B_heavy,"101_PRO, 102_GLN, 103_ARG, 145_LEU, 146_GLU, 175_TYR, 176_SER, 177_LEU, 178_GLU, 224_GLU, 227_LYS, 245_LYS, 352_ALA, 99_ARG",2.8
+4BZ1_A,4BZ1_L_light+4BZ1_H_heavy,"313_ALA, 314_GLU, 315_THR, 316_GLN, 317_HIS, 319_THR, 321_VAL, 323_LYS, 352_ILE, 353_SER, 368_GLU, 370_GLU",2.15
+1K4D_C,1K4D_B_light+1K4D_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.3
+4K94_C,4K94_L_light+4K94_H_heavy,"317_PRO, 318_MET, 319_ILE, 320_ASN, 329_GLU, 330_ASN, 331_VAL, 332_ASP, 334_ILE, 336_GLU, 358_LYS, 360_GLU, 362_TYR, 364_LYS, 365_SER, 366_GLU, 372_ARG, 376_GLU, 378_HIS, 380_THR, 381_ARG",2.4
+5D8J_A,5D8J_L_light+5D8J_H_heavy,"101_LYS, 24_VAL, 25_GLY, 26_VAL, 27_GLY, 28_PHE, 29_ALA, 30_THR, 31_ARG, 32_LYS, 33_VAL, 36_MET, 58_PHE, 77_ASP, 78_ASP, 79_ARG, 98_TRP, 99_ASP",3.0
+6E3H_B,6E3H_L_light+6E3H_H_heavy,"18_VAL, 19_ASP, 20_GLY, 21_TRP, 38_LYS, 41_THR, 42_GLN, 45_ILE, 46_ASP, 48_VAL, 49_THR, 50_ASN, 52_VAL, 53_ASN, 57_ASP",2.9
+8VVL_A,8VVL_L_light+8VVL_H_heavy,"251_MET, 252_GLU, 253_ILE, 254_ILE, 255_LEU, 256_THR, 257_LEU, 258_SER, 285_GLU, 286_TRP, 288_LYS, 289_ARG, 290_ASN, 292_GLY, 293_LEU, 317_GLU, 340_ALA, 341_GLU, 344_LYS",1.8
+3EHB_B,3EHB_D_light+3EHB_C_heavy,"166_VAL, 167_GLY, 205_SER, 206_VAL, 207_ASP, 208_GLN, 209_GLU, 235_SER, 236_GLN, 237_GLU, 238_LYS, 240_GLU, 25_SER, 26_PRO, 29_HIS, 30_ASP, 33_TRP",2.32
+7MZG_A,7MZG_L_light+7MZG_H_heavy,"403_ARG, 405_ASP, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 484_GLU, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 493_GLN, 495_TYR, 496_GLY, 498_GLN, 501_ASN, 502_GLY, 505_TYR",2.0
+8VZN_A,8VZN_C_light+8VZN_B_heavy,"138_PHE, 139_TYR, 192_LEU, 193_GLY, 195_LEU, 196_GLU, 197_PRO, 198_HIS, 199_LEU, 264_LYS, 265_ASP, 266_PRO, 267_GLU, 270_ALA, 271_TYR, 274_SER, 278_TYR",2.77
+7QU1_C,7QU1_B_light+7QU1_A_heavy,"117_VAL, 121_GLU, 122_TYR, 123_ASP, 165_ARG, 169_LYS, 170_LYS, 171_GLU",1.91
+7LF8_A,7LF8_L_light+7LF8_H_heavy,"35_TRP, 37_GLY, 39_VAL, 40_ALA, 41_ALA, 43_GLU, 44_LEU, 45_PRO, 46_ARG, 47_ASP, 49_ALA, 50_ASP",2.15
+7T9N_R,7T9N_L_light+7T9N_H_heavy,"104_THR, 105_HIS, 107_GLU, 109_ARG, 110_ASN, 129_LYS, 130_PHE, 134_PHE, 151_ASP, 152_ILE, 153_PHE, 155_ILE, 157_GLU, 159_THR, 183_LYS, 185_TYR, 208_ASN, 209_LYS, 235_GLN, 251_GLU, 253_ILE, 255_ARG, 256_ASN, 258_TRP, 274_ARG, 276_ASP, 34_GLU, 35_GLU, 36_ASP, 38_ARG, 399_THR, 56_THR, 58_LYS, 79_SER, 80_ARG, 82_TYR, 85_ILE",2.9
+7YXU_A,7YXU_L_light+7YXU_H_heavy,"32_PRO, 33_ALA, 35_THR, 40_ASN, 41_ARG, 44_ILE, 45_CYS, 47_PRO, 48_CYS, 49_PRO, 50_PRO, 51_ASN, 52_SER, 65_CYS, 78_CYS, 79_SER, 80_SER, 81_THR",2.31
+7MHR_C,7MHR_B_light+7MHR_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 63_PRO, 64_ARG",2.77
+5VIG_Z,5VIG_L_light+5VIG_H_heavy,"305_TYR, 306_SER, 307_LEU, 309_THR, 310_ALA, 311_ALA, 334_GLY, 335_THR, 336_ASP, 340_LYS, 350_GLN, 351_THR, 352_LEU, 391_VAL, 392_GLY, 393_GLU, 394_LYS, 395_LYS",3.0
+5VIG_G,5VIG_B_light+5VIG_A_heavy,"305_TYR, 306_SER, 307_LEU, 309_THR, 310_ALA, 311_ALA, 333_ALA, 334_GLY, 335_THR, 336_ASP, 340_LYS, 350_GLN, 351_THR, 352_LEU, 391_VAL, 392_GLY, 393_GLU, 394_LYS, 395_LYS",3.0
+8OXV_A,8OXV_C_light+8OXV_B_heavy,"242_THR, 243_GLY, 244_GLY, 245_ARG, 255_GLU, 258_LYS, 259_ASN, 261_LYS, 262_LYS, 263_SER, 264_GLY, 266_SER, 267_PRO, 268_VAL, 269_ARG, 270_TYR, 639_LEU, 640_GLY, 641_ASN, 643_LYS, 644_ILE, 645_ASP",1.8
+7XEI_A,7XEI_D_light+7XEI_C_heavy,"403_ARG, 405_ASP, 406_GLU, 409_GLN, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 501_ASN, 502_GLY, 505_TYR",2.76
+7XEI_B,7XEI_F_light+7XEI_E_heavy,"403_ARG, 405_ASP, 406_GLU, 409_GLN, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 501_ASN, 502_GLY, 505_TYR",2.76
+4FQI_A,4FQI_L_light+4FQI_H_heavy,"291_SER, 292_MET, 293_PRO, 38_HIS, 40_GLN, 41_ASP, 42_ILE",1.71
+4FQI_B,4FQI_L_light+4FQI_H_heavy,"18_VAL, 19_ASP, 20_GLY, 21_TRP, 36_ALA, 38_LYS, 41_THR, 42_GLN, 45_ILE, 46_ASP, 48_VAL, 49_THR",1.71
+5O1R_A,5O1R_L_light+5O1R_H_heavy,"320_ASN, 322_GLU, 337_ARG, 339_ARG, 340_PHE, 343_LYS, 352_ASP, 353_GLY, 354_ILE, 356_ASP, 360_ASP, 361_LEU, 362_HIS, 363_MET, 364_GLY, 365_THR, 366_GLN, 367_LYS, 383_GLU, 425_GLU",2.86
+5O1R_B,5O1R_M_light+5O1R_I_heavy,"320_ASN, 322_GLU, 337_ARG, 339_ARG, 340_PHE, 343_LYS, 352_ASP, 354_ILE, 356_ASP, 360_ASP, 361_LEU, 363_MET, 364_GLY, 365_THR, 366_GLN, 367_LYS, 383_GLU, 423_LYS, 425_GLU",2.86
+1BQL_Y,1BQL_L_light+1BQL_H_heavy,"41_GLN, 43_THR, 44_ASN, 45_ARG, 46_ASN, 48_ASP, 53_TYR, 67_GLY, 68_LYS, 69_THR, 70_PRO, 81_SER, 84_LEU",2.6
+3L95_X,3L95_A_light+3L95_B_heavy,"1464_CYS, 1465_SER, 1466_LEU, 1467_GLN, 1469_ASN, 1499_LYS, 1500_TYR, 1501_PHE, 1502_SER, 1503_ASP, 1505_HIS, 1507_ASP, 1708_ALA, 1709_SER, 1710_LEU, 1711_GLY, 1714_ASN, 1715_ILE, 1716_PRO, 1718_LYS",2.19
+3L95_Y,3L95_L_light+3L95_H_heavy,"1463_VAL, 1464_CYS, 1465_SER, 1466_LEU, 1467_GLN, 1469_ASN, 1498_TRP, 1499_LYS, 1500_TYR, 1501_PHE, 1502_SER, 1503_ASP, 1505_HIS, 1507_ASP, 1708_ALA, 1709_SER, 1710_LEU, 1711_GLY, 1714_ASN, 1715_ILE, 1716_PRO, 1718_LYS",2.19
+1G9M_G,1G9M_L_light+1G9M_H_heavy,"119_CYS, 120_VAL, 122_LEU, 200_VAL, 202_THR, 203_GLN, 419_ARG, 421_LYS, 422_GLN, 423_ILE, 434_MET, 437_PRO",2.2
+8DA1_I,8DA1_A_light+8DA1_B_heavy,"11_ILE, 30_ASP, 31_ALA, 32_PHE, 36_ARG, 37_GLY, 38_LYS, 39_VAL, 40_VAL, 68_HIS, 6_THR, 8_THR, 9_SER",2.67
+8VDL_C,8VDL_L_light+8VDL_H_heavy,"652_ASP, 653_SER, 655_PHE, 656_PHE, 657_GLN, 659_ILE, 660_TYR, 661_LYS, 666_GLU, 667_ALA, 669_TRP, 670_ASN, 673_LYS",2.68
+7JOO_C,7JOO_L_light+7JOO_H_heavy,"114_PHE, 115_ASP, 117_PRO, 118_PRO, 119_PHE, 50_GLN, 66_THR, 68_THR, 69_LYS, 70_GLY, 71_SER, 76_SER, 78_LYS",2.4
+6B0S_C,6B0S_L_light+6B0S_H_heavy,"314_LYS, 317_LYS, 318_GLU, 320_LEU, 321_ASN, 324_GLN, 327_LEU, 354_PRO, 355_LYS, 356_ASP, 357_GLU, 358_LEU, 359_ASP, 360_TYR, 364_ILE",1.95
+7ZYI_A,7ZYI_L_light+7ZYI_H_heavy,"146_TYR, 147_ASP, 153_LYS, 20_LYS, 212_LYS, 213_SER, 214_ILE, 216_PHE, 217_ALA, 219_THR, 272_VAL, 273_ALA, 274_PHE, 275_PRO, 276_PRO, 277_GLU, 278_VAL, 280_GLY, 281_PRO, 86_LYS, 88_ILE, 89_GLU",2.88
+4RGN_S,4RGN_L_light+4RGN_H_heavy,"135_ARG, 137_PHE, 139_ASP, 140_GLY, 141_LYS, 142_ASN, 144_LEU, 145_SER, 186_TYR, 188_LYS, 190_ILE, 192_ASN, 229_LYS, 231_GLU, 233_TYR, 5_ASP, 6_PRO, 8_PRO",2.7
+4RGN_S,4RGN_G_light+4RGN_F_heavy,"123_GLY, 125_GLN, 126_LEU, 127_ASP, 128_LYS, 129_TYR, 130_ARG, 131_SER, 149_GLN, 153_LYS, 226_LYS",2.7
+4RGN_A,4RGN_C_light+4RGN_B_heavy,"135_ARG, 137_PHE, 139_ASP, 140_GLY, 141_LYS, 142_ASN, 145_SER, 186_TYR, 188_LYS, 190_ILE, 192_ASN, 229_LYS, 231_GLU, 233_TYR, 5_ASP, 6_PRO, 8_PRO",2.7
+4RGN_A,4RGN_E_light+4RGN_D_heavy,"123_GLY, 125_GLN, 126_LEU, 127_ASP, 128_LYS, 129_TYR, 130_ARG, 131_SER, 149_GLN, 153_LYS, 226_LYS",2.7
+7UL1_A,7UL1_L_light+7UL1_H_heavy,"403_ARG, 405_ASP, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 502_GLY, 505_TYR",2.65
+6KTR_C,6KTR_B_light+6KTR_A_heavy,"126_ILE, 166_LEU, 167_PRO, 168_MET, 169_LEU, 170_PRO, 171_MET, 172_VAL, 37_GLY, 38_TRP, 39_GLY, 40_ASP, 41_PRO, 42_ILE, 43_ARG, 44_LEU, 45_ARG, 81_GLU",2.6
+6KTR_D,6KTR_G_light+6KTR_F_heavy,"126_ILE, 167_PRO, 168_MET, 169_LEU, 170_PRO, 172_VAL, 37_GLY, 38_TRP, 39_GLY, 40_ASP, 41_PRO, 42_ILE, 43_ARG, 44_LEU, 45_ARG, 81_GLU",2.6
+8E8X_1,8E8X_L_light+8E8X_H_heavy,"100_ARG, 101_ALA, 102_GLN, 103_LYS, 105_PHE, 106_ALA, 107_MET, 108_TRP, 109_ARG, 114_ASP, 168_LYS, 224_GLN, 228_SER, 229_LEU, 240_VAL, 281_TYR, 283_PRO, 87_VAL, 88_ALA, 89_ILE, 91_GLU",2.91
+6FAX_R,6FAX_L_light+6FAX_H_heavy,"21_GLU, 22_PRO, 23_PRO, 27_ARG, 29_LYS, 30_GLN, 35_SER, 36_GLN, 37_CYS, 39_SER, 42_GLN, 45_GLN, 51_CYS, 52_THR, 53_GLU, 54_PHE, 55_THR, 56_GLU, 57_THR, 59_CYS, 71_TRP",2.99
+6W0H_C,6W0H_B_light+6W0H_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.6
+6DDM_C,6DDM_A_light+6DDM_B_heavy,"240_ARG, 241_GLN, 242_ASP, 243_GLY, 244_VAL, 245_SER, 247_SER, 249_ASP, 250_THR, 279_ARG, 283_TYR, 290_HIS, 291_SER, 292_THR",1.3
+8DFG_B,8DFG_M_light+8DFG_I_heavy,"16_SER, 17_GLY, 31_LEU, 63_THR, 65_GLU, 6_GLN, 73_LYS, 75_THR, 76_CYS, 77_GLU, 78_CYS, 79_THR, 80_LYS, 81_PRO, 82_ASP, 83_SER, 84_TYR, 85_PRO, 86_LEU, 87_PHE, 88_ASP",2.0
+8DFG_A,8DFG_L_light+8DFG_H_heavy,"16_SER, 17_GLY, 31_LEU, 65_GLU, 6_GLN, 73_LYS, 75_THR, 76_CYS, 77_GLU, 78_CYS, 79_THR, 80_LYS, 81_PRO, 82_ASP, 83_SER, 84_TYR, 85_PRO, 86_LEU, 87_PHE, 88_ASP",2.0
+6AD7_B,6AD7_F_light+6AD7_E_heavy,"246_ASP, 248_PRO, 249_LEU, 250_ASN, 380_PRO, 381_GLN, 382_TYR, 383_HIS",2.95
+6AD7_A,6AD7_D_light+6AD7_C_heavy,"243_LYS, 244_LEU, 246_ASP, 248_PRO, 249_LEU, 250_ASN, 380_PRO, 381_GLN, 382_TYR, 383_HIS",2.95
+4XMP_G,4XMP_L_light+4XMP_H_heavy,"102_GLU, 103_GLN, 105_HIS, 106_THR, 109_ILE, 124_GLY, 198_GLY, 276_ASN, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 283_THR, 353_PHE, 357_LYS, 365_THR, 366_GLY, 367_GLY, 368_ASP, 371_ILE, 426_MET, 427_TRP, 428_GLN, 429_ARG, 430_VAL, 431_GLY, 455_THR, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_ASN, 461_THR, 465_ARG, 466_GLU, 467_THR, 469_ARG, 474_ASP, 476_ARG, 480_ARG, 97_LYS",1.78
+6XLQ_A,6XLQ_C_light+6XLQ_B_heavy,"100_GLN, 101_ASP, 102_GLY, 103_ASP, 105_TYR, 33_THR, 35_GLU, 37_LYS, 51_ALA, 52_ASP, 59_ARG, 98_TYR",3.0
+6XLQ_J,6XLQ_L_light+6XLQ_K_heavy,"100_GLN, 101_ASP, 102_GLY, 103_ASP, 104_PHE, 105_TYR, 33_THR, 35_GLU, 37_LYS, 51_ALA, 52_ASP, 59_ARG, 98_TYR",3.0
+6XLQ_D,6XLQ_F_light+6XLQ_E_heavy,"100_GLN, 101_ASP, 102_GLY, 103_ASP, 105_TYR, 33_THR, 35_GLU, 37_LYS, 51_ALA, 52_ASP, 59_ARG",3.0
+6XLQ_G,6XLQ_I_light+6XLQ_H_heavy,"100_GLN, 101_ASP, 102_GLY, 103_ASP, 105_TYR, 32_GLU, 33_THR, 35_GLU, 37_LYS, 49_VAL, 51_ALA, 52_ASP, 59_ARG, 98_TYR",3.0
+1NDM_C,1NDM_A_light+1NDM_B_heavy,"615_HIS, 616_GLY, 618_ASP, 619_ASN, 620_TYR, 621_ARG, 663_TRP, 673_ARG, 675_LEU, 689_THR, 693_ASN, 696_LYS, 697_LYS, 698_ILE, 700_SER, 701_ASP, 702_GLY, 703_ASN",2.1
+5EC2_C,5EC2_B_light+5EC2_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 63_PRO, 64_ARG",2.3
+4LVN_A,4LVN_B_light+4LVN_C_heavy,"363_VAL, 364_MET, 365_SER, 367_ARG, 387_GLU, 390_LEU, 391_LYS, 393_LEU, 394_HIS, 395_GLY, 396_ARG, 397_LYS, 401_ASP, 408_ASP, 410_ILE, 454_LYS",2.25
+4M62_T,4M62_M_light+4M62_I_heavy,"100_ALA, 101_GLY, 103_GLU, 119_ASN, 120_TRP, 121_PHE, 122_ASP, 123_ILE, 124_THR, 125_ASN, 127_LEU, 128_TRP, 56_ARG, 60_PHE, 64_LEU, 75_HIS, 8_ALA",1.8
+4M62_S,4M62_L_light+4M62_H_heavy,"100_ALA, 101_GLY, 119_ASN, 120_TRP, 121_PHE, 122_ASP, 123_ILE, 124_THR, 125_ASN, 127_LEU, 128_TRP, 56_ARG, 60_PHE, 75_HIS, 8_ALA",1.8
+8EUU_D,8EUU_J_light+8EUU_I_heavy,"512_ALA, 513_VAL, 514_GLY, 515_ILE, 516_GLY, 517_ALA, 518_VAL, 519_PHE, 520_LEU",2.7
+8EUU_A,8EUU_H_light+8EUU_G_heavy,"229_LYS, 80_ASN, 84_ILE, 85_HIS, 87_GLU",2.7
+8EUU_B,8EUU_H_light+8EUU_G_heavy,"512_ALA, 513_VAL, 514_GLY, 515_ILE, 516_GLY, 517_ALA, 518_VAL, 519_PHE, 520_LEU",2.7
+8EUU_F,8EUU_L_light+8EUU_K_heavy,"512_ALA, 513_VAL, 514_GLY, 515_ILE, 516_GLY, 517_ALA, 518_VAL, 519_PHE, 520_LEU",2.7
+5OCC_A,5OCC_L_light+5OCC_H_heavy,"151_ARG, 156_LYS, 157_ASP, 158_LYS, 159_PRO, 160_LEU, 161_VAL, 176_ARG, 177_SER, 179_PRO, 180_ASN, 200_ILE, 201_GLY, 202_TYR, 58_TRP, 65_ASP, 66_SER",2.5
+5F9O_G,5F9O_L_light+5F9O_H_heavy,"275_GLU, 276_ASN, 277_LEU, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 371_ILE, 429_GLY, 455_THR, 457_ASP, 458_GLY, 459_GLY, 471_GLY, 472_GLY, 480_ARG, 96_TRP",1.86
+3BE1_A,3BE1_L_light+3BE1_H_heavy,"557_PRO, 558_GLU, 560_ASP, 561_GLN, 569_LYS, 570_ASP, 571_PRO, 572_PRO, 573_PHE, 579_PRO, 593_LYS, 600_ALA, 601_CYS, 602_GLN, 603_PRO, 604_CYS",2.9
+1J1O_Y,1J1O_L_light+1J1O_H_heavy,"100_SER, 101_ASP, 102_GLY, 103_ASN, 15_HIS, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 62_TRP, 63_TRP, 73_ARG, 75_LEU, 77_ASN, 89_THR, 93_ASN, 96_LYS, 97_LYS, 98_ILE",1.8
+6S8I_E,6S8I_U_light+6S8I_Y_heavy,"114_LYS, 116_PRO, 117_ASP, 144_THR, 146_PRO, 148_ALA, 223_THR, 224_GLY, 229_GLU, 231_GLU",2.99
+6S8I_A,6S8I_L_light+6S8I_H_heavy,"114_LYS, 116_PRO, 117_ASP, 144_THR, 146_PRO, 148_ALA, 223_THR, 224_GLY, 229_GLU, 231_GLU",2.99
+6S8I_C,6S8I_O_light+6S8I_P_heavy,"114_LYS, 116_PRO, 117_ASP, 144_THR, 146_PRO, 223_THR, 224_GLY, 229_GLU, 231_GLU",2.99
+4LSQ_G,4LSQ_L_light+4LSQ_H_heavy,"276_ASN, 278_THR, 279_ASP, 280_ASN, 281_ALA, 282_LYS, 353_PHE, 354_ASN, 357_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 371_ILE, 429_GLY, 432_GLN, 455_THR, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_GLU, 461_ASN, 463_ASN, 466_GLU, 467_ILE, 472_GLY, 473_GLY, 476_LYS",2.25
+5BK1_A,5BK1_L_light+5BK1_H_heavy,"111_GLU, 12_ASN, 14_ASP, 152_GLN, 153_GLU, 154_PRO, 155_TYR, 156_PHE, 15_LYS, 207_ASP, 209_ASP, 210_TYR, 211_SER, 230_TRP, 330_MET, 334_PRO, 337_SER, 340_TRP, 341_TYR, 344_ARG, 42_LYS, 44_GLU, 45_GLU, 49_GLN, 62_TRP, 65_ASP, 66_ARG",2.15
+5BK1_B,5BK1_D_light+5BK1_C_heavy,"111_GLU, 12_ASN, 14_ASP, 153_GLU, 154_PRO, 155_TYR, 156_PHE, 15_LYS, 207_ASP, 209_ASP, 210_TYR, 211_SER, 230_TRP, 330_MET, 334_PRO, 337_SER, 340_TRP, 341_TYR, 344_ARG, 42_LYS, 44_GLU, 45_GLU, 49_GLN, 62_TRP, 65_ASP, 66_ARG",2.15
+8JQS_a,8JQS_l_light+8JQS_h_heavy,"294_THR, 297_THR, 300_PRO, 308_SER, 341_PRO, 342_ARG, 343_LYS, 344_GLU, 345_PRO, 346_GLU, 347_SER, 348_ASN, 351_ARG, 355_MET",2.63
+8JQS_A,8JQS_L_light+8JQS_H_heavy,"294_THR, 297_THR, 300_PRO, 310_ARG, 341_PRO, 342_ARG, 343_LYS, 344_GLU, 345_PRO, 346_GLU, 347_SER, 348_ASN, 351_ARG, 355_MET",2.63
+3T3M_A,3T3M_L_light+3T3M_H_heavy,"149_ASN, 154_ILE, 158_ASN, 205_SER, 206_SER, 208_ARG, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.6
+3T3M_C,3T3M_F_light+3T3M_E_heavy,"149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.6
+8CZ5_A,8CZ5_L_light+8CZ5_H_heavy,"299_ARG, 301_ASN, 303_SER, 304_TYR, 305_PRO, 306_GLY, 307_SER, 317_TYR, 319_SER, 343_PRO, 344_PHE, 345_SER, 382_THR, 383_GLY, 384_ALA, 385_PRO, 386_SER, 387_ASN, 388_PRO, 389_GLN, 396_GLY, 398_GLN, 402_LYS, 412_ASN, 414_ALA, 415_THR",2.65
+3ZTN_B,3ZTN_L_light+3ZTN_H_heavy,"18_VAL, 19_ASP, 20_GLY, 21_TRP, 38_LEU, 39_LYS, 41_THR, 42_GLN, 43_ASN, 45_ILE, 46_ASP, 49_THR, 53_ASN, 56_ILE, 57_GLU",3.0
+2NY5_G,2NY5_L_light+2NY5_H_heavy,"119_CYS, 120_VAL, 122_LEU, 200_VAL, 202_THR, 203_GLN, 205_CYS, 419_ARG, 421_LYS, 422_GLN, 423_ILE, 434_MET, 437_PRO",2.5
+8G8D_P,8G8D_L_light+8G8D_H_heavy,"656_ASN, 657_GLU, 658_GLN, 660_LEU, 661_LEU, 662_GLU, 663_LEU, 664_ASP, 665_LYS, 667_ALA, 668_SER, 669_LEU, 671_ASN, 672_TRP, 675_ILE",2.02
+8G8D_C,8G8D_B_light+8G8D_A_heavy,"656_ASN, 657_GLU, 658_GLN, 660_LEU, 661_LEU, 663_LEU, 664_ASP, 665_LYS, 667_ALA, 668_SER, 669_LEU, 671_ASN, 672_TRP, 675_ILE, 679_LEU",2.02
+5BK2_A,5BK2_L_light+5BK2_H_heavy,"1_LYS, 334_PRO, 337_SER, 338_ALA, 341_TYR, 342_ALA, 345_THR, 349_ASN, 354_ARG, 355_GLN, 359_GLU, 362_LYS, 363_ASP, 365_GLN, 366_THR, 367_ASN, 45_GLU, 48_PRO, 49_GLN, 52_ALA, 53_THR, 66_ARG, 69_GLY, 70_TYR, 72_GLN, 73_SER, 75_LEU",2.6
+5BK2_B,5BK2_D_light+5BK2_C_heavy,"334_PRO, 335_GLN, 337_SER, 338_ALA, 341_TYR, 342_ALA, 345_THR, 349_ASN, 354_ARG, 355_GLN, 359_GLU, 362_LYS, 363_ASP, 365_GLN, 366_THR, 367_ASN, 45_GLU, 48_PRO, 49_GLN, 51_ALA, 52_ALA, 53_THR, 66_ARG, 69_GLY, 70_TYR, 72_GLN, 73_SER, 75_LEU",2.6
+2NY3_A,2NY3_C_light+2NY3_D_heavy,"119_CYS, 120_VAL, 122_LEU, 200_VAL, 202_THR, 203_GLN, 419_ARG, 421_LYS, 422_GLN, 423_ILE, 434_MET, 437_PRO",2.0
+2YPV_A,2YPV_L_light+2YPV_H_heavy,"126_LYS, 128_GLN, 130_ARG, 214_TYR, 215_ASN, 217_ALA, 218_GLU, 219_LYS, 220_GLY, 221_SER, 239_GLU, 241_LYS, 242_THR, 243_VAL, 244_ASN, 245_GLY, 246_ILE, 248_HIS, 41_ARG",1.8
+2IFF_Y,2IFF_L_light+2IFF_H_heavy,"41_GLN, 43_THR, 44_ASN, 45_ARG, 46_ASN, 47_THR, 48_ASP, 49_GLY, 53_TYR, 67_GLY, 68_LYS, 69_THR, 70_PRO, 84_LEU",2.65
+3GJF_D,3GJF_K_light+3GJF_M_heavy,"150_ALA, 155_GLN, 158_ALA, 163_THR, 166_GLU, 61_ASP, 62_GLY, 65_ARG, 66_LYS, 68_LYS, 69_ALA, 72_GLN, 73_THR, 75_ARG",1.9
+3GJF_A,3GJF_L_light+3GJF_H_heavy,"150_ALA, 151_HIS, 155_GLN, 158_ALA, 163_THR, 166_GLU, 167_TRP, 58_GLU, 62_GLY, 65_ARG, 66_LYS, 69_ALA, 72_GLN, 73_THR",1.9
+4DGI_A,4DGI_L_light+4DGI_H_heavy,"138_ILE, 140_HIS, 141_PHE, 142_GLY, 143_SER, 144_ASP, 145_TYR, 146_GLU, 147_ASP, 208_ARG, 212_GLN",2.4
+7O4Y_K,7O4Y_L_light+7O4Y_H_heavy,"111_TRP, 2_VAL, 3_GLN, 41_PRO, 42_GLY, 45_ARG",1.6
+7BEJ_E,7BEJ_L_light+7BEJ_H_heavy,"403_ARG, 405_ASP, 406_GLU, 408_ARG, 409_GLN, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 503_VAL, 505_TYR",2.42
+8WSP_D,8WSP_F_light+8WSP_E_heavy,"121_GLY, 123_SER, 125_LYS, 126_GLY, 150_THR, 151_GLU, 157_SER, 158_SER, 168_PRO, 169_SER, 170_ASP, 171_ARG, 74_ARG, 78_SER, 79_GLN, 80_VAL, 81_SER, 82_TYR, 83_TYR, 84_PRO, 85_ALA, 86_GLU",2.51
+8WSP_A,8WSP_C_light+8WSP_B_heavy,"121_GLY, 123_SER, 125_LYS, 126_GLY, 150_THR, 151_GLU, 157_SER, 158_SER, 160_SER, 161_GLY, 168_PRO, 169_SER, 170_ASP, 171_ARG, 74_ARG, 78_SER, 79_GLN, 80_VAL, 81_SER, 82_TYR, 83_TYR, 84_PRO, 85_ALA, 86_GLU, 90_SER",2.51
+7LM8_A,7LM8_L_light+7LM8_H_heavy,"369_TYR, 370_ASN, 371_SER, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 408_ARG, 412_PRO, 413_GLY, 414_GLN, 415_THR, 416_GLY, 427_ASP, 428_ASP, 429_PHE",1.94
+7LM8_A,7LM8_N_light+7LM8_M_heavy,"343_ASN, 344_ALA, 345_THR, 346_ARG, 373_SER, 436_TRP, 437_ASN, 440_ASN, 441_LEU, 443_SER, 444_LYS, 445_VAL, 448_ASN, 450_ASN, 509_ARG",1.94
+7ZWI_D,7ZWI_F_light+7ZWI_E_heavy,"322_SER, 323_ALA, 324_HIS, 347_ASP, 349_ILE, 357_TYR, 358_GLN, 359_PRO, 360_GLU, 362_GLU, 363_GLU, 364_LEU, 365_GLU, 366_PRO, 367_SER, 368_ASN, 369_ILE, 409_THR, 411_ILE, 413_LYS, 416_LYS, 420_TYR",1.9
+7ZWI_A,7ZWI_C_light+7ZWI_B_heavy,"322_SER, 323_ALA, 324_HIS, 347_ASP, 349_ILE, 357_TYR, 358_GLN, 359_PRO, 360_GLU, 361_SER, 362_GLU, 363_GLU, 364_LEU, 365_GLU, 366_PRO, 367_SER, 368_ASN, 369_ILE, 409_THR, 411_ILE, 413_LYS, 415_ASP, 416_LYS, 420_TYR",1.9
+4MSW_C,4MSW_B_light+4MSW_A_heavy,"45_TYR, 49_LEU, 50_ALA, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 63_PRO, 64_ARG",2.06
+4G6F_F,4G6F_D_light+4G6F_B_heavy,"668_SER, 669_LEU, 670_TRP, 671_ASN, 672_TRP, 673_PHE, 676_THR, 677_ASN, 679_LEU, 680_TRP, 682_ILE, 683_ARG",2.1
+4G6F_P,4G6F_L_light+4G6F_H_heavy,"665_LYS, 668_SER, 669_LEU, 670_TRP, 671_ASN, 672_TRP, 673_PHE, 676_THR, 677_ASN, 679_LEU, 680_TRP, 683_ARG",2.1
+5EBM_C,5EBM_B_light+5EBM_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 63_PRO, 64_ARG",2.5
+6WIO_C,6WIO_B_light+6WIO_A_heavy,"106_VAL, 108_TYR, 109_HIS, 110_MET, 111_ASN, 147_VAL, 148_THR, 149_PRO, 150_ILE, 152_HIS, 153_HIS, 97_LEU",2.17
+8GS9_A,8GS9_K_light+8GS9_H_heavy,"403_ARG, 415_THR, 417_ASN, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_ASN, 478_LYS, 486_PHE, 487_ASN, 489_TYR, 493_ARG, 494_SER, 495_TYR, 501_TYR, 505_HIS",2.66
+3WIH_A,3WIH_L_light+3WIH_H_heavy,"18_GLY, 19_THR, 49_THR, 50_ARG, 51_TYR, 52_HIS, 53_ILE, 54_ASN, 55_LYS, 56_THR, 68_PHE, 69_LEU, 70_VAL, 71_PRO, 75_TYR",1.7
+3WIH_B,3WIH_M_light+3WIH_I_heavy,"18_GLY, 19_THR, 49_THR, 50_ARG, 51_TYR, 52_HIS, 53_ILE, 54_ASN, 55_LYS, 56_THR, 68_PHE, 69_LEU, 70_VAL, 71_PRO, 75_TYR, 99_ASP",1.7
+3D9A_C,3D9A_L_light+3D9A_H_heavy,"615_HIS, 616_GLY, 619_ASN, 620_TYR, 621_ARG, 662_TRP, 663_TRP, 673_ARG, 675_LEU, 677_ASN, 689_THR, 693_ASN, 696_LYS, 697_LYS, 698_ILE, 700_SER, 701_ASP, 702_GLY, 703_ASN",1.2
+3K2U_A,3K2U_L_light+3K2U_H_heavy,"101_HIS, 178_PRO, 179_ASN, 233_ASN, 240_ASP, 241_ARG, 243_ARG, 59_PHE, 60_SER, 61_ARG, 87_LYS, 88_TYR, 89_ILE, 90_PRO, 91_TYR, 92_THR, 93_LEU, 94_TYR, 95_SER, 96_VAL, 97_PHE",2.35
+8DPL_D,8DPL_B_light+8DPL_A_heavy,"114_LYS, 115_LYS, 116_PRO, 117_ASP, 118_GLY, 119_SER, 144_THR, 145_GLY, 146_PRO, 224_GLY, 229_GLU",2.53
+8DPL_I,8DPL_G_light+8DPL_F_heavy,"114_LYS, 115_LYS, 116_PRO, 117_ASP, 118_GLY, 119_SER, 144_THR, 145_GLY, 146_PRO, 224_GLY, 229_GLU",2.53
+8DPL_M,8DPL_L_light+8DPL_K_heavy,"114_LYS, 115_LYS, 116_PRO, 117_ASP, 118_GLY, 119_SER, 144_THR, 145_GLY, 146_PRO, 223_THR, 224_GLY, 229_GLU",2.53
+1CL7_I,1CL7_L_light+1CL7_H_heavy,"144_THR, 145_LEU, 146_GLY, 147_CYS, 148_LEU, 149_VAL, 150_LYS, 151_GLY, 152_TYR, 153_PHE, 154_PRO, 167_SER, 171_HIS, 173_PHE, 174_PRO, 176_VAL, 177_LEU, 178_GLN, 180_ASP, 181_LEU, 186_SER, 187_SER, 195_ARG, 204_VAL, 206_HIS, 208_ALA, 209_SER, 213_VAL, 215_LYS, 217_ILE, 218_VAL, 220_ARG",3.0
+2I5Y_G,2I5Y_L_light+2I5Y_H_heavy,"119_CYS, 120_VAL, 122_LEU, 200_VAL, 202_THR, 203_GLN, 204_ALA, 205_CYS, 419_ARG, 420_ILE, 421_LYS, 422_GLN, 423_ILE, 434_MET, 437_PRO",2.2
+2I5Y_P,2I5Y_Q_light+2I5Y_R_heavy,"119_CYS, 120_VAL, 122_LEU, 200_VAL, 202_THR, 203_GLN, 204_ALA, 205_CYS, 419_ARG, 420_ILE, 421_LYS, 422_GLN, 423_ILE, 434_MET, 437_PRO",2.2
+6QIG_A,6QIG_L_light+6QIG_H_heavy,"148_THR, 185_LEU, 186_PRO, 187_ASP, 188_GLY, 189_ASN, 191_GLN, 197_GLN, 198_LEU, 233_GLU, 234_HIS, 235_ASP, 236_GLY, 237_ALA, 238_PRO, 240_SER, 251_SER, 265_CYS, 269_GLN",2.8
+6W9G_C,6W9G_Y_light+6W9G_X_heavy,"129_GLU, 130_ALA, 132_SER, 141_ALA, 142_GLU, 143_LYS, 144_GLY, 146_TYR, 178_CYS, 217_PRO, 218_CYS, 220_GLN, 245_SER, 246_GLN, 247_VAL, 248_SER, 249_HIS, 250_GLY",1.82
+6W9G_B,6W9G_M_light+6W9G_K_heavy,"129_GLU, 130_ALA, 132_SER, 141_ALA, 142_GLU, 143_LYS, 144_GLY, 146_TYR, 178_CYS, 217_PRO, 218_CYS, 220_GLN, 245_SER, 246_GLN, 247_VAL, 248_SER, 249_HIS, 250_GLY",1.82
+6W9G_A,6W9G_L_light+6W9G_H_heavy,"129_GLU, 130_ALA, 132_SER, 141_ALA, 142_GLU, 143_LYS, 144_GLY, 146_TYR, 178_CYS, 217_PRO, 218_CYS, 220_GLN, 245_SER, 246_GLN, 247_VAL, 248_SER, 249_HIS, 250_GLY",1.82
+1OSP_O,1OSP_L_light+1OSP_H_heavy,"117_LYS, 118_ASP, 119_LYS, 30_VAL, 33_ASP, 34_LEU, 35_PRO, 42_VAL, 43_SER, 44_LYS, 45_GLU, 46_LYS, 52_TYR, 69_LYS, 70_ASN, 71_ASN, 92_ASP, 93_ASP, 94_LEU, 95_GLY",1.95
+7BSC_A,7BSC_L_light+7BSC_H_heavy,"281_GLU, 282_GLY, 299_ARG, 301_THR, 302_THR, 303_ALA, 304_SER, 305_GLY, 306_LYS, 307_LEU, 309_THR, 326_GLU, 327_ASP, 328_GLY, 343_GLU, 348_SER",2.31
+2QQN_A,2QQN_L_light+2QQN_H_heavy,"294_SER, 295_SER, 296_GLN, 297_TYR, 298_SER, 299_THR, 302_SER, 318_GLY, 319_GLU, 321_SER, 323_ARG, 324_GLU",2.2
+9DEZ_C,9DEZ_N_light+9DEZ_J_heavy,"185_ASN, 315_HIS, 317_ASP, 318_PHE, 319_GLY, 320_GLU, 324_ASP, 350_THR, 394_TYR",2.6
+9DEZ_B,9DEZ_L_light+9DEZ_H_heavy,"185_ASN, 315_HIS, 317_ASP, 318_PHE, 319_GLY, 324_ASP, 350_THR, 394_TYR",2.6
+9DEZ_A,9DEZ_M_light+9DEZ_I_heavy,"185_ASN, 315_HIS, 317_ASP, 318_PHE, 319_GLY, 320_GLU, 324_ASP, 350_THR, 394_TYR",2.6
+3PGF_A,3PGF_L_light+3PGF_H_heavy,"110_VAL, 259_VAL, 301_ALA, 302_VAL, 305_LYS, 308_GLU, 309_GLU, 321_MET, 324_ALA, 325_GLN, 327_GLY, 328_GLU, 329_ILE, 330_MET, 331_PRO, 332_ASN, 64_HIS, 87_ASP, 90_TYR, 91_PRO, 92_PHE, 93_THR, 95_ASP, 98_ARG",2.1
+2R56_A,2R56_L_light+2R56_H_heavy,"125_THR, 126_PRO, 127_GLU, 154_THR, 156_LEU, 157_GLU, 158_GLU, 162_ILE, 19_TRP, 20_TYR, 43_VAL, 44_GLU, 45_GLU, 47_LYS, 57_LEU, 59_GLN, 65_GLU, 66_CYS, 67_ALA, 68_GLN, 70_LYS",2.8
+2R56_B,2R56_M_light+2R56_I_heavy,"125_THR, 126_PRO, 127_GLU, 154_THR, 157_GLU, 159_GLN, 162_ILE, 18_THR, 19_TRP, 20_TYR, 43_VAL, 44_GLU, 45_GLU, 47_LYS, 59_GLN, 65_GLU, 66_CYS, 67_ALA, 68_GLN, 70_LYS",2.8
+6N6B_A,6N6B_L_light+6N6B_K_heavy,"147_ASN, 149_VAL, 150_HIS, 151_ASP, 152_ARG, 198_ASP, 199_ASN, 221_ASN, 245_SER, 246_ALA, 247_SER, 248_GLY, 292_ARG, 294_ASN, 295_TRP, 296_LYS, 342_ASN, 346_GLY, 347_HIS, 431_LYS",2.3
+6X40_D,6X40_L_light+6X40_K_heavy,"124_ASP, 170_GLU, 171_TRP, 172_THR, 173_ARG, 174_GLU, 175_PRO, 176_ALA, 177_ARG, 196_GLN, 197_THR, 198_VAL, 199_ASP, 200_SER, 42_LYS, 71_LYS",2.86
+6X40_B,6X40_I_light+6X40_J_heavy,"124_ASP, 170_GLU, 171_TRP, 172_THR, 173_ARG, 174_GLU, 175_PRO, 176_ALA, 177_ARG, 196_GLN, 197_THR, 198_VAL, 199_ASP, 200_SER, 42_LYS",2.86
+7YDS_A,7YDS_C_light+7YDS_B_heavy,"111_VAL, 113_ARG, 115_MET, 117_SER, 119_GLY, 120_GLY, 121_ALA, 122_ASP, 123_TYR, 124_LYS, 125_ARG, 51_ALA, 52_ALA, 54_ILE, 56_TYR, 58_GLU, 60_GLU, 61_ASP, 63_ASN, 66_GLN, 68_VAL, 75_LYS, 76_VAL",2.3
+8IV0_A,8IV0_C_light+8IV0_B_heavy,"117_THR, 118_TYR, 119_ASN, 120_GLY, 121_ILE, 122_ARG, 123_THR, 124_ASN, 126_VAL, 135_SER, 146_SER, 147_ASN, 148_THR, 149_ASP, 150_ASN, 151_ALA, 152_ALA, 153_PHE, 154_PRO, 155_GLN, 156_MET, 157_THR, 158_LYS, 185_LYS",2.9
+7NX8_E,7NX8_L_light+7NX8_H_heavy,"366_SER, 369_TYR, 370_ASN, 374_PHE, 375_SER, 377_PHE, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 390_LEU, 392_PHE, 412_PRO, 427_ASP, 428_ASP, 429_PHE, 517_LEU",1.95
+7NX8_E,7NX8_B_light+7NX8_A_heavy,"403_ARG, 405_ASP, 408_ARG, 415_THR, 416_GLY, 417_THR, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",1.95
+6O3B_H,6O3B_E_light+6O3B_G_heavy,"135_MET, 138_PHE, 139_GLY, 140_PHE, 141_GLN, 145_ARG, 54_ILE, 55_PRO, 56_LEU, 73_HIS, 78_ASP, 80_GLY, 81_LEU, 82_GLU, 83_VAL, 84_HIS, 85_GLN, 86_PHE, 87_TYR, 88_PRO, 89_LEU",2.5
+6O3B_C,6O3B_A_light+6O3B_B_heavy,"135_MET, 138_PHE, 139_GLY, 140_PHE, 141_GLN, 145_ARG, 54_ILE, 55_PRO, 56_LEU, 73_HIS, 77_GLU, 78_ASP, 80_GLY, 81_LEU, 82_GLU, 83_VAL, 84_HIS, 85_GLN, 86_PHE, 87_TYR, 88_PRO, 89_LEU, 91_LYS",2.5
+4KHT_A,4KHT_L_light+4KHT_H_heavy,"49_GLN, 50_HIS, 53_GLN, 54_LEU, 55_THR, 57_TRP, 58_GLY, 60_LYS, 61_GLN, 65_ARG",2.82
+7BZ5_A,7BZ5_L_light+7BZ5_H_heavy,"403_ARG, 405_ASP, 409_GLN, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 493_GLN, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",1.84
+2NY4_A,2NY4_C_light+2NY4_D_heavy,"119_CYS, 120_VAL, 122_LEU, 200_VAL, 202_THR, 203_GLN, 205_CYS, 419_ARG, 421_LYS, 422_GLN, 423_ILE, 434_MET, 437_PRO",2.0
+7YMS_A,7YMS_E_light+7YMS_F_heavy,"100_THR, 104_ASP, 166_ARG, 241_LYS, 242_LYS, 244_PRO, 97_THR, 98_MET, 99_GLY",2.9
+8Q5Y_D,8Q5Y_L_light+8Q5Y_R_heavy,"343_ASN, 345_THR, 346_ARG, 439_ASN, 440_ASN, 441_LEU, 443_SER, 444_LYS, 445_VAL, 450_ASN, 499_PRO",2.6
+8Q5Y_E,8Q5Y_H_light+8Q5Y_G_heavy,"345_THR, 346_ARG, 439_ASN, 440_ASN, 441_LEU, 442_ASP, 443_SER, 444_LYS, 445_VAL, 446_GLY, 447_GLY, 449_TYR, 450_ASN, 451_TYR, 494_SER, 499_PRO, 500_THR",2.6
+8Q5Y_C,8Q5Y_A_light+8Q5Y_B_heavy,"345_THR, 346_ARG, 439_ASN, 440_ASN, 441_LEU, 444_LYS, 445_VAL, 449_TYR, 499_PRO, 500_THR",2.6
+7M3N_A,7M3N_L_light+7M3N_H_heavy,"222_HIS, 223_THR, 224_GLY, 225_THR, 226_SER, 227_GLY, 228_THR, 88_ASP, 89_LYS, 91_ALA, 92_VAL, 93_ASN",2.4
+6CMG_A,6CMG_B_light+6CMG_C_heavy,"239_SER, 240_CYS, 241_THR, 242_ARG, 305_LEU, 458_TYR, 488_PRO, 489_GLY, 490_GLN, 504_TRP, 505_GLU, 506_GLY, 507_THR, 530_GLN, 532_ALA, 533_GLU, 555_ASP, 557_ASN, 559_GLN, 579_GLU, 580_ILE, 581_TYR, 588_ILE",2.7
+8UP2_F,8UP2_E_light+8UP2_D_heavy,"21_THR, 22_ALA, 23_PRO, 24_LEU, 25_ASP, 26_HIS, 27_LYS, 28_ASP, 29_LYS, 30_GLY, 32_GLN, 33_SER, 55_LYS, 61_ASP, 62_SER",1.6
+8UP2_C,8UP2_B_light+8UP2_A_heavy,"21_THR, 22_ALA, 23_PRO, 24_LEU, 25_ASP, 26_HIS, 27_LYS, 28_ASP, 29_LYS, 30_GLY, 32_GLN, 33_SER, 55_LYS, 61_ASP, 62_SER",1.6
+5L6Y_C,5L6Y_L_light+5L6Y_H_heavy,"100_LEU, 103_LYS, 104_LYS, 106_PHE, 107_ARG, 108_GLU, 109_GLY, 111_PHE, 13_ILE, 17_VAL, 20_THR",1.99
+7WRV_C,7WRV_V_light+7WRV_U_heavy,"345_THR, 346_ARG, 347_PHE, 348_ALA, 349_SER, 351_TYR, 352_ALA, 440_LYS, 441_LEU, 444_LYS, 445_VAL, 446_SER, 447_GLY, 448_ASN, 449_TYR, 450_ASN, 451_TYR, 452_LEU, 470_THR, 490_PHE, 492_LEU, 493_ARG, 494_SER, 509_ARG",2.47
+5KVD_E,5KVD_L_light+5KVD_H_heavy,"301_LYS, 315_THR, 316_LYS, 317_ILE, 318_PRO, 319_ALA, 320_GLU, 321_THR, 322_LEU, 327_THR, 329_GLU, 362_ASN, 364_VAL, 365_ILE, 366_THR, 367_GLU, 372_SER, 373_LYS, 374_MET, 375_MET, 377_GLU",1.65
+3DVN_V,3DVN_L_light+3DVN_H_heavy,"34_GLU, 35_GLY, 36_ILE, 37_PRO, 39_ASP, 40_GLN, 71_LEU, 72_ARG, 73_LEU, 74_ARG, 75_GLY, 7_THR, 8_LEU, 9_THR",2.7
+3DVN_Y,3DVN_A_light+3DVN_B_heavy,"34_GLU, 35_GLY, 36_ILE, 37_PRO, 39_ASP, 40_GLN, 71_LEU, 72_ARG, 73_LEU, 74_ARG, 75_GLY, 7_THR, 8_LEU, 9_THR",2.7
+3DVN_X,3DVN_A_light+3DVN_B_heavy,"18_GLU, 19_PRO, 20_SER, 55_THR, 56_LEU, 57_SER, 58_ASP, 60_ASN, 61_ILE, 62_GLN",2.7
+3DVN_U,3DVN_L_light+3DVN_H_heavy,"-1_SER, 18_GLU, 19_PRO, 20_SER, 55_THR, 56_LEU, 57_SER, 58_ASP, 60_ASN, 61_ILE, 62_GLN",2.7
+7MMO_F,7MMO_E_light+7MMO_D_heavy,"345_THR, 439_ASN, 440_ASN, 441_LEU, 443_SER, 444_LYS, 445_VAL, 446_GLY, 447_GLY, 498_GLN, 499_PRO, 500_THR, 501_ASN, 502_GLY, 506_GLN",2.43
+7MMO_C,7MMO_B_light+7MMO_A_heavy,"346_ARG, 439_ASN, 440_ASN, 443_SER, 444_LYS, 445_VAL, 446_GLY, 447_GLY, 450_ASN, 498_GLN, 499_PRO, 500_THR, 501_ASN, 502_GLY, 506_GLN",2.43
+3T3P_A,3T3P_L_light+3T3P_H_heavy,"149_ASN, 154_ILE, 158_ASN, 205_SER, 206_SER, 208_ARG, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.2
+3T3P_C,3T3P_F_light+3T3P_E_heavy,"149_ASN, 154_ILE, 158_ASN, 205_SER, 206_SER, 208_ARG, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.2
+6ZCZ_E,6ZCZ_L_light+6ZCZ_H_heavy,"369_TYR, 375_SER, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 392_PHE, 412_PRO, 413_GLY, 427_ASP, 428_ASP, 429_PHE, 517_LEU",2.65
+6BFT_G,6BFT_B_light+6BFT_A_heavy,"45_TYR, 79_GLN, 80_ILE, 81_MET, 82_ARG, 83_ILE, 86_HIS, 87_GLN, 88_GLY, 89_GLN, 90_HIS, 91_ILE, 92_GLY, 93_GLU, 94_MET",2.55
+6BFT_C,6BFT_L_light+6BFT_H_heavy,"42_GLU, 45_TYR, 79_GLN, 80_ILE, 81_MET, 82_ARG, 83_ILE, 84_LYS, 86_HIS, 87_GLN, 88_GLY, 89_GLN, 90_HIS, 91_ILE, 92_GLY, 93_GLU, 94_MET",2.55
+8SGN_G,8SGN_L_light+8SGN_H_heavy,"121_GLN, 122_GLN, 148_ASN, 150_VAL, 151_TYR, 153_ILE, 162_TRP, 164_ASN, 165_CYS, 197_SER, 199_LYS, 19_THR, 201_GLU, 208_ASP, 20_GLY, 210_GLU, 212_ILE, 214_GLU, 215_ASP, 216_GLY, 217_GLU, 21_GLU, 231_THR, 233_SER, 284_PHE, 286_SER, 288_ASN, 296_ASP, 298_CYS",2.2
+6VTW_A,6VTW_L_light+6VTW_H_heavy,"14_LYS, 15_CYS, 16_ASP, 19_LYS, 21_THR, 22_ILE, 26_ASN, 27_ARG, 28_GLY, 29_ILE, 30_ILE, 31_LYS, 32_THR, 33_VAL, 34_THR",2.6
+7CH5_R,7CH5_L_light+7CH5_H_heavy,"403_ARG, 405_ASP, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 478_THR, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 501_ASN, 505_TYR",2.7
+1YMH_F,1YMH_C_light+1YMH_D_heavy,"833_LYS, 834_ILE, 835_GLN, 836_THR, 837_ALA, 838_GLU, 839_PHE, 840_LYS, 853_TYR, 857_LEU, 860_VAL",2.6
+1YMH_E,1YMH_A_light+1YMH_B_heavy,"818_LYS, 819_GLU, 833_LYS, 834_ILE, 835_GLN, 836_THR, 837_ALA, 838_GLU, 839_PHE, 840_LYS, 849_GLU, 853_TYR, 856_LEU",2.6
+5T80_G,5T80_L_light+5T80_H_heavy,"118_GLY, 119_ASN, 120_TRP, 121_PHE, 124_THR, 125_ASN, 127_LEU, 128_TRP, 131_LYS, 135_GLN, 64_LEU, 66_VAL",2.62
+8CIM_C,8CIM_L_light+8CIM_H_heavy,"348_TYR, 446_TYR, 449_GLN, 453_PHE, 467_THR, 469_ILE, 478_ASN, 479_GLY, 480_VAL, 481_ALA, 482_GLY, 483_PHE, 485_CYS, 486_TYR, 487_PHE, 489_LEU, 490_ARG, 491_SER",3.0
+8CIM_B,8CIM_G_light+8CIM_F_heavy,"348_TYR, 446_TYR, 449_GLN, 453_PHE, 467_THR, 469_ILE, 478_ASN, 479_GLY, 480_VAL, 481_ALA, 482_GLY, 483_PHE, 485_CYS, 486_TYR, 487_PHE, 489_LEU, 490_ARG, 491_SER",3.0
+8CIM_A,8CIM_E_light+8CIM_D_heavy,"348_TYR, 446_TYR, 449_GLN, 453_PHE, 467_THR, 469_ILE, 478_ASN, 479_GLY, 480_VAL, 481_ALA, 482_GLY, 483_PHE, 485_CYS, 486_TYR, 487_PHE, 489_LEU, 490_ARG, 491_SER",3.0
+8V4F_A,8V4F_D_light+8V4F_B_heavy,"403_ARG, 405_ASP, 415_THR, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_ASN, 486_PHE, 487_ASN, 489_TYR, 493_ARG, 496_SER, 501_TYR, 505_HIS",2.68
+7X8P_D,7X8P_E_light+7X8P_F_heavy,"100_CYS, 101_THR, 102_VAL, 103_LEU, 105_GLN, 106_ALA, 108_PRO, 135_ARG, 140_HIS, 141_PHE, 142_PRO, 144_HIS, 145_GLY, 146_ALA, 147_GLU, 148_GLN, 149_ILE, 150_CYS, 151_VAL, 152_GLY, 51_ALA, 60_LEU, 90_PHE, 99_VAL",2.24
+7X8P_A,7X8P_B_light+7X8P_C_heavy,"101_THR, 102_VAL, 103_LEU, 105_GLN, 106_ALA, 108_PRO, 135_ARG, 140_HIS, 141_PHE, 142_PRO, 144_HIS, 145_GLY, 146_ALA, 147_GLU, 148_GLN, 149_ILE, 150_CYS, 151_VAL, 152_GLY, 51_ALA, 52_TYR, 60_LEU, 90_PHE",2.24
+4NZR_M,4NZR_L_light+4NZR_H_heavy,"102_ALA, 103_ASN, 105_LEU, 106_SER, 107_GLU, 109_ILE, 110_THR, 114_LYS, 116_LEU, 117_ASP, 118_ARG, 119_PRO, 120_VAL, 144_TYR, 158_TYR, 160_SER, 161_THR, 162_GLU, 163_TYR, 177_ASN, 178_GLY, 179_THR, 180_LEU, 181_MET, 187_TYR, 188_ASP, 321_ASN, 338_TYR, 340_LEU, 341_PHE, 384_ARG, 389_TYR, 390_PHE, 391_ALA, 392_GLY, 393_GLY, 394_TYR, 396_ASP, 397_LYS, 426_GLU, 429_TYR, 438_ARG, 439_VAL, 440_ASN, 441_GLU, 442_ASN, 444_TYR, 446_GLY, 447_ALA, 449_ILE, 452_ASN, 453_GLU, 456_SER, 457_ARG, 462_GLN, 99_ARG",1.65
+4XWG_A,4XWG_L_light+4XWG_H_heavy,"240_LYS, 48_TRP, 49_MET, 50_CYS, 51_TYR, 53_LYS, 54_THR, 58_PHE, 63_ASP, 65_ASN, 66_MET, 68_LEU, 69_PRO, 70_LEU, 71_GLY, 75_TRP, 78_ASN",2.65
+5KW9_A,5KW9_L_light+5KW9_H_heavy,"346_ASP, 347_THR, 348_THR, 349_PRO, 350_ASP, 351_THR, 352_PHE, 362_ASN, 380_SER, 381_HIS, 382_PRO, 383_SER, 384_GLY, 385_SER, 394_ASN, 396_GLY, 397_SER, 398_SER",2.3
+3BGF_S,3BGF_L_light+3BGF_H_heavy,"359_THR, 362_SER, 391_GLY, 392_ASP, 424_ASN, 426_ARG, 427_ASN, 486_THR, 487_THR, 488_GLY, 489_ILE, 490_GLY, 491_TYR, 492_GLN, 494_TYR",3.0
+3BGF_A,3BGF_C_light+3BGF_B_heavy,"359_THR, 362_SER, 392_ASP, 424_ASN, 426_ARG, 427_ASN, 485_THR, 486_THR, 487_THR, 488_GLY, 489_ILE, 490_GLY, 491_TYR, 492_GLN, 494_TYR",3.0
+8EZ3_A,8EZ3_L_light+8EZ3_H_heavy,"269_SER, 270_GLY, 271_SER, 283_ARG, 284_TYR, 285_PRO, 288_ARG, 304_ASP, 305_ILE, 306_ASN, 308_LYS, 309_ASP, 311_SER, 312_ILE, 313_VAL, 314_SER, 315_SER, 358_ASN, 383_TRP, 384_SER, 386_PRO, 88_SER, 89_LYS, 90_PRO",2.5
+6BAH_C,6BAH_A_light+6BAH_B_heavy,"23_ALA, 24_GLN, 27_GLY, 28_PHE, 30_GLN, 31_SER, 34_ASP, 35_ASP, 37_SER, 38_GLN, 41_ASN, 42_VAL, 45_GLU, 49_LEU",1.9
+6BAH_E,6BAH_A_light+6BAH_B_heavy,"27_LEU, 33_LYS, 34_ILE, 35_GLN, 36_THR, 37_ALA, 38_GLU, 39_PHE, 40_LYS, 49_GLU, 52_ARG, 53_TYR, 56_LEU, 57_LEU, 60_VAL, 61_ASN",1.9
+7KN3_A,7KN3_L_light+7KN3_H_heavy,"449_TYR, 452_LEU, 455_LEU, 456_PHE, 474_GLN, 475_ALA, 476_GLY, 477_SER, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 488_CYS, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER, 495_TYR, 498_GLN, 501_ASN, 502_GLY, 505_TYR",2.25
+7KN3_B,7KN3_N_light+7KN3_M_heavy,"449_TYR, 455_LEU, 456_PHE, 475_ALA, 476_GLY, 477_SER, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 488_CYS, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER, 495_TYR, 496_GLY, 501_ASN, 505_TYR",2.25
+2BOB_C,2BOB_B_light+2BOB_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 63_PRO, 64_ARG",2.76
+6UVO_D,6UVO_L_light+6UVO_H_heavy,"160_ASN, 161_ASN, 162_ASP, 163_PHE, 164_HIS, 165_PHE, 166_GLU, 167_VAL, 168_PHE, 169_ASN, 170_PHE, 172_PRO, 174_SER, 175_ILE, 176_CYS, 177_SER, 179_ASN, 182_CYS",2.9
+7QF0_A,7QF0_L_light+7QF0_H_heavy,"403_ARG, 408_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 503_VAL, 505_TYR",2.3
+4ZFG_A,4ZFG_L_light+4ZFG_H_heavy,"432_LYS, 433_CYS, 434_ILE, 435_CYS, 441_LEU, 448_ASP, 449_ALA, 450_CYS, 451_GLY, 452_PRO, 469_PHE, 475_TYR, 476_TYR, 480_SER",2.27
+7O52_U,7O52_L_light+7O52_H_heavy,"624_VAL, 625_SER, 626_HIS, 628_THR, 631_ASP, 633_ASN, 635_GLN, 636_SER, 638_PRO, 639_TYR, 640_HIS, 641_SER, 642_GLN, 645_ARG, 646_LEU, 647_GLU, 652_GLN, 653_HIS, 657_TYR",2.41
+8TFR_C,8TFR_B_light+8TFR_A_heavy,"16_THR, 17_LEU, 18_LYS, 19_GLY, 20_GLU, 21_THR, 22_THR, 38_TYR, 40_ASN, 41_ASP, 42_ASN, 43_GLY",2.99
+3LZF_A,3LZF_L_light+3LZF_H_heavy,"125_PRO, 126_SER, 128_PRO, 129_ASN, 157_LYS, 158_GLY, 159_SER, 160_SER, 161_TYR, 162_PRO, 163_LYS, 165_SER, 166_LYS, 167_SER, 169_VAL, 197_ASN, 246_GLU, 248_THR",2.8
+5EPM_C,5EPM_B_light+5EPM_A_heavy,"10_ASP, 11_PRO, 12_GLU, 24_ARG, 25_SER, 26_HIS, 27_ARG, 33_LEU, 8_SER, 9_CYS",1.75
+7VUX_A,7VUX_L_light+7VUX_H_heavy,"126_ILE, 128_LEU, 130_PRO, 131_LYS, 132_ALA, 133_GLN, 134_ILE, 59_THR, 60_SER, 61_GLU, 62_SER, 64_VAL, 81_ALA, 83_PRO, 84_GLU, 85_ASP, 86_ARG, 87_SER, 88_GLN, 89_PRO",1.64
+7B3O_E,7B3O_L_light+7B3O_H_heavy,"403_ARG, 405_ASP, 406_GLU, 408_ARG, 409_GLN, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 459_SER, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 503_VAL, 505_TYR",2.0
+2UZI_R,2UZI_L_light+2UZI_H_heavy,"17_SER, 21_ILE, 25_GLN, 27_HIS, 29_VAL, 31_GLU, 32_TYR, 33_ASP, 34_PRO, 35_THR, 36_ILE, 37_GLU, 38_ASP, 39_SER, 40_TYR, 61_GLN, 63_GLU, 64_TYR",2.0
+1MLC_F,1MLC_C_light+1MLC_D_heavy,"41_GLN, 45_ARG, 46_ASN, 47_THR, 48_ASP, 49_GLY, 50_SER, 51_THR, 53_TYR, 66_ASP, 67_GLY, 68_ARG, 70_PRO, 81_SER, 84_LEU",2.5
+1MLC_E,1MLC_A_light+1MLC_B_heavy,"41_GLN, 43_THR, 45_ARG, 46_ASN, 47_THR, 48_ASP, 49_GLY, 50_SER, 51_THR, 53_TYR, 67_GLY, 68_ARG, 70_PRO, 79_PRO, 81_SER, 84_LEU",2.5
+4IOI_E,4IOI_A_light+4IOI_B_heavy,"33_LYS, 34_ILE, 35_GLN, 36_THR, 37_ALA, 38_GLU, 39_PHE, 40_LYS, 49_GLU, 52_ARG, 53_TYR, 56_LEU, 57_LEU",1.95
+4IOI_H,4IOI_A_light+4IOI_B_heavy,"23_ALA, 24_GLN, 27_GLY, 28_PHE, 30_GLN, 31_SER, 34_ASP, 35_ASP, 37_SER, 38_GLN, 41_ASN, 42_VAL, 45_GLU, 49_LEU",1.95
+6MEJ_C,6MEJ_B_light+6MEJ_A_heavy,"472_ILE, 473_SER, 474_HIS, 514_VAL, 540_ASN, 541_ASN, 542_THR, 543_ARG, 545_PRO, 546_LEU, 547_GLY, 548_ASN, 549_TRP, 567_PRO, 591_GLU, 595_SER, 596_ARG, 634_GLY",2.8
+6MEJ_C,6MEJ_L_light+6MEJ_H_heavy,"407_ALA, 408_LYS, 409_GLN, 410_ASN, 427_LEU, 428_ASN, 429_CYS, 430_ASN, 431_ASP, 432_SER, 433_LEU, 434_ASN, 435_THR, 436_GLY, 437_TRP, 438_LEU, 439_ALA, 443_TYR, 446_LYS, 447_PHE, 448_ASN, 529_TRP, 531_GLU",2.8
+8DK6_E,8DK6_L_light+8DK6_H_heavy,"632_LYS, 634_ARG, 636_TYR, 641_GLU, 643_ARG, 644_LEU, 645_THR",2.45
+3GI8_C,3GI8_L_light+3GI8_H_heavy,"1_MET, 305_ASP, 307_GLU, 309_PRO, 310_GLU, 311_PHE, 312_PHE, 313_GLU, 316_VAL, 318_PHE, 319_LYS, 3_LEU, 435_SER, 4_LYS, 5_ASN, 6_LYS",2.59
+6MLK_A,6MLK_L_light+6MLK_H_heavy,"177_ASP, 181_ALA, 184_GLU, 185_GLU, 188_ALA, 233_PRO, 234_MET, 235_GLY, 236_PRO, 237_TRP, 238_PRO, 27_VAL, 28_SER, 29_GLY, 30_ARG",2.45
+3STL_C,3STL_B_light+3STL_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 63_PRO, 64_ARG",2.4
+6W0I_C,6W0I_B_light+6W0I_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.33
+5JQ6_A,5JQ6_L_light+5JQ6_H_heavy,"408_ILE, 428_GLN, 429_GLN, 431_THR, 432_SER, 433_ILE, 434_LYS, 460_ASN, 463_ASN, 464_ILE, 465_THR, 466_PHE, 467_PRO, 468_ASN, 473_LYS, 477_ASN, 479_PRO, 481_ASP, 510_TYR, 512_TYR, 518_TRP",2.4
+2DWD_C,2DWD_B_light+2DWD_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 63_PRO, 64_ARG",2.6
+6Z7Y_F,6Z7Y_C_light+6Z7Y_D_heavy,"25_PHE, 26_TYR, 27_THR, 28_PRO, 29_LYS, 30_THR",2.2
+6Z7Y_H,6Z7Y_A_light+6Z7Y_B_heavy,"25_PHE, 26_TYR, 27_THR, 28_PRO, 29_LYS, 30_THR",2.2
+5XJ4_A,5XJ4_L_light+5XJ4_H_heavy,"111_VAL, 113_ARG, 115_MET, 121_ALA, 122_ASP, 123_TYR, 124_LYS, 125_ARG, 23_VAL, 26_ASP, 56_TYR, 58_GLU, 59_MET, 60_GLU, 61_ASP",2.3
+1DEE_G,1DEE_C_light+1DEE_D_heavy,"1821_ASN, 1824_GLN, 1827_GLY, 1828_PHE, 1830_GLN, 1831_SER, 1834_ASP, 1835_ASP, 1838_GLN, 1841_ASN, 1842_VAL, 1845_GLU, 1849_LEU",2.7
+1DEE_H,1DEE_E_light+1DEE_F_heavy,"2824_GLN, 2827_GLY, 2828_PHE, 2830_GLN, 2831_SER, 2834_ASP, 2835_ASP, 2838_GLN, 2841_ASN, 2842_VAL, 2845_GLU, 2849_LEU",2.7
+8WRE_A,8WRE_L_light+8WRE_H_heavy,"13_GLN, 14_GLU, 159_PRO, 16_ARG, 17_ARG, 18_SER, 19_ARG, 206_MET, 210_VAL, 212_THR, 213_ASP, 215_GLU, 216_GLU, 268_PHE, 269_VAL, 270_LEU, 271_GLN, 276_GLN",2.9
+7C61_A,7C61_L_light+7C61_H_heavy,"257_GLU, 306_ILE, 309_GLY, 310_GLN, 313_ASP, 316_LYS, 317_LEU, 322_LYS, 324_LYS, 325_GLU, 327_GLN, 328_ALA, 329_ALA, 331_GLU, 332_GLN, 334_LYS, 335_THR, 336_THR, 338_ASN, 339_ALA, 340_TYR",3.0
+7JV6_B,7JV6_D_light+7JV6_C_heavy,"449_TYR, 481_ASN, 484_GLU, 485_GLY, 486_PHE, 490_PHE, 494_SER",3.0
+7JV6_A,7JV6_L_light+7JV6_H_heavy,"449_TYR, 481_ASN, 484_GLU, 485_GLY, 486_PHE, 490_PHE, 494_SER",3.0
+7JV6_E,7JV6_G_light+7JV6_F_heavy,"449_TYR, 481_ASN, 484_GLU, 485_GLY, 486_PHE, 490_PHE, 494_SER",3.0
+6A0Z_A,6A0Z_L_light+6A0Z_H_heavy,"133_SER, 135_VAL, 136_SER, 137_SER, 153_TRP, 155_ILE, 156_LYS, 157_LYS, 158_ASN, 159_SER, 186_ASN, 187_ASP, 189_ALA, 190_GLU, 193_LYS, 194_LEU, 222_LYS, 226_GLN",2.33
+1XIW_B,1XIW_G_light+1XIW_H_heavy,"10_ASP, 11_ARG, 13_PHE, 33_ASP, 34_ILE, 36_ARG, 6_GLU, 8_LEU, 9_GLU",1.9
+1XIW_A,1XIW_C_light+1XIW_D_heavy,"35_GLU, 44_ASN, 45_ILE, 46_GLY, 47_GLY, 48_ASP, 49_GLU, 56_SER, 57_ASP, 78_TYR, 80_ARG, 81_GLY, 82_SER, 83_LYS, 84_PRO, 85_GLU, 86_ASP",1.9
+1XIW_F,1XIW_C_light+1XIW_D_heavy,"10_ASP, 11_ARG, 13_PHE, 31_LEU, 33_ASP, 34_ILE, 36_ARG, 6_GLU, 8_LEU, 9_GLU",1.9
+1XIW_E,1XIW_G_light+1XIW_H_heavy,"35_GLU, 37_LEU, 44_ASN, 45_ILE, 46_GLY, 47_GLY, 48_ASP, 49_GLU, 52_LYS, 56_SER, 78_TYR, 80_ARG, 81_GLY, 82_SER, 83_LYS, 84_PRO, 85_GLU, 86_ASP",1.9
+7X2A_A,7X2A_K_light+7X2A_H_heavy,"502_LYS, 506_LEU, 510_ASP, 511_ARG, 512_THR, 513_GLU, 535_TRP, 536_GLU, 538_GLY, 539_ASP, 540_TYR, 541_TYR, 542_ARG, 553_TRP, 555_VAL, 556_ALA, 557_SER",2.49
+7M2I_C,7M2I_B_light+7M2I_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 63_PRO, 64_ARG",2.7
+8Y6I_A,8Y6I_B_light+8Y6I_C_heavy,"322_LEU, 323_SER, 324_GLY, 325_GLU, 326_TYR, 327_SER, 739_PHE, 741_ARG, 742_ILE, 743_ASP, 744_ASP, 745_PRO, 748_LYS, 77_ASP, 78_ILE, 81_ASN, 84_ASN, 85_LEU, 88_LEU, 89_MET",2.54
+2ARJ_R,2ARJ_A_light+2ARJ_B_heavy,"10_PHE, 11_PRO, 12_LYS, 14_MET, 15_ASP, 20_GLN, 21_LYS, 22_VAL, 23_ASP, 25_VAL, 27_GLU, 29_LEU, 30_GLY, 6_GLU, 82_ASN, 84_LYS, 8_ARG, 9_ILE",2.88
+2ARJ_Q,2ARJ_L_light+2ARJ_H_heavy,"10_PHE, 114_SER, 11_PRO, 12_LYS, 14_MET, 15_ASP, 20_GLN, 21_LYS, 22_VAL, 23_ASP, 25_VAL, 27_GLU, 29_LEU, 30_GLY, 6_GLU, 84_LYS, 8_ARG, 9_ILE",2.88
+6CBV_B,6CBV_L_light+6CBV_H_heavy,"27_LYS, 31_THR, 32_LYS, 34_ARG, 35_ALA, 38_LEU, 39_ASP, 41_GLN, 42_LYS, 65_PHE, 66_ASP, 69_VAL, 70_GLY, 73_ASP, 76_LEU, 77_LYS, 80_ASN",1.87
+7X66_R,7X66_L_light+7X66_H_heavy,"403_ARG, 415_THR, 416_GLY, 417_ASN, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 486_PHE, 487_ASN, 489_TYR, 493_ARG, 494_SER, 496_SER, 500_THR, 501_TYR, 502_GLY, 505_HIS",2.4
+3CXH_P,3CXH_V_light+3CXH_U_heavy,"123_PRO, 124_HIS, 126_ILE, 127_GLN, 130_ASN, 131_SER, 132_VAL, 133_ASP, 134_MET, 142_THR, 143_ASP, 144_ALA, 147_VAL, 148_LYS, 149_ASP, 150_PRO, 151_GLN",2.5
+3CXH_E,3CXH_K_light+3CXH_J_heavy,"123_PRO, 124_HIS, 126_ILE, 127_GLN, 130_ASN, 131_SER, 132_VAL, 133_ASP, 134_MET, 143_ASP, 144_ALA, 147_VAL, 148_LYS, 149_ASP, 150_PRO, 151_GLN",2.5
+8SDG_D,8SDG_B_light+8SDG_A_heavy,"345_THR, 346_ARG, 347_PHE, 348_ALA, 351_TYR, 352_ALA, 353_TRP, 354_ASN, 355_ARG, 356_LYS, 357_ARG, 394_ASN, 396_TYR, 463_PRO, 464_PHE, 465_GLU, 466_ARG, 468_ILE, 470_THR, 471_GLU",2.71
+8SDG_C,8SDG_I_light+8SDG_G_heavy,"345_THR, 346_ARG, 347_PHE, 348_ALA, 351_TYR, 352_ALA, 353_TRP, 354_ASN, 355_ARG, 357_ARG, 396_TYR, 463_PRO, 464_PHE, 466_ARG, 468_ILE, 470_THR, 471_GLU, 516_GLU",2.71
+7SI0_B,7SI0_F_light+7SI0_E_heavy,"373_THR, 374_TRP, 375_SER, 376_ARG, 377_ALA, 378_SER, 379_GLY, 380_LYS, 381_PRO, 417_GLN, 419_ARG, 421_THR, 427_ARG, 428_ALA, 430_MET",3.0
+7SI0_H,7SI0_L_light+7SI0_K_heavy,"373_THR, 374_TRP, 375_SER, 376_ARG, 377_ALA, 378_SER, 379_GLY, 380_LYS, 381_PRO, 417_GLN, 419_ARG, 421_THR, 428_ALA, 430_MET",3.0
+7SI0_A,7SI0_D_light+7SI0_C_heavy,"373_THR, 374_TRP, 375_SER, 376_ARG, 377_ALA, 378_SER, 379_GLY, 380_LYS, 381_PRO, 417_GLN, 419_ARG, 421_THR, 427_ARG, 428_ALA, 430_MET",3.0
+7SI0_G,7SI0_J_light+7SI0_I_heavy,"373_THR, 374_TRP, 375_SER, 376_ARG, 377_ALA, 378_SER, 379_GLY, 380_LYS, 381_PRO, 417_GLN, 419_ARG, 421_THR, 428_ALA, 430_MET",3.0
+5YOY_A,5YOY_F_light+5YOY_I_heavy,"104_GLU, 105_THR, 107_GLU, 108_GLY, 109_ALA, 110_GLU, 111_ALA, 112_LYS, 138_ARG, 139_PRO, 140_ASP, 141_TYR, 23_GLU, 24_GLY, 25_GLN, 65_LYS, 66_GLY, 67_GLN, 69_CYS, 70_PRO, 71_SER",2.73
+5YOY_L,5YOY_N_light+5YOY_Q_heavy,"104_GLU, 105_THR, 107_GLU, 108_GLY, 109_ALA, 110_GLU, 111_ALA, 112_LYS, 138_ARG, 139_PRO, 140_ASP, 141_TYR, 23_GLU, 24_GLY, 65_LYS, 66_GLY, 67_GLN, 69_CYS, 70_PRO, 71_SER",2.73
+5YOY_K,5YOY_M_light+5YOY_P_heavy,"104_GLU, 105_THR, 107_GLU, 108_GLY, 109_ALA, 110_GLU, 111_ALA, 112_LYS, 113_PRO, 138_ARG, 139_PRO, 140_ASP, 141_TYR, 23_GLU, 24_GLY, 65_LYS, 66_GLY, 67_GLN, 68_GLY, 69_CYS, 70_PRO, 71_SER",2.73
+5YOY_J,5YOY_O_light+5YOY_R_heavy,"104_GLU, 105_THR, 107_GLU, 108_GLY, 109_ALA, 110_GLU, 111_ALA, 112_LYS, 113_PRO, 138_ARG, 139_PRO, 140_ASP, 141_TYR, 23_GLU, 24_GLY, 65_LYS, 66_GLY, 67_GLN, 68_GLY, 69_CYS, 70_PRO, 71_SER",2.73
+5YOY_B,5YOY_D_light+5YOY_G_heavy,"104_GLU, 105_THR, 107_GLU, 108_GLY, 109_ALA, 110_GLU, 111_ALA, 112_LYS, 113_PRO, 138_ARG, 140_ASP, 141_TYR, 23_GLU, 24_GLY, 65_LYS, 66_GLY, 67_GLN, 68_GLY, 69_CYS, 70_PRO, 71_SER",2.73
+5YOY_C,5YOY_E_light+5YOY_H_heavy,"104_GLU, 105_THR, 107_GLU, 108_GLY, 109_ALA, 110_GLU, 111_ALA, 112_LYS, 138_ARG, 139_PRO, 140_ASP, 141_TYR, 142_LEU, 23_GLU, 24_GLY, 65_LYS, 66_GLY, 67_GLN, 68_GLY, 69_CYS, 70_PRO, 71_SER",2.73
+7CZP_C,7CZP_N_light+7CZP_J_heavy,"403_ARG, 406_GLU, 408_ARG, 409_GLN, 416_GLY, 417_LYS, 449_TYR, 453_TYR, 455_LEU, 456_PHE, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",3.0
+7CZP_A,7CZP_L_light+7CZP_H_heavy,"403_ARG, 406_GLU, 408_ARG, 409_GLN, 416_GLY, 417_LYS, 449_TYR, 453_TYR, 455_LEU, 456_PHE, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",3.0
+5VKD_A,5VKD_L_light+5VKD_H_heavy,"652_TYR, 653_GLN, 656_LEU, 657_LYS, 658_THR, 659_GLN, 660_GLY, 661_PRO, 662_PHE, 665_ILE, 668_TYR, 676_ILE, 677_ILE, 678_PHE, 679_SER, 680_THR, 681_SER, 682_ASP, 683_GLY, 728_ARG, 731_PHE, 732_MET, 735_LEU, 736_GLN, 738_HIS",1.75
+6BGT_C,6BGT_A_light+6BGT_B_heavy,"557_PRO, 558_GLU, 560_ASP, 561_GLN, 569_LYS, 570_ASP, 571_PRO, 572_PRO, 573_PHE, 588_TYR, 589_MET, 591_ILE, 593_LYS, 596_ASP, 600_ALA, 602_GLN, 603_PRO, 604_CYS",2.7
+7YK4_B,7YK4_M_light+7YK4_I_heavy,"114_GLN, 115_PRO, 116_LEU, 124_ASP, 125_CYS, 126_ALA, 127_PRO, 128_CYS, 129_PRO, 130_PRO, 132_HIS, 140_ALA, 141_CYS, 142_LYS, 143_PRO, 90_ARG",2.7
+7YK4_A,7YK4_L_light+7YK4_H_heavy,"114_GLN, 115_PRO, 116_LEU, 117_ASP, 118_SER, 119_TYR, 123_VAL, 124_ASP, 125_CYS, 126_ALA, 127_PRO, 128_CYS, 129_PRO, 130_PRO, 132_HIS, 141_CYS, 142_LYS, 143_PRO",2.7
+7REW_I,7REW_L_light+7REW_H_heavy,"10_LEU, 12_GLU, 65_ARG, 66_MET, 68_ASN, 69_GLY, 70_PHE, 71_CYS, 72_PRO, 73_HIS, 74_LYS, 75_VAL, 8_THR, 9_ALA",2.1
+7REW_G,7REW_B_light+7REW_A_heavy,"12_GLU, 52_ILE, 61_GLU, 65_ARG, 66_MET, 68_ASN, 69_GLY, 70_PHE, 71_CYS, 72_PRO, 73_HIS, 74_LYS, 75_VAL, 8_THR, 9_ALA",2.1
+6WZM_E,6WZM_B_light+6WZM_A_heavy,"10_ILE, 11_LYS, 12_THR, 13_TYR, 33_HIS, 34_CYS, 35_ALA, 36_ASN, 40_ILE, 47_ARG, 48_GLU, 49_LEU, 50_CYS, 5_LEU, 6_ARG, 7_CYS, 8_GLN",2.28
+6WZM_F,6WZM_D_light+6WZM_C_heavy,"10_ILE, 11_LYS, 12_THR, 13_TYR, 33_HIS, 34_CYS, 35_ALA, 36_ASN, 40_ILE, 47_ARG, 48_GLU, 49_LEU, 50_CYS, 5_LEU, 6_ARG, 7_CYS, 8_GLN",2.28
+7KPB_A,7KPB_L_light+7KPB_H_heavy,"135_GLU, 136_ILE, 137_ASN, 138_ARG, 77_THR, 79_THR, 83_ILE, 89_THR, 90_LYS, 91_VAL, 92_ASN, 93_LEU, 94_LEU, 95_SER, 96_ALA, 97_ILE, 98_LYS",3.0
+7KPB_C,7KPB_L_light+7KPB_H_heavy,"113_PRO, 115_TYR, 143_ASP, 145_ALA, 146_GLU, 149_GLN, 63_LEU, 65_LYS, 67_GLN",3.0
+7A3O_A,7A3O_L_light+7A3O_H_heavy,"149_HIS, 150_GLN, 151_VAL, 2_ARG, 310_LYS, 362_GLU, 363_LYS, 44_GLU, 46_LEU",2.8
+6VVU_D,6VVU_I_light+6VVU_G_heavy,"107_GLU, 108_LEU, 109_GLU, 110_GLU, 38_TRP, 50_GLN, 60_GLY, 62_ALA, 65_ARG, 81_GLN, 82_LEU, 83_LEU, 84_PRO, 85_VAL, 86_SER, 87_ARG",3.0
+6VVU_C,6VVU_F_light+6VVU_E_heavy,"107_GLU, 108_LEU, 109_GLU, 110_GLU, 38_TRP, 50_GLN, 60_GLY, 62_ALA, 65_ARG, 81_GLN, 82_LEU, 83_LEU, 84_PRO, 85_VAL, 86_SER, 87_ARG",3.0
+6VVU_B,6VVU_K_light+6VVU_J_heavy,"107_GLU, 108_LEU, 109_GLU, 110_GLU, 38_TRP, 50_GLN, 60_GLY, 61_LEU, 62_ALA, 65_ARG, 81_GLN, 82_LEU, 83_LEU, 84_PRO, 85_VAL, 86_SER, 87_ARG",3.0
+6VVU_A,6VVU_L_light+6VVU_H_heavy,"107_GLU, 108_LEU, 109_GLU, 110_GLU, 38_TRP, 50_GLN, 60_GLY, 61_LEU, 62_ALA, 65_ARG, 81_GLN, 82_LEU, 83_LEU, 84_PRO, 85_VAL, 86_SER, 87_ARG",3.0
+7UPW_A,7UPW_E_light+7UPW_D_heavy,"339_GLY, 343_ASN, 344_ALA, 345_THR, 346_ARG, 440_ASN, 441_LEU, 442_ASP, 443_SER, 444_LYS, 445_VAL, 448_ASN, 451_TYR, 509_ARG",2.7
+7UPW_B,7UPW_G_light+7UPW_F_heavy,"339_GLY, 343_ASN, 344_ALA, 345_THR, 346_ARG, 440_ASN, 441_LEU, 442_ASP, 443_SER, 444_LYS, 445_VAL, 448_ASN, 451_TYR, 509_ARG",2.7
+7UPW_C,7UPW_L_light+7UPW_H_heavy,"339_GLY, 343_ASN, 344_ALA, 345_THR, 346_ARG, 440_ASN, 441_LEU, 442_ASP, 443_SER, 444_LYS, 445_VAL, 448_ASN, 451_TYR, 509_ARG",2.7
+8EZ8_A,8EZ8_L_light+8EZ8_H_heavy,"266_SER, 267_PRO, 269_SER, 270_GLY, 283_ARG, 285_PRO, 286_GLY, 288_ARG, 302_ILE, 304_ASP, 305_ILE, 306_ASN, 309_ASP, 310_TYR, 311_SER, 312_ILE, 313_VAL, 314_SER, 315_SER, 318_CYS, 337_CYS, 357_GLY, 358_ASN, 383_TRP, 384_SER, 386_PRO",2.78
+3IU3_J,3IU3_D_light+3IU3_C_heavy,"119_TYR, 120_HIS, 1_GLU, 22_GLU, 23_GLY, 25_MET, 27_ASN, 29_GLU, 2_LEU, 42_LEU, 43_TYR, 45_LEU, 47_THR, 48_GLY, 4_ASP, 56_ASP, 57_ASN, 5_ASP, 6_ASP",2.9
+3IU3_K,3IU3_B_light+3IU3_A_heavy,"120_HIS, 1_GLU, 22_GLU, 23_GLY, 25_MET, 27_ASN, 29_GLU, 2_LEU, 38_LYS, 41_SER, 42_LEU, 43_TYR, 45_LEU, 47_THR, 48_GLY, 4_ASP, 56_ASP, 57_ASN, 5_ASP, 6_ASP",2.9
+7LCV_C,7LCV_B_light+7LCV_A_heavy,"112_GLU, 113_GLN, 123_MET, 125_ALA, 127_ARG, 47_LYS, 48_LEU, 49_ALA, 50_ALA, 51_GLN, 52_GLY, 53_ALA, 54_GLU, 77_ASP, 78_PHE, 79_ILE, 81_GLN, 87_GLN, 88_LEU, 89_ILE, 90_THR, 93_SER, 94_GLY, 95_GLU",1.7
+8ZD5_D,8ZD5_F_light+8ZD5_E_heavy,"121_GLU, 14_PRO, 16_GLU, 36_THR, 37_GLY, 38_LEU, 39_THR, 40_LYS, 91_LYS, 92_ASP",2.89
+8ZD5_J,8ZD5_L_light+8ZD5_K_heavy,"11_GLY, 121_GLU, 12_GLN, 13_GLY, 14_PRO, 16_GLU, 36_THR, 37_GLY, 38_LEU, 39_THR, 40_LYS, 91_LYS, 92_ASP, 93_GLY",2.89
+8ZD5_A,8ZD5_C_light+8ZD5_B_heavy,"11_GLY, 12_GLN, 13_GLY, 14_PRO, 16_GLU, 36_THR, 37_GLY, 38_LEU, 39_THR, 40_LYS, 91_LYS, 92_ASP, 93_GLY",2.89
+8ZD5_G,8ZD5_I_light+8ZD5_H_heavy,"11_GLY, 121_GLU, 12_GLN, 13_GLY, 14_PRO, 16_GLU, 36_THR, 37_GLY, 38_LEU, 39_THR, 40_LYS, 91_LYS, 92_ASP",2.89
+7KMI_C,7KMI_B_light+7KMI_A_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 503_VAL, 505_TYR",1.73
+8U03_C,8U03_L_light+8U03_H_heavy,"100_VAL, 103_LEU, 104_LYS, 107_LYS, 110_ILE, 111_LEU, 28_TYR, 31_GLU, 32_ARG, 35_ARG, 36_SER, 40_LEU, 42_VAL, 49_ILE, 51_HIS, 53_LEU, 56_TYR, 76_TRP, 77_TYR, 78_GLN, 96_TRP",2.72
+1A14_N,1A14_L_light+1A14_H_heavy,"328_PRO, 329_ASN, 330_ASP, 331_PRO, 332_THR, 341_TYR, 342_PRO, 343_GLY, 344_ASN, 366_ILE, 368_ILE, 369_ALA, 370_SER, 372_SER, 400_ASN, 401_THR, 402_ASP, 403_TRP, 432_LYS",2.5
+8ATH_A,8ATH_L_light+8ATH_H_heavy,"106_ARG, 107_ASN, 108_ALA, 109_THR, 149_ILE, 150_ASP, 151_LYS, 178_TYR, 180_SER, 181_ASN, 186_ARG, 187_GLY, 35_ASN, 80_CYS, 81_GLY, 83_GLU, 84_ASN",2.37
+8ATH_B,8ATH_F_light+8ATH_E_heavy,"106_ARG, 107_ASN, 108_ALA, 109_THR, 149_ILE, 150_ASP, 151_LYS, 178_TYR, 180_SER, 181_ASN, 186_ARG, 187_GLY, 35_ASN, 80_CYS, 81_GLY",2.37
+8TQ9_B,8TQ9_L_light+8TQ9_H_heavy,"-1_TYR, 0_ALA, 1_ILE, 2_GLN, 34_HIS, 35_ILE, 36_GLU, 38_GLN, 81_ARG, 83_LYS, 84_HIS, 85_ALA, 86_SER, 87_MET, 88_ALA, 89_GLU, 90_PRO",2.9
+7CZQ_A,7CZQ_K_light+7CZQ_H_heavy,"403_ARG, 405_ASP, 406_GLU, 408_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 449_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 487_ASN, 489_TYR, 493_GLN, 494_SER, 496_GLY, 500_THR, 501_ASN, 502_GLY, 503_VAL, 505_TYR",2.8
+7CZQ_C,7CZQ_N_light+7CZQ_J_heavy,"403_ARG, 405_ASP, 406_GLU, 408_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 449_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 487_ASN, 489_TYR, 493_GLN, 494_SER, 496_GLY, 500_THR, 501_ASN, 502_GLY, 503_VAL, 505_TYR",2.8
+7M71_A,7M71_L_light+7M71_H_heavy,"449_TYR, 456_PHE, 470_THR, 471_GLU, 481_ASN, 482_GLY, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 492_LEU, 493_GLN",2.66
+7M71_B,7M71_L_light+7M71_H_heavy,"369_TYR, 375_SER, 376_THR, 377_PHE, 378_LYS, 408_ARG, 503_VAL, 508_TYR",2.66
+6XZW_D,6XZW_L_light+6XZW_H_heavy,"141_PHE, 142_ASP, 146_GLU, 149_ARG, 173_ALA, 174_ALA, 175_LYS, 176_GLN, 178_ASN, 195_ALA, 196_ALA, 197_ASP, 199_LYS, 218_GLU",2.4
+8V91_B,8V91_I_light+8V91_J_heavy,"139_PRO, 140_SER, 60_GLY, 62_THR, 63_GLU, 64_LYS, 65_PRO",2.6
+8V91_C,8V91_I_light+8V91_J_heavy,"152_GLY, 153_ASN, 154_LEU, 155_THR, 228_GLU",2.6
+7UVO_C,7UVO_B_light+7UVO_A_heavy,"561_ASP, 563_SER, 564_TYR, 565_GLU, 566_THR, 567_THR, 570_GLY, 571_ASP, 572_THR, 573_ALA, 629_LYS, 638_ASN, 640_GLU, 642_VAL, 644_LYS, 657_LYS, 710_LYS",2.09
+8VGH_D,8VGH_L_light+8VGH_K_heavy,"107_GLU, 108_LEU, 109_GLU, 110_GLU, 36_HIS, 38_TRP, 60_GLY, 61_LEU, 62_ALA, 65_ARG, 81_GLN, 82_LEU, 83_LEU, 84_PRO, 85_VAL, 86_SER",2.9
+8VGH_C,8VGH_E_light+8VGH_F_heavy,"107_GLU, 108_LEU, 109_GLU, 110_GLU, 36_HIS, 38_TRP, 60_GLY, 61_LEU, 62_ALA, 65_ARG, 81_GLN, 82_LEU, 83_LEU, 84_PRO, 85_VAL, 86_SER",2.9
+8VGH_B,8VGH_I_light+8VGH_J_heavy,"107_GLU, 108_LEU, 109_GLU, 110_GLU, 36_HIS, 38_TRP, 60_GLY, 61_LEU, 62_ALA, 65_ARG, 81_GLN, 82_LEU, 83_LEU, 84_PRO, 85_VAL, 86_SER",2.9
+8VGH_A,8VGH_G_light+8VGH_H_heavy,"107_GLU, 108_LEU, 109_GLU, 110_GLU, 36_HIS, 38_TRP, 50_GLN, 60_GLY, 61_LEU, 62_ALA, 65_ARG, 81_GLN, 82_LEU, 83_LEU, 84_PRO, 85_VAL, 86_SER",2.9
+8JXS_A,8JXS_L_light+8JXS_B_heavy,"121_ARG, 125_ILE, 128_PRO, 129_PHE, 220_ILE, 224_GLN, 225_ILE, 227_ARG, 228_ILE, 231_LEU, 266_ARG, 270_VAL, 274_LEU, 330_ILE, 331_TYR, 334_ASN, 335_ALA, 336_ASP, 57_LYS",3.0
+8JXS_C,8JXS_L_light+8JXS_B_heavy,"377_GLN, 380_GLY, 381_PHE, 383_GLN, 384_SER, 387_ASP, 388_ASP, 391_VAL, 394_GLU, 395_ILE, 398_GLU, 516_ASP, 518_GLY",3.0
+8UKY_D,8UKY_A_light+8UKY_B_heavy,"143_TYR, 144_GLN, 147_LEU, 148_THR, 149_GLY, 150_PHE, 151_LEU, 152_GLY, 153_GLN, 155_THR, 36_ARG, 39_VAL, 43_HIS, 47_GLN, 50_GLU, 52_VAL, 54_ALA",2.4
+8UKY_C,8UKY_L_light+8UKY_H_heavy,"143_TYR, 144_GLN, 147_LEU, 148_THR, 149_GLY, 150_PHE, 151_LEU, 152_GLY, 153_GLN, 155_THR, 36_ARG, 39_VAL, 43_HIS, 47_GLN, 50_GLU",2.4
+7LSE_E,7LSE_L_light+7LSE_H_heavy,"305_THR, 306_MET, 308_ASP, 309_LYS, 310_THR, 311_LYS, 333_SER, 334_GLY, 335_THR, 336_LYS, 337_PRO, 366_ASN",2.35
+7KFV_B,7KFV_D_light+7KFV_C_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",2.1
+7KFV_A,7KFV_L_light+7KFV_H_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",2.1
+7KFV_E,7KFV_G_light+7KFV_F_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",2.1
+7CHF_R,7CHF_L_light+7CHF_H_heavy,"403_ARG, 405_ASP, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 459_SER, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 503_VAL, 504_GLY, 505_TYR",2.67
+7CHF_R,7CHF_B_light+7CHF_A_heavy,"449_TYR, 450_ASN, 452_LEU, 472_ILE, 481_ASN, 482_GLY, 483_VAL, 484_GLU, 490_PHE",2.67
+8JEN_I,8JEN_B_light+8JEN_A_heavy,"63_ASP, 64_GLN, 65_LEU, 68_ILE, 73_LEU, 74_GLY, 75_TRP, 76_HIS, 77_ILE, 78_SER, 79_PRO, 80_SER, 82_LYS",2.71
+8JEN_J,8JEN_D_light+8JEN_C_heavy,"60_GLU, 62_GLN, 63_ASP, 64_GLN, 65_LEU, 67_ALA, 68_ILE, 70_ASN, 73_LEU, 74_GLY, 76_HIS, 78_SER, 79_PRO, 80_SER, 82_LYS",2.71
+8JEN_M,8JEN_F_light+8JEN_E_heavy,"56_GLN, 62_GLN, 63_ASP, 64_GLN, 65_LEU, 67_ALA, 68_ILE, 70_ASN, 73_LEU, 74_GLY, 75_TRP, 76_HIS, 77_ILE, 78_SER, 79_PRO, 80_SER, 82_LYS",2.71
+8JEN_P,8JEN_H_light+8JEN_G_heavy,"63_ASP, 64_GLN, 65_LEU, 68_ILE, 73_LEU, 74_GLY, 75_TRP, 76_HIS, 77_ILE, 78_SER, 79_PRO, 80_SER, 82_LYS",2.71
+5XMH_B,5XMH_C_light+5XMH_D_heavy,"251_LEU, 252_MET, 253_ILE, 310_HIS, 311_GLN, 314_LEU, 382_GLU, 385_GLY, 422_VAL, 424_SER, 428_MET, 431_ALA, 432_LEU, 433_HIS, 434_ASN, 435_HIS, 436_TYR, 437_THR, 438_GLN, 439_LYS, 440_SER, 442_SER",2.8
+5XMH_A,5XMH_L_light+5XMH_H_heavy,"251_LEU, 252_MET, 253_ILE, 254_SER, 310_HIS, 311_GLN, 314_LEU, 382_GLU, 384_ASN, 385_GLY, 422_VAL, 424_SER, 431_ALA, 432_LEU, 433_HIS, 434_ASN, 435_HIS, 436_TYR, 437_THR, 438_GLN, 440_SER, 442_SER",2.8
+2EKS_C,2EKS_A_light+2EKS_B_heavy,"100_SER, 101_ASP, 102_GLY, 14_ARG, 15_HIS, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 62_TRP, 63_TRP, 73_ARG, 75_LEU, 77_ASN, 89_THR, 93_ASN, 96_LYS, 97_LYS, 98_ILE",2.0
+6M2R_A,6M2R_L_light+6M2R_H_heavy,"337_TYR, 498_TYR, 502_ARG, 505_GLU, 506_ASP, 508_ARG, 509_ASP, 511_ILE, 512_GLY, 513_PHE, 514_PRO, 598_ARG, 599_ASP, 90_HIS",2.8
+7PS4_E,7PS4_L_light+7PS4_H_heavy,"351_TYR, 449_TYR, 450_ASN, 452_LEU, 470_THR, 472_ILE, 479_PRO, 480_CYS, 481_ASN, 482_GLY, 483_VAL, 484_LYS, 486_PHE, 490_PHE, 492_LEU",1.94
+7PS4_A,7PS4_C_light+7PS4_B_heavy,"351_TYR, 449_TYR, 452_LEU, 470_THR, 472_ILE, 479_PRO, 480_CYS, 481_ASN, 482_GLY, 483_VAL, 484_LYS, 490_PHE, 492_LEU",1.94
+8G3R_G,8G3R_B_light+8G3R_C_heavy,"118_ARG, 149_VAL, 150_ARG, 151_ASP, 152_ARG, 178_TRP, 179_SER, 198_ASP, 199_LYS, 221_GLU, 222_ILE, 224_ARG, 227_GLU, 292_ARG, 294_ASN, 295_TRP, 347_HIS, 371_ARG, 406_TYR, 431_LYS",2.3
+7S5P_A,7S5P_L_light+7S5P_H_heavy,"405_ASP, 408_ARG, 415_THR, 416_GLY, 417_ASN, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 501_TYR, 502_GLY, 505_TYR",2.86
+7CVT_A,7CVT_D_light+7CVT_C_heavy,"246_ASP, 248_PRO, 249_LEU, 250_ASN, 380_PRO, 381_GLN, 382_TYR, 383_HIS",2.9
+7CVT_B,7CVT_F_light+7CVT_E_heavy,"246_ASP, 248_PRO, 249_LEU, 250_ASN, 380_PRO, 381_GLN, 382_TYR, 383_HIS",2.9
+8DA0_E,8DA0_A_light+8DA0_B_heavy,"10_GLN, 28_ASP, 29_ALA, 30_PHE, 33_GLN, 34_ARG, 35_GLY, 36_LYS, 37_ARG, 38_VAL, 6_THR, 7_PHE, 9_ASP",2.2
+7UPB_D,7UPB_F_light+7UPB_C_heavy,"394_CYS, 396_THR, 397_THR, 399_ARG, 400_ALA, 402_SER, 414_ASN, 415_THR, 416_THR, 418_PRO",3.0
+7UPB_G,7UPB_L_light+7UPB_H_heavy,"394_CYS, 396_THR, 397_THR, 399_ARG, 400_ALA, 402_SER, 415_THR, 416_THR, 418_PRO",3.0
+7UPB_A,7UPB_E_light+7UPB_B_heavy,"394_CYS, 396_THR, 397_THR, 399_ARG, 400_ALA, 402_SER, 414_ASN, 415_THR, 416_THR, 418_PRO",3.0
+7F6Y_R,7F6Y_L_light+7F6Y_H_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 449_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 484_LYS, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 493_GLN, 494_SER, 496_GLY, 498_GLN, 501_ASN, 502_GLY, 505_TYR",3.0
+3HI1_G,3HI1_L_light+3HI1_H_heavy,"109_ILE, 112_TRP, 255_VAL, 281_ALA, 365_SER, 366_GLY, 367_GLY, 368_ASP, 370_GLU, 371_ILE, 375_SER, 382_PHE, 384_TYR, 421_LYS, 426_MET, 429_GLU, 473_GLY, 474_ASP, 475_MET, 476_ARG",2.9
+3HI1_J,3HI1_A_light+3HI1_B_heavy,"109_ILE, 112_TRP, 210_PHE, 255_VAL, 257_THR, 365_SER, 366_GLY, 367_GLY, 368_ASP, 370_GLU, 371_ILE, 375_SER, 376_PHE, 382_PHE, 384_TYR, 421_LYS, 424_ILE, 426_MET, 429_GLU, 473_GLY, 474_ASP, 475_MET, 476_ARG",2.9
+6Q18_A,6Q18_L_light+6Q18_H_heavy,"135_VAL, 136_THR, 137_ALA, 145_SER, 153_TRP, 155_THR, 156_GLU, 158_ASN, 159_GLY, 160_LEU, 183_HIS, 185_PRO, 186_SER, 187_ASN, 189_ARG, 190_ASP, 192_ARG, 193_ALA, 194_ILE, 196_HIS, 225_GLY, 226_GLN, 227_GLU, 228_GLY, 95_TYR",2.55
+5LWY_A,5LWY_L_light+5LWY_H_heavy,"0_PHE, 100_GLU, 101_GLY, 102_ARG, 103_TRP, 104_ARG, 106_ILE, 107_PRO, 110_VAL, 123_HIS, 127_PRO, 128_PRO, 129_MET, 130_PRO, 131_SER, 133_ARG",2.4
+1YYL_G,1YYL_L_light+1YYL_H_heavy,"119_CYS, 120_VAL, 122_LEU, 200_VAL, 202_THR, 203_GLN, 204_ALA, 419_ARG, 420_ILE, 421_LYS, 422_GLN, 423_ILE, 434_MET, 437_PRO",2.75
+1YYL_P,1YYL_Q_light+1YYL_R_heavy,"1119_CYS, 1120_VAL, 1122_LEU, 1200_VAL, 1202_THR, 1203_GLN, 1204_ALA, 1205_CYS, 1419_ARG, 1420_ILE, 1421_LYS, 1422_GLN, 1423_ILE, 1434_MET, 1437_PRO",2.75
+7RE7_A,7RE7_J_light+7RE7_I_heavy,"105_SER, 106_ASP, 107_TRP, 108_ARG, 169_ARG, 173_GLU, 176_LYS, 180_GLN",2.55
+7RE7_A,7RE7_L_light+7RE7_H_heavy,"151_HIS, 154_GLU, 155_GLN, 158_ALA, 167_TRP, 58_GLU, 59_TYR, 62_GLY, 65_ARG, 66_LYS, 68_LYS, 69_ALA, 72_GLN, 75_ARG, 76_VAL",2.55
+6CF2_F,6CF2_B_light+6CF2_A_heavy,"11_ASP, 14_LYS, 15_ALA, 18_LEU, 21_PHE, 22_LEU, 55_ILE, 58_ARG, 59_ILE, 62_THR, 63_TYR",3.0
+8A44_A,8A44_D_light+8A44_C_heavy,"259_THR, 260_ASN, 261_PHE, 262_HIS, 263_ARG, 265_ILE, 268_ARG, 330_ASN, 333_ARG, 334_SER, 335_ILE, 336_PHE, 337_GLY, 338_THR, 339_ASP, 340_GLU, 341_LYS, 344_GLN, 345_ARG",2.49
+7Q6E_C,7Q6E_K_light+7Q6E_J_heavy,"329_ILE, 330_THR, 331_ASN, 332_LEU, 333_CYS, 334_PRO, 335_PHE, 336_GLY, 337_GLU, 339_PHE, 340_ASN, 342_THR, 343_ARG, 359_VAL, 360_ALA, 361_ASP, 362_TYR, 364_VAL, 365_LEU, 524_PRO, 526_LYS",2.7
+7Q6E_B,7Q6E_F_light+7Q6E_E_heavy,"329_ILE, 330_THR, 331_ASN, 332_LEU, 333_CYS, 334_PRO, 335_PHE, 336_GLY, 337_GLU, 339_PHE, 340_ASN, 342_THR, 343_ARG, 359_VAL, 360_ALA, 361_ASP, 362_TYR, 364_VAL, 365_LEU, 524_PRO, 526_LYS",2.7
+7Q6E_A,7Q6E_L_light+7Q6E_H_heavy,"329_ILE, 330_THR, 331_ASN, 332_LEU, 333_CYS, 334_PRO, 335_PHE, 336_GLY, 337_GLU, 340_ASN, 342_THR, 343_ARG, 359_VAL, 360_ALA, 361_ASP, 362_TYR, 364_VAL, 365_LEU, 524_PRO, 526_LYS",2.7
+4KKA_B,4KKA_F_light+4KKA_E_heavy,"243_LYS, 246_ASP, 248_PRO, 249_LEU, 250_ASN, 380_PRO, 381_GLN, 382_TYR, 383_HIS",3.0
+4KKA_A,4KKA_D_light+4KKA_C_heavy,"243_LYS, 246_ASP, 248_PRO, 249_LEU, 250_ASN, 380_PRO, 381_GLN, 382_TYR, 383_HIS",3.0
+7TN0_I,7TN0_A_light+7TN0_B_heavy,"334_ASN, 335_LEU, 337_PRO, 339_ASP, 340_GLU, 343_ASN, 344_ALA, 345_THR, 346_ARG, 356_LYS, 359_SER, 361_CYS, 441_LEU, 509_ARG",2.85
+7TN0_I,7TN0_G_light+7TN0_H_heavy,"369_TYR, 373_PRO, 375_PHE, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 390_LEU, 392_PHE, 412_PRO, 414_GLN, 427_ASP, 428_ASP, 429_PHE",2.85
+7TN0_S,7TN0_D_light+7TN0_C_heavy,"334_ASN, 335_LEU, 337_PRO, 339_ASP, 340_GLU, 343_ASN, 344_ALA, 345_THR, 346_ARG, 356_LYS, 357_ARG, 359_SER, 361_CYS, 441_LEU",2.85
+7TN0_S,7TN0_N_light+7TN0_M_heavy,"369_TYR, 370_ASN, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 392_PHE, 412_PRO, 413_GLY, 414_GLN, 427_ASP, 428_ASP, 429_PHE, 517_LEU, 528_LYS",2.85
+4K9E_C,4K9E_L_light+4K9E_H_heavy,"317_PRO, 318_MET, 319_ILE, 320_ASN, 323_VAL, 324_PHE, 325_VAL, 326_ASN, 329_GLU, 330_ASN, 331_VAL, 332_ASP, 334_ILE, 336_GLU, 358_LYS, 360_GLU, 362_TYR, 363_PRO, 364_LYS, 365_SER, 366_GLU, 372_ARG, 376_GLU, 378_HIS, 381_ARG, 497_VAL",2.7
+6MFP_A,6MFP_D_light+6MFP_C_heavy,"219_THR, 220_PRO, 221_ALA, 223_TYR, 224_VAL, 244_SER, 245_VAL, 246_GLN, 491_ILE, 53_PHE, 71_THR, 74_CYS, 75_VAL, 76_PRO, 78_ASP, 79_PRO, 80_ASN, 82_GLN, 84_ILE",3.0
+6MFP_G,6MFP_L_light+6MFP_H_heavy,"219_THR, 220_PRO, 221_ALA, 223_TYR, 224_VAL, 244_SER, 245_VAL, 246_GLN, 491_ILE, 492_GLU, 53_PHE, 71_THR, 72_HIS, 73_ALA, 74_CYS, 75_VAL, 76_PRO, 78_ASP, 79_PRO, 80_ASN, 82_GLN, 84_ILE",3.0
+2DQC_Y,2DQC_L_light+2DQC_H_heavy,"100_SER, 101_ASP, 102_GLY, 103_ASN, 15_HIS, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 62_TRP, 63_TRP, 73_ARG, 75_LEU, 77_ASN, 89_THR, 93_ASN, 96_LYS, 97_LYS",1.8
+8J7V_A,8J7V_C_light+8J7V_E_heavy,"313_GLN, 314_GLN, 315_LEU, 316_GLN, 317_GLY, 318_VAL, 319_TYR, 343_ALA, 344_PRO, 345_ASP, 346_ALA, 347_ASP, 349_ARG, 350_TRP, 353_SER, 354_GLN, 356_HIS, 373_PHE, 375_ALA, 376_MET",2.79
+8J7V_B,8J7V_D_light+8J7V_F_heavy,"313_GLN, 314_GLN, 315_LEU, 316_GLN, 317_GLY, 318_VAL, 319_TYR, 343_ALA, 344_PRO, 345_ASP, 346_ALA, 347_ASP, 349_ARG, 350_TRP, 353_SER, 354_GLN, 356_HIS, 357_ASN, 373_PHE, 376_MET",2.79
+6AL5_A,6AL5_L_light+6AL5_H_heavy,"103_SER, 120_GLU, 155_LYS, 157_TYR, 159_TRP, 162_ASP, 163_ARG, 164_PRO, 166_ILE, 217_VAL, 218_HIS, 219_PRO, 220_LYS, 221_GLY, 222_PRO, 223_LYS, 224_SER, 96_LEU",3.0
+6J5D_A,6J5D_L_light+6J5D_H_heavy,"308_ASP, 309_LYS, 310_SER, 311_LYS, 312_PHE, 313_ALA, 332_PHE, 333_SER, 334_GLY, 335_SER, 336_LYS, 387_GLU",1.8
+6PXH_A,6PXH_D_light+6PXH_C_heavy,"157_SER, 191_SER, 192_GLY, 193_ASN, 197_ALA, 198_GLY, 199_ASN, 209_THR, 26_VAL, 27_LYS, 28_SER, 29_ALA, 301_ARG, 96_THR, 97_PRO",2.3
+6PXH_B,6PXH_L_light+6PXH_H_heavy,"157_SER, 191_SER, 192_GLY, 193_ASN, 197_ALA, 198_GLY, 199_ASN, 26_VAL, 27_LYS, 28_SER, 29_ALA",2.3
+4WFF_B,4WFF_F_light+4WFF_G_heavy,"69_ARG, 70_GLU, 73_LEU, 74_ARG, 76_HIS, 77_PRO, 78_CYS, 79_VAL, 80_SER, 81_ASP, 82_GLN, 83_GLU, 86_LEU",2.5
+4WFF_A,4WFF_D_light+4WFF_E_heavy,"69_ARG, 70_GLU, 73_LEU, 74_ARG, 76_HIS, 77_PRO, 78_CYS, 79_VAL, 80_SER, 81_ASP, 82_GLN, 83_GLU, 86_LEU",2.5
+7N4I_C,7N4I_L_light+7N4I_H_heavy,"353_TRP, 355_ARG, 457_ARG, 459_SER, 462_LYS, 463_PRO, 464_PHE, 465_GLU, 466_ARG, 467_ASP, 468_ILE, 469_SER, 471_GLU, 472_ILE, 474_GLN, 479_PRO, 480_CYS, 481_ASN, 482_GLY, 518_LEU",2.28
+7T25_E,7T25_L_light+7T25_H_heavy,"228_ASN, 230_LYS, 249_ASP, 250_SER, 251_ASN, 253_THR, 254_LYS, 255_LEU, 256_GLU, 257_GLY, 258_SER",2.25
+7T25_C,7T25_B_light+7T25_A_heavy,"228_ASN, 230_LYS, 249_ASP, 250_SER, 251_ASN, 253_THR, 254_LYS, 255_LEU, 256_GLU, 257_GLY, 258_SER",2.25
+6CK9_G,6CK9_L_light+6CK9_H_heavy,"135_ASN, 136_VAL, 137_THR, 138_ASN, 322_ASP, 323_ILE, 324_GLY, 325_ASP, 326_ILE, 327_ARG, 328_GLN, 330_HIS, 417_PRO",2.71
+4MA8_C,4MA8_L_light+4MA8_H_heavy,"138_MET, 140_HIS, 141_PHE, 142_GLY, 143_ASN, 144_ASP, 145_TRP, 146_GLU, 147_ASP, 204_LYS, 208_ARG, 212_GLN",2.2
+7EPX_B,7EPX_M_light+7EPX_I_heavy,"369_TYR, 370_ASN, 372_ALA, 374_PHE, 375_SER, 376_THR, 378_LYS, 379_CYS, 404_GLY, 405_ASP, 407_VAL, 408_ARG, 502_GLY, 503_VAL, 504_GLY, 508_TYR",3.0
+7EPX_A,7EPX_L_light+7EPX_H_heavy,"369_TYR, 370_ASN, 372_ALA, 374_PHE, 375_SER, 376_THR, 378_LYS, 379_CYS, 404_GLY, 405_ASP, 407_VAL, 408_ARG, 502_GLY, 503_VAL, 504_GLY, 508_TYR",3.0
+4LU5_A,4LU5_M_light+4LU5_I_heavy,"116_TYR, 117_GLN, 118_MET, 119_PHE, 155_ASP, 156_GLY, 157_ASN, 168_ASP, 169_SER, 170_ASP, 171_VAL, 172_SER, 173_GLN, 174_GLU, 175_VAL",2.9
+4LU5_B,4LU5_L_light+4LU5_H_heavy,"116_TYR, 117_GLN, 118_MET, 119_PHE, 154_SER, 155_ASP, 156_GLY, 157_ASN, 168_ASP, 170_ASP, 171_VAL, 172_SER, 173_GLN, 174_GLU, 175_VAL",2.9
+7WLW_A,7WLW_L_light+7WLW_H_heavy,"337_TYR, 498_TYR, 502_ARG, 505_GLU, 506_ASP, 508_ARG, 509_ASP, 510_MET, 511_ILE, 512_GLY, 513_PHE, 514_PRO, 598_ARG, 599_ASP, 90_HIS",2.9
+5GJS_B,5GJS_L_light+5GJS_H_heavy,"16_GLY, 18_VAL, 19_ASP, 20_GLY, 21_TRP, 33_GLY, 34_TYR, 38_LEU, 42_GLN, 45_ILE, 46_ASP, 48_ILE, 49_THR",2.9
+1NSN_S,1NSN_L_light+1NSN_H_heavy,"105_ARG, 106_GLN, 120_THR, 121_HIS, 124_HIS, 127_LYS, 135_GLU, 57_GLU, 60_ALA, 61_PHE, 64_LYS, 68_ASN, 70_LYS, 95_ASP, 96_GLY, 97_LYS, 98_MET, 9_LYS",2.8
+6UMX_A,6UMX_L_light+6UMX_H_heavy,"170_PHE, 172_GLN, 174_LEU, 186_TYR, 187_THR, 188_GLY, 190_ARG, 191_SER, 192_LEU, 193_LYS, 228_LYS, 230_LEU, 232_GLU, 233_ASN, 234_GLY",2.79
+6UMX_B,6UMX_l_light+6UMX_h_heavy,"170_PHE, 172_GLN, 174_LEU, 186_TYR, 187_THR, 188_GLY, 190_ARG, 191_SER, 192_LEU, 193_LYS, 195_ASP, 228_LYS, 230_LEU, 232_GLU, 233_ASN, 234_GLY",2.79
+5E94_H,5E94_C_light+5E94_D_heavy,"29_THR, 30_VAL, 31_SER, 32_LEU, 33_TRP, 35_THR, 36_VAL, 39_TRP, 40_ARG, 43_ARG, 68_GLU",2.0
+5E94_G,5E94_A_light+5E94_B_heavy,"137_PRO, 138_GLU, 141_LEU, 142_LEU, 145_TYR, 29_THR, 30_VAL, 31_SER, 32_LEU, 33_TRP, 35_THR, 36_VAL, 39_TRP, 40_ARG, 43_ARG, 68_GLU",2.0
+7LM9_A,7LM9_L_light+7LM9_H_heavy,"330_ASN, 331_ALA, 332_THR, 333_LYS, 360_PHE, 423_TRP, 424_ASN, 427_ASN, 428_ILE, 430_ALA, 431_THR, 432_SER, 435_ASN, 437_ASN, 485_THR",1.53
+7UL3_A,7UL3_L_light+7UL3_C_heavy,"209_LEU, 210_LYS, 213_ARG, 223_ARG, 226_ASP, 230_ARG, 234_ARG",3.0
+4OLW_G,4OLW_L_light+4OLW_H_heavy,"102_GLU, 106_GLU, 276_ASN, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 371_ILE, 429_GLY, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_ALA, 461_ASN, 465_ASN, 466_GLU, 467_THR, 469_ARG, 473_GLY, 476_LYS, 97_LYS",2.71
+8YWR_A,8YWR_L_light+8YWR_H_heavy,"30_ARG, 34_ASP, 37_SER, 40_ARG, 67_MET, 69_GLU, 73_CYS, 74_PHE, 75_GLN, 76_SER",2.93
+5TPW_A,5TPW_L_light+5TPW_H_heavy,"36_LYS, 39_GLU, 40_GLN, 43_ARG, 64_SER, 65_VAL, 66_THR, 68_ARG, 69_PRO, 73_GLN, 76_LEU, 80_GLU, 81_ASP",2.91
+5JXE_B,5JXE_C_light+5JXE_D_heavy,"126_ILE, 127_SER, 128_LEU, 63_PHE, 64_VAL, 66_ASN, 68_TYR, 76_THR, 78_LYS, 85_ASP, 86_ARG, 87_SER, 88_GLN, 89_PRO, 90_GLY",2.9
+5JXE_A,5JXE_F_light+5JXE_G_heavy,"64_VAL, 66_ASN, 76_THR, 77_ASP, 78_LYS, 83_PRO, 84_GLU, 85_ASP, 86_ARG, 87_SER, 88_GLN, 89_PRO, 90_GLY",2.9
+3Q1S_I,3Q1S_L_light+3Q1S_H_heavy,"173_SER, 64_TRP, 65_ASN, 66_TRP, 67_ASP, 68_ASP, 69_ILE, 70_THR, 71_ASP, 76_GLY, 77_GLU, 80_PHE",2.15
+7WKX_F,7WKX_C_light+7WKX_A_heavy,"124_ARG, 127_PRO, 128_HIS, 133_PHE, 49_LEU, 51_ILE, 52_HIS, 53_ASN, 80_GLU, 81_ASP, 82_PRO, 83_GLU, 84_ARG, 85_TYR, 87_SER",2.81
+7WKX_E,7WKX_D_light+7WKX_B_heavy,"124_ARG, 127_PRO, 133_PHE, 49_LEU, 51_ILE, 80_GLU, 81_ASP, 82_PRO, 83_GLU, 84_ARG, 85_TYR, 87_SER",2.81
+7U60_C,7U60_F_light+7U60_E_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.55
+7U60_A,7U60_L_light+7U60_H_heavy,"149_ASN, 154_ILE, 158_ASN, 205_SER, 206_SER, 208_ARG, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.55
+2HJF_C,2HJF_B_light+2HJF_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 63_PRO, 64_ARG",2.9
+5WUX_G,5WUX_D_light+5WUX_C_heavy,"131_ARG, 135_GLU, 136_ILE, 137_ASN, 138_ARG, 139_PRO, 140_ASP, 24_GLY, 25_GLN, 45_ASP, 47_GLN, 72_THR, 73_HIS, 77_THR, 83_ILE, 85_VAL, 86_SER, 87_TYR, 88_GLN, 89_THR, 90_LYS",2.9
+5WUX_E,5WUX_L_light+5WUX_H_heavy,"131_ARG, 135_GLU, 136_ILE, 137_ASN, 138_ARG, 140_ASP, 24_GLY, 44_ARG, 45_ASP, 72_THR, 73_HIS, 77_THR, 83_ILE, 85_VAL, 86_SER, 87_TYR, 88_GLN, 89_THR, 90_LYS",2.9
+5WUX_F,5WUX_B_light+5WUX_A_heavy,"131_ARG, 135_GLU, 136_ILE, 137_ASN, 138_ARG, 139_PRO, 140_ASP, 24_GLY, 25_GLN, 44_ARG, 45_ASP, 47_GLN, 72_THR, 79_THR, 83_ILE, 85_VAL, 86_SER, 87_TYR, 88_GLN, 90_LYS",2.9
+7MLH_F,7MLH_A_light+7MLH_C_heavy,"100_LYS, 101_SER, 102_GLU, 103_ASN, 31_ARG, 58_ILE, 59_ASP, 60_GLY, 61_LEU, 62_GLU, 63_VAL, 64_ASP, 97_ILE, 99_PRO",2.1
+7MLH_B,7MLH_D_light+7MLH_E_heavy,"100_LYS, 101_SER, 102_GLU, 103_ASN, 31_ARG, 58_ILE, 59_ASP, 60_GLY, 61_LEU, 62_GLU, 63_VAL, 64_ASP, 97_ILE, 99_PRO",2.1
+7D00_A,7D00_K_light+7D00_H_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 496_GLY, 500_THR, 501_ASN, 502_GLY, 505_TYR",3.0
+7D00_C,7D00_N_light+7D00_J_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 496_GLY, 500_THR, 501_ASN, 502_GLY, 505_TYR",3.0
+5U3J_A,5U3J_L_light+5U3J_H_heavy,"656_ASN, 657_GLU, 668_SER, 669_LEU, 670_TRP, 671_ASN, 672_TRP, 673_PHE, 674_ASP, 675_ILE, 676_THR, 679_LEU, 680_TRP, 682_ILE, 683_ARG",2.74
+5T29_O,5T29_L_light+5T29_H_heavy,"118_GLY, 119_ASN, 120_TRP, 121_PHE, 124_THR, 125_ASN, 127_LEU, 128_TRP, 131_LYS, 134_ILE, 135_GLN, 64_LEU, 66_VAL, 79_LYS",2.03
+1C08_C,1C08_A_light+1C08_B_heavy,"100_SER, 101_ASP, 102_GLY, 103_ASN, 15_HIS, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 63_TRP, 73_ARG, 75_LEU, 89_THR, 93_ASN, 96_LYS, 97_LYS, 98_ILE",2.3
+4O58_A,4O58_L_light+4O58_H_heavy,"131_THR, 133_ASN, 134_GLY, 135_GLY, 136_SER, 137_SER, 145_SER, 153_TRP, 155_TYR, 156_LYS, 157_SER, 158_GLY, 189_LYS, 193_ASN, 194_LEU, 226_LEU, 98_TYR",2.75
+5NH3_A,5NH3_L_light+5NH3_H_heavy,"100_VAL, 102_PHE, 39_LYS, 40_ASP, 55_ASP, 61_PHE, 63_THR, 74_VAL, 75_LYS, 78_CYS, 79_TRP, 80_LEU, 81_ASP, 82_ASP, 83_ILE, 84_ASN, 86_TYR",2.35
+5NH3_B,5NH3_M_light+5NH3_I_heavy,"100_VAL, 102_PHE, 39_LYS, 40_ASP, 61_PHE, 63_THR, 74_VAL, 75_LYS, 78_CYS, 79_TRP, 80_LEU, 81_ASP, 82_ASP, 83_ILE, 84_ASN, 86_TYR",2.35
+8G4P_E,8G4P_D_light+8G4P_C_heavy,"111_GLN, 112_ILE, 114_GLU, 115_ASN, 117_SER, 118_ASP, 34_GLN, 35_SER, 37_LEU, 38_GLU, 39_ARG, 40_ALA, 41_ASN, 42_LEU, 43_ARG, 46_GLU",2.25
+8G4P_E,8G4P_B_light+8G4P_A_heavy,"139_GLN, 140_CYS, 141_GLY, 142_LEU, 39_ARG, 40_ALA, 41_ASN, 44_PRO, 86_GLN, 88_GLN, 89_GLU, 90_ARG, 92_CYS, 93_ASN, 94_GLU, 96_ASN, 97_GLU",2.25
+7UH8_C,7UH8_F_light+7UH8_E_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN",2.75
+7UH8_A,7UH8_L_light+7UH8_H_heavy,"117_GLU, 149_ASN, 154_ILE, 157_GLU, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.75
+4ZS7_A,4ZS7_L_light+4ZS7_H_heavy,"164_HIS, 168_ARG, 171_LYS, 172_GLU, 175_GLN, 176_SER, 178_LEU, 179_ARG, 182_ARG, 183_GLN, 30_ARG, 33_LEU, 34_ASP, 37_SER, 40_ARG, 67_MET, 69_GLU, 74_PHE, 75_GLN, 76_SER",2.93
+5KN5_C,5KN5_B_light+5KN5_A_heavy,"17_PHE, 18_HIS, 27_GLU, 39_VAL, 40_GLY, 41_ALA, 42_ARG, 44_GLU, 45_HIS, 46_ALA, 47_ASP, 48_LEU, 49_LEU",2.8
+5KN5_F,5KN5_E_light+5KN5_D_heavy,"17_PHE, 18_HIS, 24_LEU, 27_GLU, 29_LYS, 39_VAL, 40_GLY, 41_ALA, 42_ARG, 44_GLU, 45_HIS, 46_ALA, 47_ASP, 48_LEU, 49_LEU",2.8
+5UGY_C,5UGY_M_light+5UGY_I_heavy,"134_GLY, 135_VAL, 136_SER, 137_ALA, 145_SER, 153_TRP, 155_THR, 156_GLY, 158_ASN, 159_GLY, 160_LEU, 186_PRO, 187_ASN, 188_ILE, 189_GLY, 190_ASP, 192_ARG, 193_ALA, 194_LEU, 196_HIS, 222_LYS, 225_ASP, 226_ARG, 227_GLU, 95_TYR",2.8
+5UGY_A,5UGY_L_light+5UGY_H_heavy,"134_GLY, 135_VAL, 136_SER, 137_ALA, 138_SER, 145_SER, 153_TRP, 155_THR, 156_GLY, 158_ASN, 159_GLY, 160_LEU, 187_ASN, 188_ILE, 189_GLY, 190_ASP, 192_ARG, 193_ALA, 194_LEU, 196_HIS, 222_LYS, 225_ASP, 226_ARG, 227_GLU, 95_TYR",2.8
+5UGY_E,5UGY_N_light+5UGY_J_heavy,"131_THR, 134_GLY, 135_VAL, 136_SER, 137_ALA, 145_SER, 153_TRP, 155_THR, 156_GLY, 158_ASN, 159_GLY, 160_LEU, 187_ASN, 188_ILE, 189_GLY, 190_ASP, 192_ARG, 193_ALA, 194_LEU, 196_HIS, 222_LYS, 225_ASP, 226_ARG, 227_GLU, 95_TYR",2.8
+4GMS_E,4GMS_N_light+4GMS_J_heavy,"131_THR, 134_GLY, 135_GLY, 136_SER, 137_SER, 145_SER, 153_TRP, 155_TYR, 156_LYS, 157_SER, 158_GLY, 159_SER, 189_LYS, 190_GLU, 193_ASN, 194_LEU, 225_GLY, 226_LEU, 98_TYR",2.95
+4GMS_C,4GMS_M_light+4GMS_I_heavy,"131_THR, 135_GLY, 136_SER, 137_SER, 145_SER, 153_TRP, 155_TYR, 156_LYS, 157_SER, 158_GLY, 159_SER, 189_LYS, 190_GLU, 193_ASN, 194_LEU, 226_LEU, 228_SER, 98_TYR",2.95
+4GMS_A,4GMS_L_light+4GMS_H_heavy,"131_THR, 134_GLY, 135_GLY, 136_SER, 137_SER, 153_TRP, 155_TYR, 156_LYS, 157_SER, 158_GLY, 159_SER, 189_LYS, 190_GLU, 193_ASN, 194_LEU, 196_VAL, 226_LEU, 98_TYR",2.95
+3W9E_C,3W9E_B_light+3W9E_A_heavy,"132_GLN, 134_ARG, 139_ASP, 140_SER, 193_LEU, 196_ARG, 215_ASP, 216_SER, 218_GLY, 220_LEU, 221_PRO, 222_ARG, 223_PHE, 224_ILE, 227_ASN, 231_VAL, 234_TYR, 235_SER, 238_ILE, 239_ALA, 25_LEU, 26_ASP, 27_GLN, 38_TYR, 39_HIS",2.3
+8FJA_A,8FJA_D_light+8FJA_E_heavy,"163_THR, 166_GLU, 167_TRP, 170_ARG, 58_GLU, 59_TYR, 65_ARG, 68_LYS, 69_ALA, 72_GLN",3.0
+6UUH_F,6UUH_B_light+6UUH_A_heavy,"16_THR, 17_LEU, 18_LYS, 19_GLY, 20_GLU, 21_THR, 22_THR, 38_TYR, 40_ASN, 41_ASP, 42_ASN",2.7
+6UUH_E,6UUH_D_light+6UUH_C_heavy,"16_THR, 17_LEU, 18_LYS, 19_GLY, 20_GLU, 21_THR, 22_THR, 38_TYR, 40_ASN, 41_ASP, 42_ASN",2.7
+5VEB_Y,5VEB_L_light+5VEB_H_heavy,"503_ASP, 520_GLY, 522_GLN, 523_PHE, 524_SER, 525_PHE, 526_SER, 527_LEU, 529_PRO, 532_ALA, 533_SER, 539_ILE, 540_GLN, 541_ASP, 542_ASN, 543_LYS, 552_ARG, 571_SER, 572_ASP, 573_ASN, 574_ASP, 575_TYR, 576_PRO, 577_VAL",2.34
+2Q8A_A,2Q8A_L_light+2Q8A_H_heavy,"186_THR, 187_GLU, 188_PRO, 189_LEU, 190_MET, 194_THR, 196_ASP, 197_GLU, 199_ARG, 200_HIS, 201_PHE, 202_TYR, 203_LYS, 204_ASP, 205_ASN, 208_VAL, 222_GLY, 223_ASN, 224_MET, 225_ILE, 235_LYS",2.4
+2DQE_Y,2DQE_L_light+2DQE_H_heavy,"100_SER, 101_ASP, 102_GLY, 15_HIS, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 63_TRP, 73_ARG, 75_LEU, 77_ASN, 89_THR, 93_ASN, 96_LYS, 97_LYS, 98_ILE",1.9
+5NUZ_D,5NUZ_L_light+5NUZ_H_heavy,"111_SER, 113_ASP, 114_ASP, 115_ILE, 116_ALA, 117_VAL, 119_LEU, 165_ARG, 171_GLU, 174_ILE, 210_GLY, 211_LYS, 212_LEU, 216_LYS, 218_GLN",1.85
+5NUZ_C,5NUZ_B_light+5NUZ_A_heavy,"111_SER, 113_ASP, 114_ASP, 115_ILE, 116_ALA, 117_VAL, 119_LEU, 165_ARG, 171_GLU, 174_ILE, 211_LYS, 216_LYS, 218_GLN",1.85
+7Y3N_A,7Y3N_L_light+7Y3N_H_heavy,"145_PRO, 146_PHE, 147_GLU, 148_ARG, 150_ILE, 197_GLU, 199_LEU, 200_ASN, 201_ALA, 34_ALA, 37_ARG, 39_LYS, 76_ASN, 78_TYR",2.97
+7Y3N_B,7Y3N_D_light+7Y3N_C_heavy,"144_ARG, 145_PRO, 146_PHE, 147_GLU, 148_ARG, 150_ILE, 197_GLU, 199_LEU, 200_ASN, 201_ALA, 37_ARG, 39_LYS, 76_ASN, 78_TYR",2.97
+7Y3N_E,7Y3N_G_light+7Y3N_F_heavy,"144_ARG, 145_PRO, 146_PHE, 147_GLU, 148_ARG, 150_ILE, 197_GLU, 199_LEU, 200_ASN, 201_ALA, 34_ALA, 37_ARG, 39_LYS, 76_ASN, 78_TYR",2.97
+6B08_A,6B08_B_light+6B08_C_heavy,"105_ASN, 106_PRO, 107_VAL, 108_LYS, 109_THR, 110_GLY, 111_VAL, 122_VAL, 123_GLN, 125_GLN, 126_ASN, 127_LYS, 87_ASN, 88_GLU, 90_LYS, 91_GLN, 92_VAL",2.2
+7CHZ_I,7CHZ_L_light+7CHZ_H_heavy,"131_GLN, 146_LEU, 147_HIS, 148_LEU, 149_GLN, 152_ASP, 155_GLN, 243_GLN, 244_ALA, 245_GLU, 246_ASN, 247_MET, 257_GLY, 258_GLN",2.5
+6FGB_A,6FGB_L_light+6FGB_H_heavy,"105_VAL, 106_PRO, 107_THR, 108_ALA, 109_LYS, 115_GLU, 116_GLU, 117_PHE, 118_MET, 119_ASN, 120_PHE, 121_ASP, 122_LEU, 123_LYS, 124_GLN, 129_GLY, 130_ASP, 131_TRP, 132_PRO, 133_GLU",2.9
+5TZ2_C,5TZ2_L_light+5TZ2_H_heavy,"101_LEU, 102_THR, 35_GLU, 37_TYR, 39_LYS, 46_ASP, 49_THR, 51_ASP, 53_ALA, 54_LEU, 56_LYS, 58_THR, 59_VAL, 99_THR",2.3
+2HFE_C,2HFE_B_light+2HFE_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.25
+7WPE_A,7WPE_V_light+7WPE_U_heavy,"348_THR, 349_ARG, 352_SER, 447_LYS, 452_TYR, 453_ASN, 455_LEU, 493_PHE, 495_LEU",2.69
+7WPE_B,7WPE_Y_light+7WPE_X_heavy,"447_LYS, 448_VAL, 452_TYR, 453_ASN, 493_PHE",2.69
+7QEZ_A,7QEZ_F_light+7QEZ_E_heavy,"369_TYR, 370_ASN, 374_PHE, 376_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 390_LEU, 408_ARG, 428_ASP, 430_THR, 516_GLU, 517_LEU",2.89
+7QEZ_A,7QEZ_L_light+7QEZ_H_heavy,"455_LEU, 456_PHE, 473_TYR, 475_ALA, 476_GLY, 477_SER, 478_THR, 485_GLY, 486_PHE, 487_ASN, 488_CYS, 489_TYR, 493_GLN",2.89
+2NXZ_A,2NXZ_C_light+2NXZ_D_heavy,"119_CYS, 120_VAL, 122_LEU, 200_VAL, 202_THR, 203_GLN, 419_ARG, 421_LYS, 422_GLN, 423_ILE, 434_MET, 437_PRO",2.04
+8T9Z_A,8T9Z_L_light+8T9Z_H_heavy,"198_ARG, 202_ASN, 205_ARG, 206_GLN, 209_ASP, 210_ASN, 215_PRO, 216_ALA, 217_ILE, 218_SER, 219_LEU, 220_ASP, 224_ASP, 248_ARG, 249_ALA, 252_ARG, 253_ARG, 329_ARG, 396_ARG, 73_LEU",3.0
+4LBE_C,4LBE_B_light+4LBE_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.75
+7KD6_K,7KD6_J_light+7KD6_I_heavy,"222_PRO, 223_THR, 233_LEU, 234_ASP, 236_ARG, 237_CYS, 238_VAL, 239_GLU, 240_THR, 241_CYS, 242_PRO, 243_PRO, 244_PRO, 245_TYR, 247_HIS, 281_ASN, 282_ASN",2.6
+7KD6_E,7KD6_D_light+7KD6_C_heavy,"222_PRO, 223_THR, 233_LEU, 234_ASP, 236_ARG, 237_CYS, 238_VAL, 239_GLU, 240_THR, 241_CYS, 242_PRO, 243_PRO, 244_PRO, 245_TYR, 247_HIS, 281_ASN, 282_ASN",2.6
+7KD6_W,7KD6_V_light+7KD6_U_heavy,"222_PRO, 223_THR, 233_LEU, 234_ASP, 236_ARG, 237_CYS, 238_VAL, 239_GLU, 240_THR, 241_CYS, 242_PRO, 243_PRO, 244_PRO, 245_TYR, 247_HIS, 281_ASN, 282_ASN",2.6
+7KD6_Q,7KD6_P_light+7KD6_O_heavy,"222_PRO, 223_THR, 233_LEU, 234_ASP, 236_ARG, 238_VAL, 239_GLU, 240_THR, 241_CYS, 242_PRO, 243_PRO, 244_PRO, 245_TYR, 247_HIS, 281_ASN, 282_ASN",2.6
+1NMB_N,1NMB_L_light+1NMB_H_heavy,"328_PRO, 329_ASN, 330_ASP, 331_PRO, 332_THR, 341_TYR, 342_PRO, 343_GLY, 344_ASN, 366_ILE, 367_SER, 368_ILE, 369_ALA, 370_SER, 372_SER, 400_ASN, 401_THR, 403_TRP, 432_LYS",2.2
+6WZL_E,6WZL_B_light+6WZL_A_heavy,"11_THR, 2_GLU, 30_HIS, 31_CYS, 32_ASN, 33_GLN, 37_ILE, 3_LEU, 44_ARG, 45_LYS, 46_ILE, 47_CYS, 4_ARG, 5_CYS, 6_LEU, 8_MET, 9_LYS",2.29
+6WZL_F,6WZL_D_light+6WZL_C_heavy,"11_THR, 2_GLU, 30_HIS, 31_CYS, 32_ASN, 33_GLN, 37_ILE, 3_LEU, 44_ARG, 45_LYS, 46_ILE, 47_CYS, 4_ARG, 5_CYS, 6_LEU, 7_CYS, 8_MET, 9_LYS",2.29
+8VUI_D,8VUI_G_light+8VUI_A_heavy,"101_THR, 102_ALA, 103_ALA, 105_GLY, 107_PRO, 110_HIS, 54_SER, 58_GLN, 60_GLU, 61_ASP, 62_GLU, 63_PRO, 64_TRP, 66_LEU, 95_PRO, 97_GLU, 99_ARG",2.1
+1MHH_F,1MHH_C_light+1MHH_D_heavy,"1833_LYS, 1834_ILE, 1835_GLN, 1836_THR, 1837_ALA, 1838_GLU, 1839_PHE, 1840_LYS, 1849_GLU, 1853_TYR, 1856_LEU, 1857_LEU",2.1
+1MHH_E,1MHH_A_light+1MHH_B_heavy,"833_LYS, 834_ILE, 835_GLN, 836_THR, 837_ALA, 838_GLU, 839_PHE, 840_LYS, 849_GLU, 853_TYR, 856_LEU",2.1
+2VDO_A,2VDO_L_light+2VDO_H_heavy,"149_ASN, 154_ILE, 158_ASN, 205_SER, 206_SER, 208_ARG, 209_PRO, 210_GLY, 211_ILE, 213_LEU, 214_TRP, 215_HIS, 77_ARG, 78_ASN, 79_VAL, 80_GLY, 81_SER, 82_GLN, 84_LEU",2.51
+5EC1_C,5EC1_B_light+5EC1_A_heavy,"45_TYR, 49_LEU, 52_ARG, 53_GLY, 54_ALA, 55_PRO, 56_GLY, 57_ALA, 58_GLN, 60_ILE, 61_THR, 62_TYR, 64_ARG",2.75
+6NMU_C,6NMU_A_light+6NMU_B_heavy,"17_ALA, 40_ARG, 44_PRO, 45_GLY, 46_ARG, 47_GLU, 48_LEU, 49_ILE, 54_GLU, 56_HIS, 57_PHE, 58_PRO, 59_ARG, 80_ALA, 82_THR, 83_PRO, 84_ALA, 85_ASP",2.55
+6NMU_S,6NMU_L_light+6NMU_H_heavy,"17_ALA, 40_ARG, 44_PRO, 45_GLY, 46_ARG, 47_GLU, 48_LEU, 49_ILE, 54_GLU, 55_GLY, 56_HIS, 57_PHE, 58_PRO, 59_ARG, 80_ALA, 82_THR, 83_PRO, 84_ALA, 85_ASP",2.55
+6V4N_W,6V4N_E_light+6V4N_D_heavy,"116_ARG, 147_ARG, 148_GLU, 149_ASP, 150_ARG, 177_TRP, 178_SER, 223_ARG, 226_GLU, 292_ARG, 373_LYS, 374_ARG, 375_MET, 408_TRP, 409_TYR, 432_ASP, 433_GLY, 434_GLY, 435_LYS, 436_THR, 464_MET, 465_THR",2.5
+6V4N_I,6V4N_B_light+6V4N_A_heavy,"116_ARG, 147_ARG, 148_GLU, 149_ASP, 150_ARG, 177_TRP, 178_SER, 226_GLU, 292_ARG, 373_LYS, 374_ARG, 375_MET, 408_TRP, 409_TYR, 432_ASP, 433_GLY, 434_GLY, 435_LYS, 436_THR, 464_MET, 465_THR",2.5
+6V4N_N,6V4N_L_light+6V4N_H_heavy,"116_ARG, 147_ARG, 149_ASP, 150_ARG, 177_TRP, 178_SER, 226_GLU, 292_ARG, 373_LYS, 374_ARG, 375_MET, 408_TRP, 409_TYR, 432_ASP, 433_GLY, 434_GLY, 435_LYS, 436_THR, 464_MET, 465_THR",2.5
+6V4N_M,6V4N_G_light+6V4N_C_heavy,"116_ARG, 147_ARG, 149_ASP, 150_ARG, 177_TRP, 178_SER, 226_GLU, 292_ARG, 373_LYS, 374_ARG, 375_MET, 408_TRP, 409_TYR, 432_ASP, 433_GLY, 434_GLY, 435_LYS, 436_THR, 464_MET, 465_THR",2.5
+6IUT_A,6IUT_L_light+6IUT_H_heavy,"115_GLN, 116_ILE, 118_PRO, 120_SER, 121_SER, 122_TRP, 123_SER, 124_ASP, 158_PRO, 159_THR, 160_ILE, 161_LYS, 162_ARG, 163_SER, 164_TYR, 165_ASN, 167_THR, 168_ASN, 240_ASN",2.3
+2VH5_R,2VH5_L_light+2VH5_H_heavy,"17_SER, 25_GLN, 27_HIS, 29_VAL, 31_GLU, 32_TYR, 33_ASP, 34_PRO, 35_THR, 36_ILE, 37_GLU, 38_ASP, 39_SER, 40_TYR, 61_GLN, 63_GLU, 64_TYR",2.7
+6U36_B,6U36_L_light+6U36_H_heavy,"153_SER, 194_ARG, 195_GLU, 238_ASP, 239_ALA, 369_ILE, 374_ASP, 375_CYS, 376_SER, 377_THR, 378_CYS, 379_PHE",2.7
+5XEZ_A,5XEZ_D_light+5XEZ_C_heavy,"108_ARG, 110_PRO, 111_ARG, 112_GLY, 113_GLN, 116_ARG, 122_GLN, 127_GLU, 202_TYR, 204_GLN, 205_LYS, 206_ILE, 207_GLY, 208_ASP, 32_LEU, 36_TRP, 64_LYS, 65_TYR, 84_TYR, 87_TRP",3.0
+5XEZ_B,5XEZ_L_light+5XEZ_H_heavy,"108_ARG, 110_PRO, 111_ARG, 112_GLY, 113_GLN, 116_ARG, 122_GLN, 127_GLU, 202_TYR, 204_GLN, 205_LYS, 206_ILE, 207_GLY, 208_ASP, 32_LEU, 36_TRP, 64_LYS, 65_TYR, 84_TYR, 87_TRP",3.0
+5ANM_G,5ANM_A_light+5ANM_B_heavy,"340_LEU, 342_ARG, 427_ARG, 428_ALA, 429_LEU, 430_MET, 431_ARG, 432_SER, 433_THR, 434_THR, 436_THR, 437_SER, 472_GLU",2.85
+5ANM_E,5ANM_C_light+5ANM_D_heavy,"390_GLU, 391_LYS, 392_GLN, 393_ARG, 394_ASN",2.85
+5ANM_E,5ANM_L_light+5ANM_H_heavy,"340_LEU, 342_ARG, 428_ALA, 429_LEU, 430_MET, 431_ARG, 432_SER, 433_THR, 434_THR, 436_THR, 437_SER, 472_GLU, 497_LYS",2.85
+5ANM_F,5ANM_C_light+5ANM_D_heavy,"340_LEU, 342_ARG, 427_ARG, 428_ALA, 429_LEU, 430_MET, 431_ARG, 432_SER, 433_THR, 434_THR, 436_THR, 437_SER, 472_GLU",2.85
+5ANM_F,5ANM_L_light+5ANM_H_heavy,"390_GLU, 391_LYS, 392_GLN, 393_ARG, 394_ASN, 513_ARG, 514_ALA",2.85
+6PZW_D,6PZW_I_light+6PZW_K_heavy,"358_ALA, 378_LYS, 380_PRO, 392_ILE, 393_GLN, 450_THR, 451_GLU, 452_PHE, 93_THR, 95_ASN",3.0
+6PZW_D,6PZW_E_light+6PZW_F_heavy,"216_ASN, 218_TRP, 220_ARG, 253_ARG, 255_TYR, 262_ILE, 263_LEU, 265_TRP",3.0
+6PZW_C,6PZW_E_light+6PZW_F_heavy,"358_ALA, 378_LYS, 380_PRO, 392_ILE, 393_GLN, 450_THR, 451_GLU, 452_PHE, 93_THR, 95_ASN",3.0
+6PZW_C,6PZW_G_light+6PZW_J_heavy,"216_ASN, 218_TRP, 220_ARG, 253_ARG, 255_TYR, 262_ILE, 263_LEU, 265_TRP",3.0
+6PZW_B,6PZW_G_light+6PZW_J_heavy,"358_ALA, 378_LYS, 380_PRO, 392_ILE, 393_GLN, 450_THR, 451_GLU, 452_PHE, 93_THR, 95_ASN",3.0
+6PZW_B,6PZW_L_light+6PZW_H_heavy,"216_ASN, 218_TRP, 220_ARG, 253_ARG, 255_TYR, 262_ILE, 263_LEU, 265_TRP",3.0
+6PZW_A,6PZW_I_light+6PZW_K_heavy,"216_ASN, 218_TRP, 220_ARG, 253_ARG, 255_TYR, 262_ILE, 263_LEU, 265_TRP",3.0
+6PZW_A,6PZW_L_light+6PZW_H_heavy,"358_ALA, 378_LYS, 380_PRO, 392_ILE, 393_GLN, 450_THR, 451_GLU, 452_PHE, 93_THR, 95_ASN",3.0
+1FSK_D,1FSK_E_light+1FSK_F_heavy,"42_GLU, 43_ASN, 44_ILE, 45_GLU, 46_GLY, 47_ASN, 48_GLY, 49_GLY, 50_PRO, 51_GLY, 52_THR, 53_ILE, 70_ARG, 72_ASP, 76_HIS, 97_LYS",2.9
+1FSK_G,1FSK_H_light+1FSK_I_heavy,"42_GLU, 43_ASN, 44_ILE, 45_GLU, 46_GLY, 47_ASN, 48_GLY, 49_GLY, 50_PRO, 51_GLY, 52_THR, 53_ILE, 70_ARG, 72_ASP, 76_HIS, 87_GLU, 97_LYS",2.9
+1FSK_J,1FSK_K_light+1FSK_L_heavy,"42_GLU, 43_ASN, 44_ILE, 45_GLU, 46_GLY, 47_ASN, 48_GLY, 49_GLY, 50_PRO, 51_GLY, 52_THR, 53_ILE, 70_ARG, 72_ASP, 76_HIS, 87_GLU, 97_LYS",2.9
+1FSK_A,1FSK_B_light+1FSK_C_heavy,"42_GLU, 43_ASN, 44_ILE, 45_GLU, 46_GLY, 47_ASN, 48_GLY, 49_GLY, 50_PRO, 51_GLY, 52_THR, 53_ILE, 70_ARG, 72_ASP, 76_HIS, 87_GLU, 97_LYS",2.9
+7JIE_A,7JIE_D_light+7JIE_C_heavy,"233_THR, 236_GLU, 479_ASN, 481_ASP, 482_THR, 484_ARG, 486_LEU, 508_VAL, 509_ILE, 510_PRO, 511_PRO, 512_ASN",2.25
+7JIE_B,7JIE_F_light+7JIE_E_heavy,"233_THR, 236_GLU, 479_ASN, 481_ASP, 482_THR, 484_ARG, 486_LEU, 508_VAL, 510_PRO, 511_PRO, 512_ASN",2.25
+7XSA_E,7XSA_C_light+7XSA_B_heavy,"345_THR, 439_ASN, 440_ASN, 441_LEU, 443_SER, 444_LYS, 445_VAL, 446_GLY, 447_GLY, 449_TYR, 450_ASN, 498_GLN, 499_PRO, 500_THR, 501_TYR, 502_GLY, 506_GLN",2.2
+7XSA_J,7XSA_I_light+7XSA_H_heavy,"345_THR, 439_ASN, 440_ASN, 441_LEU, 443_SER, 444_LYS, 445_VAL, 446_GLY, 447_GLY, 498_GLN, 499_PRO, 500_THR, 501_TYR, 502_GLY, 506_GLN",2.2
+7A3Q_A,7A3Q_L_light+7A3Q_H_heavy,"101_TRP, 102_GLY, 103_ASN, 104_GLY, 105_CYS, 106_GLY, 247_ARG, 249_ASP, 67_ASN, 69_THR, 70_THR, 71_ALA, 72_THR, 74_CYS, 97_VAL, 99_ARG",2.7
+7A3Q_B,7A3Q_M_light+7A3Q_I_heavy,"101_TRP, 102_GLY, 103_ASN, 104_GLY, 105_CYS, 106_GLY, 248_GLN, 70_THR, 71_ALA, 72_THR, 74_CYS, 97_VAL, 99_ARG",2.7
+6O41_O,6O41_A_light+6O41_B_heavy,"15_LYS, 16_THR, 17_LEU, 18_LYS, 19_GLY, 20_GLU, 21_THR, 22_THR, 38_TYR, 40_ASN, 41_ASP, 42_ASN, 43_GLY",2.47
+6O41_N,6O41_C_light+6O41_D_heavy,"15_LYS, 16_THR, 17_LEU, 18_LYS, 19_GLY, 20_GLU, 21_THR, 22_THR, 38_TYR, 40_ASN, 41_ASP, 42_ASN, 43_GLY",2.47
+6O41_M,6O41_L_light+6O41_H_heavy,"15_LYS, 16_THR, 17_LEU, 18_LYS, 19_GLY, 20_GLU, 21_THR, 22_THR, 38_TYR, 40_ASN, 41_ASP, 42_ASN, 43_GLY",2.47
+7C01_A,7C01_L_light+7C01_H_heavy,"403_ARG, 405_ASP, 406_GLU, 408_ARG, 409_GLN, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 495_TYR, 502_GLY, 505_TYR",2.88
+7C01_B,7C01_D_light+7C01_C_heavy,"403_ARG, 405_ASP, 406_GLU, 408_ARG, 409_GLN, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 495_TYR, 504_GLY, 505_TYR",2.88
+6WTU_D,6WTU_F_light+6WTU_E_heavy,"184_SER, 185_ASN, 348_LYS, 351_TYR, 352_LEU, 356_ASP, 359_ARG, 360_LYS, 379_LYS, 382_GLY, 383_TYR, 385_ILE, 386_ASP, 387_ARG, 389_GLU, 390_TYR",2.55
+6WTU_G,6WTU_I_light+6WTU_H_heavy,"183_GLU, 185_ASN, 348_LYS, 349_ASN, 351_TYR, 352_LEU, 356_ASP, 359_ARG, 360_LYS, 363_LYS, 379_LYS, 382_GLY, 383_TYR, 385_ILE, 386_ASP, 387_ARG, 389_GLU, 390_TYR, 393_GLN",2.55
+6WTU_J,6WTU_L_light+6WTU_K_heavy,"183_GLU, 184_SER, 185_ASN, 348_LYS, 349_ASN, 351_TYR, 352_LEU, 356_ASP, 359_ARG, 360_LYS, 363_LYS, 379_LYS, 382_GLY, 383_TYR, 385_ILE, 386_ASP, 387_ARG, 389_GLU, 390_TYR, 393_GLN",2.55
+6WTU_A,6WTU_C_light+6WTU_B_heavy,"183_GLU, 184_SER, 185_ASN, 348_LYS, 351_TYR, 352_LEU, 356_ASP, 359_ARG, 360_LYS, 379_LYS, 382_GLY, 383_TYR, 385_ILE, 386_ASP, 387_ARG, 389_GLU, 390_TYR, 393_GLN",2.55
+4FP8_D,4FP8_O_light+4FP8_K_heavy,"131_THR, 133_ASN, 134_GLY, 135_GLY, 136_SER, 137_ASN, 145_SER, 153_TRP, 155_THR, 156_LYS, 189_GLN, 190_GLU, 192_THR, 193_SER, 194_LEU, 226_LEU, 98_TYR",2.95
+4FP8_A,4FP8_L_light+4FP8_H_heavy,"131_THR, 133_ASN, 134_GLY, 135_GLY, 136_SER, 137_ASN, 145_SER, 153_TRP, 155_THR, 156_LYS, 186_SER, 189_GLN, 190_GLU, 192_THR, 193_SER, 194_LEU, 226_LEU, 98_TYR",2.95
+4FP8_B,4FP8_M_light+4FP8_I_heavy,"131_THR, 133_ASN, 134_GLY, 135_GLY, 136_SER, 137_ASN, 145_SER, 153_TRP, 155_THR, 156_LYS, 159_SER, 189_GLN, 190_GLU, 192_THR, 193_SER, 194_LEU, 226_LEU, 98_TYR",2.95
+4FP8_C,4FP8_N_light+4FP8_J_heavy,"131_THR, 133_ASN, 134_GLY, 135_GLY, 136_SER, 137_ASN, 145_SER, 153_TRP, 155_THR, 156_LYS, 189_GLN, 190_GLU, 192_THR, 193_SER, 194_LEU, 226_LEU, 98_TYR",2.95
+4JLR_C,4JLR_B_light+4JLR_A_heavy,"65_SER, 68_LEU, 69_SER, 72_ASN, 78_ASN, 79_ASP, 82_LYS, 83_LEU, 85_SER, 86_ASN, 89_LEU",2.71
+4JLR_S,4JLR_L_light+4JLR_H_heavy,"65_SER, 68_LEU, 69_SER, 72_ASN, 73_ASP, 78_ASN, 79_ASP, 82_LYS, 83_LEU, 85_SER, 86_ASN, 89_LEU",2.71
+7KMG_F,7KMG_E_light+7KMG_D_heavy,"351_TYR, 452_LEU, 455_LEU, 470_THR, 481_ASN, 482_GLY, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 488_CYS, 490_PHE, 492_LEU, 493_GLN, 494_SER",2.16
+7KMG_C,7KMG_B_light+7KMG_A_heavy,"351_TYR, 449_TYR, 455_LEU, 470_THR, 481_ASN, 482_GLY, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 488_CYS, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER",2.16
+7PR0_D,7PR0_F_light+7PR0_G_heavy,"340_GLU, 341_VAL, 344_ALA, 345_THR, 346_ARG, 347_PHE, 348_ALA, 349_SER, 351_TYR, 352_ALA, 354_ASN, 356_LYS, 440_ASN, 441_LEU, 444_LYS, 445_VAL, 446_GLY, 447_GLY, 448_ASN, 449_TYR, 450_ASN, 452_LEU, 494_SER",2.92
+7PR0_E,7PR0_L_light+7PR0_H_heavy,"344_ALA, 345_THR, 346_ARG, 347_PHE, 348_ALA, 349_SER, 351_TYR, 352_ALA, 440_ASN, 444_LYS, 445_VAL, 446_GLY, 447_GLY, 448_ASN, 449_TYR, 450_ASN",2.92
+7PR0_A,7PR0_B_light+7PR0_C_heavy,"344_ALA, 345_THR, 346_ARG, 347_PHE, 348_ALA, 349_SER, 351_TYR, 352_ALA, 354_ASN, 440_ASN, 441_LEU, 444_LYS, 445_VAL, 446_GLY, 447_GLY, 448_ASN, 449_TYR, 450_ASN, 452_LEU",2.92
+7Z2M_I,7Z2M_L_light+7Z2M_H_heavy,"118_SER, 124_VAL, 54_HIS, 55_ARG, 56_ASN, 57_GLU, 58_ASP, 68_GLU, 69_ALA, 70_LYS, 71_CYS, 73_HIS, 74_LEU, 88_ASN, 89_SER, 90_VAL, 91_PRO, 93_GLN",1.9
+7Z2M_G,7Z2M_C_light+7Z2M_D_heavy,"118_SER, 52_ASN, 54_HIS, 55_ARG, 56_ASN, 57_GLU, 58_ASP, 61_ARG, 68_GLU, 69_ALA, 70_LYS, 71_CYS, 72_ARG, 73_HIS, 74_LEU, 85_TYR, 88_ASN, 89_SER, 90_VAL, 91_PRO, 93_GLN",1.9
+7Z2M_J,7Z2M_E_light+7Z2M_F_heavy,"118_SER, 124_VAL, 54_HIS, 55_ARG, 56_ASN, 57_GLU, 58_ASP, 61_ARG, 68_GLU, 69_ALA, 70_LYS, 71_CYS, 72_ARG, 73_HIS, 74_LEU, 85_TYR, 88_ASN, 89_SER, 90_VAL, 91_PRO, 93_GLN",1.9
+7Z2M_K,7Z2M_A_light+7Z2M_B_heavy,"118_SER, 52_ASN, 54_HIS, 55_ARG, 56_ASN, 57_GLU, 58_ASP, 68_GLU, 69_ALA, 70_LYS, 71_CYS, 72_ARG, 73_HIS, 74_LEU, 88_ASN, 89_SER, 90_VAL, 91_PRO, 93_GLN",1.9
+6A3W_I,6A3W_H_light+6A3W_G_heavy,"100_SER, 101_MET, 102_CYS, 103_GLU, 114_LYS, 128_GLN, 130_ARG, 131_GLY, 132_ILE, 134_ARG, 135_PRO, 66_ARG, 95_LEU, 96_GLY, 97_ALA",2.0
+6A3W_F,6A3W_B_light+6A3W_A_heavy,"120_CYS, 121_CYS, 122_PHE, 124_THR, 136_TRP, 151_THR, 152_LYS",2.0
+6A3W_F,6A3W_E_light+6A3W_D_heavy,"100_SER, 101_MET, 102_CYS, 103_GLU, 113_THR, 114_LYS, 128_GLN, 130_ARG, 131_GLY, 132_ILE, 134_ARG, 135_PRO, 66_ARG, 95_LEU, 96_GLY, 97_ALA",2.0
+6A3W_C,6A3W_B_light+6A3W_A_heavy,"100_SER, 101_MET, 102_CYS, 114_LYS, 128_GLN, 130_ARG, 131_GLY, 132_ILE, 134_ARG, 135_PRO, 66_ARG, 95_LEU, 96_GLY, 97_ALA",2.0
+6A3W_C,6A3W_E_light+6A3W_D_heavy,"107_LYS, 120_CYS, 121_CYS, 122_PHE, 124_THR, 136_TRP, 151_THR, 152_LYS",2.0
+6A3W_L,6A3W_H_light+6A3W_G_heavy,"107_LYS, 120_CYS, 121_CYS, 122_PHE, 123_GLY, 124_THR, 136_TRP, 151_THR, 152_LYS",2.0
+6A3W_L,6A3W_K_light+6A3W_J_heavy,"100_SER, 101_MET, 102_CYS, 103_GLU, 114_LYS, 128_GLN, 130_ARG, 131_GLY, 132_ILE, 134_ARG, 135_PRO, 66_ARG, 95_LEU, 96_GLY, 97_ALA",2.0
+4ZFF_D,4ZFF_B_light+4ZFF_A_heavy,"48_LYS, 81_MET, 83_ILE, 84_LYS, 85_PRO, 86_HIS, 87_GLN, 88_GLY, 89_GLN, 90_HIS, 91_ILE",2.75
+4ZFF_D,4ZFF_L_light+4ZFF_H_heavy,"106_PRO, 18_MET, 21_TYR, 22_GLN, 25_TYR, 62_ASN, 63_ASP, 65_GLY, 66_LEU",2.75
+4ZFF_C,4ZFF_B_light+4ZFF_A_heavy,"18_MET, 21_TYR, 22_GLN, 25_TYR, 62_ASN, 63_ASP, 65_GLY",2.75
+4ZFF_C,4ZFF_L_light+4ZFF_H_heavy,"48_LYS, 81_MET, 83_ILE, 84_LYS, 85_PRO, 86_HIS, 87_GLN, 88_GLY, 89_GLN, 90_HIS, 91_ILE",2.75
+6FXN_C,6FXN_I_light+6FXN_H_heavy,"160_LYS, 161_GLY, 162_SER, 163_TYR, 205_THR, 206_TYR, 208_MET, 209_GLY, 215_LYS, 221_GLY, 222_ASP, 224_LEU, 225_SER, 226_LEU, 231_ARG, 233_ILE, 264_PRO, 265_ARG, 266_GLU",2.9
+6FXN_K,6FXN_R_light+6FXN_Q_heavy,"160_LYS, 161_GLY, 162_SER, 163_TYR, 205_THR, 206_TYR, 207_ALA, 208_MET, 209_GLY, 215_LYS, 221_GLY, 222_ASP, 224_LEU, 225_SER, 226_LEU, 231_ARG, 233_ILE, 264_PRO, 265_ARG, 266_GLU",2.9
+6FXN_B,6FXN_G_light+6FXN_F_heavy,"160_LYS, 161_GLY, 162_SER, 163_TYR, 205_THR, 206_TYR, 207_ALA, 208_MET, 209_GLY, 215_LYS, 221_GLY, 222_ASP, 224_LEU, 225_SER, 226_LEU, 231_ARG, 232_CYS, 233_ILE, 264_PRO, 265_ARG, 266_GLU",2.9
+6FXN_J,6FXN_N_light+6FXN_M_heavy,"160_LYS, 161_GLY, 162_SER, 163_TYR, 205_THR, 206_TYR, 208_MET, 209_GLY, 215_LYS, 221_GLY, 222_ASP, 224_LEU, 225_SER, 226_LEU, 231_ARG, 233_ILE, 264_PRO, 265_ARG, 266_GLU",2.9
+6FXN_A,6FXN_E_light+6FXN_D_heavy,"160_LYS, 161_GLY, 162_SER, 163_TYR, 205_THR, 206_TYR, 207_ALA, 208_MET, 209_GLY, 215_LYS, 221_GLY, 222_ASP, 224_LEU, 225_SER, 226_LEU, 231_ARG, 233_ILE, 264_PRO, 265_ARG, 266_GLU",2.9
+6FXN_L,6FXN_P_light+6FXN_O_heavy,"160_LYS, 161_GLY, 162_SER, 163_TYR, 205_THR, 206_TYR, 207_ALA, 208_MET, 209_GLY, 215_LYS, 221_GLY, 222_ASP, 224_LEU, 225_SER, 226_LEU, 231_ARG, 233_ILE, 264_PRO, 265_ARG, 266_GLU",2.9
+8G4M_G,8G4M_K_light+8G4M_J_heavy,"241_SER, 79_PRO, 80_ASN, 81_PRO, 82_GLN, 83_GLU, 85_HIS, 87_GLU",2.95
+8G4M_I,8G4M_L_light+8G4M_H_heavy,"79_PRO, 80_ASN, 81_PRO, 83_GLU, 85_HIS, 87_GLU",2.95
+8G4M_A,8G4M_E_light+8G4M_D_heavy,"513_VAL, 514_GLY, 515_ILE, 516_GLY, 517_ALA, 518_VAL, 519_PHE, 520_LEU, 528_SER, 532_ALA, 542_ARG",2.95
+8G4M_A,8G4M_K_light+8G4M_J_heavy,"592_LEU, 640_GLN, 643_TYR, 644_GLY, 647_GLU, 648_GLU",2.95
+8G4M_B,8G4M_L_light+8G4M_H_heavy,"592_LEU, 640_GLN, 644_GLY, 647_GLU, 648_GLU",2.95
+8G4M_B,8G4M_K_light+8G4M_J_heavy,"514_GLY, 515_ILE, 516_GLY, 517_ALA, 518_VAL, 519_PHE, 520_LEU, 528_SER, 542_ARG",2.95
+8G4M_C,8G4M_E_light+8G4M_D_heavy,"229_LYS, 79_PRO, 80_ASN, 81_PRO, 83_GLU, 85_HIS, 87_GLU",2.95
+8G4M_F,8G4M_L_light+8G4M_H_heavy,"514_GLY, 515_ILE, 516_GLY, 517_ALA, 518_VAL, 519_PHE, 520_LEU, 532_ALA, 542_ARG",2.95
+6KYZ_D,6KYZ_C_light+6KYZ_B_heavy,"125_ASN, 127_SER, 128_SER, 129_THR, 22_LYS",1.85
+6KYZ_D,6KYZ_F_light+6KYZ_E_heavy,"107_ASN, 108_PHE, 109_THR, 110_ASN, 111_THR, 112_ILE, 113_LEU, 114_GLU, 11_LEU, 138_ASP, 139_TYR, 140_ALA, 141_THR, 142_LYS, 143_THR, 145_GLN, 8_LEU",1.85
+6KYZ_A,6KYZ_C_light+6KYZ_B_heavy,"107_ASN, 108_PHE, 109_THR, 110_ASN, 111_THR, 112_ILE, 113_LEU, 114_GLU, 11_LEU, 138_ASP, 139_TYR, 140_ALA, 141_THR, 142_LYS, 143_THR, 145_GLN, 2_PRO, 4_THR, 8_LEU",1.85
+6KYZ_A,6KYZ_F_light+6KYZ_E_heavy,"125_ASN, 127_SER, 128_SER, 129_THR, 22_LYS",1.85
+6K7O_P,6K7O_C_light+6K7O_B_heavy,"30_LEU, 31_GLU, 33_ARG, 34_GLU, 50_ASN, 51_PRO, 53_GLU, 55_LYS, 78_ARG, 80_PRO, 81_VAL",3.0
+6K7O_Q,6K7O_E_light+6K7O_D_heavy,"30_LEU, 31_GLU, 32_ALA, 33_ARG, 34_GLU, 50_ASN, 51_PRO, 53_GLU, 55_LYS, 78_ARG, 80_PRO, 81_VAL",3.0
+6K7O_R,6K7O_G_light+6K7O_F_heavy,"30_LEU, 31_GLU, 32_ALA, 33_ARG, 34_GLU, 50_ASN, 51_PRO, 53_GLU, 55_LYS, 78_ARG, 80_PRO, 81_VAL",3.0
+6K7O_A,6K7O_L_light+6K7O_H_heavy,"30_LEU, 31_GLU, 32_ALA, 33_ARG, 34_GLU, 50_ASN, 51_PRO, 53_GLU, 55_LYS, 78_ARG, 80_PRO, 81_VAL",3.0
+7CZX_C,7CZX_N_light+7CZX_J_heavy,"446_GLY, 448_ASN, 449_TYR, 452_LEU, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER",2.8
+7CZX_B,7CZX_M_light+7CZX_I_heavy,"446_GLY, 448_ASN, 449_TYR, 452_LEU, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER",2.8
+7CZX_A,7CZX_K_light+7CZX_H_heavy,"446_GLY, 448_ASN, 449_TYR, 452_LEU, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER",2.8
+6A78_B,6A78_L_light+6A78_H_heavy,"39_THR, 41_LEU, 48_LEU, 49_VAL, 50_SER, 52_GLN, 82_SER",2.1
+6A78_B,6A78_M_light+6A78_I_heavy,"23_ASP, 24_GLY, 25_THR, 27_VAL, 29_SER, 57_LYS, 59_LEU, 60_GLU, 61_ASN, 63_VAL, 65_GLN, 67_ARG, 68_TYR",2.1
+6A78_A,6A78_L_light+6A78_H_heavy,"23_ASP, 24_GLY, 25_THR, 27_VAL, 29_SER, 57_LYS, 59_LEU, 60_GLU, 61_ASN, 63_VAL, 65_GLN, 67_ARG, 68_TYR",2.1
+6A78_A,6A78_M_light+6A78_I_heavy,"41_LEU, 48_LEU, 49_VAL, 50_SER, 52_GLN, 82_SER",2.1
+7Q0I_D,7Q0I_B_light+7Q0I_A_heavy,"13_SER, 140_PHE, 144_TYR, 145_TYR, 146_HIS, 147_LYS, 148_ASN, 14_GLN, 153_MET, 155_SER, 156_GLU, 158_ARG, 16_VAL, 17_ASN, 18_PHE, 19_THR, 246_LEU, 247_THR, 248_PRO, 71_SER, 75_GLY, 76_THR",2.39
+7Q0I_C,7Q0I_L_light+7Q0I_H_heavy,"13_SER, 140_PHE, 144_TYR, 145_TYR, 146_HIS, 147_LYS, 148_ASN, 14_GLN, 153_MET, 155_SER, 156_GLU, 158_ARG, 15_CYS, 16_VAL, 17_ASN, 18_PHE, 19_THR, 246_LEU, 247_THR, 248_PRO, 76_THR",2.39
+8Y3U_K,8Y3U_B_light+8Y3U_A_heavy,"525_ALA, 526_ALA, 527_ILE, 528_GLY, 529_LEU, 531_TRP, 535_PHE",2.98
+8Y3U_K,8Y3U_J_light+8Y3U_I_heavy,"507_ALA, 563_ASN, 564_GLU, 566_THR, 567_GLN, 568_ALA",2.98
+8Y3U_G,8Y3U_F_light+8Y3U_E_heavy,"507_ALA, 563_ASN, 564_GLU, 566_THR, 567_GLN, 568_ALA",2.98
+8Y3U_G,8Y3U_J_light+8Y3U_I_heavy,"526_ALA, 527_ILE, 528_GLY, 529_LEU, 531_TRP, 535_PHE",2.98
+8Y3U_C,8Y3U_F_light+8Y3U_E_heavy,"525_ALA, 526_ALA, 527_ILE, 528_GLY, 529_LEU, 531_TRP, 535_PHE",2.98
+4RDQ_C,4RDQ_J_light+4RDQ_K_heavy,"148_TYR, 149_LYS, 150_ARG, 151_PHE, 152_PRO, 153_SER, 155_GLU, 156_HIS, 159_ARG, 160_ALA",2.85
+4RDQ_C,4RDQ_L_light+4RDQ_M_heavy,"344_GLN, 345_PRO, 346_PRO, 347_TYR, 348_THR, 349_ALA, 350_ALA, 352_ALA, 355_LYS, 7_ASN",2.85
+4RDQ_D,4RDQ_L_light+4RDQ_M_heavy,"148_TYR, 149_LYS, 150_ARG, 151_PHE, 152_PRO, 153_SER, 155_GLU, 156_HIS, 159_ARG, 160_ALA",2.85
+4RDQ_D,4RDQ_N_light+4RDQ_O_heavy,"344_GLN, 345_PRO, 346_PRO, 347_TYR, 348_THR, 349_ALA, 350_ALA, 352_ALA, 355_LYS, 7_ASN",2.85
+4RDQ_B,4RDQ_H_light+4RDQ_I_heavy,"148_TYR, 149_LYS, 150_ARG, 151_PHE, 152_PRO, 153_SER, 155_GLU, 156_HIS, 159_ARG, 160_ALA",2.85
+4RDQ_B,4RDQ_J_light+4RDQ_K_heavy,"344_GLN, 345_PRO, 346_PRO, 347_TYR, 348_THR, 349_ALA, 350_ALA, 352_ALA, 355_LYS, 7_ASN",2.85
+4RDQ_A,4RDQ_F_light+4RDQ_G_heavy,"148_TYR, 149_LYS, 150_ARG, 151_PHE, 152_PRO, 153_SER, 155_GLU, 156_HIS, 159_ARG, 160_ALA",2.85
+4RDQ_A,4RDQ_H_light+4RDQ_I_heavy,"344_GLN, 345_PRO, 346_PRO, 347_TYR, 348_THR, 349_ALA, 350_ALA, 352_ALA, 355_LYS, 7_ASN",2.85
+4RDQ_E,4RDQ_F_light+4RDQ_G_heavy,"336_LEU, 344_GLN, 345_PRO, 346_PRO, 347_TYR, 348_THR, 349_ALA, 350_ALA, 352_ALA, 355_LYS",2.85
+4RDQ_E,4RDQ_N_light+4RDQ_O_heavy,"148_TYR, 149_LYS, 150_ARG, 151_PHE, 152_PRO, 153_SER, 155_GLU, 156_HIS, 159_ARG, 160_ALA",2.85
+7LOP_Z,7LOP_V_light+7LOP_W_heavy,"369_TYR, 370_ASN, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 390_LEU, 392_PHE, 408_ARG, 430_THR, 517_LEU",2.25
+7LOP_Z,7LOP_C_light+7LOP_B_heavy,"445_VAL, 499_PRO, 500_THR, 502_GLY, 503_VAL",2.25
+7LOP_Z,7LOP_Y_light+7LOP_X_heavy,"446_GLY, 447_GLY, 449_TYR, 456_PHE, 470_THR, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 488_CYS, 489_TYR, 490_PHE, 491_PRO, 492_LEU, 493_GLN, 494_SER, 498_GLN",2.25
+7LOP_A,7LOP_V_light+7LOP_W_heavy,"445_VAL, 499_PRO, 500_THR, 502_GLY, 503_VAL, 504_GLY",2.25
+7LOP_A,7LOP_C_light+7LOP_B_heavy,"369_TYR, 370_ASN, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 390_LEU, 392_PHE, 428_ASP, 517_LEU",2.25
+7LOP_A,7LOP_L_light+7LOP_H_heavy,"446_GLY, 447_GLY, 449_TYR, 456_PHE, 470_THR, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 488_CYS, 489_TYR, 490_PHE, 491_PRO, 492_LEU, 493_GLN, 494_SER, 498_GLN",2.25
+3LHP_T,3LHP_L_light+3LHP_I_heavy,"74_ASN, 81_GLY, 88_TRP, 89_PHE, 91_ILE, 92_SER, 93_GLN, 95_LEU, 96_TRP",2.7
+3LHP_T,3LHP_M_light+3LHP_I_heavy,"74_ASN, 81_GLY, 86_ILE, 87_ASN, 88_TRP, 89_PHE, 91_ILE, 92_SER, 93_GLN, 95_LEU, 96_TRP",2.7
+3LHP_S,3LHP_L_light+3LHP_H_heavy,"74_ASN, 75_ASP, 78_ALA, 81_GLY, 84_LYS, 86_ILE, 87_ASN, 88_TRP, 89_PHE, 90_ASP, 91_ILE, 92_SER, 93_GLN, 95_LEU, 96_TRP",2.7
+6AYZ_M,6AYZ_C_light+6AYZ_R_heavy,"109_VAL, 10_LYS, 110_GLY, 112_TYR, 113_VAL, 114_ASN, 138_ASN, 140_LEU, 142_GLU, 143_LYS, 144_PRO, 145_ARG, 146_VAL, 147_THR, 148_ARG, 45_VAL, 47_SER, 48_SER, 51_LEU, 52_ASP, 5_SER, 6_LEU, 7_LEU, 8_GLY, 92_VAL, 94_VAL, 9_ILE",2.1
+6AYZ_M,6AYZ_D_light+6AYZ_B_heavy,"49_ARG, 50_SER, 51_LEU, 52_ASP, 53_HIS, 82_GLU, 83_LEU",2.1
+6AYZ_A,6AYZ_C_light+6AYZ_R_heavy,"50_SER, 51_LEU, 52_ASP, 53_HIS, 54_ASP, 82_GLU, 83_LEU",2.1
+6AYZ_A,6AYZ_D_light+6AYZ_B_heavy,"110_GLY, 112_TYR, 113_VAL, 114_ASN, 138_ASN, 140_LEU, 142_GLU, 143_LYS, 144_PRO, 145_ARG, 146_VAL, 147_THR, 148_ARG, 47_SER, 48_SER, 49_ARG, 51_LEU, 52_ASP, 6_LEU, 7_LEU, 8_GLY, 94_VAL, 96_LEU, 9_ILE",2.1
+7T0L_D,7T0L_K_light+7T0L_G_heavy,"121_LYS, 122_ASP, 136_ALA, 137_ASP, 138_THR, 223_ASP, 225_THR, 226_GLN, 227_ASP, 228_THR, 229_GLU, 230_LEU, 232_GLU, 243_LYS, 87_GLN",3.0
+7T0L_B,7T0L_L_light+7T0L_H_heavy,"1_ILE, 2_GLN, 3_ARG, 4_THR, 58_LYS, 59_ASP, 60_TRP, 6_LYS, 86_THR",3.0
+7T0L_A,7T0L_L_light+7T0L_H_heavy,"121_LYS, 122_ASP, 136_ALA, 137_ASP, 138_THR, 223_ASP, 225_THR, 226_GLN, 227_ASP, 228_THR, 229_GLU, 232_GLU, 87_GLN",3.0
+7T0L_E,7T0L_K_light+7T0L_G_heavy,"1_ILE, 2_GLN, 3_ARG, 4_THR, 59_ASP, 60_TRP, 6_LYS, 86_THR",3.0
+5VTA_D,5VTA_L_light+5VTA_H_heavy,"116_TYR, 117_ASN, 138_ARG, 139_GLN, 140_LEU, 241_ASP, 89_GLU, 91_SER, 92_THR, 94_GLU, 95_ILE, 96_PHE, 97_GLY, 98_ASP, 99_SER",2.8
+5VTA_A,5VTA_J_light+5VTA_K_heavy,"116_TYR, 117_ASN, 133_TYR, 138_ARG, 139_GLN, 140_LEU, 241_ASP, 242_GLU, 89_GLU, 91_SER, 92_THR, 94_GLU, 95_ILE, 96_PHE, 97_GLY, 98_ASP, 99_SER",2.8
+5VTA_B,5VTA_G_light+5VTA_I_heavy,"116_TYR, 117_ASN, 138_ARG, 139_GLN, 140_LEU, 241_ASP, 243_SER, 747_THR, 89_GLU, 91_SER, 92_THR, 94_GLU, 95_ILE, 96_PHE, 97_GLY, 98_ASP, 99_SER",2.8
+5VTA_C,5VTA_E_light+5VTA_F_heavy,"116_TYR, 117_ASN, 138_ARG, 139_GLN, 140_LEU, 241_ASP, 89_GLU, 91_SER, 92_THR, 94_GLU, 95_ILE, 96_PHE, 97_GLY, 98_ASP, 99_SER",2.8
+7YCN_G,7YCN_I_light+7YCN_H_heavy,"338_PHE, 342_PHE, 365_TYR, 366_SER, 367_VAL, 368_LEU, 369_TYR, 371_SER, 372_ALA, 373_SER, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 383_SER, 384_PRO, 385_THR, 386_LYS, 387_LEU, 388_ASN, 513_LEU",2.85
+7YCN_J,7YCN_L_light+7YCN_K_heavy,"342_PHE, 365_TYR, 366_SER, 367_VAL, 368_LEU, 369_TYR, 371_SER, 372_ALA, 373_SER, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 383_SER, 384_PRO, 385_THR, 386_LYS, 387_LEU, 388_ASN, 513_LEU",2.85
+7YCN_J,7YCN_F_light+7YCN_E_heavy,"405_ASP, 408_ARG, 413_GLY, 414_GLN, 415_THR, 449_TYR, 455_LEU, 456_PHE, 475_ALA, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 505_TYR",2.85
+7YCN_A,7YCN_L_light+7YCN_K_heavy,"408_ARG, 413_GLY, 414_GLN, 415_THR, 453_TYR, 455_LEU, 475_ALA, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 494_SER, 505_TYR",2.85
+7YCN_A,7YCN_F_light+7YCN_E_heavy,"342_PHE, 365_TYR, 366_SER, 367_VAL, 368_LEU, 371_SER, 372_ALA, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 383_SER, 384_PRO, 385_THR, 387_LEU, 388_ASN, 513_LEU",2.85
+7YCN_B,7YCN_D_light+7YCN_C_heavy,"338_PHE, 342_PHE, 364_ASP, 365_TYR, 366_SER, 367_VAL, 368_LEU, 369_TYR, 371_SER, 372_ALA, 373_SER, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 383_SER, 384_PRO, 385_THR, 387_LEU, 388_ASN",2.85
+5B8C_I,5B8C_G_light+5B8C_H_heavy,"128_LEU, 129_ALA, 131_LYS, 132_ALA, 62_SER, 63_PHE, 64_VAL, 66_ASN, 68_TYR, 76_THR, 78_LYS, 82_PHE, 83_PRO, 85_ASP, 86_ARG, 87_SER, 88_GLN, 89_PRO, 90_GLY",2.15
+5B8C_C,5B8C_A_light+5B8C_B_heavy,"128_LEU, 129_ALA, 130_PRO, 131_LYS, 132_ALA, 134_ILE, 60_SER, 61_GLU, 62_SER, 63_PHE, 64_VAL, 66_ASN, 68_TYR, 75_GLN, 76_THR, 77_ASP, 78_LYS, 83_PRO, 85_ASP, 86_ARG, 87_SER, 88_GLN, 89_PRO, 90_GLY",2.15
+5B8C_C,5B8C_G_light+5B8C_H_heavy,"118_SER, 119_GLY, 120_THR, 141_GLU, 143_ARG, 40_ALA, 41_LEU, 43_VAL, 70_MET, 71_SER, 72_PRO, 73_SER, 74_ASN",2.15
+5B8C_L,5B8C_J_light+5B8C_K_heavy,"128_LEU, 131_LYS, 132_ALA, 62_SER, 63_PHE, 64_VAL, 66_ASN, 68_TYR, 76_THR, 78_LYS, 82_PHE, 83_PRO, 85_ASP, 86_ARG, 87_SER, 88_GLN, 89_PRO, 90_GLY",2.15
+5B8C_F,5B8C_D_light+5B8C_E_heavy,"128_LEU, 129_ALA, 130_PRO, 132_ALA, 59_THR, 60_SER, 61_GLU, 62_SER, 63_PHE, 64_VAL, 66_ASN, 68_TYR, 76_THR, 78_LYS, 82_PHE, 83_PRO, 85_ASP, 86_ARG, 87_SER, 88_GLN, 89_PRO, 90_GLY",2.15
+5B8C_F,5B8C_J_light+5B8C_K_heavy,"118_SER, 119_GLY, 120_THR, 141_GLU, 143_ARG, 40_ALA, 41_LEU, 43_VAL, 70_MET, 71_SER, 72_PRO, 73_SER",2.15
+5D96_D,5D96_I_light+5D96_J_heavy,"114_ARG, 117_ASN, 118_ILE, 119_ALA, 120_GLY, 121_PHE, 122_PRO, 125_ARG, 135_SER, 136_GLY, 137_ALA, 138_THR, 139_LEU, 140_PRO, 142_ALA, 143_GLY, 72_TRP, 73_CYS, 74_GLY, 75_HIS",2.3
+5D96_A,5D96_B_light+5D96_C_heavy,"104_CYS, 110_SER, 114_ARG, 117_ASN, 118_ILE, 119_ALA, 120_GLY, 121_PHE, 122_PRO, 125_ARG, 135_SER, 136_GLY, 137_ALA, 138_THR, 139_LEU, 140_PRO, 142_ALA, 143_GLY, 72_TRP, 74_GLY, 75_HIS",2.3
+7KI4_E,7KI4_C_light+7KI4_D_heavy,"187_ILE, 188_ASP, 189_LYS, 190_ILE, 191_SER, 192_CYS, 193_LYS, 194_GLN, 70_GLN, 71_CYS",2.9
+7KI4_E,7KI4_F_light+7KI4_G_heavy,"147_LEU, 150_SER, 153_SER, 154_THR, 155_ASN, 156_GLU, 158_VAL, 159_VAL, 160_LYS, 161_LEU, 162_GLN, 163_GLU, 180_ASN, 67_ASN",2.9
+7KI4_B,7KI4_L_light+7KI4_H_heavy,"187_ILE, 188_ASP, 189_LYS, 190_ILE, 191_SER, 192_CYS, 193_LYS, 194_GLN, 70_GLN, 71_CYS",2.9
+7KI4_B,7KI4_C_light+7KI4_D_heavy,"147_LEU, 150_SER, 153_SER, 154_THR, 155_ASN, 156_GLU, 158_VAL, 159_VAL, 160_LYS, 161_LEU, 162_GLN, 163_GLU, 180_ASN, 67_ASN",2.9
+7KI4_A,7KI4_L_light+7KI4_H_heavy,"147_LEU, 150_SER, 153_SER, 154_THR, 155_ASN, 156_GLU, 158_VAL, 159_VAL, 160_LYS, 161_LEU, 162_GLN, 163_GLU, 180_ASN, 67_ASN",2.9
+7KI4_A,7KI4_F_light+7KI4_G_heavy,"187_ILE, 188_ASP, 189_LYS, 190_ILE, 191_SER, 192_CYS, 193_LYS, 194_GLN, 70_GLN, 71_CYS",2.9
+8WSN_D,8WSN_C_light+8WSN_B_heavy,"251_SER, 252_SER, 253_SER, 256_PHE, 275_LYS, 278_GLY, 284_MET, 287_CYS, 288_LYS, 289_VAL, 290_ALA, 291_GLY, 292_CYS, 293_GLU, 294_HIS",2.8
+8WSN_D,8WSN_F_light+8WSN_E_heavy,"224_PRO, 226_ASP, 244_LYS, 320_GLY, 332_ARG, 83_TYR, 84_PRO, 86_GLU, 91_ARG",2.8
+8WSN_A,8WSN_C_light+8WSN_B_heavy,"226_ASP, 244_LYS, 317_SER, 320_GLY, 332_ARG, 83_TYR, 84_PRO, 86_GLU, 91_ARG",2.8
+8WSN_A,8WSN_F_light+8WSN_E_heavy,"251_SER, 252_SER, 253_SER, 275_LYS, 278_GLY, 284_MET, 287_CYS, 288_LYS, 289_VAL, 290_ALA, 291_GLY, 292_CYS, 293_GLU, 294_HIS",2.8
+5D93_D,5D93_E_light+5D93_F_heavy,"142_ALA, 143_GLY, 144_ALA, 145_ASN, 146_VAL, 148_THR, 37_VAL, 38_LEU, 71_SER, 74_GLY, 75_HIS, 77_ILE, 78_ALA, 81_PRO, 82_THR, 84_LYS, 85_GLU",2.2
+5D93_A,5D93_B_light+5D93_C_heavy,"142_ALA, 143_GLY, 144_ALA, 145_ASN, 146_VAL, 148_THR, 35_MET, 36_SER, 37_VAL, 38_LEU, 71_SER, 72_TRP, 74_GLY, 75_HIS, 77_ILE, 78_ALA, 81_PRO, 82_THR, 84_LYS, 85_GLU",2.2
+4DKE_A,4DKE_L_light+4DKE_H_heavy,"103_GLU, 104_SER, 106_GLN, 107_ASP, 108_VAL, 109_LEU, 110_LEU, 111_GLU, 116_TRP, 117_LYS, 120_GLN, 123_GLU, 127_LEU, 152_PRO",3.0
+4DKE_B,4DKE_M_light+4DKE_I_heavy,"103_GLU, 104_SER, 106_GLN, 107_ASP, 108_VAL, 109_LEU, 110_LEU, 111_GLU, 116_TRP, 117_LYS, 120_GLN, 155_ASN",3.0
+4F37_A,4F37_L_light+4F37_H_heavy,"10_TYR, 1_ASP, 2_ALA, 3_GLU, 4_PHE, 5_ARG, 6_HIS, 7_ASP, 8_SER",2.57
+4F37_B,4F37_K_light+4F37_F_heavy,"10_TYR, 3_GLU, 4_PHE, 5_ARG, 6_HIS, 7_ASP, 8_SER",2.57
+8SZY_U,8SZY_D_light+8SZY_C_heavy,"100_ILE, 101_PHE, 102_LEU, 103_GLU, 10_ASN, 11_ILE, 12_SER, 19_ILE, 20_ILE, 6_GLU, 78_VAL, 79_ASN, 7_THR, 81_THR, 83_GLU, 8_THR, 99_ARG, 9_GLY",2.31
+8SZY_U,8SZY_L_light+8SZY_H_heavy,"20_ILE, 22_GLN, 28_THR, 29_THR, 30_ALA, 31_GLN, 67_PRO",2.31
+8SZY_U,8SZY_M_light+8SZY_I_heavy,"33_THR, 34_GLN, 36_ASN, 38_GLU, 40_GLN, 41_ASP, 42_GLN, 43_LEU, 46_ILE, 48_ASN, 49_ALA, 50_ASP, 51_LEU, 52_GLY, 53_TRP, 54_HIS, 56_SER, 57_PRO, 58_SER, 59_PHE, 60_LYS, 89_HIS, 91_TYR",2.31
+8SZY_T,8SZY_B_light+8SZY_A_heavy,"100_ILE, 101_PHE, 102_LEU, 103_GLU, 10_ASN, 11_ILE, 12_SER, 19_ILE, 20_ILE, 6_GLU, 78_VAL, 79_ASN, 7_THR, 81_THR, 83_GLU, 8_THR, 99_ARG, 9_GLY",2.31
+8SZY_T,8SZY_L_light+8SZY_H_heavy,"33_THR, 34_GLN, 36_ASN, 38_GLU, 40_GLN, 41_ASP, 42_GLN, 43_LEU, 46_ILE, 48_ASN, 50_ASP, 51_LEU, 52_GLY, 53_TRP, 54_HIS, 56_SER, 57_PRO, 58_SER, 59_PHE, 60_LYS, 89_HIS, 91_TYR, 92_PRO",2.31
+8SZY_T,8SZY_M_light+8SZY_I_heavy,"20_ILE, 22_GLN, 28_THR, 29_THR, 30_ALA, 31_GLN, 67_PRO",2.31
+6FLA_I,6FLA_L_light+6FLA_H_heavy,"301_MET, 303_THR, 305_LYS, 327_GLU, 328_GLY, 329_ASP, 336_PRO, 337_PHE, 338_GLU, 361_LYS, 379_ILE, 381_GLY, 382_VAL, 383_GLU, 384_PRO, 385_GLY, 386_GLN",2.9
+6FLA_G,6FLA_B_light+6FLA_A_heavy,"301_MET, 303_THR, 305_LYS, 327_GLU, 328_GLY, 329_ASP, 336_PRO, 337_PHE, 338_GLU, 361_LYS, 381_GLY, 382_VAL, 383_GLU, 384_PRO, 385_GLY, 386_GLN",2.9
+7XY8_A,7XY8_L_light+7XY8_H_heavy,"100_GLN, 106_ARG, 108_LYS, 109_ALA, 110_VAL, 127_LYS, 128_SER, 129_GLU, 130_SER, 131_VAL, 132_PRO, 133_PRO, 134_VAL, 135_THR, 164_GLN, 165_GLY, 30_VAL, 31_GLU, 98_ASN",2.3
+7XY8_B,7XY8_M_light+7XY8_I_heavy,"106_ARG, 108_LYS, 109_ALA, 110_VAL, 127_LYS, 128_SER, 129_GLU, 130_SER, 131_VAL, 132_PRO, 133_PRO, 134_VAL, 164_GLN, 165_GLY",2.3
+6HF1_D,6HF1_E_light+6HF1_F_heavy,"142_ALA, 143_GLY, 144_ALA, 145_ASN, 146_VAL, 148_THR, 37_VAL, 38_LEU, 71_SER, 74_GLY, 75_ALA, 77_ILE, 78_ALA, 81_PRO, 82_THR, 84_LYS, 85_GLU",1.94
+6HF1_A,6HF1_B_light+6HF1_C_heavy,"142_ALA, 143_GLY, 144_ALA, 145_ASN, 146_VAL, 148_THR, 37_VAL, 38_LEU, 71_SER, 72_TRP, 74_GLY, 75_ALA, 77_ILE, 78_ALA, 81_PRO, 82_THR, 84_LYS, 85_GLU",1.94
+6UMG_r,6UMG_l_light+6UMG_h_heavy,"70_ALA, 71_ASP, 74_TRP, 78_GLU, 83_PHE, 84_TRP",2.7
+6UMG_c,6UMG_l_light+6UMG_h_heavy,"114_HIS, 116_ALA, 117_SER, 119_ARG, 120_THR, 121_TRP, 122_THR, 124_TYR, 128_ASN, 131_THR, 21_GLY, 22_SER, 24_LEU, 25_GLU, 26_GLU, 38_ARG, 41_ILE, 70_ASP, 71_GLY, 72_TRP, 92_PHE, 94_ASP, 95_PHE",2.7
+6UMG_C,6UMG_L_light+6UMG_H_heavy,"114_HIS, 116_ALA, 117_SER, 119_ARG, 120_THR, 121_TRP, 122_THR, 124_TYR, 131_THR, 132_HIS, 133_GLU, 21_GLY, 22_SER, 23_GLU, 24_LEU, 25_GLU, 26_GLU, 38_ARG, 41_ILE, 70_ASP, 71_GLY, 72_TRP, 92_PHE, 94_ASP",2.7
+6UMG_R,6UMG_L_light+6UMG_H_heavy,"70_ALA, 71_ASP, 74_TRP, 78_GLU, 83_PHE, 84_TRP",2.7
+5WK3_D,5WK3_V_light+5WK3_W_heavy,"21_LEU, 22_ARG, 23_LYS, 44_VAL, 45_GLN, 60_ASN, 64_TYR, 67_SER, 68_LEU",1.9
+5WK3_C,5WK3_T_light+5WK3_U_heavy,"21_LEU, 22_ARG, 23_LYS, 44_VAL, 45_GLN, 57_ARG, 60_ASN, 64_TYR, 67_SER, 68_LEU, 70_ARG",1.9
+5WK3_B,5WK3_R_light+5WK3_S_heavy,"21_LEU, 22_ARG, 23_LYS, 44_VAL, 45_GLN, 60_ASN, 64_TYR, 67_SER, 68_LEU",1.9
+5WK3_A,5WK3_P_light+5WK3_Q_heavy,"21_LEU, 22_ARG, 23_LYS, 44_VAL, 45_GLN, 60_ASN, 64_TYR, 67_SER, 68_LEU",1.9
+4Z5R_H,4Z5R_R_light+4Z5R_S_heavy,"125_ARG, 141_GLU, 144_ARG, 145_ALA, 148_MET, 149_ARG, 153_LEU, 19_ALA, 22_ARG, 24_ILE, 25_SER, 26_LEU, 27_PHE, 30_LEU, 33_ARG, 34_HIS, 35_ASP, 37_GLY, 40_GLN, 41_GLU",3.0
+4Z5R_N,4Z5R_A_light+4Z5R_B_heavy,"125_ARG, 141_GLU, 144_ARG, 145_ALA, 148_MET, 149_ARG, 152_SER, 153_LEU, 19_ALA, 22_ARG, 24_ILE, 25_SER, 26_LEU, 27_PHE, 30_LEU, 33_ARG, 34_HIS, 35_ASP, 40_GLN",3.0
+4Z5R_G,4Z5R_P_light+4Z5R_Q_heavy,"125_ARG, 141_GLU, 144_ARG, 149_ARG, 153_LEU, 22_ARG, 24_ILE, 25_SER, 26_LEU, 27_PHE, 30_LEU, 33_ARG, 34_HIS, 35_ASP, 36_PHE, 37_GLY, 40_GLN, 41_GLU",3.0
+4Z5R_D,4Z5R_J_light+4Z5R_K_heavy,"125_ARG, 141_GLU, 144_ARG, 145_ALA, 148_MET, 149_ARG, 152_SER, 153_LEU, 19_ALA, 22_ARG, 24_ILE, 25_SER, 26_LEU, 27_PHE, 30_LEU, 33_ARG, 34_HIS, 35_ASP, 37_GLY, 40_GLN, 41_GLU",3.0
+4Z5R_F,4Z5R_V_light+4Z5R_W_heavy,"125_ARG, 141_GLU, 144_ARG, 145_ALA, 148_MET, 149_ARG, 152_SER, 153_LEU, 19_ALA, 22_ARG, 24_ILE, 25_SER, 26_LEU, 27_PHE, 30_LEU, 33_ARG, 34_HIS, 35_ASP, 37_GLY, 40_GLN, 41_GLU",3.0
+4Z5R_X,4Z5R_Y_light+4Z5R_Z_heavy,"125_ARG, 141_GLU, 144_ARG, 145_ALA, 148_MET, 149_ARG, 152_SER, 153_LEU, 19_ALA, 22_ARG, 24_ILE, 25_SER, 26_LEU, 27_PHE, 30_LEU, 33_ARG, 34_HIS, 35_ASP, 37_GLY, 40_GLN, 41_GLU",3.0
+4Z5R_I,4Z5R_T_light+4Z5R_U_heavy,"125_ARG, 141_GLU, 144_ARG, 145_ALA, 148_MET, 149_ARG, 152_SER, 153_LEU, 19_ALA, 22_ARG, 24_ILE, 25_SER, 26_LEU, 27_PHE, 30_LEU, 33_ARG, 34_HIS, 35_ASP, 37_GLY, 40_GLN, 41_GLU",3.0
+4Z5R_E,4Z5R_L_light+4Z5R_M_heavy,"125_ARG, 141_GLU, 144_ARG, 145_ALA, 148_MET, 149_ARG, 152_SER, 153_LEU, 19_ALA, 22_ARG, 24_ILE, 25_SER, 26_LEU, 27_PHE, 30_LEU, 33_ARG, 34_HIS, 35_ASP, 37_GLY, 40_GLN, 41_GLU",3.0
+5WNA_B,5WNA_L_light+5WNA_H_heavy,"161_ASN, 163_PHE, 165_PHE, 167_VAL, 168_PHE, 169_ASN, 170_PHE, 171_VAL, 172_PRO, 174_SER, 175_ILE, 182_CYS, 189_ILE, 190_PRO",2.4
+5WNA_B,5WNA_D_light+5WNA_C_heavy,"174_SER, 175_ILE, 177_SER, 178_ASN, 179_ASN",2.4
+5WNA_A,5WNA_L_light+5WNA_H_heavy,"174_SER, 175_ILE, 177_SER, 178_ASN, 179_ASN",2.4
+5WNA_A,5WNA_D_light+5WNA_C_heavy,"161_ASN, 162_ASP, 163_PHE, 165_PHE, 168_PHE, 169_ASN, 170_PHE, 171_VAL, 172_PRO, 174_SER, 175_ILE, 179_ASN, 182_CYS, 187_LYS, 189_ILE, 190_PRO",2.4
+7WG3_K,7WG3_C_light+7WG3_G_heavy,"131_GLU, 135_SER, 21_HIS, 22_THR, 23_LEU, 24_THR, 25_PRO, 26_GLY, 27_ASP, 28_LEU, 29_ARG, 30_ASP, 56_ARG",2.19
+7WG3_I,7WG3_A_light+7WG3_E_heavy,"20_ARG, 22_THR, 23_LEU, 24_THR, 25_PRO, 26_GLY, 27_ASP, 28_LEU, 29_ARG, 30_ASP, 56_ARG",2.19
+7WG3_J,7WG3_B_light+7WG3_F_heavy,"118_PHE, 139_ASN, 20_ARG, 21_HIS, 22_THR, 23_LEU, 24_THR, 25_PRO, 26_GLY, 27_ASP, 28_LEU, 29_ARG, 56_ARG",2.19
+7WG3_L,7WG3_D_light+7WG3_H_heavy,"116_VAL, 118_PHE, 139_ASN, 141_THR, 19_VAL, 20_ARG, 21_HIS, 22_THR, 23_LEU, 24_THR, 25_PRO, 26_GLY, 27_ASP, 28_LEU, 29_ARG",2.19
+5DO2_A,5DO2_L_light+5DO2_H_heavy,"398_ASN, 400_LYS, 495_LEU, 496_LYS, 527_VAL, 528_SER, 529_ILE, 530_VAL, 531_PRO, 532_SER, 535_TRP, 536_GLU, 539_ASP, 540_TYR, 542_ARG, 543_LYS, 544_GLN",2.41
+5DO2_B,5DO2_D_light+5DO2_C_heavy,"397_TYR, 398_ASN, 400_LYS, 495_LEU, 496_LYS, 527_VAL, 528_SER, 529_ILE, 530_VAL, 531_PRO, 532_SER, 535_TRP, 536_GLU, 539_ASP, 540_TYR, 542_ARG, 543_LYS, 544_GLN",2.41
+5BJZ_B,5BJZ_H_light+5BJZ_D_heavy,"171_TYR, 174_GLY, 175_LYS, 176_TYR, 256_LYS, 305_LYS, 308_GLU, 311_LEU, 312_ALA, 318_ALA, 319_ALA, 321_MET, 322_GLU, 324_ALA, 325_GLN, 326_LYS, 327_GLY, 328_GLU, 329_ILE, 90_TYR, 91_PRO, 92_PHE, 95_ASP, 98_ARG",1.95
+5BJZ_A,5BJZ_L_light+5BJZ_C_heavy,"171_TYR, 174_GLY, 175_LYS, 176_TYR, 256_LYS, 305_LYS, 308_GLU, 311_LEU, 312_ALA, 318_ALA, 319_ALA, 321_MET, 322_GLU, 324_ALA, 325_GLN, 327_GLY, 328_GLU, 329_ILE, 90_TYR, 91_PRO, 92_PHE, 95_ASP, 98_ARG",1.95
+8BK2_C,8BK2_G_light+8BK2_F_heavy,"145_PHE, 146_ASN, 150_ASP, 152_LYS, 174_ASP, 176_ALA, 177_ALA, 178_LYS, 179_GLN, 181_HIS, 198_ALA, 199_ALA, 200_GLU, 202_LYS, 216_ARG",2.41
+8BK2_B,8BK2_E_light+8BK2_D_heavy,"145_PHE, 146_ASN, 150_ASP, 152_LYS, 174_ASP, 176_ALA, 177_ALA, 178_LYS, 179_GLN, 181_HIS, 198_ALA, 199_ALA, 200_GLU, 216_ARG",2.41
+8BK2_A,8BK2_L_light+8BK2_H_heavy,"145_PHE, 146_ASN, 150_ASP, 152_LYS, 174_ASP, 176_ALA, 177_ALA, 178_LYS, 179_GLN, 181_HIS, 198_ALA, 199_ALA, 200_GLU, 202_LYS, 216_ARG",2.41
+3QA3_I,3QA3_C_light+3QA3_D_heavy,"148_HIS, 151_ARG, 152_ARG, 159_THR, 194_ASN, 196_ARG, 200_LYS, 303_GLU, 306_LYS, 310_ASN",3.0
+3QA3_I,3QA3_F_light+3QA3_H_heavy,"142_SER, 143_GLY, 144_SER, 178_GLU, 179_GLU, 180_PHE, 181_ARG, 205_LEU, 206_LEU, 207_GLY, 208_ARG, 209_THR, 243_GLY, 244_GLU, 246_PHE, 247_GLY, 274_ALA",3.0
+3QA3_G,3QA3_C_light+3QA3_D_heavy,"142_SER, 143_GLY, 144_SER, 178_GLU, 179_GLU, 180_PHE, 181_ARG, 205_LEU, 206_LEU, 207_GLY, 208_ARG, 209_THR, 243_GLY, 244_GLU, 246_PHE, 247_GLY, 274_ALA",3.0
+3QA3_G,3QA3_F_light+3QA3_H_heavy,"148_HIS, 151_ARG, 152_ARG, 159_THR, 194_ASN, 196_ARG, 197_SER, 200_LYS, 303_GLU, 306_LYS, 309_GLN, 310_ASN",3.0
+3QA3_L,3QA3_J_light+3QA3_K_heavy,"142_SER, 143_GLY, 144_SER, 178_GLU, 179_GLU, 180_PHE, 181_ARG, 205_LEU, 206_LEU, 207_GLY, 208_ARG, 209_THR, 243_GLY, 244_GLU, 246_PHE, 247_GLY, 279_LYS",3.0
+3QA3_E,3QA3_A_light+3QA3_B_heavy,"142_SER, 143_GLY, 144_SER, 178_GLU, 179_GLU, 180_PHE, 181_ARG, 205_LEU, 206_LEU, 207_GLY, 208_ARG, 209_THR, 243_GLY, 244_GLU, 246_PHE, 247_GLY, 274_ALA",3.0
+7K37_C,7K37_I_light+7K37_J_heavy,"269_ARG, 270_SER, 271_ASP, 272_ALA, 273_PRO, 284_PRO, 285_ASN, 48_THR, 49_GLY, 91_SER, 92_LYS",2.8
+7K37_C,7K37_K_light+7K37_L_heavy,"170_ASN, 171_ASN, 172_ASP, 173_ASN, 239_PRO, 240_GLY",2.8
+7K37_A,7K37_G_light+7K37_H_heavy,"269_ARG, 270_SER, 271_ASP, 272_ALA, 273_PRO, 284_PRO, 285_ASN, 48_THR, 49_GLY, 91_SER, 92_LYS",2.8
+7K37_A,7K37_I_light+7K37_J_heavy,"171_ASN, 172_ASP, 173_ASN, 239_PRO, 240_GLY",2.8
+7K37_E,7K37_G_light+7K37_H_heavy,"171_ASN, 172_ASP, 173_ASN, 239_PRO, 240_GLY",2.8
+7K37_E,7K37_K_light+7K37_L_heavy,"269_ARG, 270_SER, 271_ASP, 272_ALA, 273_PRO, 284_PRO, 285_ASN, 48_THR, 49_GLY, 91_SER, 92_LYS",2.8
+7LR3_D,7LR3_L_light+7LR3_H_heavy,"539_GLN, 541_SER, 543_SER, 565_ARG, 595_ILE, 597_LYS, 598_ASP, 599_SER, 600_ASN, 601_GLY, 602_LYS, 603_GLU",2.8
+7LR3_C,7LR3_B_light+7LR3_A_heavy,"539_GLN, 540_PHE, 541_SER, 543_SER, 565_ARG, 595_ILE, 597_LYS, 598_ASP, 599_SER, 600_ASN, 601_GLY, 602_LYS, 603_GLU, 606_ILE",2.8
+8I5H_D,8I5H_F_light+8I5H_E_heavy,"403_ARG, 406_GLU, 409_GLN, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",2.38
+8I5H_R,8I5H_L_light+8I5H_H_heavy,"403_ARG, 405_ASP, 406_GLU, 409_GLN, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 457_ARG, 458_LYS, 459_SER, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",2.38
+8I5H_A,8I5H_C_light+8I5H_B_heavy,"403_ARG, 406_GLU, 409_GLN, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 503_VAL, 505_TYR",2.38
+5VJQ_I,5VJQ_B_light+5VJQ_A_heavy,"100_SER, 101_ASP, 102_GLY, 15_LEU, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 62_TRP, 63_TRP, 73_LYS, 75_ALA, 93_ARG, 96_LYS, 97_ARG",1.9
+5VJQ_I,5VJQ_D_light+5VJQ_C_heavy,"102_GLY, 103_ASN, 106_ASN, 112_ARG, 116_ARG, 22_GLY",1.9
+5VJQ_K,5VJQ_F_light+5VJQ_E_heavy,"100_SER, 101_ASP, 102_GLY, 15_LEU, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 62_TRP, 63_TRP, 73_LYS, 75_ALA, 89_THR, 93_ARG, 96_LYS, 97_ARG, 98_ILE",1.9
+5VJQ_J,5VJQ_B_light+5VJQ_A_heavy,"102_GLY, 103_ASN, 106_ASN, 112_ARG, 116_ARG",1.9
+5VJQ_J,5VJQ_D_light+5VJQ_C_heavy,"100_SER, 101_ASP, 102_GLY, 15_LEU, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 62_TRP, 63_TRP, 73_LYS, 75_ALA, 93_ARG, 96_LYS, 97_ARG",1.9
+5VJQ_L,5VJQ_F_light+5VJQ_E_heavy,"102_GLY, 103_ASN, 106_ASN, 109_VAL, 112_ARG, 116_ARG, 22_GLY",1.9
+5VJQ_L,5VJQ_H_light+5VJQ_G_heavy,"100_SER, 101_ASP, 102_GLY, 15_LEU, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 62_TRP, 63_TRP, 73_LYS, 75_ALA, 93_ARG, 96_LYS, 97_ARG",1.9
+6MFT_G,6MFT_L_light+6MFT_H_heavy,"275_LYS, 276_ASN, 278_SER, 279_ASP, 280_ASN, 281_ALA, 282_LYS, 356_LYS, 364_SER, 365_GLY, 366_GLY, 367_ASP, 370_ILE, 430_VAL, 455_LEU, 456_ARG, 457_ASP, 458_GLY, 459_CYS, 460_ASN, 461_THR, 465_THR, 466_GLU, 467_ILE, 469_ARG, 472_GLY, 473_GLY",2.32
+6MFT_C,6MFT_B_light+6MFT_A_heavy,"275_LYS, 276_ASN, 278_SER, 279_ASP, 280_ASN, 281_ALA, 282_LYS, 364_SER, 365_GLY, 366_GLY, 367_ASP, 370_ILE, 430_VAL, 455_LEU, 456_ARG, 457_ASP, 458_GLY, 459_CYS, 460_ASN, 461_THR, 462_THR, 465_THR, 466_GLU, 467_ILE, 469_ARG, 473_GLY",2.32
+7QF1_F,7QF1_D_light+7QF1_E_heavy,"456_PHE, 475_ALA, 476_GLY, 477_SER, 478_THR, 479_PRO, 483_VAL, 485_GLY, 486_PHE, 487_ASN, 488_CYS, 489_TYR, 493_GLN",2.8
+7QF1_A,7QF1_B_light+7QF1_C_heavy,"456_PHE, 475_ALA, 476_GLY, 477_SER, 478_THR, 479_PRO, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 488_CYS, 489_TYR, 493_GLN",2.8
+3U2S_G,3U2S_L_light+3U2S_H_heavy,"157_CYS, 158_SER, 160_ASN, 162_THR, 166_LYS, 167_ASP, 168_ARG, 169_LYS, 170_GLN, 171_LYS, 172_VAL, 173_ASN, 223_ASP",1.8
+3U2S_C,3U2S_B_light+3U2S_A_heavy,"157_CYS, 158_SER, 160_ASN, 162_THR, 166_LYS, 167_ASP, 168_ARG, 169_LYS, 170_GLN, 171_LYS, 172_VAL, 173_ASN, 223_ASP",1.8
+2VXS_D,2VXS_N_light+2VXS_J_heavy,"124_VAL, 126_PRO, 127_ILE, 74_LEU, 85_TYR, 86_HIS, 88_ASN",2.63
+2VXS_D,2VXS_O_light+2VXS_K_heavy,"39_ARG, 40_SER, 41_SER, 42_ASP, 43_TYR, 46_ARG",2.63
+2VXS_A,2VXS_L_light+2VXS_H_heavy,"39_ARG, 40_SER, 41_SER, 42_ASP, 43_TYR, 46_ARG",2.63
+2VXS_A,2VXS_M_light+2VXS_I_heavy,"124_VAL, 126_PRO, 127_ILE, 74_LEU, 85_TYR, 86_HIS, 88_ASN",2.63
+2VXS_B,2VXS_L_light+2VXS_H_heavy,"124_VAL, 126_PRO, 127_ILE, 74_LEU, 85_TYR, 86_HIS, 88_ASN, 93_GLN",2.63
+2VXS_B,2VXS_M_light+2VXS_I_heavy,"39_ARG, 40_SER, 41_SER, 42_ASP, 43_TYR, 46_ARG",2.63
+2VXS_C,2VXS_N_light+2VXS_J_heavy,"38_LYS, 39_ARG, 40_SER, 41_SER, 42_ASP, 43_TYR, 46_ARG",2.63
+2VXS_C,2VXS_O_light+2VXS_K_heavy,"124_VAL, 126_PRO, 127_ILE, 74_LEU, 85_TYR, 86_HIS, 88_ASN",2.63
+8CWJ_I,8CWJ_L_light+8CWJ_H_heavy,"445_VAL, 446_GLY, 447_GLY, 449_TYR, 498_HIS, 500_THR",2.45
+8CWJ_I,8CWJ_B_light+8CWJ_A_heavy,"369_TYR, 370_ASN, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 390_LEU, 392_PHE, 408_ARG, 428_ASP, 430_THR, 517_LEU",2.45
+8CWJ_I,8CWJ_V_light+8CWJ_U_heavy,"349_SER, 351_TYR, 352_ALA, 353_TRP, 355_ARG, 357_ARG, 450_ASN, 452_LEU, 464_PHE, 465_GLU, 466_ARG, 467_ASP, 468_ILE, 469_SER, 470_THR, 484_GLU, 490_PHE",2.45
+8CWJ_G,8CWJ_L_light+8CWJ_H_heavy,"369_TYR, 370_ASN, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 390_LEU, 392_PHE, 408_ARG, 428_ASP, 429_PHE, 430_THR, 515_PHE, 516_GLU, 517_LEU",2.45
+8CWJ_G,8CWJ_B_light+8CWJ_A_heavy,"444_LYS, 445_VAL, 446_GLY, 447_GLY, 449_TYR, 498_HIS, 500_THR",2.45
+8CWJ_G,8CWJ_K_light+8CWJ_J_heavy,"349_SER, 351_TYR, 352_ALA, 353_TRP, 355_ARG, 357_ARG, 450_ASN, 452_LEU, 464_PHE, 466_ARG, 467_ASP, 468_ILE, 470_THR, 490_PHE",2.45
+6I8S_D,6I8S_I_light+6I8S_E_heavy,"190_HIS, 192_SER, 193_ASP, 194_GLY, 243_LYS, 244_GLU, 246_PRO",2.9
+6I8S_D,6I8S_L_light+6I8S_H_heavy,"210_TYR, 211_THR, 212_GLU, 220_TYR, 241_TYR, 268_ARG, 269_LEU, 270_PRO, 271_ARG, 272_LEU, 345_ALA, 346_ARG, 347_MET, 348_ALA, 349_PRO, 350_GLU, 351_GLU",2.9
+6I8S_C,6I8S_J_light+6I8S_F_heavy,"18_ARG, 293_LEU, 294_GLY, 53_GLU, 56_GLN, 60_ALA, 61_ALA, 65_LYS",2.9
+6I8S_C,6I8S_K_light+6I8S_G_heavy,"210_TYR, 211_THR, 212_GLU, 220_TYR, 241_TYR, 269_LEU, 270_PRO, 271_ARG, 272_LEU, 346_ARG, 347_MET, 348_ALA, 349_PRO, 350_GLU, 351_GLU",2.9
+6I8S_B,6I8S_J_light+6I8S_F_heavy,"210_TYR, 211_THR, 212_GLU, 220_TYR, 241_TYR, 268_ARG, 269_LEU, 270_PRO, 271_ARG, 272_LEU, 345_ALA, 346_ARG, 347_MET, 348_ALA, 349_PRO, 350_GLU, 351_GLU",2.9
+6I8S_B,6I8S_K_light+6I8S_G_heavy,"188_LEU, 190_HIS, 192_SER, 193_ASP, 194_GLY, 196_THR, 243_LYS, 244_GLU, 246_PRO",2.9
+6I8S_A,6I8S_I_light+6I8S_E_heavy,"210_TYR, 211_THR, 212_GLU, 220_TYR, 241_TYR, 269_LEU, 270_PRO, 271_ARG, 345_ALA, 346_ARG, 347_MET, 348_ALA, 349_PRO, 350_GLU, 351_GLU",2.9
+6I8S_A,6I8S_L_light+6I8S_H_heavy,"18_ARG, 293_LEU, 294_GLY, 56_GLN, 60_ALA, 61_ALA, 65_LYS",2.9
+6WIT_I,6WIT_B_light+6WIT_A_heavy,"179_LEU, 180_ASP, 192_ARG, 194_ILE, 195_ASN, 196_CYS, 197_GLN, 198_THR, 199_THR, 200_THR, 216_TYR, 219_ASP, 220_ASN",2.79
+6WIT_C,6WIT_L_light+6WIT_H_heavy,"179_LEU, 180_ASP, 192_ARG, 194_ILE, 195_ASN, 196_CYS, 197_GLN, 198_THR, 199_THR, 200_THR, 216_TYR, 219_ASP, 220_ASN, 221_GLY",2.79
+7WHD_C,7WHD_E_light+7WHD_D_heavy,"445_VAL, 449_TYR, 450_ASN, 452_LEU, 456_PHE, 470_THR, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER, 498_GLN, 500_THR",2.65
+7WHD_B,7WHD_K_light+7WHD_J_heavy,"445_VAL, 446_GLY, 449_TYR, 450_ASN, 452_LEU, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER, 498_GLN",2.65
+7WHD_A,7WHD_H_light+7WHD_G_heavy,"445_VAL, 449_TYR, 450_ASN, 452_LEU, 483_VAL, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER, 500_THR",2.65
+4XRC_D,4XRC_A_light+4XRC_B_heavy,"133_SER, 135_VAL, 136_SER, 137_SER, 142_GLN, 143_GLY, 144_LYS, 145_SER, 153_TRP, 156_LYS, 158_ASN, 190_GLU, 193_LYS, 194_LEU, 226_LEU, 98_TYR",2.74
+4XRC_C,4XRC_L_light+4XRC_H_heavy,"133_SER, 135_VAL, 136_SER, 137_SER, 142_GLN, 143_GLY, 144_LYS, 145_SER, 153_TRP, 155_ILE, 156_LYS, 157_LYS, 158_ASN, 159_SER, 190_GLU, 193_LYS, 194_LEU, 226_LEU, 98_TYR",2.74
+8JNK_E,8JNK_M_light+8JNK_I_heavy,"167_GLU, 168_ASN, 169_THR, 170_GLY, 75_GLU, 77_VAL, 79_GLU, 89_ARG, 93_TYR, 94_PRO",2.69
+8JNK_E,8JNK_N_light+8JNK_J_heavy,"100_LYS, 101_GLU, 104_TRP, 105_ILE, 108_GLN, 111_GLN, 112_ASP, 155_HIS, 156_PRO, 157_VAL, 160_THR, 164_ARG, 97_VAL, 98_ASP",2.69
+8JNK_A,8JNK_M_light+8JNK_I_heavy,"100_LYS, 101_GLU, 104_TRP, 105_ILE, 107_GLU, 108_GLN, 112_ASP, 155_HIS, 156_PRO, 157_VAL, 160_THR, 164_ARG, 97_VAL, 98_ASP, 99_VAL",2.69
+8JNK_A,8JNK_N_light+8JNK_J_heavy,"167_GLU, 168_ASN, 169_THR, 170_GLY, 75_GLU, 77_VAL, 79_GLU, 82_LEU, 89_ARG, 93_TYR, 94_PRO, 96_PHE",2.69
+8JNK_C,8JNK_P_light+8JNK_L_heavy,"100_LYS, 101_GLU, 104_TRP, 105_ILE, 108_GLN, 111_GLN, 112_ASP, 155_HIS, 156_PRO, 157_VAL, 160_THR, 164_ARG, 97_VAL, 98_ASP",2.69
+8JNK_G,8JNK_O_light+8JNK_K_heavy,"100_LYS, 101_GLU, 104_TRP, 105_ILE, 107_GLU, 108_GLN, 111_GLN, 112_ASP, 155_HIS, 156_PRO, 157_VAL, 160_THR, 164_ARG, 98_ASP",2.69
+7LJ5_A,7LJ5_D_light+7LJ5_E_heavy,"69_ARG, 70_GLU, 73_LEU, 74_ARG, 76_HIS, 77_PRO, 78_CYS, 79_VAL, 80_SER, 81_ASP, 82_GLN, 83_GLU, 86_LEU",2.26
+7LJ5_B,7LJ5_F_light+7LJ5_G_heavy,"70_GLU, 73_LEU, 74_ARG, 76_HIS, 77_PRO, 78_CYS, 79_VAL, 80_SER, 81_ASP, 82_GLN, 83_GLU",2.26
+8EL2_A,8EL2_L_light+8EL2_H_heavy,"444_LYS, 449_TYR, 455_LEU, 456_PHE, 484_GLU, 485_GLY, 486_PHE, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER",2.89
+8EL2_A,8EL2_K_light+8EL2_I_heavy,"405_ASP, 475_ALA, 487_ASN, 489_TYR, 505_TYR",2.89
+8EL2_B,8EL2_L_light+8EL2_H_heavy,"405_ASP, 408_ARG, 475_ALA, 487_ASN, 489_TYR, 505_TYR",2.89
+8EL2_B,8EL2_K_light+8EL2_I_heavy,"444_LYS, 446_GLY, 449_TYR, 455_LEU, 456_PHE, 484_GLU, 485_GLY, 486_PHE, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER",2.89
+5MHR_A,5MHR_J_light+5MHR_K_heavy,"321_ILE, 323_ILE, 334_ARG, 336_GLN, 337_VAL, 338_ASN, 340_ASP, 353_PRO, 358_PHE",3.0
+5MHR_A,5MHR_Q_light+5MHR_R_heavy,"329_SER, 363_GLY, 364_GLY, 391_VAL, 392_VAL, 393_THR, 417_TRP, 419_GLU, 422_GLN, 427_ARG, 429_ARG",3.0
+5MHR_C,5MHR_J_light+5MHR_K_heavy,"329_SER, 363_GLY, 364_GLY, 365_ASP, 391_VAL, 392_VAL, 393_THR, 417_TRP, 419_GLU, 427_ARG, 429_ARG",3.0
+5MHR_C,5MHR_H_light+5MHR_I_heavy,"321_ILE, 323_ILE, 334_ARG, 336_GLN, 337_VAL, 338_ASN, 340_ASP, 353_PRO, 358_PHE",3.0
+5MHR_D,5MHR_L_light+5MHR_M_heavy,"329_SER, 363_GLY, 364_GLY, 391_VAL, 392_VAL, 393_THR, 417_TRP, 419_GLU, 427_ARG, 429_ARG",3.0
+5MHR_D,5MHR_G_light+5MHR_N_heavy,"321_ILE, 323_ILE, 334_ARG, 336_GLN, 337_VAL, 338_ASN, 340_ASP, 353_PRO, 358_PHE",3.0
+5MHR_B,5MHR_Q_light+5MHR_R_heavy,"321_ILE, 323_ILE, 334_ARG, 336_GLN, 337_VAL, 338_ASN, 340_ASP, 353_PRO, 358_PHE",3.0
+5MHR_B,5MHR_H_light+5MHR_I_heavy,"329_SER, 363_GLY, 364_GLY, 365_ASP, 391_VAL, 392_VAL, 393_THR, 417_TRP, 419_GLU, 427_ARG, 429_ARG",3.0
+5MHR_F,5MHR_G_light+5MHR_N_heavy,"329_SER, 363_GLY, 364_GLY, 365_ASP, 391_VAL, 392_VAL, 393_THR, 417_TRP, 419_GLU, 427_ARG, 429_ARG",3.0
+5MHR_F,5MHR_O_light+5MHR_P_heavy,"321_ILE, 323_ILE, 334_ARG, 336_GLN, 337_VAL, 338_ASN, 340_ASP, 353_PRO, 358_PHE",3.0
+5MHR_E,5MHR_L_light+5MHR_M_heavy,"321_ILE, 323_ILE, 334_ARG, 336_GLN, 337_VAL, 338_ASN, 340_ASP, 353_PRO, 358_PHE",3.0
+5MHR_E,5MHR_O_light+5MHR_P_heavy,"329_SER, 363_GLY, 365_ASP, 391_VAL, 392_VAL, 393_THR, 417_TRP, 419_GLU, 427_ARG, 429_ARG, 431_GLU",3.0
+7UOV_b,7UOV_P_light+7UOV_O_heavy,"264_TRP, 265_THR, 267_SER, 321_GLN, 324_GLN, 325_ARG",2.75
+7UOV_b,7UOV_R_light+7UOV_Q_heavy,"356_ARG, 361_ILE, 362_PRO, 384_LYS, 387_LEU, 394_LEU, 395_ASN, 397_THR, 398_HIS, 399_PHE, 400_SER, 401_ASP",2.75
+7UOV_c,7UOV_T_light+7UOV_S_heavy,"352_LYS, 356_ARG, 361_ILE, 362_PRO, 387_LEU, 394_LEU, 395_ASN, 397_THR, 398_HIS, 399_PHE, 400_SER, 401_ASP, 402_ASP",2.75
+7UOV_c,7UOV_R_light+7UOV_Q_heavy,"264_TRP, 265_THR, 267_SER, 321_GLN, 324_GLN, 325_ARG",2.75
+7UOV_a,7UOV_T_light+7UOV_S_heavy,"264_TRP, 265_THR, 267_SER, 321_GLN, 324_GLN, 325_ARG",2.75
+7UOV_a,7UOV_P_light+7UOV_O_heavy,"352_LYS, 356_ARG, 361_ILE, 362_PRO, 384_LYS, 394_LEU, 395_ASN, 397_THR, 398_HIS, 399_PHE, 400_SER, 401_ASP, 402_ASP",2.75
+7UOV_A,7UOV_L_light+7UOV_H_heavy,"107_LEU, 108_THR, 109_ASN, 110_THR, 111_SER, 112_ILE, 114_ASN, 161_LYS, 217_TYR, 219_TYR, 91_SER, 92_HIS",2.75
+7UOV_B,7UOV_E_light+7UOV_D_heavy,"107_LEU, 108_THR, 109_ASN, 110_THR, 111_SER, 112_ILE, 113_ILE, 114_ASN, 161_LYS, 217_TYR, 219_TYR, 91_SER, 92_HIS",2.75
+7UOV_C,7UOV_G_light+7UOV_F_heavy,"108_THR, 109_ASN, 110_THR, 111_SER, 112_ILE, 113_ILE, 114_ASN, 115_HIS, 156_ASP, 161_LYS, 217_TYR, 219_TYR, 91_SER, 92_HIS",2.75
+8CAF_G,8CAF_A_light+8CAF_B_heavy,"20_THR, 22_LYS, 24_GLU, 25_ARG, 28_GLU, 32_GLU, 53_GLU",2.66
+8CAF_G,8CAF_C_light+8CAF_D_heavy,"28_GLU, 31_GLU, 37_PRO, 38_PRO, 39_GLN, 75_GLY, 76_GLY",2.66
+8CAF_E,8CAF_C_light+8CAF_D_heavy,"717_ARG, 718_ILE, 721_MET, 722_ARG, 723_LYS, 724_VAL, 725_LEU, 729_GLN, 732_GLY, 733_GLU, 736_THR, 737_GLN, 740_SER",2.66
+8CAF_F,8CAF_A_light+8CAF_B_heavy,"28_GLU, 31_GLU, 37_PRO, 39_GLN, 75_GLY, 76_GLY",2.66
+8CAF_F,8CAF_C_light+8CAF_D_heavy,"20_THR, 22_LYS, 25_ARG, 28_GLU, 32_GLU",2.66
+8CAF_H,8CAF_A_light+8CAF_B_heavy,"717_ARG, 718_ILE, 721_MET, 722_ARG, 723_LYS, 724_VAL, 729_GLN, 732_GLY, 733_GLU, 736_THR, 737_GLN, 740_SER",2.66
+8JKF_b,8JKF_N_light+8JKF_M_heavy,"209_THR, 232_TRP, 258_GLU, 259_GLY, 315_GLU, 316_CYS, 317_THR",2.83
+8JKF_b,8JKF_n_light+8JKF_m_heavy,"100_GLY, 101_PRO, 102_GLN, 145_LEU, 146_LYS, 174_ASP, 175_TYR, 176_SER, 177_LEU, 178_GLU, 224_GLU, 227_LYS, 245_LYS, 99_ARG",2.83
+8JKF_a,8JKF_L_light+8JKF_H_heavy,"209_THR, 232_TRP, 258_GLU, 315_GLU, 316_CYS, 317_THR",2.83
+8JKF_a,8JKF_l_light+8JKF_h_heavy,"100_GLY, 101_PRO, 102_GLN, 145_LEU, 146_LYS, 147_HIS, 174_ASP, 175_TYR, 176_SER, 177_LEU, 178_GLU, 224_GLU, 227_LYS, 245_LYS, 99_ARG",2.83
+8JKF_B,8JKF_N_light+8JKF_M_heavy,"100_GLY, 101_PRO, 102_GLN, 146_LYS, 147_HIS, 174_ASP, 175_TYR, 176_SER, 177_LEU, 178_GLU, 224_GLU, 227_LYS, 99_ARG",2.83
+8JKF_A,8JKF_L_light+8JKF_H_heavy,"100_GLY, 101_PRO, 102_GLN, 145_LEU, 146_LYS, 174_ASP, 175_TYR, 176_SER, 177_LEU, 178_GLU, 224_GLU, 227_LYS, 99_ARG",2.83
+8JKF_A,8JKF_l_light+8JKF_h_heavy,"208_ASP, 232_TRP, 258_GLU, 315_GLU, 316_CYS, 317_THR",2.83
+7VGR_A,7VGR_C_light+7VGR_D_heavy,"138_LEU, 180_LYS, 181_LEU, 182_GLY, 183_ALA, 184_SER, 197_SER, 198_ARG, 199_TYR, 201_ILE",2.7
+7VGR_A,7VGR_E_light+7VGR_F_heavy,"146_ARG, 148_HIS, 158_ARG, 188_ALA, 189_GLY, 190_ASP, 191_SER",2.7
+7VGR_B,7VGR_C_light+7VGR_D_heavy,"146_ARG, 148_HIS, 158_ARG, 188_ALA, 189_GLY, 190_ASP, 191_SER",2.7
+7VGR_B,7VGR_E_light+7VGR_F_heavy,"138_LEU, 180_LYS, 181_LEU, 182_GLY, 183_ALA, 184_SER, 197_SER, 198_ARG, 199_TYR, 201_ILE",2.7
+7DR4_K,7DR4_L_light+7DR4_H_heavy,"111_THR, 34_PRO, 35_LYS, 37_THR, 38_ARG, 40_LEU, 41_THR, 42_PHE, 43_LYS, 45_TYR, 62_GLU, 65_PRO, 68_GLU",2.49
+7DR4_I,7DR4_L_light+7DR4_H_heavy,"12_LEU, 15_GLU, 16_HIS, 19_LEU, 79_HIS, 81_ARG, 88_ASN",2.49
+7DR4_J,7DR4_B_light+7DR4_A_heavy,"111_THR, 34_PRO, 35_LYS, 37_THR, 38_ARG, 40_LEU, 41_THR, 42_PHE, 43_LYS, 45_TYR, 62_GLU, 65_PRO, 68_GLU",2.49
+7DR4_G,7DR4_B_light+7DR4_A_heavy,"12_LEU, 15_GLU, 16_HIS, 19_LEU, 79_HIS, 81_ARG, 88_ASN",2.49
+6LYN_D,6LYN_L_light+6LYN_H_heavy,"351_ALA, 352_ALA, 354_GLU, 356_GLN, 424_ASN, 426_ALA, 428_PHE, 457_HIS, 458_PRO, 459_ARG, 474_ASP, 475_GLN, 476_ASP, 477_PRO, 478_GLN, 504_ASP, 505_LEU",2.78
+6LYN_C,6LYN_B_light+6LYN_A_heavy,"351_ALA, 352_ALA, 354_GLU, 356_GLN, 424_ASN, 426_ALA, 428_PHE, 457_HIS, 458_PRO, 474_ASP, 475_GLN, 476_ASP, 477_PRO, 478_GLN, 504_ASP, 505_LEU",2.78
+3QWO_C,3QWO_L_light+3QWO_A_heavy,"10_LYS, 13_SER, 14_ASN, 17_ALA, 24_GLU, 27_ARG, 28_SER, 31_LEU, 32_SER, 34_ILE, 35_ASN, 36_ASP, 6_ASN, 9_LYS",1.9
+3QWO_C,3QWO_B_light+3QWO_A_heavy,"10_LYS, 13_SER, 14_ASN, 17_ALA, 24_GLU, 27_ARG, 28_SER, 31_LEU, 32_SER, 34_ILE, 35_ASN, 36_ASP, 4_SER, 5_TYR, 6_ASN, 7_ASP, 9_LYS",1.9
+4XVU_B,4XVU_D_light+4XVU_C_heavy,"246_PHE, 249_LEU, 250_TYR, 253_GLU, 254_ARG, 256_ILE, 257_GLN, 260_ILE, 301_GLN, 302_ILE, 304_GLU, 305_LEU, 306_TYR, 307_GLU, 308_ASP, 309_PHE",2.35
+4XVU_B,4XVU_F_light+4XVU_E_heavy,"322_ARG, 63_SER, 64_ASP, 67_GLY, 68_GLU, 69_LYS, 75_ARG, 78_THR",2.35
+4XVU_H,4XVU_J_light+4XVU_I_heavy,"246_PHE, 249_LEU, 250_TYR, 253_GLU, 254_ARG, 256_ILE, 257_GLN, 260_ILE, 301_GLN, 302_ILE, 304_GLU, 305_LEU, 306_TYR, 307_GLU, 308_ASP, 309_PHE",2.35
+4XVU_H,4XVU_L_light+4XVU_K_heavy,"322_ARG, 63_SER, 64_ASP, 67_GLY, 68_GLU, 69_LYS, 75_ARG",2.35
+4XVU_A,4XVU_D_light+4XVU_C_heavy,"322_ARG, 63_SER, 64_ASP, 65_ALA, 67_GLY, 68_GLU, 69_LYS, 78_THR",2.35
+4XVU_A,4XVU_F_light+4XVU_E_heavy,"246_PHE, 249_LEU, 250_TYR, 253_GLU, 254_ARG, 256_ILE, 257_GLN, 260_ILE, 298_TYR, 301_GLN, 302_ILE, 304_GLU, 305_LEU, 306_TYR, 307_GLU, 308_ASP, 309_PHE",2.35
+4XVU_G,4XVU_J_light+4XVU_I_heavy,"322_ARG, 63_SER, 64_ASP, 65_ALA, 67_GLY, 68_GLU, 69_LYS, 78_THR",2.35
+4XVU_G,4XVU_L_light+4XVU_K_heavy,"246_PHE, 249_LEU, 250_TYR, 253_GLU, 254_ARG, 256_ILE, 257_GLN, 260_ILE, 301_GLN, 302_ILE, 304_GLU, 305_LEU, 306_TYR, 307_GLU, 308_ASP, 309_PHE",2.35
+7N8H_K,7N8H_G_light+7N8H_H_heavy,"342_PHE, 368_LEU, 371_SER, 372_ALA, 373_SER, 374_PHE, 436_TRP, 441_LEU",2.3
+7N8H_K,7N8H_L_light+7N8H_M_heavy,"446_GLY, 449_TYR, 455_LEU, 456_PHE, 484_GLU, 485_GLY, 486_PHE, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER",2.3
+7N8H_K,7N8H_N_light+7N8H_O_heavy,"108_THR, 109_THR, 113_LYS, 236_THR, 237_ARG, 269_TYR, 28_TYR, 83_VAL, 85_PRO, 87_ASN",2.3
+7N8H_A,7N8H_D_light+7N8H_E_heavy,"446_GLY, 449_TYR, 455_LEU, 456_PHE, 484_GLU, 485_GLY, 486_PHE, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER",2.3
+7N8H_A,7N8H_L_light+7N8H_M_heavy,"342_PHE, 368_LEU, 371_SER, 372_ALA, 373_SER, 374_PHE, 436_TRP, 441_LEU",2.3
+7N8H_A,7N8H_B_light+7N8H_C_heavy,"108_THR, 109_THR, 113_LYS, 236_THR, 237_ARG, 269_TYR, 28_TYR, 83_VAL, 85_PRO, 87_ASN",2.3
+7N8H_F,7N8H_D_light+7N8H_E_heavy,"342_PHE, 368_LEU, 371_SER, 372_ALA, 373_SER, 374_PHE, 436_TRP, 441_LEU",2.3
+7N8H_F,7N8H_G_light+7N8H_H_heavy,"446_GLY, 449_TYR, 455_LEU, 456_PHE, 484_GLU, 485_GLY, 486_PHE, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER",2.3
+7N8H_F,7N8H_I_light+7N8H_J_heavy,"108_THR, 109_THR, 113_LYS, 236_THR, 237_ARG, 269_TYR, 28_TYR, 83_VAL, 85_PRO, 87_ASN",2.3
+5OTJ_D,5OTJ_L_light+5OTJ_H_heavy,"10_LYS, 11_ARG, 13_ASP, 14_THR, 15_ASN, 16_GLY, 7_ARG",2.35
+5OTJ_D,5OTJ_A_light+5OTJ_B_heavy,"19_LYS, 21_SER, 22_LEU, 23_SER, 38_ASP, 41_GLN, 42_ARG, 44_MET, 45_ALA, 46_GLU, 48_ASP, 49_THR, 50_ASP, 51_GLY, 52_ASP, 53_GLY, 54_PHE",2.35
+5OTJ_C,5OTJ_L_light+5OTJ_H_heavy,"19_LYS, 21_SER, 22_LEU, 23_SER, 38_ASP, 41_GLN, 42_ARG, 44_MET, 45_ALA, 46_GLU, 48_ASP, 49_THR, 50_ASP, 51_GLY, 52_ASP, 53_GLY, 54_PHE",2.35
+5OTJ_C,5OTJ_A_light+5OTJ_B_heavy,"10_LYS, 11_ARG, 13_ASP, 14_THR, 15_ASN, 16_GLY, 7_ARG",2.35
+5USL_A,5USL_C_light+5USL_B_heavy,"102_SER, 103_SER, 104_TYR, 105_GLU, 140_SER, 143_SER, 145_ASN, 146_THR, 147_SER, 148_ALA, 149_PRO, 32_LYS, 34_THR, 35_THR, 37_GLN, 39_THR, 46_ASN, 47_PHE, 48_LYS, 98_LYS",2.9
+5USL_X,5USL_L_light+5USL_H_heavy,"102_SER, 103_SER, 104_TYR, 105_GLU, 140_SER, 145_ASN, 146_THR, 147_SER, 148_ALA, 149_PRO, 151_ASP, 34_THR, 35_THR, 37_GLN, 38_ASN, 39_THR, 46_ASN, 47_PHE, 48_LYS, 96_TRP, 98_LYS",2.9
+2DQF_F,2DQF_A_light+2DQF_B_heavy,"65_ASN, 71_GLY, 72_SER, 73_ARG, 74_ASN, 77_ASN, 79_PRO",2.5
+2DQF_F,2DQF_D_light+2DQF_E_heavy,"100_SER, 101_ASP, 102_GLY, 15_HIS, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 62_TRP, 75_LEU, 93_ASN, 96_LYS, 97_LYS",2.5
+2DQF_C,2DQF_A_light+2DQF_B_heavy,"100_SER, 101_ASP, 102_GLY, 15_HIS, 16_GLY, 19_ASN, 20_TYR, 21_ARG, 62_TRP, 73_ARG, 75_LEU, 89_THR, 93_ASN, 96_LYS, 97_LYS",2.5
+2DQF_C,2DQF_D_light+2DQF_E_heavy,"65_ASN, 71_GLY, 72_SER, 73_ARG, 74_ASN, 77_ASN, 79_PRO",2.5
+7VYT_T,7VYT_C_light+7VYT_B_heavy,"100_VAL, 101_ASN, 107_PHE, 121_ARG, 123_PHE, 125_GLU, 128_GLU, 62_GLN",1.53
+7VYT_T,7VYT_L_light+7VYT_H_heavy,"111_HIS, 113_TYR, 114_PRO, 115_ASP, 116_GLY, 117_THR, 56_GLN, 58_ASN, 60_GLU, 62_GLN, 63_ASP, 65_LEU, 68_ILE, 70_ASN, 72_ASP, 73_LEU, 74_GLY, 76_HIS",1.53
+7VYT_A,7VYT_C_light+7VYT_B_heavy,"111_HIS, 113_TYR, 114_PRO, 115_ASP, 116_GLY, 117_THR, 56_GLN, 58_ASN, 60_GLU, 62_GLN, 63_ASP, 65_LEU, 68_ILE, 70_ASN, 72_ASP, 73_LEU, 74_GLY, 76_HIS",1.53
+7VYT_A,7VYT_L_light+7VYT_H_heavy,"100_VAL, 101_ASN, 103_THR, 104_GLY, 107_PHE, 121_ARG, 123_PHE, 125_GLU, 128_GLU, 37_LYS, 62_GLN",1.53
+6AZ2_D,6AZ2_F_light+6AZ2_A_heavy,"31_LEU, 32_GLU, 33_SER, 66_PRO, 67_VAL",2.48
+6AZ2_D,6AZ2_E_light+6AZ2_C_heavy,"10_LYS, 11_VAL, 12_LEU, 136_VAL, 13_ASN, 14_ASN, 15_PRO, 16_ALA, 17_LYS, 20_ASP, 21_PRO, 23_GLU, 25_GLU, 28_PHE, 60_ILE, 61_LEU, 62_VAL, 63_GLY, 64_PRO, 66_PRO, 70_ASN, 71_LYS, 72_PHE, 73_VAL, 75_SER",2.48
+6AZ2_B,6AZ2_F_light+6AZ2_A_heavy,"10_LYS, 11_VAL, 12_LEU, 136_VAL, 13_ASN, 14_ASN, 15_PRO, 16_ALA, 17_LYS, 20_ASP, 21_PRO, 23_GLU, 25_GLU, 28_PHE, 60_ILE, 61_LEU, 62_VAL, 63_GLY, 64_PRO, 66_PRO, 70_ASN, 71_LYS, 72_PHE, 73_VAL, 75_SER",2.48
+6AZ2_B,6AZ2_E_light+6AZ2_C_heavy,"31_LEU, 32_GLU, 33_SER, 66_PRO, 67_VAL, 68_GLY",2.48
+7LXZ_K,7LXZ_D_light+7LXZ_E_heavy,"342_PHE, 343_ASN, 368_LEU, 371_SER, 372_ALA, 373_SER, 374_PHE, 436_TRP, 440_ASN, 441_LEU",2.6
+7LXZ_K,7LXZ_L_light+7LXZ_M_heavy,"444_LYS, 446_GLY, 449_TYR, 452_LEU, 455_LEU, 456_PHE, 484_GLU, 485_GLY, 486_PHE, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER",2.6
+7LXZ_K,7LXZ_O_light+7LXZ_N_heavy,"144_TYR, 246_ARG, 247_SER, 248_TYR, 250_THR, 251_PRO, 252_GLY, 253_ASP, 254_SER, 255_SER, 257_GLY",2.6
+7LXZ_B,7LXZ_C_light+7LXZ_F_heavy,"444_LYS, 446_GLY, 449_TYR, 452_LEU, 455_LEU, 456_PHE, 484_GLU, 485_GLY, 486_PHE, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER",2.6
+7LXZ_B,7LXZ_L_light+7LXZ_M_heavy,"342_PHE, 343_ASN, 368_LEU, 371_SER, 372_ALA, 373_SER, 374_PHE, 436_TRP, 440_ASN, 441_LEU",2.6
+7LXZ_B,7LXZ_I_light+7LXZ_G_heavy,"144_TYR, 246_ARG, 247_SER, 248_TYR, 250_THR, 251_PRO, 252_GLY, 253_ASP, 254_SER, 255_SER, 257_GLY",2.6
+7LXZ_A,7LXZ_D_light+7LXZ_E_heavy,"444_LYS, 446_GLY, 449_TYR, 452_LEU, 455_LEU, 456_PHE, 484_GLU, 485_GLY, 486_PHE, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER",2.6
+7LXZ_A,7LXZ_C_light+7LXZ_F_heavy,"342_PHE, 343_ASN, 368_LEU, 371_SER, 372_ALA, 373_SER, 374_PHE, 436_TRP, 440_ASN, 441_LEU",2.6
+7LXZ_A,7LXZ_J_light+7LXZ_H_heavy,"144_TYR, 246_ARG, 247_SER, 248_TYR, 250_THR, 251_PRO, 252_GLY, 253_ASP, 254_SER, 255_SER, 257_GLY",2.6
+8V9D_A,8V9D_J_light+8V9D_I_heavy,"139_PRO, 140_SER, 141_VAL, 59_TRP, 63_GLU",2.9
+8V9D_C,8V9D_L_light+8V9D_K_heavy,"151_HIS, 152_GLY, 153_ASN, 155_THR, 62_THR, 63_GLU, 65_PRO",2.9
+8V9D_C,8V9D_N_light+8V9D_M_heavy,"139_PRO, 140_SER, 141_VAL, 59_TRP, 63_GLU",2.9
+8V9D_B,8V9D_J_light+8V9D_I_heavy,"151_HIS, 152_GLY, 153_ASN, 155_THR, 62_THR, 63_GLU, 65_PRO",2.9
+8V9D_B,8V9D_L_light+8V9D_K_heavy,"139_PRO, 140_SER, 141_VAL, 59_TRP, 63_GLU",2.9
+8V9D_D,8V9D_N_light+8V9D_M_heavy,"151_HIS, 152_GLY, 153_ASN, 155_THR, 62_THR, 63_GLU, 65_PRO",2.9
+7MZK_A,7MZK_E_light+7MZK_F_heavy,"403_ARG, 405_ASP, 415_THR, 416_GLY, 417_LYS, 421_TYR, 455_LEU, 456_PHE, 458_LYS, 473_TYR, 475_ALA, 476_GLY, 477_SER, 478_THR, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 505_TYR",2.25
+7MZK_A,7MZK_L_light+7MZK_H_heavy,"373_SER, 436_TRP, 437_ASN, 439_ASN, 440_ASN, 441_LEU, 443_SER, 444_LYS, 445_VAL, 446_GLY, 499_PRO, 500_THR",2.25
+7MZK_B,7MZK_D_light+7MZK_C_heavy,"405_ASP, 416_GLY, 417_LYS, 421_TYR, 455_LEU, 456_PHE, 458_LYS, 473_TYR, 475_ALA, 476_GLY, 477_SER, 478_THR, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 505_TYR",2.25
+7MZK_B,7MZK_M_light+7MZK_N_heavy,"373_SER, 436_TRP, 437_ASN, 439_ASN, 440_ASN, 441_LEU, 443_SER, 444_LYS, 445_VAL, 499_PRO, 500_THR",2.25
+6VMJ_W,6VMJ_A_light+6VMJ_B_heavy,"144_VAL, 145_ASN, 147_ALA, 150_ARG, 152_PRO, 153_ASP, 154_SER, 19_GLY, 20_ARG, 21_GLU",2.95
+6VMJ_W,6VMJ_I_light+6VMJ_J_heavy,"59_LEU, 61_ASP, 87_ARG, 88_ALA, 89_VAL, 90_PRO, 92_PRO, 95_GLN",2.95
+6VMJ_W,6VMJ_L_light+6VMJ_M_heavy,"164_ASP, 165_ARG, 166_ALA, 167_THR, 169_ASN, 170_ARG, 173_ASP, 174_GLY, 178_GLU, 223_ARG",2.95
+6VMJ_Z,6VMJ_A_light+6VMJ_B_heavy,"164_ASP, 165_ARG, 166_ALA, 167_THR, 169_ASN, 170_ARG, 173_ASP, 174_GLY, 178_GLU, 223_ARG",2.95
+6VMJ_Z,6VMJ_E_light+6VMJ_F_heavy,"59_LEU, 61_ASP, 87_ARG, 88_ALA, 89_VAL, 90_PRO, 92_PRO, 95_GLN",2.95
+6VMJ_Z,6VMJ_L_light+6VMJ_M_heavy,"144_VAL, 145_ASN, 147_ALA, 150_ARG, 152_PRO, 153_ASP, 154_SER, 156_GLN, 19_GLY, 20_ARG, 21_GLU",2.95
+6VMJ_X,6VMJ_E_light+6VMJ_F_heavy,"144_VAL, 145_ASN, 147_ALA, 150_ARG, 152_PRO, 153_ASP, 154_SER, 19_GLY, 20_ARG, 21_GLU, 75_GLN",2.95
+6VMJ_X,6VMJ_I_light+6VMJ_J_heavy,"164_ASP, 165_ARG, 166_ALA, 167_THR, 169_ASN, 170_ARG, 173_ASP, 174_GLY, 178_GLU, 223_ARG",2.95
+6VMJ_X,6VMJ_L_light+6VMJ_M_heavy,"59_LEU, 61_ASP, 87_ARG, 88_ALA, 89_VAL, 90_PRO, 92_PRO, 95_GLN",2.95
+6VMJ_Y,6VMJ_A_light+6VMJ_B_heavy,"59_LEU, 61_ASP, 87_ARG, 88_ALA, 89_VAL, 90_PRO, 92_PRO, 95_GLN",2.95
+6VMJ_Y,6VMJ_E_light+6VMJ_F_heavy,"164_ASP, 165_ARG, 166_ALA, 167_THR, 169_ASN, 170_ARG, 173_ASP, 174_GLY, 178_GLU, 223_ARG",2.95
+6VMJ_Y,6VMJ_I_light+6VMJ_J_heavy,"144_VAL, 145_ASN, 147_ALA, 150_ARG, 152_PRO, 153_ASP, 154_SER, 19_GLY, 20_ARG, 21_GLU",2.95
+4UTA_A,4UTA_M_light+4UTA_I_heavy,"101_TRP, 102_GLY, 103_ASN, 104_GLY, 105_CYS, 106_GLY, 113_ILE, 246_LYS, 247_LYS, 248_GLN, 249_ASP, 68_THR, 69_THR, 70_THR, 71_GLU, 72_SER, 73_ARG, 74_CYS, 77_GLN, 83_ASN, 84_GLU, 97_VAL, 99_ARG",3.0
+4UTA_B,4UTA_L_light+4UTA_H_heavy,"101_TRP, 102_GLY, 103_ASN, 104_GLY, 105_CYS, 106_GLY, 113_ILE, 246_LYS, 247_LYS, 248_GLN, 249_ASP, 68_THR, 69_THR, 70_THR, 71_GLU, 72_SER, 73_ARG, 74_CYS, 77_GLN, 83_ASN, 84_GLU, 97_VAL, 99_ARG",3.0
+3NH7_C,3NH7_N_light+3NH7_J_heavy,"43_HIS, 62_ILE, 64_GLU, 67_ASP, 78_MET, 79_LYS, 81_GLU, 84_ASP, 86_GLN, 90_SER, 91_PRO, 92_LYS, 93_ALA, 94_GLN, 95_LEU, 96_ARG, 97_ARG",2.7
+3NH7_D,3NH7_M_light+3NH7_I_heavy,"103_ARG, 35_PHE, 58_HIS, 80_TYR, 82_GLY, 83_SER, 85_PHE, 86_GLN, 87_CYS, 88_LYS, 89_ASP",2.7
+3NH7_D,3NH7_O_light+3NH7_K_heavy,"43_HIS, 60_PHE, 62_ILE, 64_GLU, 67_ASP, 74_ALA, 77_CYS, 78_MET, 79_LYS, 80_TYR, 81_GLU, 84_ASP, 86_GLN, 90_SER, 91_PRO, 92_LYS, 93_ALA, 94_GLN, 95_LEU, 96_ARG, 97_ARG",2.7
+3NH7_B,3NH7_M_light+3NH7_I_heavy,"43_HIS, 60_PHE, 62_ILE, 64_GLU, 67_ASP, 74_ALA, 77_CYS, 78_MET, 79_LYS, 80_TYR, 81_GLU, 84_ASP, 86_GLN, 90_SER, 91_PRO, 92_LYS, 93_ALA, 94_GLN, 95_LEU, 96_ARG, 97_ARG",2.7
+3NH7_B,3NH7_O_light+3NH7_K_heavy,"103_ARG, 35_PHE, 58_HIS, 80_TYR, 82_GLY, 83_SER, 85_PHE, 86_GLN, 87_CYS, 88_LYS, 89_ASP",2.7
+3NH7_A,3NH7_L_light+3NH7_H_heavy,"43_HIS, 60_PHE, 62_ILE, 64_GLU, 67_ASP, 74_ALA, 77_CYS, 78_MET, 79_LYS, 80_TYR, 81_GLU, 84_ASP, 86_GLN, 90_SER, 91_PRO, 92_LYS, 93_ALA, 94_GLN, 95_LEU, 96_ARG, 97_ARG",2.7
+4HC1_A,4HC1_L_light+4HC1_H_heavy,"34_ALA, 35_SER, 37_GLN, 40_GLY, 41_LEU, 42_ASP, 43_THR, 44_SER, 45_LEU, 46_GLY, 47_ALA, 48_VAL, 49_GLN, 50_SER, 51_ASP, 52_THR, 65_SER",2.87
+4HC1_B,4HC1_N_light+4HC1_M_heavy,"35_SER, 37_GLN, 40_GLY, 41_LEU, 42_ASP, 43_THR, 44_SER, 45_LEU, 46_GLY, 47_ALA, 48_VAL, 49_GLN, 50_SER, 51_ASP, 52_THR",2.87
+3LH2_T,3LH2_M_light+3LH2_I_heavy,"15_ASN, 16_TRP, 17_PHE, 18_ASP, 19_ILE, 20_THR, 21_GLY, 24_TRP, 33_ILE, 36_SER",2.65
+3LH2_T,3LH2_N_light+3LH2_J_heavy,"37_ASP, 38_VAL, 39_GLN, 40_ALA, 42_VAL, 63_PHE, 64_SER, 65_GLY",2.65
+3LH2_V,3LH2_L_light+3LH2_H_heavy,"37_ASP, 38_VAL, 39_GLN, 40_ALA, 42_VAL, 63_PHE, 64_SER, 65_GLY",2.65
+3LH2_V,3LH2_O_light+3LH2_K_heavy,"15_ASN, 16_TRP, 17_PHE, 18_ASP, 19_ILE, 20_THR, 21_GLY, 24_TRP, 33_ILE, 36_SER",2.65
+3LH2_U,3LH2_M_light+3LH2_I_heavy,"37_ASP, 39_GLN, 40_ALA, 63_PHE, 64_SER",2.65
+3LH2_U,3LH2_N_light+3LH2_J_heavy,"15_ASN, 16_TRP, 17_PHE, 18_ASP, 19_ILE, 20_THR, 21_GLY, 24_TRP, 36_SER",2.65
+3LH2_S,3LH2_L_light+3LH2_H_heavy,"15_ASN, 16_TRP, 17_PHE, 19_ILE, 20_THR, 21_GLY, 23_LEU, 24_TRP, 33_ILE, 36_SER, 38_VAL",2.65
+3LH2_S,3LH2_O_light+3LH2_K_heavy,"37_ASP, 38_VAL, 39_GLN, 40_ALA, 42_VAL, 63_PHE, 64_SER",2.65
+8DAO_F,8DAO_D_light+8DAO_C_heavy,"108_ARG, 140_TYR, 142_ARG, 165_GLU, 166_GLN, 171_SER, 173_TYR",2.8
+8DAO_F,8DAO_B_light+8DAO_A_heavy,"151_ASP, 152_ASN, 153_ALA, 154_LEU, 155_GLN, 182_SER, 184_ALA, 185_ASP, 188_LYS, 189_HIS",2.8
+8DAO_H,8DAO_D_light+8DAO_C_heavy,"151_ASP, 153_ALA, 154_LEU, 155_GLN, 182_SER, 184_ALA, 185_ASP, 188_LYS, 189_HIS",2.8
+8DAO_H,8DAO_B_light+8DAO_A_heavy,"108_ARG, 140_TYR, 165_GLU, 166_GLN, 171_SER, 173_TYR",2.8
+7S13_C,7S13_L_light+7S13_H_heavy,"123_TYR, 124_PRO, 125_GLU, 49_GLU, 50_LYS, 52_PHE, 96_LYS, 97_GLY",2.12
+7S13_C,7S13_M_light+7S13_I_heavy,"71_TYR, 73_PRO, 74_GLN, 75_TYR, 76_GLY, 77_LEU, 78_TYR, 79_CYS, 81_GLN, 82_GLU, 83_HIS, 86_GLU, 90_ALA, 91_ALA",2.12
+7S13_D,7S13_L_light+7S13_H_heavy,"71_TYR, 73_PRO, 74_GLN, 75_TYR, 76_GLY, 77_LEU, 78_TYR, 79_CYS, 81_GLN, 82_GLU, 83_HIS, 86_GLU, 91_ALA",2.12
+7S13_D,7S13_M_light+7S13_I_heavy,"123_TYR, 124_PRO, 125_GLU, 50_LYS, 52_PHE, 96_LYS",2.12
+2FJG_W,2FJG_L_light+2FJG_H_heavy,"106_PRO, 17_PHE, 18_MET, 21_TYR, 22_GLN, 25_TYR, 62_ASN, 63_ASP, 64_GLU, 66_LEU",2.8
+2FJG_W,2FJG_A_light+2FJG_B_heavy,"48_LYS, 81_MET, 83_ILE, 84_LYS, 85_PRO, 86_HIS, 87_GLN, 88_GLY, 89_GLN, 90_HIS, 91_ILE",2.8
+2FJG_V,2FJG_L_light+2FJG_H_heavy,"48_LYS, 81_MET, 83_ILE, 84_LYS, 86_HIS, 87_GLN, 88_GLY, 89_GLN, 90_HIS, 91_ILE",2.8
+2FJG_V,2FJG_A_light+2FJG_B_heavy,"106_PRO, 18_MET, 21_TYR, 22_GLN, 25_TYR, 62_ASN, 63_ASP, 64_GLU, 66_LEU",2.8
+6WO5_E,6WO5_B_light+6WO5_A_heavy,"510_THR, 514_VAL, 522_SER, 523_GLY, 524_ALA, 525_PRO, 538_VAL, 540_ASN, 541_ASN, 542_THR, 543_ARG, 545_PRO, 546_LEU, 547_GLY, 548_ASN, 549_TRP",2.62
+6WO5_E,6WO5_L_light+6WO5_H_heavy,"425_THR, 427_LEU, 428_ASN, 429_CYS, 431_GLU, 433_LEU, 434_ASN, 435_THR, 436_GLY, 438_LEU, 503_CYS, 504_GLY, 529_TRP, 531_ALA, 613_TYR, 616_TRP",2.62
+6WO5_F,6WO5_D_light+6WO5_C_heavy,"510_THR, 514_VAL, 522_SER, 523_GLY, 524_ALA, 525_PRO, 527_TYR, 538_VAL, 540_ASN, 541_ASN, 542_THR, 543_ARG, 545_PRO, 546_LEU, 547_GLY, 548_ASN, 549_TRP",2.62
+6WO5_F,6WO5_I_light+6WO5_G_heavy,"425_THR, 427_LEU, 428_ASN, 429_CYS, 431_GLU, 433_LEU, 434_ASN, 435_THR, 436_GLY, 438_LEU, 503_CYS, 504_GLY, 529_TRP, 531_ALA, 613_TYR, 616_TRP",2.62
+4XTR_B,4XTR_D_light+4XTR_C_heavy,"246_PHE, 249_LEU, 250_TYR, 253_GLU, 254_ARG, 256_ILE, 257_GLN, 260_ILE, 301_GLN, 302_ILE, 304_GLU, 305_LEU, 306_TYR, 307_GLU, 308_ASP, 309_PHE",2.05
+4XTR_B,4XTR_F_light+4XTR_E_heavy,"322_ARG, 63_SER, 64_ASP, 67_GLY, 68_GLU, 69_LYS, 75_ARG, 78_THR",2.05
+4XTR_A,4XTR_D_light+4XTR_C_heavy,"322_ARG, 63_SER, 64_ASP, 65_ALA, 67_GLY, 68_GLU, 69_LYS, 75_ARG",2.05
+4XTR_A,4XTR_F_light+4XTR_E_heavy,"246_PHE, 249_LEU, 250_TYR, 253_GLU, 254_ARG, 256_ILE, 257_GLN, 260_ILE, 301_GLN, 302_ILE, 304_GLU, 305_LEU, 306_TYR, 307_GLU, 308_ASP, 309_PHE",2.05
+7KEO_D,7KEO_B_light+7KEO_A_heavy,"11_LYS, 34_GLU, 36_ILE, 37_PRO, 39_ASP, 40_GLN, 69_LEU, 70_VAL, 71_LEU, 72_ARG, 73_LEU, 74_ARG, 75_GLY, 76_GLY, 7_THR, 8_LEU, 9_THR",2.9
+7KEO_G,7KEO_F_light+7KEO_E_heavy,"16_GLU, 18_GLU, 20_SER, 21_ASP, 22_THR, 24_GLU, 25_ASN, 28_ALA, 29_LYS",2.9
+7KEO_H,7KEO_F_light+7KEO_E_heavy,"34_GLU, 36_ILE, 37_PRO, 39_ASP, 40_GLN, 69_LEU, 70_VAL, 71_LEU, 72_ARG, 73_LEU, 74_ARG, 75_GLY, 76_GLY, 7_THR, 8_LEU, 9_THR",2.9
+7KEO_C,7KEO_B_light+7KEO_A_heavy,"16_GLU, 18_GLU, 20_SER, 21_ASP, 22_THR, 24_GLU, 25_ASN, 28_ALA, 29_LYS",2.9
+7UOT_b,7UOT_P_light+7UOT_O_heavy,"264_TRP, 265_THR, 267_SER, 321_GLN, 324_GLN, 325_ARG",2.77
+7UOT_b,7UOT_R_light+7UOT_Q_heavy,"352_LYS, 356_ARG, 361_ILE, 362_PRO, 384_LYS, 387_LEU, 394_LEU, 395_ASN, 396_GLU, 397_THR, 398_HIS, 399_PHE, 400_SER, 401_ASP, 402_ASP, 404_GLU",2.77
+7UOT_c,7UOT_T_light+7UOT_S_heavy,"352_LYS, 356_ARG, 361_ILE, 362_PRO, 384_LYS, 387_LEU, 394_LEU, 395_ASN, 396_GLU, 397_THR, 398_HIS, 399_PHE, 400_SER, 401_ASP, 404_GLU",2.77
+7UOT_c,7UOT_R_light+7UOT_Q_heavy,"264_TRP, 265_THR, 267_SER, 321_GLN, 324_GLN, 325_ARG",2.77
+7UOT_a,7UOT_T_light+7UOT_S_heavy,"264_TRP, 265_THR, 267_SER, 321_GLN, 324_GLN, 325_ARG",2.77
+7UOT_a,7UOT_P_light+7UOT_O_heavy,"352_LYS, 356_ARG, 361_ILE, 362_PRO, 384_LYS, 387_LEU, 394_LEU, 395_ASN, 396_GLU, 397_THR, 398_HIS, 399_PHE, 400_SER, 401_ASP, 402_ASP",2.77
+7SX7_G,7SX7_L_light+7SX7_H_heavy,"123_THR, 124_GLY, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 354_ASN, 365_SER, 366_GLY, 367_GLY, 368_ASP, 371_ILE, 427_TRP, 430_THR, 455_THR, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_ALA, 461_ASN, 462_ASN, 469_ARG, 472_GLY, 473_GLY, 474_ASN, 97_LYS",2.15
+7SX7_A,7SX7_C_light+7SX7_B_heavy,"124_GLY, 276_ASN, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 371_ILE, 427_TRP, 430_THR, 455_THR, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_ALA, 461_ASN, 469_ARG, 473_GLY, 97_LYS",2.15
+8TFT_G,8TFT_L_light+8TFT_H_heavy,"12_ASN, 52_ARG, 53_ASP, 54_VAL, 57_TRP, 62_TRP, 63_LEU, 64_ASN, 65_GLY, 66_ASP, 67_PHE, 68_ARG, 69_LYS, 77_GLU",2.3
+8TFT_F,8TFT_M_light+8TFT_I_heavy,"12_ASN, 52_ARG, 53_ASP, 54_VAL, 57_TRP, 62_TRP, 63_LEU, 64_ASN, 65_GLY, 66_ASP, 67_PHE, 68_ARG, 69_LYS, 77_GLU",2.3
+7WWL_A,7WWL_N_light+7WWL_J_heavy,"343_ASN, 436_TRP, 437_ASN, 438_SER, 439_ASN, 440_ASN, 441_LEU, 445_VAL, 499_PRO, 500_THR, 501_ASN, 503_VAL, 508_TYR",3.0
+7WWL_B,7WWL_L_light+7WWL_H_heavy,"446_GLY, 449_TYR, 486_PHE, 493_GLN, 494_SER, 498_GLN, 502_GLY, 505_TYR",3.0
+7WWL_B,7WWL_M_light+7WWL_I_heavy,"343_ASN, 436_TRP, 437_ASN, 438_SER, 439_ASN, 440_ASN, 441_LEU, 445_VAL, 499_PRO, 500_THR, 501_ASN, 503_VAL, 506_GLN, 508_TYR",3.0
+7WWL_C,7WWL_L_light+7WWL_H_heavy,"343_ASN, 436_TRP, 437_ASN, 438_SER, 439_ASN, 440_ASN, 441_LEU, 445_VAL, 499_PRO, 500_THR, 501_ASN, 502_GLY, 503_VAL, 506_GLN, 508_TYR",3.0
+7WWL_C,7WWL_N_light+7WWL_J_heavy,"449_TYR, 486_PHE, 493_GLN, 494_SER, 498_GLN, 502_GLY, 505_TYR",3.0
+5N7W_Y,5N7W_L_light+5N7W_H_heavy,"112_LEU, 15_ASP, 16_LYS, 17_ASN, 18_PHE, 20_ARG",1.96
+5N7W_X,5N7W_B_light+5N7W_A_heavy,"112_LEU, 15_ASP, 16_LYS, 17_ASN, 18_PHE, 20_ARG",1.96
+6PIS_A,6PIS_L_light+6PIS_H_heavy,"78_CYS, 79_VAL, 80_SER, 83_SER, 90_LEU",2.77
+6PIS_A,6PIS_M_light+6PIS_I_heavy,"57_HIS, 60_GLN, 63_LYS, 64_LYS, 67_HIS, 68_GLY, 71_GLN, 72_PHE, 74_ARG, 75_ASP, 76_HIS, 77_PRO",2.77
+6PIS_B,6PIS_L_light+6PIS_H_heavy,"60_GLN, 64_LYS, 67_HIS, 68_GLY, 71_GLN, 72_PHE, 74_ARG, 75_ASP, 76_HIS, 77_PRO",2.77
+6PIS_B,6PIS_M_light+6PIS_I_heavy,"139_HIS, 78_CYS, 79_VAL, 80_SER, 82_LYS, 83_SER, 90_LEU",2.77
+4YK4_E,4YK4_Y_light+4YK4_Z_heavy,"135_VAL, 136_SER, 137_ALA, 153_TRP, 159_GLY, 190_ASP, 192_ARG, 193_ALA, 194_LEU, 196_HIS, 198_GLU, 226_GLN",2.8
+4YK4_A,4YK4_B_light+4YK4_C_heavy,"135_VAL, 136_SER, 137_ALA, 153_TRP, 159_GLY, 190_ASP, 192_ARG, 193_ALA, 194_LEU, 196_HIS, 198_GLU, 226_GLN",2.8
+7WWM_C,7WWM_N_light+7WWM_J_heavy,"343_ASN, 373_SER, 374_PHE, 436_TRP, 440_ASN, 441_LEU, 444_LYS, 445_VAL, 499_PRO, 501_ASN, 506_GLN",2.8
+7WWM_B,7WWM_M_light+7WWM_I_heavy,"343_ASN, 373_SER, 374_PHE, 436_TRP, 440_ASN, 441_LEU, 444_LYS, 445_VAL, 499_PRO, 501_ASN, 506_GLN",2.8
+7WWM_A,7WWM_L_light+7WWM_H_heavy,"343_ASN, 373_SER, 374_PHE, 436_TRP, 440_ASN, 441_LEU, 444_LYS, 445_VAL, 499_PRO, 501_ASN, 506_GLN",2.8
+6UYD_E,6UYD_L_light+6UYD_H_heavy,"427_LEU, 428_ASN, 429_CYS, 431_GLU, 436_GLY, 438_LEU, 439_ALA, 441_LEU, 442_PHE, 443_TYR, 529_TRP",1.9
+6UYD_E,6UYD_B_light+6UYD_A_heavy,"606_ARG, 625_THR, 627_PHE, 628_LYS, 629_VAL, 630_ARG, 644_CYS",1.9
+6UYD_F,6UYD_L_light+6UYD_H_heavy,"606_ARG, 607_CYS, 625_THR, 627_PHE, 628_LYS, 629_VAL, 630_ARG, 639_ARG, 644_CYS",1.9
+6UYD_F,6UYD_B_light+6UYD_A_heavy,"422_ILE, 427_LEU, 429_CYS, 431_GLU, 436_GLY, 438_LEU, 439_ALA, 441_LEU, 442_PHE, 443_TYR, 529_TRP",1.9
+8BG1_I,8BG1_H_light+8BG1_G_heavy,"368_LEU, 369_TYR, 370_ASN, 371_SER, 372_ALA, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 383_SER, 384_PRO, 385_THR, 386_LYS",2.88
+8BG1_F,8BG1_E_light+8BG1_D_heavy,"369_TYR, 370_ASN, 371_SER, 372_ALA, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 383_SER, 384_PRO, 385_THR",2.88
+8BG1_L,8BG1_K_light+8BG1_J_heavy,"369_TYR, 370_ASN, 371_SER, 372_ALA, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 383_SER, 384_PRO, 385_THR, 386_LYS",2.88
+8BG1_C,8BG1_B_light+8BG1_A_heavy,"369_TYR, 370_ASN, 371_SER, 372_ALA, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 383_SER, 384_PRO, 385_THR",2.88
+7RKS_S,7RKS_M_light+7RKS_I_heavy,"355_LEU, 356_TYR, 358_SER, 361_PHE, 362_SER, 364_PHE, 365_LYS, 366_CYS, 367_TYR, 368_GLY, 369_VAL, 370_SER, 371_ALA, 372_THR, 373_LYS, 395_ARG, 399_PRO, 414_ASP, 415_ASP, 416_PHE, 489_ILE, 494_TYR",2.7
+7RKS_R,7RKS_L_light+7RKS_H_heavy,"355_LEU, 356_TYR, 359_THR, 361_PHE, 364_PHE, 365_LYS, 366_CYS, 367_TYR, 368_GLY, 369_VAL, 370_SER, 371_ALA, 372_THR, 373_LYS, 392_ASP, 395_ARG, 399_PRO, 401_GLN, 414_ASP, 415_ASP, 416_PHE, 417_MET, 489_ILE, 494_TYR",2.7
+6KZ0_D,6KZ0_F_light+6KZ0_E_heavy,"0_SER, 10_LEU, 12_ARG, 13_LYS, 14_ASN, 153_THR, 154_GLY, 1_GLY, 2_PRO, 5_GLU, 6_PHE, 84_ARG, 86_ILE, 89_PHE, 9_SER",2.4
+6KZ0_J,6KZ0_L_light+6KZ0_K_heavy,"-1_GLY, 0_SER, 10_LEU, 12_ARG, 13_LYS, 14_ASN, 153_THR, 154_GLY, 1_GLY, 2_PRO, 5_GLU, 6_PHE, 84_ARG, 86_ILE, 89_PHE, 9_SER",2.4
+6KZ0_A,6KZ0_C_light+6KZ0_B_heavy,"0_SER, 10_LEU, 12_ARG, 13_LYS, 14_ASN, 153_THR, 154_GLY, 1_GLY, 2_PRO, 5_GLU, 6_PHE, 84_ARG, 85_ASP, 86_ILE, 89_PHE, 9_SER",2.4
+6KZ0_G,6KZ0_I_light+6KZ0_H_heavy,"0_SER, 12_ARG, 13_LYS, 14_ASN, 153_THR, 154_GLY, 1_GLY, 2_PRO, 5_GLU, 6_PHE, 84_ARG, 85_ASP, 86_ILE, 89_PHE, 9_SER",2.4
+5GZN_B,5GZN_D_light+5GZN_C_heavy,"136_GLU, 138_ARG, 140_MET, 156_THR, 159_GLU, 166_LYS, 168_GLU, 276_GLU, 277_MET, 278_ASP, 279_GLY, 280_ALA, 281_LYS, 283_ARG, 46_VAL, 47_THR, 52_ASN",3.0
+5GZN_G,5GZN_D_light+5GZN_C_heavy,"100_GLY, 101_TRP, 102_GLY, 103_ASN, 108_PHE, 110_LYS, 247_ASP, 98_ASP",3.0
+5GZN_A,5GZN_L_light+5GZN_H_heavy,"136_GLU, 138_ARG, 140_MET, 156_THR, 158_HIS, 159_GLU, 166_LYS, 168_GLU, 276_GLU, 277_MET, 278_ASP, 279_GLY, 280_ALA, 281_LYS, 283_ARG, 46_VAL, 47_THR, 52_ASN",3.0
+5GZN_E,5GZN_L_light+5GZN_H_heavy,"100_GLY, 101_TRP, 102_GLY, 103_ASN, 108_PHE, 110_LYS, 247_ASP, 98_ASP",3.0
+5O14_A,5O14_L_light+5O14_H_heavy,"161_ASP, 163_GLY, 180_LYS, 181_ILE, 182_GLU, 183_HIS, 184_LEU, 185_LYS, 187_PRO, 190_ASN, 191_VAL, 192_ASP, 213_LEU, 214_TYR, 215_ASN, 216_GLN, 243_VAL",2.2
+5O14_B,5O14_D_light+5O14_C_heavy,"161_ASP, 163_GLY, 180_LYS, 181_ILE, 182_GLU, 183_HIS, 185_LYS, 187_PRO, 190_ASN, 191_VAL, 192_ASP, 213_LEU, 214_TYR, 215_ASN, 216_GLN, 243_VAL",2.2
+6XKP_A,6XKP_L_light+6XKP_H_heavy,"346_ARG, 347_PHE, 349_SER, 351_TYR, 444_LYS, 446_GLY, 447_GLY, 448_ASN, 449_TYR, 450_ASN, 451_TYR, 452_LEU, 470_THR, 484_GLU, 490_PHE, 492_LEU, 493_GLN, 494_SER",2.72
+6XKP_A,6XKP_N_light+6XKP_M_heavy,"462_LYS, 465_GLU, 466_ARG, 468_ILE, 469_SER",2.72
+6XKP_B,6XKP_L_light+6XKP_H_heavy,"462_LYS, 465_GLU, 466_ARG, 468_ILE, 469_SER, 519_HIS",2.72
+6XKP_B,6XKP_N_light+6XKP_M_heavy,"346_ARG, 347_PHE, 349_SER, 444_LYS, 446_GLY, 447_GLY, 448_ASN, 449_TYR, 450_ASN, 451_TYR, 452_LEU, 470_THR, 484_GLU, 490_PHE, 492_LEU, 494_SER",2.72
+7TUF_C,7TUF_L_light+7TUF_H_heavy,"157_LEU, 216_VAL, 218_PHE, 219_ALA, 220_ALA, 237_TRP",2.8
+7TUF_C,7TUF_B_light+7TUF_A_heavy,"165_MET, 166_PRO, 168_THR, 171_ALA, 172_ALA, 173_SER, 174_SER, 175_LEU, 176_ALA, 177_PRO, 223_ASP, 225_GLU, 226_PRO, 227_TRP, 228_GLY, 229_PRO, 230_TRP",2.8
+7TUF_D,7TUF_L_light+7TUF_H_heavy,"165_MET, 166_PRO, 167_PRO, 168_THR, 171_ALA, 172_ALA, 173_SER, 174_SER, 175_LEU, 176_ALA, 177_PRO, 223_ASP, 225_GLU, 226_PRO, 227_TRP, 228_GLY, 229_PRO, 230_TRP",2.8
+7TUF_D,7TUF_B_light+7TUF_A_heavy,"157_LEU, 216_VAL, 218_PHE, 220_ALA, 237_TRP",2.8
+6IEK_D,6IEK_F_light+6IEK_E_heavy,"120_PRO, 138_SER, 139_LEU, 13_HIS, 140_LYS, 141_LYS, 20_THR, 21_GLN, 23_ASP, 24_ALA, 27_LYS, 28_PRO",2.7
+6IEK_A,6IEK_C_light+6IEK_B_heavy,"11_LYS, 120_PRO, 121_LYS, 138_SER, 139_LEU, 13_HIS, 141_LYS, 20_THR, 21_GLN, 23_ASP, 242_LYS, 24_ALA, 27_LYS, 28_PRO",2.7
+7MZJ_B,7MZJ_D_light+7MZJ_C_heavy,"403_ARG, 405_ASP, 417_LYS, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 458_LYS, 473_TYR, 475_ALA, 476_GLY, 477_SER, 478_THR, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 505_TYR",2.4
+7MZJ_B,7MZJ_M_light+7MZJ_N_heavy,"345_THR, 346_ARG, 351_TYR, 352_ALA, 440_ASN, 441_LEU, 442_ASP, 443_SER, 444_LYS, 445_VAL, 446_GLY, 447_GLY, 448_ASN, 449_TYR, 450_ASN, 452_LEU, 470_THR, 472_ILE, 484_GLU, 490_PHE, 494_SER",2.4
+7MZJ_A,7MZJ_E_light+7MZJ_F_heavy,"403_ARG, 405_ASP, 417_LYS, 421_TYR, 455_LEU, 456_PHE, 458_LYS, 473_TYR, 475_ALA, 476_GLY, 477_SER, 478_THR, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 505_TYR",2.4
+7MZJ_A,7MZJ_L_light+7MZJ_H_heavy,"345_THR, 346_ARG, 351_TYR, 352_ALA, 440_ASN, 441_LEU, 442_ASP, 443_SER, 444_LYS, 445_VAL, 446_GLY, 447_GLY, 448_ASN, 449_TYR, 450_ASN, 452_LEU, 468_ILE, 470_THR, 472_ILE, 490_PHE, 493_GLN, 494_SER",2.4
+7SU0_C,7SU0_L_light+7SU0_H_heavy,"102_PRO, 103_PRO, 104_TYR, 105_TYR, 106_LEU, 107_GLY, 108_ILE, 2_ALA, 33_GLU, 35_ARG, 39_LEU, 3_MET, 41_GLN, 46_VAL, 47_THR, 48_GLU, 4_HIS, 5_VAL, 95_LYS, 97_GLU, 99_MET",2.41
+7SU0_D,7SU0_M_light+7SU0_I_heavy,"100_TYR, 102_PRO, 103_PRO, 104_TYR, 105_TYR, 106_LEU, 108_ILE, 2_ALA, 33_GLU, 35_ARG, 39_LEU, 3_MET, 44_SER, 45_GLN, 46_VAL, 48_GLU, 4_HIS, 95_LYS, 97_GLU, 99_MET",2.41
+4KXZ_D,4KXZ_L_light+4KXZ_H_heavy,"100_GLN, 102_SER, 103_ASN, 110_LYS, 55_ASP, 81_GLN, 83_LEU, 85_PRO, 97_LYS, 99_GLU",2.83
+4KXZ_D,4KXZ_M_light+4KXZ_N_heavy,"50_TYR, 51_LEU, 57_GLN, 60_ARG, 64_LEU, 67_THR",2.83
+4KXZ_D,4KXZ_P_light+4KXZ_Q_heavy,"28_LEU, 29_GLY, 30_TRP, 31_LYS, 32_TRP, 90_TYR, 92_ILE, 93_GLY, 94_LYS, 99_GLU",2.83
+4KXZ_A,4KXZ_L_light+4KXZ_H_heavy,"101_LEU, 28_LEU, 29_GLY, 30_TRP, 31_LYS, 32_TRP, 90_TYR, 92_ILE, 93_GLY, 94_LYS",2.83
+4KXZ_A,4KXZ_I_light+4KXZ_J_heavy,"50_TYR, 51_LEU, 57_GLN, 60_ARG, 64_LEU, 67_THR",2.83
+4KXZ_B,4KXZ_L_light+4KXZ_H_heavy,"50_TYR, 51_LEU, 57_GLN, 60_ARG, 67_THR",2.83
+4KXZ_B,4KXZ_I_light+4KXZ_J_heavy,"101_LEU, 28_LEU, 29_GLY, 30_TRP, 31_LYS, 32_TRP, 90_TYR, 92_ILE, 93_GLY, 94_LYS, 99_GLU",2.83
+4KXZ_B,4KXZ_M_light+4KXZ_N_heavy,"100_GLN, 102_SER, 103_ASN, 110_LYS, 55_ASP, 81_GLN, 83_LEU, 85_PRO, 97_LYS, 99_GLU",2.83
+4KXZ_E,4KXZ_M_light+4KXZ_N_heavy,"28_LEU, 29_GLY, 30_TRP, 31_LYS, 32_TRP, 90_TYR, 92_ILE, 93_GLY, 94_LYS, 99_GLU",2.83
+4KXZ_E,4KXZ_P_light+4KXZ_Q_heavy,"50_TYR, 51_LEU, 57_GLN, 60_ARG, 64_LEU, 67_THR",2.83
+8UDZ_A,8UDZ_D_light+8UDZ_C_heavy,"298_LEU, 299_TYR, 301_ASP, 303_ARG, 304_LYS, 305_ASP, 306_LEU, 307_GLY",2.21
+8UDZ_A,8UDZ_F_light+8UDZ_E_heavy,"37_ASP, 39_GLU, 40_LEU, 43_ARG, 46_ILE, 47_GLU",2.21
+8UDZ_B,8UDZ_D_light+8UDZ_C_heavy,"37_ASP, 39_GLU, 40_LEU, 42_LYS, 43_ARG, 46_ILE, 47_GLU, 50_ARG",2.21
+8UDZ_B,8UDZ_F_light+8UDZ_E_heavy,"298_LEU, 299_TYR, 301_ASP, 303_ARG, 304_LYS, 305_ASP, 306_LEU, 307_GLY, 309_LYS",2.21
+8XN9_C,8XN9_E_light+8XN9_G_heavy,"193_LYS, 196_GLU, 200_ASP, 204_SER, 69_SER, 70_GLN, 73_GLY, 74_SER",1.99
+8XN9_C,8XN9_F_light+8XN9_I_heavy,"162_GLN, 163_GLU, 170_TYR, 241_THR, 244_ARG",1.99
+8XN9_B,8XN9_D_light+8XN9_H_heavy,"162_GLN, 163_GLU, 170_TYR, 241_THR, 244_ARG",1.99
+8XN9_B,8XN9_F_light+8XN9_I_heavy,"193_LYS, 196_GLU, 200_ASP, 204_SER, 69_SER, 70_GLN, 73_GLY, 74_SER",1.99
+8XN9_A,8XN9_E_light+8XN9_G_heavy,"162_GLN, 163_GLU, 170_TYR, 241_THR, 244_ARG",1.99
+8XN9_A,8XN9_D_light+8XN9_H_heavy,"193_LYS, 196_GLU, 197_LEU, 200_ASP, 204_SER, 69_SER, 70_GLN, 73_GLY, 74_SER",1.99
+5VQM_A,5VQM_L_light+5VQM_H_heavy,"116_ASN, 322_LYS, 323_TYR, 324_LYS, 325_GLU, 328_PRO, 340_GLU, 341_GLU, 343_GLN, 344_SER, 347_GLU, 348_SER, 350_LEU, 351_ALA, 352_SER, 353_LYS, 354_SER, 355_ASP",2.79
+5VQM_B,5VQM_C_light+5VQM_D_heavy,"116_ASN, 120_ASP, 322_LYS, 323_TYR, 324_LYS, 325_GLU, 328_PRO, 340_GLU, 341_GLU, 343_GLN, 344_SER, 347_GLU, 348_SER, 350_LEU, 351_ALA, 352_SER, 353_LYS, 354_SER, 355_ASP",2.79
+7DM1_B,7DM1_E_light+7DM1_F_heavy,"134_THR, 136_LYS, 137_THR, 139_ASP, 140_ASP, 141_PRO, 142_GLN, 144_ALA, 145_ALA, 152_LEU, 156_ALA",2.1
+7DM1_A,7DM1_C_light+7DM1_D_heavy,"134_THR, 136_LYS, 137_THR, 139_ASP, 140_ASP, 141_PRO, 142_GLN, 144_ALA, 145_ALA, 148_PRO, 152_LEU, 156_ALA",2.1
+7K39_E,7K39_K_light+7K39_L_heavy,"269_ARG, 270_SER, 271_ASP, 272_ALA, 273_PRO, 284_PRO, 285_ASN, 48_THR, 49_GLY, 50_LYS, 91_SER, 92_LYS",3.0
+7K39_C,7K39_I_light+7K39_J_heavy,"269_ARG, 270_SER, 271_ASP, 272_ALA, 273_PRO, 284_PRO, 285_ASN, 48_THR, 49_GLY, 50_LYS, 91_SER, 92_LYS",3.0
+7K39_C,7K39_K_light+7K39_L_heavy,"171_ASN, 172_ASP, 173_ASN, 239_PRO, 240_GLY",3.0
+7K39_A,7K39_G_light+7K39_H_heavy,"269_ARG, 270_SER, 271_ASP, 272_ALA, 273_PRO, 284_PRO, 285_ASN, 48_THR, 49_GLY, 91_SER, 92_LYS",3.0
+7K39_A,7K39_I_light+7K39_J_heavy,"171_ASN, 172_ASP, 173_ASN, 239_PRO, 240_GLY",3.0
+6IUV_A,6IUV_L_light+6IUV_H_heavy,"114_ILE, 115_GLN, 116_ILE, 118_PRO, 120_SER, 121_SER, 123_SER, 161_LYS, 162_ARG, 163_SER, 164_TYR, 165_ASN, 167_THR, 168_ASN, 201_GLY, 202_THR, 203_SER, 238_ALA, 240_ASN",2.33
+6IUV_A,6IUV_D_light+6IUV_C_heavy,"115_GLN, 119_LYS, 137_TYR, 138_LEU, 139_GLY, 140_ARG, 145_ARG, 147_VAL, 148_VAL, 251_GLU, 252_TYR, 69_GLU, 71_ILE",2.33
+6IUV_B,6IUV_L_light+6IUV_H_heavy,"115_GLN, 119_LYS, 137_TYR, 138_LEU, 139_GLY, 140_ARG, 145_ARG, 147_VAL, 148_VAL, 251_GLU, 252_TYR, 69_GLU, 71_ILE",2.33
+6IUV_B,6IUV_D_light+6IUV_C_heavy,"114_ILE, 115_GLN, 116_ILE, 118_PRO, 120_SER, 121_SER, 123_SER, 161_LYS, 162_ARG, 163_SER, 164_TYR, 165_ASN, 167_THR, 168_ASN, 201_GLY, 202_THR, 203_SER, 238_ALA, 240_ASN",2.33
+1OB1_C,1OB1_A_light+1OB1_B_heavy,"10_LYS, 11_GLN, 13_PRO, 14_GLN, 23_ASP, 24_GLU, 25_ARG, 26_GLU, 28_CYS, 38_GLY, 39_ASP, 8_VAL, 9_LYS",2.9
+1OB1_C,1OB1_D_light+1OB1_E_heavy,"21_HIS, 51_GLU, 58_ALA, 59_ASP, 80_LYS",2.9
+1OB1_F,1OB1_A_light+1OB1_B_heavy,"21_HIS, 51_GLU, 52_ASN, 58_ALA, 59_ASP, 80_LYS, 96_HIS",2.9
+1OB1_F,1OB1_D_light+1OB1_E_heavy,"10_LYS, 11_GLN, 13_PRO, 23_ASP, 24_GLU, 25_ARG, 26_GLU, 28_CYS, 38_GLY, 39_ASP, 8_VAL, 9_LYS",2.9
+4HCR_B,4HCR_L_light+4HCR_H_heavy,"33_GLY, 35_SER, 36_VAL, 37_GLN, 46_GLY, 47_ALA, 48_VAL, 49_GLN, 50_SER, 51_ASP",2.3
+4HCR_B,4HCR_N_light+4HCR_M_heavy,"1_VAL, 27_LEU, 2_LYS, 32_ARG, 35_SER, 36_VAL, 37_GLN, 39_ARG, 3_PRO, 41_LEU, 71_VAL, 73_VAL, 74_GLY, 75_SER, 77_GLY, 78_GLY, 79_ARG, 80_THR, 81_PHE, 82_GLN, 83_HIS, 84_THR",2.3
+4HCR_A,4HCR_L_light+4HCR_H_heavy,"1_VAL, 2_LYS, 35_SER, 36_VAL, 37_GLN, 39_ARG, 3_PRO, 41_LEU, 71_VAL, 73_VAL, 74_GLY, 75_SER, 77_GLY, 78_GLY, 79_ARG, 80_THR, 81_PHE, 82_GLN, 83_HIS, 84_THR",2.3
+4HCR_A,4HCR_N_light+4HCR_M_heavy,"33_GLY, 35_SER, 37_GLN, 38_TRP, 39_ARG, 46_GLY, 47_ALA, 48_VAL, 49_GLN, 50_SER, 51_ASP",2.3
+4RRP_O,4RRP_C_light+4RRP_I_heavy,"10_LYS, 11_VAL, 12_LEU, 136_VAL, 13_ASN, 14_ASN, 15_PRO, 16_ALA, 17_LYS, 20_ASP, 21_PRO, 23_GLU, 25_GLU, 60_ILE, 62_VAL, 63_GLY, 66_PRO, 71_LYS, 72_PHE, 73_VAL, 75_SER",2.79
+4RRP_Q,4RRP_E_light+4RRP_K_heavy,"10_LYS, 11_VAL, 12_LEU, 136_VAL, 13_ASN, 14_ASN, 15_PRO, 16_ALA, 17_LYS, 20_ASP, 21_PRO, 23_GLU, 25_GLU, 28_PHE, 60_ILE, 63_GLY, 64_PRO, 70_ASN, 71_LYS, 72_PHE, 73_VAL, 75_SER",2.79
+4RRP_P,4RRP_D_light+4RRP_J_heavy,"10_LYS, 11_VAL, 12_LEU, 136_VAL, 13_ASN, 14_ASN, 15_PRO, 16_ALA, 17_LYS, 20_ASP, 21_PRO, 23_GLU, 25_GLU, 60_ILE, 62_VAL, 63_GLY, 66_PRO, 70_ASN, 71_LYS, 72_PHE, 73_VAL, 75_SER",2.79
+4RRP_M,4RRP_A_light+4RRP_G_heavy,"10_LYS, 11_VAL, 12_LEU, 136_VAL, 13_ASN, 14_ASN, 15_PRO, 16_ALA, 17_LYS, 20_ASP, 21_PRO, 23_GLU, 25_GLU, 28_PHE, 60_ILE, 61_LEU, 62_VAL, 63_GLY, 66_PRO, 70_ASN, 71_LYS, 72_PHE, 73_VAL, 75_SER",2.79
+4RRP_R,4RRP_F_light+4RRP_L_heavy,"10_LYS, 11_VAL, 12_LEU, 136_VAL, 13_ASN, 14_ASN, 15_PRO, 16_ALA, 17_LYS, 20_ASP, 21_PRO, 23_GLU, 25_GLU, 60_ILE, 63_GLY, 70_ASN, 71_LYS, 72_PHE, 73_VAL, 75_SER",2.79
+4RRP_N,4RRP_B_light+4RRP_H_heavy,"10_LYS, 11_VAL, 12_LEU, 136_VAL, 13_ASN, 14_ASN, 15_PRO, 16_ALA, 17_LYS, 20_ASP, 21_PRO, 23_GLU, 25_GLU, 60_ILE, 61_LEU, 62_VAL, 63_GLY, 66_PRO, 70_ASN, 71_LYS, 72_PHE, 73_VAL, 75_SER",2.79
+8Q7B_A,8Q7B_C_light+8Q7B_D_heavy,"590_ASN, 593_PRO, 594_GLY, 595_LEU, 596_ASN",2.56
+8Q7B_A,8Q7B_E_light+8Q7B_F_heavy,"599_GLY, 600_ASN, 601_ASN, 603_CYS, 604_ASN, 605_TYR, 611_GLU, 612_GLU, 615_VAL, 620_ASP, 621_LEU",2.56
+8Q7B_B,8Q7B_C_light+8Q7B_D_heavy,"599_GLY, 600_ASN, 601_ASN, 603_CYS, 604_ASN, 605_TYR, 611_GLU, 612_GLU, 615_VAL, 620_ASP, 621_LEU",2.56
+8Q7B_B,8Q7B_E_light+8Q7B_F_heavy,"590_ASN, 593_PRO, 594_GLY, 595_LEU, 596_ASN",2.56
+8XNH_C,8XNH_F_light+8XNH_G_heavy,"371_SER, 390_VAL, 422_LEU, 423_GLY, 424_ASN",2.3
+8XNH_C,8XNH_D_light+8XNH_I_heavy,"129_THR, 284_ILE, 286_THR, 287_GLU, 289_GLN, 290_GLN, 49_LYS, 50_SER, 51_ASN, 52_PRO, 53_LEU, 54_THR",2.3
+8XNH_B,8XNH_E_light+8XNH_H_heavy,"371_SER, 390_VAL, 422_LEU, 423_GLY, 424_ASN",2.3
+8XNH_A,8XNH_E_light+8XNH_H_heavy,"284_ILE, 286_THR, 287_GLU, 289_GLN, 290_GLN, 49_LYS, 50_SER, 51_ASN, 52_PRO",2.3
+7KN4_B,7KN4_N_light+7KN4_M_heavy,"403_ARG, 449_TYR, 456_PHE, 475_ALA, 484_GLU, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER, 496_GLY, 501_ASN, 505_TYR",2.7
+7KN4_A,7KN4_L_light+7KN4_H_heavy,"403_ARG, 449_TYR, 456_PHE, 475_ALA, 476_GLY, 484_GLU, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER, 496_GLY, 498_GLN, 501_ASN, 505_TYR",2.7
+7KN4_A,7KN4_L_light+7KN4_M_heavy,"403_ARG, 449_TYR, 456_PHE, 484_GLU, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER, 496_GLY, 498_GLN, 501_ASN, 505_TYR",2.7
+4XNQ_D,4XNQ_A_light+4XNQ_B_heavy,"133_SER, 135_VAL, 136_SER, 137_SER, 142_GLN, 143_GLY, 144_LYS, 145_SER, 153_TRP, 155_ILE, 156_LYS, 157_LYS, 158_ASP, 159_SER, 190_GLU, 193_LYS, 194_LEU, 98_TYR",2.0
+4XNQ_C,4XNQ_L_light+4XNQ_H_heavy,"133_SER, 135_VAL, 136_SER, 137_SER, 142_GLN, 143_GLY, 144_LYS, 145_SER, 153_TRP, 155_ILE, 156_LYS, 157_LYS, 158_ASP, 159_SER, 190_GLU, 193_LYS, 194_LEU, 98_TYR",2.0
+7RCO_B,7RCO_C_light+7RCO_D_heavy,"367_TYR, 368_ASN, 369_THR, 370_ILE, 371_ASN, 372_PRO, 373_GLU, 374_ALA, 375_SER, 376_ALA, 377_SER",2.9
+7RCO_B,7RCO_E_light+7RCO_F_heavy,"314_GLN, 315_ASP, 320_ARG, 321_PRO, 322_LEU, 323_TYR, 328_ARG, 329_ASP, 345_PHE, 347_ALA",2.9
+7RCO_A,7RCO_C_light+7RCO_D_heavy,"313_VAL, 315_ASP, 320_ARG, 321_PRO, 322_LEU, 323_TYR, 328_ARG, 329_ASP, 345_PHE",2.9
+7RCO_A,7RCO_E_light+7RCO_F_heavy,"352_TYR, 368_ASN, 369_THR, 370_ILE, 371_ASN, 372_PRO, 373_GLU, 374_ALA, 375_SER, 376_ALA, 377_SER",2.9
+6WOZ_D,6WOZ_F_light+6WOZ_E_heavy,"183_GLU, 184_SER, 186_TYR, 187_TYR, 290_ARG, 291_LYS, 293_LEU, 294_ASP, 295_GLN, 297_LYS, 298_LYS, 300_ASN, 301_ASN, 304_ARG, 305_ASP, 308_GLU, 424_PHE, 428_ASN",2.9
+6WOZ_D,6WOZ_C_light+6WOZ_B_heavy,"185_ASN, 337_TYR, 390_TYR, 393_GLN, 394_LYS, 396_LYS, 397_HIS, 407_ASP, 408_GLY, 409_VAL",2.9
+6WOZ_J,6WOZ_L_light+6WOZ_K_heavy,"183_GLU, 186_TYR, 187_TYR, 290_ARG, 291_LYS, 293_LEU, 294_ASP, 295_GLN, 297_LYS, 298_LYS, 300_ASN, 301_ASN, 304_ARG, 305_ASP, 308_GLU, 417_ASN, 424_PHE",2.9
+6WOZ_A,6WOZ_C_light+6WOZ_B_heavy,"183_GLU, 184_SER, 186_TYR, 187_TYR, 290_ARG, 291_LYS, 293_LEU, 294_ASP, 295_GLN, 297_LYS, 298_LYS, 300_ASN, 301_ASN, 304_ARG, 305_ASP, 308_GLU, 424_PHE, 425_ASN, 428_ASN",2.9
+6WOZ_G,6WOZ_I_light+6WOZ_H_heavy,"183_GLU, 184_SER, 186_TYR, 187_TYR, 290_ARG, 291_LYS, 293_LEU, 294_ASP, 295_GLN, 297_LYS, 298_LYS, 300_ASN, 301_ASN, 304_ARG, 305_ASP, 308_GLU, 417_ASN, 424_PHE, 425_ASN, 428_ASN",2.9
+6WOZ_G,6WOZ_L_light+6WOZ_K_heavy,"185_ASN, 337_TYR, 390_TYR, 393_GLN, 394_LYS, 396_LYS, 397_HIS, 407_ASP, 408_GLY, 409_VAL",2.9
+7NP1_D,7NP1_O_light+7NP1_K_heavy,"415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 502_GLY, 505_TYR",2.8
+7NP1_C,7NP1_N_light+7NP1_J_heavy,"408_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 459_SER, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 501_ASN, 502_GLY, 505_TYR",2.8
+7NP1_B,7NP1_M_light+7NP1_I_heavy,"415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 459_SER, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 505_TYR",2.8
+7NP1_A,7NP1_L_light+7NP1_H_heavy,"415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 505_TYR",2.8
+7LXY_J,7LXY_D_light+7LXY_E_heavy,"342_PHE, 343_ASN, 368_LEU, 371_SER, 372_ALA, 373_SER, 374_PHE, 436_TRP, 441_LEU",2.2
+7LXY_J,7LXY_K_light+7LXY_M_heavy,"444_LYS, 446_GLY, 449_TYR, 452_LEU, 455_LEU, 456_PHE, 484_GLU, 485_GLY, 486_PHE, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER",2.2
+7LXY_J,7LXY_O_light+7LXY_N_heavy,"144_TYR, 145_TYR, 146_HIS, 147_LYS, 156_GLU, 17_ASN, 246_ARG",2.2
+7LXY_A,7LXY_D_light+7LXY_E_heavy,"444_LYS, 446_GLY, 449_TYR, 452_LEU, 455_LEU, 456_PHE, 484_GLU, 485_GLY, 486_PHE, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER",2.2
+7LXY_A,7LXY_C_light+7LXY_F_heavy,"342_PHE, 343_ASN, 368_LEU, 371_SER, 372_ALA, 373_SER, 374_PHE, 436_TRP, 441_LEU",2.2
+7LXY_A,7LXY_L_light+7LXY_H_heavy,"144_TYR, 145_TYR, 146_HIS, 147_LYS, 156_GLU, 17_ASN, 246_ARG",2.2
+7LXY_B,7LXY_C_light+7LXY_F_heavy,"444_LYS, 446_GLY, 449_TYR, 452_LEU, 455_LEU, 456_PHE, 484_GLU, 485_GLY, 486_PHE, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER",2.2
+7LXY_B,7LXY_K_light+7LXY_M_heavy,"342_PHE, 343_ASN, 368_LEU, 371_SER, 372_ALA, 373_SER, 374_PHE, 436_TRP, 441_LEU",2.2
+7LXY_B,7LXY_I_light+7LXY_G_heavy,"144_TYR, 145_TYR, 146_HIS, 147_LYS, 156_GLU, 17_ASN, 246_ARG",2.2
+8SIT_C,8SIT_P_light+8SIT_O_heavy,"369_TYR, 371_SER, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 382_VAL, 383_SER, 384_PRO, 385_THR, 408_ARG, 412_PRO, 413_GLY, 414_GLN, 415_THR, 427_ASP, 428_ASP, 429_PHE",2.91
+8SIT_A,8SIT_L_light+8SIT_H_heavy,"368_LEU, 369_TYR, 371_SER, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 382_VAL, 383_SER, 384_PRO, 385_THR, 408_ARG, 412_PRO, 413_GLY, 414_GLN, 415_THR, 420_ASP, 427_ASP, 429_PHE, 460_ASN",2.91
+7LF7_K,7LF7_L_light+7LF7_H_heavy,"100_ALA, 102_GLU, 103_LEU, 104_PRO, 105_ARG, 106_ASN, 108_ALA, 109_ASP, 96_GLY, 98_VAL, 99_ALA",2.03
+7LF7_M,7LF7_B_light+7LF7_A_heavy,"100_ALA, 102_GLU, 103_LEU, 104_PRO, 105_ARG, 106_ASN, 108_ALA, 109_ASP, 96_GLY, 98_VAL, 99_ALA",2.03
+3Q3G_I,3Q3G_F_light+3Q3G_H_heavy,"142_SER, 143_GLY, 144_SER, 178_GLU, 179_GLU, 180_PHE, 181_ARG, 205_LEU, 206_LEU, 207_GLY, 208_ARG, 209_THR, 243_GLY, 244_GLU, 246_PHE, 247_GLY, 274_ALA",2.7
+3Q3G_G,3Q3G_C_light+3Q3G_D_heavy,"142_SER, 143_GLY, 144_SER, 178_GLU, 179_GLU, 180_PHE, 181_ARG, 205_LEU, 206_LEU, 207_GLY, 208_ARG, 209_THR, 243_GLY, 244_GLU, 246_PHE, 247_GLY, 274_ALA, 279_LYS",2.7
+3Q3G_E,3Q3G_A_light+3Q3G_B_heavy,"142_SER, 143_GLY, 144_SER, 178_GLU, 179_GLU, 180_PHE, 181_ARG, 205_LEU, 206_LEU, 207_GLY, 208_ARG, 209_THR, 243_GLY, 244_GLU, 246_PHE, 247_GLY, 274_ALA",2.7
+3Q3G_L,3Q3G_J_light+3Q3G_K_heavy,"142_SER, 143_GLY, 144_SER, 178_GLU, 179_GLU, 180_PHE, 181_ARG, 205_LEU, 206_LEU, 207_GLY, 208_ARG, 209_THR, 243_GLY, 244_GLU, 246_PHE, 247_GLY, 274_ALA",2.7
+6MHR_C,6MHR_B_light+6MHR_A_heavy,"25_GLN, 27_PRO, 30_ASN, 32_PRO, 35_THR, 39_ASN, 40_ASN, 41_ARG, 42_ASN, 43_GLN, 44_ILE, 45_CYS",2.8
+6MHR_F,6MHR_E_light+6MHR_D_heavy,"25_GLN, 26_ASP, 27_PRO, 30_ASN, 31_CYS, 32_PRO, 39_ASN, 40_ASN, 41_ARG, 42_ASN, 43_GLN, 44_ILE, 45_CYS, 60_ARG",2.8
+8BE1_F,8BE1_E_light+8BE1_D_heavy,"421_TYR, 456_PHE, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 478_THR, 479_PRO, 486_PHE, 487_ASN, 489_TYR",1.98
+8BE1_C,8BE1_B_light+8BE1_A_heavy,"456_PHE, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 478_THR, 479_PRO, 486_PHE, 487_ASN, 489_TYR",1.98
+6BAN_N,6BAN_C_light+6BAN_D_heavy,"330_SER, 331_GLY, 332_ARG, 368_GLN, 369_LYS, 370_GLU, 378_ASP, 380_ASN, 381_PHE, 382_LYS, 383_SER, 384_ASP, 385_LEU",1.95
+6BAN_O,6BAN_E_light+6BAN_F_heavy,"330_SER, 331_GLY, 332_ARG, 368_GLN, 369_LYS, 370_GLU, 378_ASP, 380_ASN, 381_PHE, 382_LYS, 383_SER, 384_ASP, 385_LEU",1.95
+6BAN_M,6BAN_A_light+6BAN_B_heavy,"330_SER, 331_GLY, 332_ARG, 368_GLN, 369_LYS, 370_GLU, 378_ASP, 380_ASN, 381_PHE, 382_LYS, 383_SER, 384_ASP, 385_LEU",1.95
+6BAN_P,6BAN_G_light+6BAN_H_heavy,"330_SER, 331_GLY, 332_ARG, 368_GLN, 369_LYS, 370_GLU, 378_ASP, 380_ASN, 381_PHE, 382_LYS, 383_SER, 384_ASP, 385_LEU",1.95
+7PS0_E,7PS0_L_light+7PS0_H_heavy,"446_GLY, 449_TYR, 493_GLN, 494_SER, 495_TYR, 498_GLN, 499_PRO, 500_THR, 501_TYR, 502_GLY, 505_TYR",2.92
+7PS0_A,7PS0_C_light+7PS0_B_heavy,"445_VAL, 446_GLY, 449_TYR, 456_PHE, 489_TYR, 493_GLN, 494_SER, 498_GLN, 499_PRO, 500_THR, 501_TYR, 502_GLY, 505_TYR",2.92
+8HN6_E,8HN6_B_light+8HN6_A_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 449_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 474_GLN, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",2.07
+8HN6_F,8HN6_D_light+8HN6_C_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 495_TYR, 496_GLY, 498_GLN, 500_THR, 501_ASN, 502_GLY, 505_TYR",2.07
+7L7E_G,7L7E_B_light+7L7E_A_heavy,"417_LYS, 455_LEU, 475_ALA, 476_GLY, 477_SER, 478_THR, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 488_CYS, 489_TYR, 493_GLN",3.0
+7L7E_G,7L7E_D_light+7L7E_C_heavy,"408_ARG, 437_ASN, 439_ASN, 499_PRO, 503_VAL, 506_GLN",3.0
+7L7E_G,7L7E_F_light+7L7E_E_heavy,"346_ARG, 439_ASN, 440_ASN, 441_LEU, 443_SER, 444_LYS, 445_VAL, 446_GLY, 449_TYR, 450_ASN, 452_LEU, 484_GLU, 490_PHE, 493_GLN, 494_SER, 499_PRO",3.0
+7L7E_b,7L7E_U_light+7L7E_T_heavy,"417_LYS, 455_LEU, 456_PHE, 475_ALA, 476_GLY, 477_SER, 478_THR, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 488_CYS, 489_TYR, 493_GLN",3.0
+7L7E_b,7L7E_D_light+7L7E_C_heavy,"346_ARG, 439_ASN, 440_ASN, 443_SER, 444_LYS, 445_VAL, 446_GLY, 449_TYR, 450_ASN, 452_LEU, 484_GLU, 490_PHE, 492_LEU, 493_GLN, 494_SER, 499_PRO",3.0
+7L7E_b,7L7E_F_light+7L7E_E_heavy,"408_ARG, 437_ASN, 439_ASN, 499_PRO, 506_GLN",3.0
+7L7E_K,7L7E_J_light+7L7E_I_heavy,"455_LEU, 456_PHE, 475_ALA, 476_GLY, 477_SER, 478_THR, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 488_CYS, 489_TYR, 493_GLN",3.0
+7L7E_K,7L7E_P_light+7L7E_O_heavy,"346_ARG, 439_ASN, 440_ASN, 441_LEU, 443_SER, 444_LYS, 445_VAL, 446_GLY, 449_TYR, 450_ASN, 452_LEU, 484_GLU, 492_LEU, 493_GLN, 494_SER, 499_PRO",3.0
+7L7E_S,7L7E_L_light+7L7E_H_heavy,"455_LEU, 475_ALA, 476_GLY, 477_SER, 478_THR, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 493_GLN",3.0
+7L7E_S,7L7E_N_light+7L7E_M_heavy,"346_ARG, 439_ASN, 440_ASN, 441_LEU, 443_SER, 444_LYS, 445_VAL, 446_GLY, 449_TYR, 450_ASN, 452_LEU, 484_GLU, 490_PHE, 492_LEU, 493_GLN, 494_SER, 499_PRO",3.0
+6M58_B,6M58_L_light+6M58_H_heavy,"11_PHE, 121_ASP, 122_VAL, 125_THR, 128_HIS, 129_ASP, 12_LYS, 131_GLU, 13_ASP, 14_LEU, 15_GLY, 162_LYS, 166_THR, 16_GLU, 170_GLN, 171_ALA, 172_ALA, 174_LYS, 51_LYS, 9_HIS",2.95
+6M58_B,6M58_D_light+6M58_C_heavy,"225_LYS, 227_GLU, 228_PHE, 229_ALA, 230_GLU, 232_SER, 233_LYS, 236_THR, 259_ASP, 262_LYS, 263_TYR, 266_GLU, 267_ASN, 269_ASP, 270_SER, 308_ASP, 318_ASN, 325_VAL, 326_PHE, 329_MET, 333_GLU, 9_HIS",2.95
+6M58_A,6M58_L_light+6M58_H_heavy,"225_LYS, 227_GLU, 228_PHE, 229_ALA, 230_GLU, 232_SER, 233_LYS, 236_THR, 259_ASP, 262_LYS, 263_TYR, 266_GLU, 267_ASN, 269_ASP, 270_SER, 308_ASP, 318_ASN, 325_VAL, 326_PHE, 329_MET, 9_HIS",2.95
+6M58_A,6M58_D_light+6M58_C_heavy,"121_ASP, 125_THR, 128_HIS, 129_ASP, 12_LYS, 131_GLU, 13_ASP, 14_LEU, 15_GLY, 162_LYS, 166_THR, 16_GLU, 170_GLN, 171_ALA, 172_ALA, 174_LYS, 51_LYS, 9_HIS",2.95
+4MWF_D,4MWF_B_light+4MWF_A_heavy,"427_LEU, 428_ASN, 429_CYS, 430_ASN, 431_GLU, 432_SER, 433_LEU, 438_LEU, 439_ALA, 442_PHE, 443_TYR, 529_TRP",2.65
+4MWF_C,4MWF_L_light+4MWF_H_heavy,"427_LEU, 428_ASN, 429_CYS, 430_ASN, 431_GLU, 432_SER, 433_LEU, 436_GLY, 438_LEU, 439_ALA, 441_LEU, 442_PHE, 443_TYR, 446_LYS, 529_TRP",2.65
+1FE8_C,1FE8_L_light+1FE8_H_heavy,"1004_PRO, 1006_GLN, 1009_ASP, 1016_ARG, 1023_HIS, 1041_SER, 1042_VAL, 1043_ASP, 1072_GLN",2.03
+1FE8_C,1FE8_M_light+1FE8_I_heavy,"1026_ARG, 1027_PRO, 925_GLN, 926_PRO, 963_ARG",2.03
+1FE8_C,1FE8_N_light+1FE8_J_heavy,"1023_HIS, 1024_GLY, 1026_ARG, 962_PRO, 963_ARG, 966_GLN, 981_PRO, 982_TRP, 983_ASN, 984_VAL, 985_VAL, 986_PRO, 987_GLU, 989_ALA, 990_HIS, 993_SER, 994_LEU, 996_ASP, 997_VAL",2.03
+1FE8_B,1FE8_M_light+1FE8_I_heavy,"1023_HIS, 1024_GLY, 1026_ARG, 962_PRO, 963_ARG, 966_GLN, 981_PRO, 982_TRP, 983_ASN, 984_VAL, 985_VAL, 986_PRO, 987_GLU, 989_ALA, 990_HIS, 993_SER, 994_LEU, 996_ASP, 997_VAL",2.03
+1FE8_B,1FE8_N_light+1FE8_J_heavy,"1021_GLU, 1026_ARG, 1027_PRO, 1028_GLY, 925_GLN, 926_PRO, 963_ARG",2.03
+1FE8_A,1FE8_L_light+1FE8_H_heavy,"1023_HIS, 1024_GLY, 1026_ARG, 962_PRO, 963_ARG, 966_GLN, 981_PRO, 982_TRP, 983_ASN, 984_VAL, 985_VAL, 986_PRO, 987_GLU, 989_ALA, 990_HIS, 993_SER, 994_LEU, 996_ASP, 997_VAL",2.03
+1FE8_A,1FE8_N_light+1FE8_J_heavy,"1004_PRO, 1006_GLN, 1009_ASP, 1016_ARG, 1023_HIS, 1041_SER, 1042_VAL, 1043_ASP, 1044_SER, 1046_ASP, 1072_GLN",2.03
+5LSP_P,5LSP_L_light+5LSP_H_heavy,"534_PRO, 537_GLN, 559_GLN, 593_ASN, 594_ASN, 595_LYS",2.61
+5LSP_P,5LSP_T_light+5LSP_S_heavy,"592_ARG, 593_ASN, 595_LYS, 599_LYS, 600_LYS, 602_ARG, 612_LEU, 613_THR, 614_LEU, 615_SER",2.61
+5LSP_A,5LSP_L_light+5LSP_H_heavy,"592_ARG, 593_ASN, 595_LYS, 599_LYS, 600_LYS, 602_ARG, 612_LEU, 613_THR, 614_LEU, 615_SER",2.61
+5LSP_A,5LSP_T_light+5LSP_S_heavy,"534_PRO, 537_GLN, 559_GLN, 593_ASN, 594_ASN, 595_LYS",2.61
+6XXV_F,6XXV_E_light+6XXV_D_heavy,"68_LEU, 69_SER, 72_ASN, 73_ASP, 74_MET, 75_PRO, 76_ILE, 77_THR, 78_ASN, 79_ASP, 81_LYS, 82_LYS, 85_SER, 86_ASN, 89_LEU",2.2
+6XXV_C,6XXV_B_light+6XXV_A_heavy,"68_LEU, 69_SER, 72_ASN, 73_ASP, 75_PRO, 76_ILE, 77_THR, 78_ASN, 79_ASP, 81_LYS, 82_LYS, 85_SER, 86_ASN, 89_LEU",2.2
+6B0H_I,6B0H_A_light+6B0H_B_heavy,"42_LEU, 45_ASP, 46_GLU, 47_LYS, 66_PRO, 67_VAL, 69_TYR, 81_ASN, 82_ASN",2.7
+6B0H_I,6B0H_C_light+6B0H_D_heavy,"102_ASP, 105_ASN, 106_PRO, 107_VAL, 108_LYS, 109_THR, 110_GLY, 111_VAL, 112_CYS, 120_PRO, 122_VAL, 125_GLN, 126_ASN, 87_ASN, 88_GLU, 90_LYS, 91_GLN, 92_VAL",2.7
+6B0H_J,6B0H_A_light+6B0H_B_heavy,"102_ASP, 105_ASN, 106_PRO, 107_VAL, 109_THR, 110_GLY, 111_VAL, 112_CYS, 120_PRO, 122_VAL, 125_GLN, 126_ASN, 87_ASN, 88_GLU, 90_LYS, 91_GLN, 92_VAL",2.7
+6B0H_J,6B0H_C_light+6B0H_D_heavy,"42_LEU, 45_ASP, 46_GLU, 47_LYS, 65_ASN, 66_PRO, 67_VAL, 69_TYR, 81_ASN, 82_ASN",2.7
+4QNP_A,4QNP_F_light+4QNP_E_heavy,"216_ILE, 219_TRP, 220_ARG, 221_ASN, 250_GLN, 251_ALA, 252_SER, 254_LYS, 263_ILE, 264_VAL, 266_SER, 267_VAL, 268_GLU",2.8
+4QNP_B,4QNP_L_light+4QNP_H_heavy,"216_ILE, 219_TRP, 220_ARG, 221_ASN, 250_GLN, 251_ALA, 252_SER, 254_LYS, 263_ILE, 264_VAL, 266_SER, 267_VAL, 268_GLU, 270_ASN",2.8
+5HYS_G,5HYS_B_light+5HYS_A_heavy,"373_THR, 374_TRP, 375_SER, 376_ARG, 377_ALA, 378_SER, 379_GLY, 380_LYS, 412_GLU, 414_GLU, 417_GLN, 419_ARG, 430_MET",2.5
+5HYS_K,5HYS_D_light+5HYS_C_heavy,"373_THR, 374_TRP, 375_SER, 376_ARG, 377_ALA, 378_SER, 379_GLY, 380_LYS, 417_GLN, 419_ARG, 428_ALA, 430_MET",2.5
+8EE8_B,8EE8_C_light+8EE8_D_heavy,"69_ALA, 73_ARG, 79_GLU, 81_TYR, 82_LEU, 83_ASP, 84_LYS, 86_SER, 87_ASP, 88_THR, 90_TYR",2.8
+8EE8_Z,8EE8_L_light+8EE8_H_heavy,"67_ASP, 69_ALA, 73_ARG, 79_GLU, 81_TYR, 82_LEU, 83_ASP, 84_LYS, 86_SER, 87_ASP, 88_THR, 90_TYR",2.8
+6VGR_A,6VGR_D_light+6VGR_C_heavy,"256_GLU, 260_ARG, 283_GLN, 411_VAL, 413_GLU, 414_GLU, 415_SER, 416_ARG, 417_ALA, 418_GLN, 419_SER, 420_PRO, 422_GLU",2.84
+6VGR_A,6VGR_L_light+6VGR_H_heavy,"112_LEU, 113_GLN, 166_ARG, 169_ALA, 170_SER, 172_SER",2.84
+6VGR_B,6VGR_D_light+6VGR_C_heavy,"112_LEU, 113_GLN, 166_ARG, 169_ALA, 170_SER, 172_SER",2.84
+6VGR_B,6VGR_L_light+6VGR_H_heavy,"256_GLU, 260_ARG, 283_GLN, 411_VAL, 413_GLU, 414_GLU, 415_SER, 416_ARG, 417_ALA, 418_GLN, 419_SER, 420_PRO, 422_GLU",2.84
+6EWB_D,6EWB_I_light+6EWB_G_heavy,"339_ARG, 340_THR, 341_ASP, 342_GLY, 343_SER, 345_ARG",2.78
+6EWB_D,6EWB_K_light+6EWB_J_heavy,"250_PHE, 256_ALA, 391_ASP, 392_GLY, 393_GLY, 394_THR, 395_THR, 397_ARG, 398_ASN, 401_GLN, 435_ARG, 444_TYR, 445_PRO, 446_ASN, 448_ASP, 504_GLN, 506_ASP",2.78
+6EWB_C,6EWB_I_light+6EWB_G_heavy,"250_PHE, 253_PRO, 256_ALA, 391_ASP, 392_GLY, 393_GLY, 394_THR, 395_THR, 397_ARG, 398_ASN, 401_GLN, 435_ARG, 444_TYR, 446_ASN, 448_ASP, 504_GLN, 506_ASP",2.78
+6EWB_C,6EWB_K_light+6EWB_J_heavy,"340_THR, 341_ASP, 342_GLY, 343_SER, 345_ARG",2.78
+6EWB_B,6EWB_F_light+6EWB_E_heavy,"340_THR, 341_ASP, 342_GLY, 343_SER, 345_ARG",2.78
+6EWB_B,6EWB_L_light+6EWB_H_heavy,"250_PHE, 256_ALA, 391_ASP, 392_GLY, 393_GLY, 394_THR, 395_THR, 397_ARG, 398_ASN, 401_GLN, 435_ARG, 444_TYR, 445_PRO, 446_ASN, 448_ASP, 504_GLN, 506_ASP",2.78
+6EWB_A,6EWB_F_light+6EWB_E_heavy,"250_PHE, 253_PRO, 256_ALA, 391_ASP, 392_GLY, 393_GLY, 394_THR, 395_THR, 397_ARG, 401_GLN, 435_ARG, 444_TYR, 445_PRO, 446_ASN, 448_ASP, 504_GLN, 506_ASP",2.78
+6EWB_A,6EWB_L_light+6EWB_H_heavy,"340_THR, 341_ASP, 342_GLY, 343_SER, 345_ARG",2.78
+6A79_B,6A79_M_light+6A79_I_heavy,"23_ASP, 24_GLY, 25_THR, 27_VAL, 29_SER, 57_LYS, 59_LEU, 60_GLU, 61_ASN, 63_VAL, 65_GLN, 67_ARG, 68_TYR",2.31
+6A79_A,6A79_L_light+6A79_H_heavy,"23_ASP, 24_GLY, 25_THR, 27_VAL, 29_SER, 57_LYS, 59_LEU, 60_GLU, 61_ASN, 63_VAL, 65_GLN, 67_ARG, 68_TYR",2.31
+8GF2_A,8GF2_D_light+8GF2_C_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 494_SER, 496_GLY, 498_GLN, 501_ASN, 505_TYR",2.85
+8GF2_A,8GF2_L_light+8GF2_H_heavy,"369_TYR, 370_ASN, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 390_LEU, 408_ARG, 428_ASP, 429_PHE, 430_THR, 517_LEU",2.85
+8GF2_B,8GF2_F_light+8GF2_E_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 453_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 493_GLN, 496_GLY, 498_GLN, 501_ASN, 505_TYR",2.85
+8GF2_B,8GF2_Y_light+8GF2_X_heavy,"368_LEU, 369_TYR, 371_SER, 374_PHE, 375_SER, 376_THR, 377_PHE, 378_LYS, 379_CYS, 380_TYR, 381_GLY, 382_VAL, 383_SER, 384_PRO, 385_THR, 386_LYS, 390_LEU, 408_ARG, 428_ASP, 429_PHE, 430_THR, 517_LEU",2.85
+8R8D_B,8R8D_I_light+8R8D_E_heavy,"100_GLN, 101_HIS, 174_SER, 175_SER, 177_LEU, 189_ASP, 190_ALA, 191_CYS, 192_GLN, 195_SER, 213_ILE, 215_TRP, 216_GLY, 217_SER, 219_GLY, 220_CYS, 226_GLY, 57_HIS, 60_GLN, 94_PHE, 95_SER, 96_PRO, 97_VAL, 98_SER, 99_TYR",2.6
+8R8D_B,8R8D_L_light+8R8D_H_heavy,"144_GLN, 145_PHE, 146_GLU, 147_GLY, 17_VAL, 20_LEU, 221_GLY",2.6
+8R8D_A,8R8D_I_light+8R8D_E_heavy,"144_GLN, 145_PHE, 146_GLU, 147_GLY, 17_VAL, 20_LEU, 221_GLY",2.6
+8R8D_A,8R8D_L_light+8R8D_H_heavy,"100_GLN, 101_HIS, 174_SER, 175_SER, 177_LEU, 189_ASP, 190_ALA, 191_CYS, 192_GLN, 195_SER, 213_ILE, 215_TRP, 216_GLY, 217_SER, 219_GLY, 220_CYS, 226_GLY, 57_HIS, 60_GLN, 94_PHE, 95_SER, 96_PRO, 97_VAL, 98_SER, 99_TYR",2.6
+8FHY_D,8FHY_L_light+8FHY_H_heavy,"353_TRP, 354_ASN, 355_ARG, 357_ARG, 396_TYR, 462_LYS, 464_PHE, 465_GLU, 466_ARG, 467_ASP, 468_ILE, 469_SER, 481_ASN, 516_GLU",2.53
+8FHY_D,8FHY_E_light+8FHY_F_heavy,"417_LYS, 420_ASP, 421_TYR, 449_TYR, 453_TYR, 455_LEU, 456_PHE, 460_ASN, 475_ALA, 476_GLY, 477_SER, 478_THR, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 488_CYS, 489_TYR, 490_PHE, 493_GLN, 494_SER, 496_GLY, 498_GLN, 501_ASN, 502_GLY, 505_TYR",2.53
+8FHY_I,8FHY_L_light+8FHY_H_heavy,"409_GLN, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 449_TYR, 453_TYR, 455_LEU, 456_PHE, 460_ASN, 475_ALA, 476_GLY, 477_SER, 478_THR, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 489_TYR, 490_PHE, 493_GLN, 494_SER, 496_GLY, 498_GLN, 501_ASN, 502_GLY, 505_TYR",2.53
+8FHY_I,8FHY_B_light+8FHY_C_heavy,"353_TRP, 355_ARG, 357_ARG, 396_TYR, 462_LYS, 463_PRO, 464_PHE, 465_GLU, 466_ARG, 467_ASP, 468_ILE, 469_SER, 481_ASN",2.53
+8FHY_A,8FHY_B_light+8FHY_C_heavy,"409_GLN, 417_LYS, 420_ASP, 421_TYR, 449_TYR, 453_TYR, 455_LEU, 456_PHE, 460_ASN, 475_ALA, 476_GLY, 477_SER, 478_THR, 484_GLU, 485_GLY, 486_PHE, 487_ASN, 488_CYS, 489_TYR, 490_PHE, 493_GLN, 494_SER, 496_GLY, 501_ASN, 502_GLY, 505_TYR",2.53
+8FHY_A,8FHY_E_light+8FHY_F_heavy,"353_TRP, 354_ASN, 355_ARG, 357_ARG, 396_TYR, 457_ARG, 462_LYS, 464_PHE, 465_GLU, 466_ARG, 467_ASP, 468_ILE, 469_SER, 481_ASN, 516_GLU",2.53
+1AHW_F,1AHW_D_light+1AHW_E_heavy,"149_LYS, 152_ILE, 154_THR, 156_TYR, 165_LYS, 166_LYS, 167_THR, 168_ALA, 169_LYS, 170_THR, 171_ASN, 178_ASP, 190_GLN, 192_VAL, 194_PRO, 196_ARG, 197_THR, 198_VAL, 200_ARG, 201_LYS, 204_ASP",3.0
+1AHW_C,1AHW_A_light+1AHW_B_heavy,"149_LYS, 152_ILE, 154_THR, 156_TYR, 165_LYS, 166_LYS, 167_THR, 168_ALA, 169_LYS, 170_THR, 171_ASN, 190_GLN, 192_VAL, 194_PRO, 196_ARG, 198_VAL, 200_ARG, 201_LYS, 204_ASP",3.0
+5F9W_G,5F9W_C_light+5F9W_B_heavy,"122_LEU, 202_LYS, 325_ASP, 327_ARG, 419_LYS, 422_GLN, 423_ILE, 432_GLN, 437_PRO",2.89
+5F9W_G,5F9W_L_light+5F9W_H_heavy,"275_GLU, 276_ASN, 277_LEU, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 370_GLU, 371_ILE, 425_ASN, 426_MET, 427_TRP, 455_THR, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 461_ASN, 471_GLY, 472_GLY, 473_GLY, 476_LYS, 480_ARG, 96_TRP",2.89
+5F9W_A,5F9W_C_light+5F9W_B_heavy,"102_GLU, 275_GLU, 276_ASN, 277_LEU, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 365_SER, 367_GLY, 368_ASP, 370_GLU, 371_ILE, 425_ASN, 426_MET, 427_TRP, 455_THR, 457_ASP, 458_GLY, 459_GLY, 461_ASN, 471_GLY, 472_GLY, 473_GLY, 480_ARG, 96_TRP",2.89
+5F9W_A,5F9W_L_light+5F9W_H_heavy,"122_LEU, 202_LYS, 325_ASP, 327_ARG, 419_LYS, 421_LYS, 422_GLN, 423_ILE, 432_GLN, 437_PRO",2.89
+3NGB_D,3NGB_F_light+3NGB_E_heavy,"124_GLY, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 371_ILE, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_ALA, 463_THR, 465_ASN, 467_THR, 469_ARG, 473_GLY, 97_LYS",2.68
+3NGB_I,3NGB_C_light+3NGB_B_heavy,"120_VAL, 122_LEU, 124_GLY, 198_GLY, 423_ILE, 432_GLN, 434_MET, 437_PRO, 438_PRO",2.68
+3NGB_I,3NGB_K_light+3NGB_J_heavy,"124_GLY, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 365_SER, 366_GLY, 367_GLY, 368_ASP, 371_ILE, 430_THR, 455_THR, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_ALA, 461_ASN, 462_ASN, 465_ASN, 466_GLU, 467_THR, 469_ARG, 97_LYS",2.68
+3NGB_G,3NGB_L_light+3NGB_H_heavy,"124_GLY, 198_GLY, 276_ASN, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 365_SER, 367_GLY, 368_ASP, 371_ILE, 430_THR, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_ALA, 461_ASN, 463_THR, 465_ASN, 467_THR, 469_ARG, 473_GLY, 97_LYS",2.68
+3NGB_G,3NGB_F_light+3NGB_E_heavy,"120_VAL, 122_LEU, 124_GLY, 198_GLY, 423_ILE, 432_GLN, 434_MET",2.68
+3NGB_A,3NGB_C_light+3NGB_B_heavy,"124_GLY, 276_ASN, 278_THR, 279_ASN, 280_ASN, 281_ALA, 282_LYS, 283_THR, 365_SER, 366_GLY, 367_GLY, 368_ASP, 371_ILE, 430_THR, 455_THR, 456_ARG, 457_ASP, 458_GLY, 459_GLY, 460_ALA, 461_ASN, 463_THR, 465_ASN, 466_GLU, 467_THR, 469_ARG, 97_LYS",2.68
+3NGB_A,3NGB_K_light+3NGB_J_heavy,"120_VAL, 122_LEU, 124_GLY, 198_GLY, 421_LYS, 423_ILE, 432_GLN, 434_MET",2.68
+4ETQ_C,4ETQ_L_light+4ETQ_H_heavy,"174_ILE, 175_ASN, 176_HIS, 177_SER, 178_ALA, 179_ASP, 204_SER, 205_LEU, 215_ILE, 217_GLU, 219_TYR, 220_ARG, 221_ASN, 223_TYR, 224_LYS, 226_ASN, 227_ASP, 39_THR, 40_GLY, 41_LYS, 44_ARG, 5_LEU",2.1
+4ETQ_X,4ETQ_B_light+4ETQ_A_heavy,"108_LYS, 145_ASN, 174_ILE, 175_ASN, 176_HIS, 177_SER, 204_SER, 205_LEU, 215_ILE, 217_GLU, 219_TYR, 220_ARG, 221_ASN, 223_TYR, 224_LYS, 39_THR, 3_GLN, 40_GLY, 41_LYS, 44_ARG, 5_LEU",2.1
+6U9S_C,6U9S_B_light+6U9S_A_heavy,"157_CYS, 163_THR, 164_ALA, 166_THR, 167_THR, 168_SER, 170_LEU, 171_LYS, 177_SER, 178_GLY, 179_SER, 180_ASN, 184_ASN, 185_LEU, 186_PHE, 187_LYS, 192_GLN, 202_HIS",2.4
+6U9S_F,6U9S_E_light+6U9S_D_heavy,"157_CYS, 163_THR, 164_ALA, 166_THR, 167_THR, 168_SER, 170_LEU, 177_SER, 178_GLY, 179_SER, 180_ASN, 184_ASN, 185_LEU, 186_PHE, 187_LYS, 188_GLU, 192_GLN",2.4
+8SDH_A,8SDH_D_light+8SDH_C_heavy,"353_TRP, 355_ARG, 426_PRO, 428_ASP, 457_ARG, 458_LYS, 459_SER, 462_LYS, 463_PRO, 464_PHE, 465_GLU, 466_ARG, 467_ASP, 468_ILE, 469_SER, 470_THR, 471_GLU, 481_ASN, 482_GLY, 516_GLU, 518_LEU",2.84
+8SDH_B,8SDH_L_light+8SDH_H_heavy,"353_TRP, 426_PRO, 428_ASP, 457_ARG, 458_LYS, 462_LYS, 463_PRO, 464_PHE, 465_GLU, 466_ARG, 467_ASP, 468_ILE, 469_SER, 470_THR, 471_GLU, 481_ASN, 482_GLY, 518_LEU",2.84
+7LJ4_B,7LJ4_F_light+7LJ4_G_heavy,"70_GLU, 73_LEU, 74_ARG, 76_HIS, 77_PRO, 78_CYS, 79_VAL, 80_SER, 81_ASP, 82_GLN, 83_GLU",2.78
+7LJ4_A,7LJ4_D_light+7LJ4_E_heavy,"69_ARG, 70_GLU, 73_LEU, 74_ARG, 76_HIS, 77_PRO, 78_CYS, 79_VAL, 80_SER, 81_ASP, 82_GLN, 83_GLU",2.78
+7XSC_E,7XSC_B_light+7XSC_A_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 486_PHE, 487_ASN, 489_TYR, 498_GLN, 501_ASN, 502_GLY, 505_TYR",2.88
+7XSC_F,7XSC_H_light+7XSC_D_heavy,"403_ARG, 415_THR, 416_GLY, 417_LYS, 420_ASP, 421_TYR, 455_LEU, 456_PHE, 457_ARG, 458_LYS, 460_ASN, 473_TYR, 475_ALA, 476_GLY, 477_SER, 486_PHE, 487_ASN, 489_TYR, 495_TYR, 496_GLY, 501_ASN, 502_GLY, 505_TYR",2.88
+7D85_D,7D85_C_light+7D85_B_heavy,"448_SER, 449_LEU, 451_TRP, 460_GLU, 461_GLU, 463_LEU, 465_ILE, 466_LYS, 471_ARG, 472_ARG, 475_VAL, 476_ALA",2.5
+7D85_D,7D85_F_light+7D85_E_heavy,"316_GLY, 317_SER, 318_GLY, 319_SER, 322_GLN, 345_LEU, 381_GLN, 402_ARG, 403_SER, 404_LEU, 405_TYR, 406_ASN, 407_ARG, 408_GLY, 409_PHE, 412_LEU, 414_MET, 434_ARG, 436_TYR, 464_ASP, 466_LYS, 467_HIS",2.5
+7D85_A,7D85_C_light+7D85_B_heavy,"316_GLY, 317_SER, 318_GLY, 319_SER, 322_GLN, 345_LEU, 381_GLN, 402_ARG, 403_SER, 404_LEU, 405_TYR, 406_ASN, 407_ARG, 408_GLY, 409_PHE, 414_MET, 434_ARG, 436_TYR, 464_ASP, 466_LYS, 467_HIS",2.5
+7D85_A,7D85_F_light+7D85_E_heavy,"447_HIS, 448_SER, 449_LEU, 451_TRP, 459_THR, 460_GLU, 461_GLU, 463_LEU, 466_LYS, 471_ARG, 472_ARG, 475_VAL, 476_ALA",2.5
+7UPK_D,7UPK_L_light+7UPK_H_heavy,"246_LEU, 247_GLY, 248_TYR, 249_ALA, 250_THR, 251_GLU, 256_LEU, 282_PHE, 283_PRO, 284_ILE, 287_GLU, 290_GLN, 291_ALA, 292_TYR, 335_LYS, 336_ARG, 42_VAL, 43_THR, 44_ARG, 45_LYS, 47_LYS, 53_LEU, 55_LYS",2.8
+7UPK_D,7UPK_F_light+7UPK_C_heavy,"118_VAL, 119_ALA, 88_THR, 89_PRO, 92_GLY, 96_ILE",2.8
+7UPK_B,7UPK_L_light+7UPK_H_heavy,"118_VAL, 119_ALA, 88_THR, 89_PRO, 92_GLY, 96_ILE",2.8
+7UPK_B,7UPK_G_light+7UPK_E_heavy,"246_LEU, 247_GLY, 248_TYR, 250_THR, 251_GLU, 256_LEU, 282_PHE, 283_PRO, 284_ILE, 287_GLU, 290_GLN, 291_ALA, 292_TYR, 335_LYS, 336_ARG, 42_VAL, 43_THR, 44_ARG, 45_LYS, 47_LYS, 53_LEU, 55_LYS",2.8
+7UPK_A,7UPK_F_light+7UPK_C_heavy,"246_LEU, 247_GLY, 248_TYR, 249_ALA, 250_THR, 251_GLU, 256_LEU, 282_PHE, 283_PRO, 284_ILE, 287_GLU, 291_ALA, 292_TYR, 335_LYS, 336_ARG, 42_VAL, 43_THR, 44_ARG, 45_LYS, 47_LYS, 53_LEU, 55_LYS",2.8
+7UPK_A,7UPK_G_light+7UPK_E_heavy,"118_VAL, 119_ALA, 88_THR, 89_PRO, 92_GLY, 96_ILE",2.8
+7C88_M,7C88_L_light+7C88_H_heavy,"113_ARG, 115_MET, 116_ILE, 117_SER, 119_GLY, 120_GLY, 121_ALA, 122_ASP, 19_PHE, 54_ILE, 56_TYR, 58_GLU, 60_GLU, 61_ASP, 63_ASN, 66_GLN, 68_VAL, 69_HIS, 71_GLU, 76_VAL",2.0
+7C88_C,7C88_B_light+7C88_A_heavy,"113_ARG, 115_MET, 116_ILE, 117_SER, 119_GLY, 120_GLY, 121_ALA, 122_ASP, 19_PHE, 51_ALA, 54_ILE, 56_TYR, 58_GLU, 60_GLU, 61_ASP, 63_ASN, 66_GLN, 68_VAL, 69_HIS, 71_GLU, 76_VAL",2.0
+8JEL_J,8JEL_D_light+8JEL_C_heavy,"56_GLN, 60_GLU, 63_ASP, 64_GLN, 65_LEU, 67_ALA, 68_ILE, 70_ASN, 73_LEU, 74_GLY, 75_TRP, 76_HIS, 77_ILE, 78_SER, 79_PRO, 80_SER, 82_LYS",2.45
+8JEL_L,8JEL_K_light+8JEL_I_heavy,"56_GLN, 60_GLU, 63_ASP, 64_GLN, 65_LEU, 68_ILE, 70_ASN, 73_LEU, 74_GLY, 75_TRP, 76_HIS, 77_ILE, 78_SER, 79_PRO, 80_SER, 82_LYS",2.45
+8JEL_H,8JEL_G_light+8JEL_F_heavy,"56_GLN, 60_GLU, 63_ASP, 64_GLN, 65_LEU, 68_ILE, 70_ASN, 73_LEU, 74_GLY, 75_TRP, 76_HIS, 77_ILE, 78_SER, 79_PRO, 80_SER, 82_LYS",2.45
+8JEL_E,8JEL_B_light+8JEL_A_heavy,"56_GLN, 60_GLU, 62_GLN, 63_ASP, 64_GLN, 65_LEU, 68_ILE, 70_ASN, 73_LEU, 74_GLY, 75_TRP, 76_HIS, 77_ILE, 78_SER, 79_PRO, 80_SER, 82_LYS",2.45
+4XWO_T,4XWO_V_light+4XWO_U_heavy,"322_ARG, 63_SER, 64_ASP, 66_PHE, 67_GLY, 68_GLU, 69_LYS, 75_ARG",2.75
+4XWO_T,4XWO_X_light+4XWO_W_heavy,"246_PHE, 250_TYR, 253_GLU, 254_ARG, 256_ILE, 257_GLN, 260_ILE, 2_ASP, 301_GLN, 304_GLU, 305_LEU, 306_TYR, 307_GLU, 308_ASP, 309_PHE",2.75
+4XWO_A,4XWO_D_light+4XWO_C_heavy,"246_PHE, 249_LEU, 250_TYR, 253_GLU, 254_ARG, 256_ILE, 257_GLN, 301_GLN, 304_GLU, 305_LEU, 306_TYR, 307_GLU, 308_ASP, 309_PHE",2.75
+4XWO_A,4XWO_F_light+4XWO_E_heavy,"322_ARG, 63_SER, 64_ASP, 67_GLY, 68_GLU, 69_LYS, 75_ARG, 78_THR",2.75
+4XWO_G,4XWO_J_light+4XWO_I_heavy,"249_LEU, 250_TYR, 253_GLU, 254_ARG, 256_ILE, 257_GLN, 260_ILE, 301_GLN, 304_GLU, 305_LEU, 306_TYR, 307_GLU, 308_ASP, 309_PHE",2.75
+4XWO_G,4XWO_L_light+4XWO_K_heavy,"322_ARG, 63_SER, 64_ASP, 67_GLY, 68_GLU, 69_LYS",2.75
+4XWO_S,4XWO_V_light+4XWO_U_heavy,"246_PHE, 249_LEU, 250_TYR, 253_GLU, 254_ARG, 256_ILE, 257_GLN, 260_ILE, 301_GLN, 304_GLU, 305_LEU, 306_TYR, 307_GLU, 308_ASP, 309_PHE",2.75
+4XWO_S,4XWO_X_light+4XWO_W_heavy,"63_SER, 64_ASP, 67_GLY, 69_LYS, 75_ARG",2.75
+4XWO_M,4XWO_P_light+4XWO_O_heavy,"246_PHE, 249_LEU, 250_TYR, 253_GLU, 254_ARG, 256_ILE, 257_GLN, 260_ILE, 301_GLN, 304_GLU, 305_LEU, 306_TYR, 307_GLU, 308_ASP",2.75
+4XWO_M,4XWO_R_light+4XWO_Q_heavy,"322_ARG, 63_SER, 64_ASP, 67_GLY, 69_LYS, 75_ARG",2.75
+4XWO_B,4XWO_D_light+4XWO_C_heavy,"322_ARG, 63_SER, 64_ASP, 67_GLY, 68_GLU, 69_LYS, 75_ARG",2.75
+4XWO_B,4XWO_F_light+4XWO_E_heavy,"249_LEU, 250_TYR, 253_GLU, 254_ARG, 256_ILE, 257_GLN, 260_ILE, 301_GLN, 304_GLU, 305_LEU, 306_TYR, 307_GLU, 308_ASP",2.75
+4XWO_H,4XWO_J_light+4XWO_I_heavy,"322_ARG, 352_ASP, 63_SER, 64_ASP, 67_GLY, 68_GLU, 69_LYS",2.75
+4XWO_H,4XWO_L_light+4XWO_K_heavy,"246_PHE, 249_LEU, 250_TYR, 253_GLU, 254_ARG, 256_ILE, 257_GLN, 260_ILE, 301_GLN, 304_GLU, 305_LEU, 306_TYR, 307_GLU, 308_ASP, 309_PHE",2.75
+4XWO_N,4XWO_P_light+4XWO_O_heavy,"63_SER, 64_ASP, 67_GLY, 68_GLU, 69_LYS, 75_ARG",2.75
+4XWO_N,4XWO_R_light+4XWO_Q_heavy,"246_PHE, 249_LEU, 250_TYR, 253_GLU, 254_ARG, 256_ILE, 257_GLN, 260_ILE, 266_VAL, 267_ASN, 301_GLN, 304_GLU, 305_LEU, 306_TYR, 307_GLU, 308_ASP, 309_PHE",2.75
+7OX1_Z,7OX1_D_light+7OX1_C_heavy,"136_LYS, 140_MET, 143_LYS, 22_PRO, 23_THR, 24_LEU, 25_ALA, 27_ILE, 28_LEU, 29_ASP, 31_ASN, 32_PHE, 35_ASN, 36_LYS, 39_GLU, 85_TYR, 88_ILE, 91_ARG",2.49
+7OX1_X,7OX1_L_light+7OX1_H_heavy,"136_LYS, 140_MET, 143_LYS, 22_PRO, 23_THR, 24_LEU, 25_ALA, 27_ILE, 28_LEU, 29_ASP, 31_ASN, 32_PHE, 35_ASN, 36_LYS, 39_GLU, 85_TYR, 87_LEU, 88_ILE, 91_ARG",2.49
+7OX1_Y,7OX1_F_light+7OX1_E_heavy,"136_LYS, 140_MET, 22_PRO, 23_THR, 24_LEU, 25_ALA, 27_ILE, 28_LEU, 29_ASP, 31_ASN, 32_PHE, 35_ASN, 36_LYS, 39_GLU, 85_TYR, 88_ILE, 91_ARG",2.49
+7OX1_G,7OX1_B_light+7OX1_A_heavy,"136_LYS, 140_MET, 22_PRO, 23_THR, 24_LEU, 25_ALA, 27_ILE, 28_LEU, 29_ASP, 31_ASN, 32_PHE, 35_ASN, 36_LYS, 39_GLU, 85_TYR, 88_ILE, 91_ARG",2.49
+8QCM_A,8QCM_C_light+8QCM_D_heavy,"590_ASN, 593_PRO, 594_GLY, 595_LEU, 596_ASN, 602_PRO",2.39
+8QCM_A,8QCM_E_light+8QCM_F_heavy,"599_GLY, 601_ASN, 603_CYS, 604_ASN, 605_TYR, 612_GLU, 615_VAL, 616_LYS, 620_ASP, 621_LEU, 622_SER",2.39
+8QCM_B,8QCM_C_light+8QCM_D_heavy,"599_GLY, 601_ASN, 602_PRO, 603_CYS, 604_ASN, 605_TYR, 612_GLU, 615_VAL, 616_LYS, 620_ASP, 621_LEU, 622_SER",2.39
+8QCM_B,8QCM_E_light+8QCM_F_heavy,"590_ASN, 593_PRO, 594_GLY, 595_LEU, 596_ASN",2.39
+7K43_E,7K43_D_light+7K43_C_heavy,"342_PHE, 343_ASN, 368_LEU, 371_SER, 372_ALA, 373_SER, 436_TRP, 440_ASN, 441_LEU",2.6
+7K43_E,7K43_G_light+7K43_F_heavy,"446_GLY, 449_TYR, 452_LEU, 455_LEU, 456_PHE, 484_GLU, 485_GLY, 486_PHE, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER, 496_GLY",2.6
+7K43_A,7K43_L_light+7K43_H_heavy,"446_GLY, 449_TYR, 452_LEU, 455_LEU, 456_PHE, 484_GLU, 485_GLY, 486_PHE, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER, 496_GLY",2.6
+7K43_A,7K43_G_light+7K43_F_heavy,"342_PHE, 343_ASN, 368_LEU, 371_SER, 372_ALA, 373_SER, 436_TRP, 440_ASN, 441_LEU",2.6
+7K43_B,7K43_L_light+7K43_H_heavy,"342_PHE, 343_ASN, 368_LEU, 371_SER, 372_ALA, 373_SER, 436_TRP, 440_ASN, 441_LEU",2.6
+7K43_B,7K43_D_light+7K43_C_heavy,"446_GLY, 449_TYR, 452_LEU, 455_LEU, 456_PHE, 484_GLU, 485_GLY, 486_PHE, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER, 496_GLY",2.6
+7SHU_B,7SHU_F_light+7SHU_E_heavy,"373_THR, 374_TRP, 375_SER, 376_ARG, 377_ALA, 378_SER, 379_GLY, 380_LYS, 381_PRO, 412_GLU, 413_GLY, 414_GLU, 417_GLN, 419_ARG, 421_THR, 427_ARG, 428_ALA, 430_MET",2.75
+7SHU_A,7SHU_D_light+7SHU_C_heavy,"373_THR, 374_TRP, 375_SER, 376_ARG, 377_ALA, 378_SER, 380_LYS, 381_PRO, 412_GLU, 413_GLY, 414_GLU, 417_GLN, 419_ARG, 428_ALA, 430_MET",2.75
+8RUU_Y,8RUU_C_light+8RUU_B_heavy,"36_GLN, 37_ARG, 38_VAL, 39_SER, 41_SER, 42_ARG, 47_ARG",2.81
+8RUU_Y,8RUU_L_light+8RUU_H_heavy,"117_LEU, 119_THR, 125_VAL, 127_PRO, 128_VAL, 129_ILE, 130_HIS, 132_VAL, 133_GLN, 53_ASN, 70_ALA, 71_GLN, 72_CYS, 73_ARG, 74_ASN, 75_LEU, 84_GLU, 86_ILE, 87_SER, 88_MET, 89_ASN, 90_SER, 91_VAL, 92_PRO, 94_GLN",2.81
+8RUU_X,8RUU_C_light+8RUU_B_heavy,"117_LEU, 119_THR, 125_VAL, 127_PRO, 128_VAL, 129_ILE, 130_HIS, 53_ASN, 70_ALA, 71_GLN, 72_CYS, 73_ARG, 74_ASN, 75_LEU, 84_GLU, 86_ILE, 87_SER, 88_MET, 89_ASN, 90_SER, 91_VAL, 92_PRO, 94_GLN",2.81
+8RUU_X,8RUU_L_light+8RUU_H_heavy,"36_GLN, 37_ARG, 38_VAL, 39_SER, 41_SER, 42_ARG, 47_ARG",2.81
+8C7M_B,8C7M_D_light+8C7M_C_heavy,"251_ASP, 253_ARG, 255_ARG, 296_LYS, 299_ARG, 300_THR, 301_LEU, 302_HIS, 304_GLY, 305_LYS, 308_TYR, 425_GLU, 426_LYS, 427_LEU, 428_THR, 429_LEU",2.56
+8C7M_A,8C7M_L_light+8C7M_H_heavy,"251_ASP, 253_ARG, 255_ARG, 283_ARG, 299_ARG, 300_THR, 301_LEU, 302_HIS, 304_GLY, 305_LYS, 308_TYR, 425_GLU, 426_LYS, 427_LEU, 428_THR, 429_LEU",2.56
+7LY2_J,7LY2_D_light+7LY2_E_heavy,"342_PHE, 343_ASN, 368_LEU, 371_SER, 372_ALA, 373_SER, 374_PHE, 436_TRP, 441_LEU",2.5
+7LY2_J,7LY2_K_light+7LY2_M_heavy,"444_LYS, 446_GLY, 449_TYR, 452_LEU, 455_LEU, 456_PHE, 484_GLU, 485_GLY, 486_PHE, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER",2.5
+7LY2_J,7LY2_O_light+7LY2_N_heavy,"144_TYR, 145_TYR, 146_HIS, 147_LYS, 148_ASN, 155_SER, 17_ASN, 249_LEU, 250_THR, 251_PRO, 252_GLY, 253_ASP, 254_SER",2.5
+7LY2_A,7LY2_D_light+7LY2_E_heavy,"444_LYS, 446_GLY, 449_TYR, 452_LEU, 455_LEU, 456_PHE, 484_GLU, 485_GLY, 486_PHE, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER",2.5
+7LY2_A,7LY2_C_light+7LY2_F_heavy,"342_PHE, 343_ASN, 368_LEU, 371_SER, 372_ALA, 373_SER, 374_PHE, 436_TRP, 441_LEU",2.5
+7LY2_A,7LY2_L_light+7LY2_H_heavy,"144_TYR, 145_TYR, 146_HIS, 147_LYS, 148_ASN, 155_SER, 17_ASN, 249_LEU, 250_THR, 251_PRO, 252_GLY, 253_ASP, 254_SER",2.5
+7LY2_B,7LY2_C_light+7LY2_F_heavy,"444_LYS, 446_GLY, 449_TYR, 452_LEU, 455_LEU, 456_PHE, 484_GLU, 485_GLY, 486_PHE, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER",2.5
+7LY2_B,7LY2_K_light+7LY2_M_heavy,"342_PHE, 343_ASN, 368_LEU, 371_SER, 372_ALA, 373_SER, 374_PHE, 436_TRP, 441_LEU",2.5
+7LY2_B,7LY2_I_light+7LY2_G_heavy,"144_TYR, 145_TYR, 146_HIS, 147_LYS, 148_ASN, 155_SER, 17_ASN, 249_LEU, 250_THR, 251_PRO, 252_GLY, 253_ASP, 254_SER",2.5
+7YCL_D,7YCL_B_light+7YCL_A_heavy,"381_GLY, 383_SER, 386_LYS, 389_ASP, 390_LEU, 391_CYS, 428_ASP, 430_THR, 517_LEU, 518_LEU, 519_HIS",2.13
+7YCL_D,7YCL_L_light+7YCL_H_heavy,"369_TYR, 370_ASN, 371_SER, 372_ALA, 373_SER, 374_PHE, 375_SER, 378_LYS, 379_CYS, 385_THR, 404_GLY, 408_ARG, 503_VAL, 504_GLY, 508_TYR",2.13
+7YCL_R,7YCL_B_light+7YCL_A_heavy,"369_TYR, 370_ASN, 371_SER, 372_ALA, 373_SER, 374_PHE, 375_SER, 377_PHE, 378_LYS, 379_CYS, 384_PRO, 385_THR, 404_GLY, 405_ASP, 408_ARG, 503_VAL, 504_GLY, 508_TYR",2.13
+7YCL_R,7YCL_L_light+7YCL_H_heavy,"381_GLY, 382_VAL, 383_SER, 386_LYS, 389_ASP, 390_LEU, 391_CYS, 428_ASP, 430_THR, 517_LEU, 518_LEU, 519_HIS",2.13
+6SVL_F,6SVL_E_light+6SVL_D_heavy,"10_ASP, 12_ARG, 13_PRO, 16_VAL, 18_HIS, 20_PHE, 41_GLY, 42_THR, 43_ASN, 45_GLN, 47_GLN, 66_PRO, 7_VAL, 8_ALA, 9_PHE",1.58
+6SVL_Q,6SVL_B_light+6SVL_A_heavy,"125_LYS, 14_GLY, 39_GLN, 40_GLY, 73_TYR",1.58
+6SVL_Q,6SVL_L_light+6SVL_H_heavy,"10_ASP, 12_ARG, 13_PRO, 16_VAL, 18_HIS, 20_PHE, 41_GLY, 42_THR, 43_ASN, 45_GLN, 47_GLN, 7_VAL, 8_ALA, 9_PHE",1.58
+6SVL_Q,6SVL_P_light+6SVL_O_heavy,"19_SER, 21_SER, 31_THR, 33_MET, 82_ARG",1.58
+6SVL_J,6SVL_I_light+6SVL_G_heavy,"10_ASP, 12_ARG, 13_PRO, 16_VAL, 18_HIS, 20_PHE, 41_GLY, 42_THR, 43_ASN, 45_GLN, 47_GLN, 65_ARG, 66_PRO, 67_GLN, 7_VAL, 8_ALA, 9_PHE",1.58
+6SVL_R,6SVL_L_light+6SVL_H_heavy,"19_SER, 21_SER, 31_THR, 33_MET, 82_ARG",1.58
+6SVL_R,6SVL_M_light+6SVL_K_heavy,"122_GLY, 125_LYS, 14_GLY, 39_GLN, 40_GLY, 41_GLY, 73_TYR",1.58
+6SVL_R,6SVL_P_light+6SVL_O_heavy,"10_ASP, 12_ARG, 13_PRO, 16_VAL, 18_HIS, 20_PHE, 41_GLY, 42_THR, 43_ASN, 45_GLN, 47_GLN, 66_PRO, 7_VAL, 8_ALA, 9_PHE",1.58
+6SVL_N,6SVL_M_light+6SVL_K_heavy,"10_ASP, 12_ARG, 13_PRO, 16_VAL, 18_HIS, 20_PHE, 41_GLY, 42_THR, 43_ASN, 45_GLN, 47_GLN, 66_PRO, 7_VAL, 8_ALA, 9_PHE",1.58
+6SVL_C,6SVL_B_light+6SVL_A_heavy,"10_ASP, 12_ARG, 13_PRO, 16_VAL, 18_HIS, 20_PHE, 41_GLY, 42_THR, 43_ASN, 45_GLN, 47_GLN, 7_VAL, 8_ALA, 9_PHE",1.58
+6SVL_C,6SVL_L_light+6SVL_H_heavy,"122_GLY, 125_LYS, 14_GLY, 39_GLN, 40_GLY, 73_TYR",1.58
+6XM2_I,6XM2_C_light+6XM2_D_heavy,"313_VAL, 314_GLN, 315_ASP, 320_ARG, 321_PRO, 322_LEU, 323_TYR, 328_ARG, 329_ASP, 345_PHE, 347_ALA",1.91
+6XM2_I,6XM2_E_light+6XM2_F_heavy,"352_TYR, 368_ASN, 369_THR, 370_ILE, 371_ASN, 372_PRO, 373_GLU, 374_ALA, 375_SER, 376_ALA, 377_SER",1.91
+6XM2_J,6XM2_C_light+6XM2_D_heavy,"368_ASN, 369_THR, 370_ILE, 371_ASN, 372_PRO, 373_GLU, 374_ALA, 375_SER, 376_ALA, 377_SER",1.91
+6XM2_J,6XM2_E_light+6XM2_F_heavy,"313_VAL, 314_GLN, 315_ASP, 320_ARG, 321_PRO, 322_LEU, 323_TYR, 328_ARG, 329_ASP, 345_PHE, 347_ALA",1.91
+6XM2_L,6XM2_A_light+6XM2_B_heavy,"368_ASN, 369_THR, 370_ILE, 371_ASN, 372_PRO, 373_GLU, 374_ALA, 375_SER, 376_ALA, 377_SER",1.91
+6XM2_L,6XM2_G_light+6XM2_H_heavy,"313_VAL, 314_GLN, 315_ASP, 321_PRO, 322_LEU, 323_TYR, 328_ARG, 329_ASP, 345_PHE, 347_ALA",1.91
+6XM2_K,6XM2_A_light+6XM2_B_heavy,"313_VAL, 314_GLN, 315_ASP, 320_ARG, 321_PRO, 322_LEU, 323_TYR, 328_ARG, 329_ASP, 345_PHE, 347_ALA",1.91
+6XM2_K,6XM2_G_light+6XM2_H_heavy,"368_ASN, 369_THR, 370_ILE, 371_ASN, 372_PRO, 373_GLU, 374_ALA, 375_SER, 376_ALA, 377_SER",1.91
+1V7M_V,1V7M_L_light+1V7M_H_heavy,"102_GLY, 105_GLN, 106_SER, 109_GLY, 110_THR, 111_GLN, 112_LEU, 113_PRO, 114_PRO, 115_GLN, 57_GLU, 58_THR, 61_GLN, 68_THR, 71_LEU, 75_MET, 98_ARG",2.51
+1V7M_X,1V7M_M_light+1V7M_I_heavy,"102_GLY, 105_GLN, 106_SER, 109_GLY, 110_THR, 111_GLN, 112_LEU, 113_PRO, 115_GLN, 57_GLU, 58_THR, 61_GLN, 71_LEU, 75_MET, 98_ARG",2.51
+6RP8_C,6RP8_L_light+6RP8_H_heavy,"100_TYR, 102_PRO, 103_PRO, 104_TYR, 105_TYR, 106_LEU, 107_GLY, 108_ILE, 33_GLU, 35_ARG, 39_LEU, 3_MET, 46_VAL, 47_THR, 48_GLU, 95_LYS, 97_GLU, 99_MET",2.6
+6RP8_c,6RP8_l_light+6RP8_h_heavy,"100_TYR, 101_PRO, 102_PRO, 103_PRO, 104_TYR, 105_TYR, 106_LEU, 107_GLY, 108_ILE, 33_GLU, 35_ARG, 3_MET, 44_SER, 46_VAL, 48_GLU, 95_LYS, 97_GLU, 99_MET",2.6
+4PLJ_A,4PLJ_L_light+4PLJ_H_heavy,"476_THR, 479_GLU, 549_GLU, 551_GLY, 552_THR, 553_THR, 554_LYS, 586_THR, 587_ASN, 588_LEU, 589_GLY, 590_SER, 591_GLY, 592_PRO, 593_VAL, 594_SER",2.3
+4PLJ_B,4PLJ_C_light+4PLJ_D_heavy,"479_GLU, 549_GLU, 551_GLY, 552_THR, 553_THR, 554_LYS, 586_THR, 587_ASN, 588_LEU, 589_GLY, 590_SER, 591_GLY, 592_PRO, 593_VAL, 594_SER",2.3
+6N5D_K,6N5D_N_light+6N5D_L_heavy,"137_ASN, 138_ALA, 219_SER, 220_ARG, 221_PRO, 222_TRP, 223_VAL, 224_ARG, 225_GLY, 91_SER, 94_PHE, 96_ASN",3.0
+6N5D_B,6N5D_F_light+6N5D_E_heavy,"136_SER, 137_ASN, 138_ALA, 219_SER, 220_ARG, 221_PRO, 222_TRP, 223_VAL, 224_ARG, 225_GLY, 92_LYS, 94_PHE",3.0
+6N5D_A,6N5D_D_light+6N5D_C_heavy,"136_SER, 137_ASN, 138_ALA, 219_SER, 220_ARG, 221_PRO, 222_TRP, 223_VAL, 224_ARG, 225_GLY, 226_LEU, 229_ARG, 94_PHE",3.0
+7SO9_K,7SO9_D_light+7SO9_E_heavy,"342_PHE, 343_ASN, 368_LEU, 371_SER, 372_ALA, 373_SER, 374_PHE, 436_TRP, 441_LEU",2.4
+7SO9_K,7SO9_L_light+7SO9_M_heavy,"446_GLY, 449_TYR, 456_PHE, 484_GLU, 485_GLY, 486_PHE, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER",2.4
+7SO9_K,7SO9_N_light+7SO9_O_heavy,"108_THR, 109_THR, 111_ASP, 113_LYS, 114_THR, 236_THR, 237_ARG, 269_TYR, 26_PRO, 270_LEU, 28_TYR, 323_THR, 63_THR, 82_PRO, 83_VAL, 85_PRO, 87_ASN, 88_ASP",2.4
+7SO9_A,7SO9_D_light+7SO9_E_heavy,"446_GLY, 449_TYR, 456_PHE, 484_GLU, 485_GLY, 486_PHE, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER",2.4
+7SO9_A,7SO9_G_light+7SO9_H_heavy,"342_PHE, 343_ASN, 368_LEU, 371_SER, 372_ALA, 373_SER, 374_PHE, 436_TRP, 441_LEU",2.4
+7SO9_A,7SO9_B_light+7SO9_C_heavy,"108_THR, 109_THR, 111_ASP, 113_LYS, 114_THR, 236_THR, 237_ARG, 269_TYR, 26_PRO, 270_LEU, 28_TYR, 323_THR, 63_THR, 82_PRO, 83_VAL, 85_PRO, 87_ASN, 88_ASP",2.4
+7SO9_F,7SO9_G_light+7SO9_H_heavy,"446_GLY, 449_TYR, 456_PHE, 484_GLU, 485_GLY, 486_PHE, 489_TYR, 490_PHE, 492_LEU, 493_GLN, 494_SER",2.4
+7SO9_F,7SO9_L_light+7SO9_M_heavy,"342_PHE, 343_ASN, 368_LEU, 371_SER, 372_ALA, 373_SER, 374_PHE, 436_TRP, 441_LEU",2.4
+7SO9_F,7SO9_I_light+7SO9_J_heavy,"108_THR, 109_THR, 111_ASP, 113_LYS, 114_THR, 236_THR, 237_ARG, 269_TYR, 26_PRO, 270_LEU, 28_TYR, 323_THR, 63_THR, 82_PRO, 83_VAL, 85_PRO, 87_ASN, 88_ASP",2.4
+4XNM_C,4XNM_L_light+4XNM_H_heavy,"135_VAL, 136_SER, 137_SER, 142_GLN, 143_GLY, 144_LYS, 153_TRP, 156_LYS, 157_LYS, 158_ASN, 190_GLU, 193_LYS, 194_LEU, 225_GLY, 226_GLN, 98_TYR",2.51
+4XNM_D,4XNM_A_light+4XNM_B_heavy,"133_SER, 135_VAL, 136_SER, 137_SER, 142_GLN, 143_GLY, 144_LYS, 153_TRP, 156_LYS, 158_ASN, 190_GLU, 193_LYS, 194_LEU, 225_GLY, 226_GLN, 98_TYR",2.51
+4XZU_G,4XZU_F_light+4XZU_E_heavy,"2181_GLU, 2182_SER, 2183_LYS, 2184_ALA, 2186_SER, 2187_ASP, 2211_HIS, 2213_GLN, 2214_GLY, 2215_ARG",2.61
+4XZU_M,4XZU_B_light+4XZU_A_heavy,"2181_GLU, 2182_SER, 2183_LYS, 2184_ALA, 2186_SER, 2187_ASP, 2211_HIS, 2213_GLN, 2214_GLY, 2215_ARG",2.61
+6N81_A,6N81_L_light+6N81_H_heavy,"225_LYS, 269_ASP, 271_VAL, 316_GLU, 462_TYR, 463_GLN, 465_ALA, 466_ALA, 467_PRO, 468_ALA, 469_GLN, 470_SER, 493_LYS",2.58
+6N81_B,6N81_D_light+6N81_C_heavy,"225_LYS, 269_ASP, 271_VAL, 315_GLU, 316_GLU, 317_ILE, 462_TYR, 463_GLN, 465_ALA, 466_ALA, 467_PRO, 468_ALA, 469_GLN, 470_SER, 493_LYS",2.58
+7NX3_F,7NX3_C_light+7NX3_B_heavy,"787_ASN, 788_GLN, 789_LEU, 790_ILE, 793_VAL, 814_TRP, 890_LEU, 904_HIS, 905_SER, 907_PRO, 910_MET, 911_LYS, 912_LYS, 913_TRP, 914_GLY, 915_TRP, 916_GLU, 918_ARG",2.81
+7NX3_F,7NX3_D_light+7NX3_E_heavy,"680_THR, 682_HIS, 683_TRP, 684_LEU, 686_THR, 687_THR, 689_GLY, 690_ALA, 691_SER, 703_ASN, 704_ALA, 707_ASN, 708_SER, 745_LYS, 749_ASN, 753_ARG, 778_GLN, 781_ASP, 784_PRO, 786_THR, 795_ILE, 973_MET, 974_GLU, 975_GLY, 976_HIS, 977_GLY, 978_GLU, 982_LYS, 985_LEU",2.81
+7NX3_A,7NX3_C_light+7NX3_B_heavy,"680_THR, 682_HIS, 683_TRP, 684_LEU, 686_THR, 687_THR, 689_GLY, 690_ALA, 691_SER, 703_ASN, 704_ALA, 707_ASN, 708_SER, 709_ASN, 749_ASN, 753_ARG, 778_GLN, 781_ASP, 784_PRO, 786_THR, 791_GLN, 795_ILE, 973_MET, 974_GLU, 975_GLY, 976_HIS, 977_GLY, 978_GLU, 980_ASN, 982_LYS, 985_LEU",2.81
+7NX3_A,7NX3_D_light+7NX3_E_heavy,"787_ASN, 788_GLN, 789_LEU, 790_ILE, 793_VAL, 814_TRP, 890_LEU, 904_HIS, 905_SER, 907_PRO, 910_MET, 911_LYS, 912_LYS, 913_TRP, 914_GLY, 915_TRP, 916_GLU, 918_ARG",2.81
+8PY4_B,8PY4_C_light+8PY4_D_heavy,"599_GLY, 600_ASN, 601_ASN, 602_PRO, 603_CYS, 604_ASN, 605_TYR, 612_GLU, 615_VAL, 620_ASP",3.0
+8PY4_B,8PY4_E_light+8PY4_F_heavy,"590_ASN, 593_PRO, 594_GLY, 595_LEU, 596_ASN",3.0
+8PY4_A,8PY4_C_light+8PY4_D_heavy,"590_ASN, 593_PRO, 594_GLY, 595_LEU, 596_ASN",3.0
+8PY4_A,8PY4_E_light+8PY4_F_heavy,"599_GLY, 600_ASN, 601_ASN, 602_PRO, 603_CYS, 604_ASN, 605_TYR, 612_GLU, 615_VAL, 620_ASP, 621_LEU",3.0
diff --git a/data/species.json b/data/species.json
new file mode 100644
index 0000000000000000000000000000000000000000..196d5f926f219fe2ec6532a3a5a8ca4b11543d1b
--- /dev/null
+++ b/data/species.json
@@ -0,0 +1,3562 @@
+{
+  "4uuj": {
+    "chain": "C",
+    "organism": "STREPTOMYCES LIVIDANS",
+    "classification": "other"
+  },
+  "7amp": {
+    "chain": "B",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8f5n": {
+    "chain": "A",
+    "organism": "Streptococcus pyogenes",
+    "classification": "bacterial"
+  },
+  "5bo1": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "1ncc": {
+    "chain": "N",
+    "organism": "Influenza A virus",
+    "classification": "viral"
+  },
+  "8x0t": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8g4t": {
+    "chain": "I",
+    "organism": "Human immunodeficiency virus 1",
+    "classification": "viral"
+  },
+  "6o39": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "3wfd": {
+    "chain": "C",
+    "organism": "Pseudomonas aeruginosa",
+    "classification": "bacterial"
+  },
+  "8w85": {
+    "chain": "H",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6rps": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "9ivk": {
+    "chain": "A",
+    "organism": "artificial sequences",
+    "classification": "other"
+  },
+  "7kql": {
+    "chain": "T",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7kyo": {
+    "chain": "B",
+    "organism": "Streptococcus pneumoniae serotype 2 (strain D39 / NCTC 7466)",
+    "classification": "bacterial"
+  },
+  "3o2d": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "1uwx": {
+    "chain": "B",
+    "organism": "STREPTOCOCCUS SP.",
+    "classification": "bacterial"
+  },
+  "2oz4": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6xkq": {
+    "chain": "A",
+    "organism": "Severe acute respiratory syndrome coronavirus 2",
+    "classification": "viral"
+  },
+  "8d9z": {
+    "chain": "D",
+    "organism": "Naja nivea",
+    "classification": "other"
+  },
+  "8v52": {
+    "chain": "B",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4xnm": {
+    "chain": "C",
+    "organism": "Influenza A virus",
+    "classification": "viral"
+  },
+  "6p67": {
+    "chain": "G",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6qig": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "9dez": {
+    "chain": "C",
+    "organism": "Porcine deltacoronavirus",
+    "classification": "viral"
+  },
+  "8cdd": {
+    "chain": "B",
+    "organism": "Plasmodium falciparum 3D7",
+    "classification": "other"
+  },
+  "4qnp": {
+    "chain": "A",
+    "organism": "Influenza A virus",
+    "classification": "viral"
+  },
+  "4lss": {
+    "chain": "G",
+    "organism": "Human immunodeficiency virus 1",
+    "classification": "viral"
+  },
+  "4zs7": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "9b2w": {
+    "chain": "D",
+    "organism": "Human respirovirus 3",
+    "classification": "viral"
+  },
+  "6vgr": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4nnp": {
+    "chain": "B",
+    "organism": "Staphylococcus aureus subsp. aureus",
+    "classification": "bacterial"
+  },
+  "5lsp": {
+    "chain": "P",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7ec5": {
+    "chain": "B",
+    "organism": "Human enterovirus D68",
+    "classification": "viral"
+  },
+  "8ffe": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "1mhp": {
+    "chain": "B",
+    "organism": "Rattus norvegicus",
+    "classification": "other"
+  },
+  "4uu9": {
+    "chain": "D",
+    "organism": "HOMO SAPIENS",
+    "classification": "human"
+  },
+  "7tuf": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7sue": {
+    "chain": "D",
+    "organism": "Severe acute respiratory syndrome coronavirus 2",
+    "classification": "viral"
+  },
+  "7ul3": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8dk6": {
+    "chain": "E",
+    "organism": "Hepatitis C virus isolate HC-J6",
+    "classification": "viral"
+  },
+  "8y0q": {
+    "chain": "2",
+    "organism": "Foot-and-mouth disease virus O",
+    "classification": "viral"
+  },
+  "7upw": {
+    "chain": "A",
+    "organism": "Severe acute respiratory syndrome coronavirus",
+    "classification": "viral"
+  },
+  "4qti": {
+    "chain": "U",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4o58": {
+    "chain": "A",
+    "organism": "Influenza A virus",
+    "classification": "viral"
+  },
+  "4krp": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4yxl": {
+    "chain": "A",
+    "organism": "Mesocricetus auratus",
+    "classification": "other"
+  },
+  "7lr3": {
+    "chain": "C",
+    "organism": "Plasmodium berghei",
+    "classification": "other"
+  },
+  "4f2m": {
+    "chain": "E",
+    "organism": "TGEV virulent Purdue",
+    "classification": "other"
+  },
+  "5jxe": {
+    "chain": "B",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8tzw": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6elu": {
+    "chain": "A",
+    "organism": "Trypanosoma brucei rhodesiense",
+    "classification": "other"
+  },
+  "6nyq": {
+    "chain": "C",
+    "organism": "Mus musculus",
+    "classification": "other"
+  },
+  "9jbq": {
+    "chain": "C",
+    "organism": "Pseudomonas aeruginosa",
+    "classification": "bacterial"
+  },
+  "7wn8": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7quh": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7o9s": {
+    "chain": "A",
+    "organism": "Hantaan orthohantavirus",
+    "classification": "viral"
+  },
+  "8pg0": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "1g9n": {
+    "chain": "G",
+    "organism": "Human immunodeficiency virus 1",
+    "classification": "viral"
+  },
+  "5bk2": {
+    "chain": "A",
+    "organism": "Escherichia coli",
+    "classification": "bacterial"
+  },
+  "7uvf": {
+    "chain": "B",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6mei": {
+    "chain": "C",
+    "organism": "Hepacivirus C",
+    "classification": "viral"
+  },
+  "5w3l": {
+    "chain": "B",
+    "organism": "Human rhinovirus 14",
+    "classification": "viral"
+  },
+  "8tco": {
+    "chain": "C",
+    "organism": "Human betaherpesvirus 5",
+    "classification": "viral"
+  },
+  "3u9p": {
+    "chain": "C",
+    "organism": "Mus musculus",
+    "classification": "other"
+  },
+  "6wio": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8vzn": {
+    "chain": "A",
+    "organism": "Mus musculus",
+    "classification": "other"
+  },
+  "5bv7": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7jkt": {
+    "chain": "G",
+    "organism": "Human immunodeficiency virus 1",
+    "classification": "viral"
+  },
+  "7x28": {
+    "chain": "F",
+    "organism": "Middle East respiratory syndrome-related coronavirus",
+    "classification": "viral"
+  },
+  "6xxv": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "3ld8": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4lst": {
+    "chain": "G",
+    "organism": "Human immunodeficiency virus 1",
+    "classification": "viral"
+  },
+  "4d3c": {
+    "chain": "A",
+    "organism": "HOMO SAPIENS",
+    "classification": "human"
+  },
+  "8a44": {
+    "chain": "A",
+    "organism": "Plasmodium vivax",
+    "classification": "other"
+  },
+  "4hwb": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "3v6o": {
+    "chain": "B",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6ogx": {
+    "chain": "G",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "2xra": {
+    "chain": "A",
+    "organism": "SYNTHETIC CONSTRUCT",
+    "classification": "other"
+  },
+  "6q20": {
+    "chain": "A",
+    "organism": "Influenza A virus (strain A/Japan/305/1957 H2N2)",
+    "classification": "viral"
+  },
+  "4jqi": {
+    "chain": "A",
+    "organism": "Rattus norvegicus",
+    "classification": "other"
+  },
+  "5ush": {
+    "chain": "X",
+    "organism": "Vaccinia virus",
+    "classification": "viral"
+  },
+  "3hae": {
+    "chain": "K",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "2hmi": {
+    "chain": "B",
+    "organism": "Human immunodeficiency virus 1",
+    "classification": "viral"
+  },
+  "3vg9": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6h2y": {
+    "chain": "D",
+    "organism": "Neisseria meningitidis",
+    "classification": "other"
+  },
+  "8u03": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6bkc": {
+    "chain": "E",
+    "organism": "Recombinant Hepatitis C virus HK6a/JFH-1",
+    "classification": "viral"
+  },
+  "6z3q": {
+    "chain": "C",
+    "organism": "Enterovirus A71",
+    "classification": "viral"
+  },
+  "8dn6": {
+    "chain": "A",
+    "organism": "Arabidopsis thaliana",
+    "classification": "other"
+  },
+  "4qci": {
+    "chain": "D",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "3d85": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "3hi6": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8c7h": {
+    "chain": "D",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7mrz": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7zqt": {
+    "chain": "E",
+    "organism": "Helicobacter pylori",
+    "classification": "other"
+  },
+  "7ued": {
+    "chain": "M",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6wgl": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "1nsn": {
+    "chain": "S",
+    "organism": "Staphylococcus aureus",
+    "classification": "bacterial"
+  },
+  "8y31": {
+    "chain": "E",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6pe8": {
+    "chain": "U",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "1xiw": {
+    "chain": "B",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7wg3": {
+    "chain": "K",
+    "organism": "Bos taurus",
+    "classification": "other"
+  },
+  "8dn7": {
+    "chain": "C",
+    "organism": "Pisum sativum",
+    "classification": "other"
+  },
+  "2hfg": {
+    "chain": "R",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "5xez": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8v91": {
+    "chain": "B",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "2j4w": {
+    "chain": "D",
+    "organism": "PLASMODIUM VIVAX",
+    "classification": "other"
+  },
+  "8e8x": {
+    "chain": "1",
+    "organism": "Human poliovirus 3 strain Sabin",
+    "classification": "viral"
+  },
+  "1kb5": {
+    "chain": "A",
+    "organism": "Mus musculus",
+    "classification": "other"
+  },
+  "6gv4": {
+    "chain": "B",
+    "organism": "Human parechovirus 3",
+    "classification": "viral"
+  },
+  "8sxp": {
+    "chain": "C",
+    "organism": "Ophiophagus hannah",
+    "classification": "other"
+  },
+  "6ztr": {
+    "chain": "I",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6cw3": {
+    "chain": "F",
+    "organism": "Saccharomyces cerevisiae S288c",
+    "classification": "fungal"
+  },
+  "8tq9": {
+    "chain": "B",
+    "organism": "Mus musculus",
+    "classification": "other"
+  },
+  "6wfw": {
+    "chain": "G",
+    "organism": "Streptococcus sp. group G",
+    "classification": "bacterial"
+  },
+  "8ee0": {
+    "chain": "A",
+    "organism": "Saccharopolyspora erythraea",
+    "classification": "other"
+  },
+  "3lev": {
+    "chain": "A",
+    "organism": "Thermus aquaticus",
+    "classification": "other"
+  },
+  "6y9b": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7v23": {
+    "chain": "A",
+    "organism": "Severe acute respiratory syndrome coronavirus 2",
+    "classification": "viral"
+  },
+  "8bk2": {
+    "chain": "B",
+    "organism": "Neisseria meningitidis serogroup B",
+    "classification": "other"
+  },
+  "5h35": {
+    "chain": "C",
+    "organism": "Sulfolobus solfataricus",
+    "classification": "other"
+  },
+  "5ldn": {
+    "chain": "A",
+    "organism": "Human adenovirus C serotype 5",
+    "classification": "viral"
+  },
+  "1ors": {
+    "chain": "C",
+    "organism": "Aeropyrum pernix",
+    "classification": "other"
+  },
+  "6fgb": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7tyv": {
+    "chain": "B",
+    "organism": "Lassa virus",
+    "classification": "viral"
+  },
+  "3qwo": {
+    "chain": "C",
+    "organism": "Staphylococcus aureus",
+    "classification": "bacterial"
+  },
+  "7n8q": {
+    "chain": "A",
+    "organism": "Human immunodeficiency virus 1",
+    "classification": "viral"
+  },
+  "4ye4": {
+    "chain": "G",
+    "organism": "Human immunodeficiency virus",
+    "classification": "viral"
+  },
+  "3uc0": {
+    "chain": "B",
+    "organism": "Dengue virus 4",
+    "classification": "viral"
+  },
+  "6mlk": {
+    "chain": "A",
+    "organism": "Saccharopolyspora erythraea",
+    "classification": "other"
+  },
+  "6hga": {
+    "chain": "B",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "1fns": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
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+  "7n0u": {
+    "chain": "C",
+    "organism": "Betula pendula",
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+  },
+  "8db4": {
+    "chain": "E",
+    "organism": "Arachis hypogaea",
+    "classification": "other"
+  },
+  "6id4": {
+    "chain": "E",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6svl": {
+    "chain": "N",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4wfg": {
+    "chain": "B",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7shu": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8k3c": {
+    "chain": "B",
+    "organism": "Henipavirus nipahense",
+    "classification": "viral"
+  },
+  "5e94": {
+    "chain": "G",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8byu": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4cad": {
+    "chain": "I",
+    "organism": "METHANOCOCCUS MARIPALUDIS",
+    "classification": "other"
+  },
+  "4o9h": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "5utz": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "5eu7": {
+    "chain": "B",
+    "organism": "Human immunodeficiency virus 1",
+    "classification": "viral"
+  },
+  "8e8r": {
+    "chain": "2",
+    "organism": "Human poliovirus 3 strain Sabin",
+    "classification": "viral"
+  },
+  "8uky": {
+    "chain": "D",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "9dh2": {
+    "chain": "T",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7x8q": {
+    "chain": "D",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8yxi": {
+    "chain": "A",
+    "organism": "Severe fever with thrombocytopenia syndrome virus",
+    "classification": "viral"
+  },
+  "5mo9": {
+    "chain": "X",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "3skj": {
+    "chain": "F",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "3grw": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7xw6": {
+    "chain": "R",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6wzk": {
+    "chain": "E",
+    "organism": "Macaca fascicularis",
+    "classification": "other"
+  },
+  "8u2c": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6ks0": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7yms": {
+    "chain": "A",
+    "organism": "Coxsackievirus A16",
+    "classification": "viral"
+  },
+  "8j8r": {
+    "chain": "B",
+    "organism": "Bos taurus",
+    "classification": "other"
+  },
+  "6ba5": {
+    "chain": "N",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7daa": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "1dee": {
+    "chain": "H",
+    "organism": "Staphylococcus aureus",
+    "classification": "bacterial"
+  },
+  "7ox3": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6was": {
+    "chain": "G",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8tq8": {
+    "chain": "C",
+    "organism": "Mus musculus",
+    "classification": "other"
+  },
+  "4yxk": {
+    "chain": "A",
+    "organism": "Cervus elaphus nelsoni",
+    "classification": "other"
+  },
+  "6u36": {
+    "chain": "B",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "2qqn": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "3cvh": {
+    "chain": "M",
+    "organism": "Mus musculus",
+    "classification": "other"
+  },
+  "4jpk": {
+    "chain": "A",
+    "organism": "Human immunodeficiency virus 1",
+    "classification": "viral"
+  },
+  "1v7m": {
+    "chain": "V",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "2ybr": {
+    "chain": "I",
+    "organism": "CENTRUROIDES NOXIUS HOFFMANN",
+    "classification": "other"
+  },
+  "4ht1": {
+    "chain": "T",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4bz2": {
+    "chain": "A",
+    "organism": "DENGUE VIRUS 4",
+    "classification": "viral"
+  },
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+    "chain": "A",
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+    "classification": "viral"
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+    "chain": "C",
+    "organism": "Ceratogyrus marshalli",
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+  },
+  "7lxx": {
+    "chain": "A",
+    "organism": "Severe acute respiratory syndrome coronavirus 2",
+    "classification": "viral"
+  },
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+    "chain": "P",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6nms": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6vep": {
+    "chain": "Q",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8tv3": {
+    "chain": "A",
+    "organism": "Borreliella burgdorferi B31",
+    "classification": "other"
+  },
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+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7zoz": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4rwy": {
+    "chain": "A",
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+    "classification": "viral"
+  },
+  "3u4e": {
+    "chain": "J",
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+  },
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+    "chain": "E",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7rew": {
+    "chain": "I",
+    "organism": "Macaca fascicularis",
+    "classification": "other"
+  },
+  "8tlm": {
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+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8vsj": {
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+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6ks1": {
+    "chain": "A",
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+    "classification": "human"
+  },
+  "4leo": {
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+    "classification": "human"
+  },
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+    "organism": "Mus musculus",
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+  },
+  "6pi7": {
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+    "classification": "human"
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+    "organism": "Escherichia coli",
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+  },
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+    "organism": "Homo sapiens",
+    "classification": "human"
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+    "classification": "human"
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+  "7s7i": {
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+    "classification": "human"
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+    "chain": "I",
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+  },
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+    "classification": "human"
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+    "organism": "SUID HERPESVIRUS",
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+    "organism": "Salmonella typhimurium (strain LT2 / SGSC1412 / ATCC 700720)",
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+    "organism": "Dengue virus type 1 (strain Nauru/West Pac/1974)",
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+    "organism": "Staphylococcus aureus (strain NCTC 8325)",
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+    "classification": "human"
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+    "classification": "human"
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+    "organism": "Mus musculus",
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+    "classification": "human"
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+    "classification": "human"
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+    "organism": "PLASMODIUM FALCIPARUM",
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+    "organism": "Homo sapiens",
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+    "organism": "Borrelia burgdorferi",
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+    "chain": "B",
+    "organism": "Homo sapiens",
+    "classification": "human"
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+    "organism": "Human Cytomegalovirus",
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+    "chain": "C",
+    "organism": "Argentinian mammarenavirus",
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+    "chain": "B",
+    "organism": "Homo sapiens",
+    "classification": "human"
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+    "chain": "A",
+    "organism": "Plasmodium falciparum (isolate 3D7)",
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+    "organism": "Saccharomyces cerevisiae",
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+  },
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+    "organism": "Homo sapiens",
+    "classification": "human"
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+  "8e8y": {
+    "chain": "1",
+    "organism": "Human poliovirus 2 strain Sabin",
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+    "chain": "P",
+    "organism": "HIV-1 M\\:B_HXB2R",
+    "classification": "other"
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+    "organism": "Homo sapiens",
+    "classification": "human"
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+    "organism": "Human immunodeficiency virus 1",
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+    "organism": "Hendra henipavirus",
+    "classification": "viral"
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+  "2uzi": {
+    "chain": "R",
+    "organism": "HOMO SAPIENS",
+    "classification": "human"
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+    "organism": "Homo sapiens",
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+    "organism": "Severe acute respiratory syndrome coronavirus 2",
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+    "organism": "Human immunodeficiency virus 1",
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+    "organism": "Severe acute respiratory syndrome coronavirus 2",
+    "classification": "viral"
+  },
+  "8vyn": {
+    "chain": "C",
+    "organism": "Human betaherpesvirus 5",
+    "classification": "viral"
+  },
+  "8jkf": {
+    "chain": "A",
+    "organism": "Zika virus",
+    "classification": "viral"
+  },
+  "6iek": {
+    "chain": "D",
+    "organism": "Rift valley fever virus",
+    "classification": "viral"
+  },
+  "6wbv": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6hx4": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7rxl": {
+    "chain": "F",
+    "organism": "Plasmodium falciparum (isolate 3D7)",
+    "classification": "other"
+  },
+  "5fb8": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "5l0q": {
+    "chain": "A",
+    "organism": "Bos taurus",
+    "classification": "other"
+  },
+  "7a3o": {
+    "chain": "A",
+    "organism": "Dengue virus 1",
+    "classification": "viral"
+  },
+  "6vn1": {
+    "chain": "C",
+    "organism": "Human alphaherpesvirus 3",
+    "classification": "viral"
+  },
+  "8ol9": {
+    "chain": "H",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "3mj9": {
+    "chain": "A",
+    "organism": "Mus musculus",
+    "classification": "other"
+  },
+  "6lz9": {
+    "chain": "B",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "5d93": {
+    "chain": "A",
+    "organism": "Mus musculus",
+    "classification": "other"
+  },
+  "3i50": {
+    "chain": "E",
+    "organism": "West Nile virus",
+    "classification": "viral"
+  },
+  "6blh": {
+    "chain": "G",
+    "organism": "Human respiratory syncytial virus A (strain rsb6256)",
+    "classification": "viral"
+  },
+  "8jel": {
+    "chain": "J",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "5te4": {
+    "chain": "G",
+    "organism": "Human immunodeficiency virus 1",
+    "classification": "viral"
+  },
+  "7m3n": {
+    "chain": "A",
+    "organism": "Canine parvovirus type 2",
+    "classification": "viral"
+  },
+  "6o3b": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "5lcv": {
+    "chain": "A",
+    "organism": "Zika virus",
+    "classification": "viral"
+  },
+  "8ath": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "5nmv": {
+    "chain": "K",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8hrx": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7uvi": {
+    "chain": "C",
+    "organism": "Plasmodium falciparum",
+    "classification": "other"
+  },
+  "9fjk": {
+    "chain": "B",
+    "organism": "Severe acute respiratory syndrome coronavirus 2",
+    "classification": "viral"
+  },
+  "6lxj": {
+    "chain": "D",
+    "organism": "Influenza A virus (A/Anhui/1-BALF_RG44/2013(H7N9))",
+    "classification": "viral"
+  },
+  "7ttm": {
+    "chain": "A",
+    "organism": "Bat SARS-like coronavirus RsSHC014",
+    "classification": "viral"
+  },
+  "2aep": {
+    "chain": "A",
+    "organism": "Influenza A virus",
+    "classification": "viral"
+  },
+  "1sy6": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8udz": {
+    "chain": "B",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "1xf5": {
+    "chain": "L",
+    "organism": "Finegoldia magna",
+    "classification": "other"
+  },
+  "1egj": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6xcj": {
+    "chain": "G",
+    "organism": "Human immunodeficiency virus 1",
+    "classification": "viral"
+  },
+  "6ml8": {
+    "chain": "A",
+    "organism": "Influenza A virus",
+    "classification": "viral"
+  },
+  "5jq6": {
+    "chain": "A",
+    "organism": "Staphylococcus aureus",
+    "classification": "bacterial"
+  },
+  "4i2x": {
+    "chain": "E",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6wo4": {
+    "chain": "E",
+    "organism": "Recombinant Hepatitis C virus HK6a/JFH-1",
+    "classification": "viral"
+  },
+  "4np4": {
+    "chain": "A",
+    "organism": "Clostridium difficile",
+    "classification": "other"
+  },
+  "4ps4": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "3idx": {
+    "chain": "G",
+    "organism": "Human immunodeficiency virus 1",
+    "classification": "viral"
+  },
+  "6iuv": {
+    "chain": "A",
+    "organism": "Influenza A virus (A/Hong Kong/482/97(H5N1))",
+    "classification": "viral"
+  },
+  "8h07": {
+    "chain": "B",
+    "organism": "Severe acute respiratory syndrome coronavirus 2",
+    "classification": "viral"
+  },
+  "3gi9": {
+    "chain": "C",
+    "organism": "Methanocaldococcus jannaschii",
+    "classification": "other"
+  },
+  "6bgt": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8t9z": {
+    "chain": "A",
+    "organism": "Human metapneumovirus",
+    "classification": "viral"
+  },
+  "2vxs": {
+    "chain": "D",
+    "organism": "HOMO SAPIENS",
+    "classification": "human"
+  },
+  "2vxt": {
+    "chain": "I",
+    "organism": "HOMO SAPIENS",
+    "classification": "human"
+  },
+  "5u3d": {
+    "chain": "E",
+    "organism": "Finegoldia magna",
+    "classification": "other"
+  },
+  "6phc": {
+    "chain": "I",
+    "organism": "Plasmodium falciparum",
+    "classification": "other"
+  },
+  "1fbi": {
+    "chain": "Y",
+    "organism": "Numida meleagris",
+    "classification": "other"
+  },
+  "6adb": {
+    "chain": "A",
+    "organism": "Escherichia coli (strain K12)",
+    "classification": "bacterial"
+  },
+  "4plj": {
+    "chain": "A",
+    "organism": "Hepatitis E virus",
+    "classification": "viral"
+  },
+  "1rjl": {
+    "chain": "C",
+    "organism": "Borrelia burgdorferi",
+    "classification": "other"
+  },
+  "5vyf": {
+    "chain": "F",
+    "organism": "Felis catus",
+    "classification": "other"
+  },
+  "4jzj": {
+    "chain": "D",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "5xj4": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8ruu": {
+    "chain": "Y",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6v4p": {
+    "chain": "B",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4rdq": {
+    "chain": "C",
+    "organism": "Gallus gallus",
+    "classification": "other"
+  },
+  "4jlr": {
+    "chain": "C",
+    "organism": "Unknown",
+    "classification": "other"
+  },
+  "8tqa": {
+    "chain": "A",
+    "organism": "Mus musculus",
+    "classification": "other"
+  },
+  "8dcn": {
+    "chain": "F",
+    "organism": "Clostridioides difficile",
+    "classification": "other"
+  },
+  "6v4n": {
+    "chain": "W",
+    "organism": "Influenza B virus",
+    "classification": "viral"
+  },
+  "6vzi": {
+    "chain": "G",
+    "organism": "Human immunodeficiency virus 1",
+    "classification": "viral"
+  },
+  "5if0": {
+    "chain": "I",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "5k9k": {
+    "chain": "F",
+    "organism": "Influenza A virus (strain A/Hong Kong/1/1968 H3N2)",
+    "classification": "viral"
+  },
+  "5veb": {
+    "chain": "Y",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "1tqb": {
+    "chain": "A",
+    "organism": "Ovis aries",
+    "classification": "other"
+  },
+  "3nh7": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4lvo": {
+    "chain": "A",
+    "organism": "Plasmodium falciparum",
+    "classification": "other"
+  },
+  "8r8d": {
+    "chain": "B",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4rgo": {
+    "chain": "S",
+    "organism": "Staphylococcus aureus",
+    "classification": "bacterial"
+  },
+  "7r58": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4ala": {
+    "chain": "C",
+    "organism": "DENGUE VIRUS 3",
+    "classification": "viral"
+  },
+  "4f37": {
+    "chain": "A",
+    "organism": "Escherichia coli",
+    "classification": "bacterial"
+  },
+  "7yru": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4aei": {
+    "chain": "C",
+    "organism": "ANDROCTONUS AUSTRALIS HECTOR",
+    "classification": "other"
+  },
+  "6u2f": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "5w5x": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6q0l": {
+    "chain": "A",
+    "organism": "Influenza A virus (A/Beijing/262/1995(H1N1))",
+    "classification": "viral"
+  },
+  "5tq0": {
+    "chain": "A",
+    "organism": "Xenopus laevis",
+    "classification": "other"
+  },
+  "3jwd": {
+    "chain": "A",
+    "organism": "Human immunodeficiency virus 1",
+    "classification": "viral"
+  },
+  "6oor": {
+    "chain": "A",
+    "organism": "Mus musculus",
+    "classification": "other"
+  },
+  "8g8d": {
+    "chain": "P",
+    "organism": "Human immunodeficiency virus 1",
+    "classification": "viral"
+  },
+  "7uot": {
+    "chain": "C",
+    "organism": "Lassa virus",
+    "classification": "viral"
+  },
+  "7lsg": {
+    "chain": "C",
+    "organism": "Tick-borne encephalitis virus",
+    "classification": "viral"
+  },
+  "8sak": {
+    "chain": "B",
+    "organism": "unclassified Merbecovirus",
+    "classification": "viral"
+  },
+  "6uvo": {
+    "chain": "D",
+    "organism": "Human respiratory syncytial virus A (strain A2)",
+    "classification": "viral"
+  },
+  "7uij": {
+    "chain": "D",
+    "organism": "Borreliella burgdorferi B31",
+    "classification": "other"
+  },
+  "7lse": {
+    "chain": "E",
+    "organism": "Tick-borne encephalitis virus Far Eastern subtype",
+    "classification": "viral"
+  },
+  "6cw2": {
+    "chain": "C",
+    "organism": "Saccharomyces cerevisiae",
+    "classification": "fungal"
+  },
+  "4j6r": {
+    "chain": "G",
+    "organism": "HUMAN IMMUNODEFICIENCY VIRUS 1",
+    "classification": "viral"
+  },
+  "7n3c": {
+    "chain": "C",
+    "organism": "Severe acute respiratory syndrome coronavirus 2",
+    "classification": "viral"
+  },
+  "3wkm": {
+    "chain": "B",
+    "organism": "Aquifex aeolicus",
+    "classification": "other"
+  },
+  "6s5a": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "5ikc": {
+    "chain": "N",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6s3d": {
+    "chain": "M",
+    "organism": "synthetic construct",
+    "classification": "other"
+  },
+  "7ahu": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8c7m": {
+    "chain": "B",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7xnf": {
+    "chain": "A",
+    "organism": "Pangolin coronavirus",
+    "classification": "viral"
+  },
+  "8y0r": {
+    "chain": "2",
+    "organism": "Foot-and-mouth disease virus A",
+    "classification": "viral"
+  },
+  "8y6i": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "3t2n": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4zff": {
+    "chain": "D",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4m62": {
+    "chain": "T",
+    "organism": "synthetic construct",
+    "classification": "other"
+  },
+  "4ag4": {
+    "chain": "A",
+    "organism": "HOMO SAPIENS",
+    "classification": "human"
+  },
+  "5ukr": {
+    "chain": "G",
+    "organism": "Human immunodeficiency virus 1",
+    "classification": "viral"
+  },
+  "8d1t": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4nzr": {
+    "chain": "M",
+    "organism": "Mycoplasma genitalium",
+    "classification": "other"
+  },
+  "6xlq": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8da1": {
+    "chain": "I",
+    "organism": "Bungarus multicinctus",
+    "classification": "other"
+  },
+  "7u5b": {
+    "chain": "I",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "3q1s": {
+    "chain": "I",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6by3": {
+    "chain": "C",
+    "organism": "Streptomyces coelicolor",
+    "classification": "other"
+  },
+  "6lyn": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "5occ": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8jxs": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4lmq": {
+    "chain": "D",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "5w5z": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7k9j": {
+    "chain": "C",
+    "organism": "Severe acute respiratory syndrome coronavirus 2",
+    "classification": "viral"
+  },
+  "5k59": {
+    "chain": "B",
+    "organism": "Staphylococcus aureus (strain USA300)",
+    "classification": "bacterial"
+  },
+  "8kdm": {
+    "chain": "B",
+    "organism": "Severe acute respiratory syndrome coronavirus 2",
+    "classification": "viral"
+  },
+  "7lr4": {
+    "chain": "C",
+    "organism": "Plasmodium berghei",
+    "classification": "other"
+  },
+  "7lf7": {
+    "chain": "M",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8w86": {
+    "chain": "D",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7wvg": {
+    "chain": "B",
+    "organism": "Influenza A virus",
+    "classification": "viral"
+  },
+  "6wix": {
+    "chain": "G",
+    "organism": "Human immunodeficiency virus 1",
+    "classification": "viral"
+  },
+  "7xrz": {
+    "chain": "Y",
+    "organism": "Escherichia coli",
+    "classification": "bacterial"
+  },
+  "1jrh": {
+    "chain": "I",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "3ks0": {
+    "chain": "A",
+    "organism": "Saccharomyces cerevisiae",
+    "classification": "fungal"
+  },
+  "7x29": {
+    "chain": "C",
+    "organism": "Severe acute respiratory syndrome coronavirus 2",
+    "classification": "viral"
+  },
+  "5x2n": {
+    "chain": "C",
+    "organism": "Oryzias latipes",
+    "classification": "other"
+  },
+  "7lkf": {
+    "chain": "A",
+    "organism": "Gallus gallus",
+    "classification": "other"
+  },
+  "6mi2": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "5dur": {
+    "chain": "A",
+    "organism": "Influenza A virus (A/Anhui/1/2005(H5N1))",
+    "classification": "viral"
+  },
+  "7kyl": {
+    "chain": "Z",
+    "organism": "Powassan virus",
+    "classification": "viral"
+  },
+  "7tuy": {
+    "chain": "R",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4qhu": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7e72": {
+    "chain": "E",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4g7v": {
+    "chain": "S",
+    "organism": "Ciona intestinalis",
+    "classification": "other"
+  },
+  "8f38": {
+    "chain": "C",
+    "organism": "synthetic construct",
+    "classification": "other"
+  },
+  "8hpk": {
+    "chain": "A",
+    "organism": "Oxalobacter formigenes",
+    "classification": "other"
+  },
+  "6rlo": {
+    "chain": "J",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4dw2": {
+    "chain": "U",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6uj9": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8g3q": {
+    "chain": "G",
+    "organism": "Influenza A virus",
+    "classification": "viral"
+  },
+  "4kxz": {
+    "chain": "D",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8v5q": {
+    "chain": "G",
+    "organism": "Human alphaherpesvirus 3",
+    "classification": "viral"
+  },
+  "6z7z": {
+    "chain": "F",
+    "organism": "Sus scrofa",
+    "classification": "other"
+  },
+  "8vgn": {
+    "chain": "I",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "1ztx": {
+    "chain": "E",
+    "organism": "West Nile virus",
+    "classification": "viral"
+  },
+  "6jbt": {
+    "chain": "F",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "2r56": {
+    "chain": "A",
+    "organism": "Bos taurus",
+    "classification": "other"
+  },
+  "1iqd": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "2q8a": {
+    "chain": "A",
+    "organism": "Plasmodium falciparum",
+    "classification": "other"
+  },
+  "6pis": {
+    "chain": "B",
+    "organism": "Mus musculus",
+    "classification": "other"
+  },
+  "5cbe": {
+    "chain": "F",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4xwo": {
+    "chain": "A",
+    "organism": "Saccharomyces cerevisiae (ATCC 204508 / S288c)",
+    "classification": "fungal"
+  },
+  "7upb": {
+    "chain": "D",
+    "organism": "Nipah henipavirus",
+    "classification": "viral"
+  },
+  "6uyf": {
+    "chain": "E",
+    "organism": "Recombinant Hepatitis C virus HK6a/JFH-1",
+    "classification": "viral"
+  },
+  "8tbq": {
+    "chain": "P",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7ox4": {
+    "chain": "C",
+    "organism": "Mus musculus",
+    "classification": "other"
+  },
+  "6j5d": {
+    "chain": "A",
+    "organism": "Louping ill virus",
+    "classification": "viral"
+  },
+  "4dtg": {
+    "chain": "K",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6cmi": {
+    "chain": "B",
+    "organism": "Hendra virus",
+    "classification": "viral"
+  },
+  "3efd": {
+    "chain": "K",
+    "organism": "Escherichia coli",
+    "classification": "bacterial"
+  },
+  "4k3j": {
+    "chain": "B",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8rp8": {
+    "chain": "D",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7a3q": {
+    "chain": "A",
+    "organism": "Dengue virus 4",
+    "classification": "viral"
+  },
+  "1uac": {
+    "chain": "Y",
+    "organism": "Meleagris gallopavo",
+    "classification": "other"
+  },
+  "7usl": {
+    "chain": "C",
+    "organism": "Bordetella pertussis",
+    "classification": "other"
+  },
+  "6kyz": {
+    "chain": "A",
+    "organism": "Human rhinovirus 14",
+    "classification": "viral"
+  },
+  "3ehb": {
+    "chain": "B",
+    "organism": "Paracoccus denitrificans",
+    "classification": "other"
+  },
+  "4u6v": {
+    "chain": "B",
+    "organism": "Staphylococcus aureus subsp. aureus TCH60",
+    "classification": "bacterial"
+  },
+  "5i9q": {
+    "chain": "A",
+    "organism": "Human immunodeficiency virus 1",
+    "classification": "viral"
+  },
+  "1wej": {
+    "chain": "F",
+    "organism": "Equus caballus",
+    "classification": "other"
+  },
+  "8xi6": {
+    "chain": "C",
+    "organism": "Severe acute respiratory syndrome coronavirus 2",
+    "classification": "viral"
+  },
+  "5te7": {
+    "chain": "G",
+    "organism": "Human immunodeficiency virus 1",
+    "classification": "viral"
+  },
+  "7nx3": {
+    "chain": "F",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7q6c": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4xmp": {
+    "chain": "G",
+    "organism": "Human immunodeficiency virus 1",
+    "classification": "viral"
+  },
+  "4dkf": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7jtg": {
+    "chain": "E",
+    "organism": "Recombinant Hepatitis C virus HK6a/JFH-1",
+    "classification": "viral"
+  },
+  "5d8j": {
+    "chain": "A",
+    "organism": "Mus musculus",
+    "classification": "other"
+  },
+  "7xy8": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "5ob5": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6iap": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6vvu": {
+    "chain": "B",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8vvm": {
+    "chain": "I",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6ktr": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6iw2": {
+    "chain": "D",
+    "organism": "Yellow fever virus (strain 17D vaccine)",
+    "classification": "viral"
+  },
+  "6otc": {
+    "chain": "A",
+    "organism": "Lake Victoria marburgvirus (strain Popp-67)",
+    "classification": "viral"
+  },
+  "5otj": {
+    "chain": "D",
+    "organism": "Phleum pratense",
+    "classification": "other"
+  },
+  "6mej": {
+    "chain": "C",
+    "organism": "Hepacivirus C",
+    "classification": "viral"
+  },
+  "3l5w": {
+    "chain": "J",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4okv": {
+    "chain": "F",
+    "organism": "Anopheles stephensi",
+    "classification": "other"
+  },
+  "6p9h": {
+    "chain": "A",
+    "organism": "Staphylococcus aureus",
+    "classification": "bacterial"
+  },
+  "6hig": {
+    "chain": "B",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8zd5": {
+    "chain": "D",
+    "organism": "Canis lupus familiaris",
+    "classification": "other"
+  },
+  "8ulj": {
+    "chain": "B",
+    "organism": "Respiratory syncytial virus A2",
+    "classification": "viral"
+  },
+  "5th9": {
+    "chain": "B",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "3pnw": {
+    "chain": "R",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "5vjo": {
+    "chain": "F",
+    "organism": "Anas platyrhynchos",
+    "classification": "other"
+  },
+  "8j80": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "5l6y": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6cxy": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "1jhl": {
+    "chain": "A",
+    "organism": "Phasianus colchicus",
+    "classification": "other"
+  },
+  "1nl0": {
+    "chain": "G",
+    "organism": "Unknown",
+    "classification": "other"
+  },
+  "3l5x": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8vvk": {
+    "chain": "B",
+    "organism": "Crimean-Congo hemorrhagic fever virus",
+    "classification": "viral"
+  },
+  "7tpd": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6j15": {
+    "chain": "D",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7zli": {
+    "chain": "A",
+    "organism": "Caenorhabditis elegans",
+    "classification": "other"
+  },
+  "2h9g": {
+    "chain": "R",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "3lh2": {
+    "chain": "V",
+    "organism": "ARTIFICIAL GENE",
+    "classification": "other"
+  },
+  "8dao": {
+    "chain": "F",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4i3r": {
+    "chain": "G",
+    "organism": "Human Immunodeficiency Virus",
+    "classification": "viral"
+  },
+  "7vgr": {
+    "chain": "A",
+    "organism": "Severe acute respiratory syndrome coronavirus 2",
+    "classification": "viral"
+  },
+  "3thm": {
+    "chain": "F",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8vgm": {
+    "chain": "A",
+    "organism": "Aliarcobacter butzleri RM4018",
+    "classification": "other"
+  },
+  "8dfh": {
+    "chain": "A",
+    "organism": "Plasmodium falciparum 3D7",
+    "classification": "other"
+  },
+  "7wtf": {
+    "chain": "D",
+    "organism": "Severe acute respiratory syndrome coronavirus 2",
+    "classification": "viral"
+  },
+  "8y6h": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "5mhr": {
+    "chain": "D",
+    "organism": "Reovirus sp.",
+    "classification": "viral"
+  },
+  "8h3n": {
+    "chain": "C",
+    "organism": "Severe acute respiratory syndrome coronavirus 2",
+    "classification": "viral"
+  },
+  "7ujd": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8d9y": {
+    "chain": "K",
+    "organism": "Oxyuranus scutellatus scutellatus",
+    "classification": "other"
+  },
+  "1uj3": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "3nfp": {
+    "chain": "I",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6o9i": {
+    "chain": "C",
+    "organism": "Mus musculus",
+    "classification": "other"
+  },
+  "4k94": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7bbj": {
+    "chain": "B",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7mlh": {
+    "chain": "F",
+    "organism": "Dermatophagoides pteronyssinus",
+    "classification": "other"
+  },
+  "7o52": {
+    "chain": "U",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6umx": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7rah": {
+    "chain": "E",
+    "organism": "Bordetella pertussis",
+    "classification": "other"
+  },
+  "5e8e": {
+    "chain": "H",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4edw": {
+    "chain": "V",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7t0l": {
+    "chain": "D",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8y6a": {
+    "chain": "B",
+    "organism": "Severe acute respiratory syndrome coronavirus 2",
+    "classification": "viral"
+  },
+  "4irz": {
+    "chain": "A",
+    "organism": "Oryctolagus cuniculus",
+    "classification": "other"
+  },
+  "4lu5": {
+    "chain": "A",
+    "organism": "Vaccinia virus",
+    "classification": "viral"
+  },
+  "6sv2": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8tui": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8dyx": {
+    "chain": "I",
+    "organism": "Plasmodium falciparum",
+    "classification": "other"
+  },
+  "8w83": {
+    "chain": "L",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4rrp": {
+    "chain": "R",
+    "organism": "Saccharomyces cerevisiae",
+    "classification": "fungal"
+  },
+  "5gzn": {
+    "chain": "E",
+    "organism": "Zika virus",
+    "classification": "viral"
+  },
+  "5tl5": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4ypg": {
+    "chain": "D",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "5kw9": {
+    "chain": "A",
+    "organism": "Norwalk virus",
+    "classification": "viral"
+  },
+  "2q8b": {
+    "chain": "A",
+    "organism": "Plasmodium falciparum",
+    "classification": "other"
+  },
+  "8a1e": {
+    "chain": "A",
+    "organism": "Rabies virus strain Pasteur vaccin",
+    "classification": "viral"
+  },
+  "4d9q": {
+    "chain": "A",
+    "organism": "Macaca mulatta",
+    "classification": "other"
+  },
+  "6wo5": {
+    "chain": "F",
+    "organism": "Hepatitis C virus (isolate H)",
+    "classification": "viral"
+  },
+  "2nyy": {
+    "chain": "A",
+    "organism": "Clostridium botulinum",
+    "classification": "other"
+  },
+  "7lfa": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6cyf": {
+    "chain": "Q",
+    "organism": "Pseudomonas aeruginosa",
+    "classification": "bacterial"
+  },
+  "4zfg": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8ts0": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6n6b": {
+    "chain": "A",
+    "organism": "Influenza A virus (A/Minnesota/11/2010(H3N2))",
+    "classification": "viral"
+  },
+  "6lxi": {
+    "chain": "B",
+    "organism": "Influenza A virus (strain A/Brevig Mission/1/1918 H1N1)",
+    "classification": "viral"
+  },
+  "7phw": {
+    "chain": "D",
+    "organism": "Plasmodium falciparum (isolate 3D7)",
+    "classification": "other"
+  },
+  "5vcn": {
+    "chain": "A",
+    "organism": "Dermatophagoides pteronyssinus",
+    "classification": "other"
+  },
+  "5n7w": {
+    "chain": "X",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4uta": {
+    "chain": "B",
+    "organism": "DENGUE VIRUS 2",
+    "classification": "viral"
+  },
+  "3zkn": {
+    "chain": "B",
+    "organism": "HOMO SAPIENS",
+    "classification": "human"
+  },
+  "3w9e": {
+    "chain": "C",
+    "organism": "Human herpesvirus 2",
+    "classification": "viral"
+  },
+  "8vdl": {
+    "chain": "C",
+    "organism": "Plasmodium falciparum HB3",
+    "classification": "other"
+  },
+  "3u2s": {
+    "chain": "G",
+    "organism": "Human immunodeficiency virus 1",
+    "classification": "viral"
+  },
+  "7z2m": {
+    "chain": "G",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "5vkd": {
+    "chain": "A",
+    "organism": "Bundibugyo ebolavirus",
+    "classification": "viral"
+  },
+  "6xqw": {
+    "chain": "E",
+    "organism": "Plasmodium falciparum",
+    "classification": "other"
+  },
+  "6ck9": {
+    "chain": "G",
+    "organism": "Human immunodeficiency virus 1",
+    "classification": "viral"
+  },
+  "4xvu": {
+    "chain": "H",
+    "organism": "Saccharomyces cerevisiae (ATCC 204508 / S288c)",
+    "classification": "fungal"
+  },
+  "4oii": {
+    "chain": "B",
+    "organism": "West Nile virus",
+    "classification": "viral"
+  },
+  "8gkl": {
+    "chain": "E",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6i8s": {
+    "chain": "D",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7unb": {
+    "chain": "R",
+    "organism": "Plasmodium falciparum",
+    "classification": "other"
+  },
+  "8dg9": {
+    "chain": "C",
+    "organism": "Respiratory syncytial virus A2",
+    "classification": "viral"
+  },
+  "7zjl": {
+    "chain": "A",
+    "organism": "Severe acute respiratory syndrome coronavirus 2",
+    "classification": "viral"
+  },
+  "7x8p": {
+    "chain": "D",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "5wk3": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6vtw": {
+    "chain": "A",
+    "organism": "Human respiratory syncytial virus A2",
+    "classification": "viral"
+  },
+  "8tp7": {
+    "chain": "C",
+    "organism": "Influenza A virus (A/Singapore/1/1957(H2N2))",
+    "classification": "viral"
+  },
+  "6u9s": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8slb": {
+    "chain": "A",
+    "organism": "Thermotoga maritima MSB8",
+    "classification": "other"
+  },
+  "2adf": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "2vxq": {
+    "chain": "A",
+    "organism": "PHLEUM PRATENSE",
+    "classification": "other"
+  },
+  "4jr9": {
+    "chain": "A",
+    "organism": "Escherichia coli",
+    "classification": "bacterial"
+  },
+  "7kpj": {
+    "chain": "E",
+    "organism": "Ruminococcus gnavus",
+    "classification": "other"
+  },
+  "8jnk": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7qu1": {
+    "chain": "C",
+    "organism": "Machupo mammarenavirus",
+    "classification": "viral"
+  },
+  "8urf": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6u8c": {
+    "chain": "B",
+    "organism": "Streptococcus sp. 'group G'",
+    "classification": "bacterial"
+  },
+  "6tyb": {
+    "chain": "G",
+    "organism": "Simian immunodeficiency virus",
+    "classification": "viral"
+  },
+  "7q6e": {
+    "chain": "A",
+    "organism": "Severe acute respiratory syndrome coronavirus 2",
+    "classification": "viral"
+  },
+  "5o1r": {
+    "chain": "A",
+    "organism": "Neisseria meningitidis",
+    "classification": "other"
+  },
+  "7ket": {
+    "chain": "C",
+    "organism": "Neisseria meningitidis",
+    "classification": "other"
+  },
+  "8t03": {
+    "chain": "B",
+    "organism": "Mus musculus",
+    "classification": "other"
+  },
+  "8da0": {
+    "chain": "E",
+    "organism": "Dendroaspis polylepis polylepis",
+    "classification": "other"
+  },
+  "7kq7": {
+    "chain": "B",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "2jel": {
+    "chain": "P",
+    "organism": "Escherichia coli",
+    "classification": "bacterial"
+  },
+  "8tfl": {
+    "chain": "B",
+    "organism": "Ricinus communis",
+    "classification": "other"
+  },
+  "5w2b": {
+    "chain": "A",
+    "organism": "Reston ebolavirus",
+    "classification": "viral"
+  },
+  "6s8j": {
+    "chain": "E",
+    "organism": "Ebola virus",
+    "classification": "viral"
+  },
+  "3b9k": {
+    "chain": "F",
+    "organism": "Mus musculus",
+    "classification": "other"
+  },
+  "4khx": {
+    "chain": "A",
+    "organism": "Human Immunodeficiency Virus 1",
+    "classification": "viral"
+  },
+  "8tfn": {
+    "chain": "B",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8d7e": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "9gwt": {
+    "chain": "P",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6n81": {
+    "chain": "A",
+    "organism": "Norovirus Hu/GII.4/Farmington Hills/2004/USA",
+    "classification": "viral"
+  },
+  "4mwf": {
+    "chain": "D",
+    "organism": "Hepatitis C virus (isolate H)",
+    "classification": "viral"
+  },
+  "7chz": {
+    "chain": "I",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "3mxw": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "2ypv": {
+    "chain": "A",
+    "organism": "NEISSERIA MENINGITIDIS MC58",
+    "classification": "other"
+  },
+  "8ahn": {
+    "chain": "A",
+    "organism": "Sin Nombre orthohantavirus",
+    "classification": "viral"
+  },
+  "5b71": {
+    "chain": "E",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7c61": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7uvs": {
+    "chain": "C",
+    "organism": "Plasmodium falciparum",
+    "classification": "other"
+  },
+  "8f60": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "1yjd": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6m58": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6osv": {
+    "chain": "K",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8jlw": {
+    "chain": "A",
+    "organism": "Crimean-Congo hemorrhagic fever orthonairovirus",
+    "classification": "viral"
+  },
+  "1cl7": {
+    "chain": "I",
+    "organism": "Mus musculus",
+    "classification": "other"
+  },
+  "3rkd": {
+    "chain": "A",
+    "organism": "Hepatitis E virus",
+    "classification": "viral"
+  },
+  "5wi9": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8y3u": {
+    "chain": "K",
+    "organism": "Ebola virus",
+    "classification": "viral"
+  },
+  "6mvl": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7l7r": {
+    "chain": "G",
+    "organism": "Crimean-Congo hemorrhagic fever virus (strain Nigeria/IbAr10200/1970)",
+    "classification": "viral"
+  },
+  "8jlx": {
+    "chain": "A",
+    "organism": "Crimean-Congo hemorrhagic fever orthonairovirus",
+    "classification": "viral"
+  },
+  "8b7h": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "2qqk": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "5d72": {
+    "chain": "B",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8tv1": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "5ggv": {
+    "chain": "Y",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "5y9j": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6c9u": {
+    "chain": "A",
+    "organism": "Saccharopolyspora erythraea",
+    "classification": "other"
+  },
+  "8ut3": {
+    "chain": "C",
+    "organism": "Influenza A virus",
+    "classification": "viral"
+  },
+  "5wux": {
+    "chain": "E",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6uyn": {
+    "chain": "A",
+    "organism": "Influenza A virus",
+    "classification": "viral"
+  },
+  "4ij3": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8a99": {
+    "chain": "C",
+    "organism": "Severe acute respiratory syndrome coronavirus 2",
+    "classification": "viral"
+  },
+  "6ddm": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "4ffv": {
+    "chain": "B",
+    "organism": "Rattus norvegicus",
+    "classification": "other"
+  },
+  "2bdn": {
+    "chain": "A",
+    "organism": "Unknown",
+    "classification": "other"
+  },
+  "6h3t": {
+    "chain": "B",
+    "organism": "Bovine Schmallenberg virus",
+    "classification": "viral"
+  },
+  "6x3x": {
+    "chain": "D",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7yk4": {
+    "chain": "B",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6qb6": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "2wuc": {
+    "chain": "A",
+    "organism": "HOMO SAPIENS",
+    "classification": "human"
+  },
+  "6gku": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "5eii": {
+    "chain": "I",
+    "organism": "Saccharomyces cerevisiae",
+    "classification": "fungal"
+  },
+  "6k7o": {
+    "chain": "P",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8e8s": {
+    "chain": "2",
+    "organism": "Poliovirus 2",
+    "classification": "viral"
+  },
+  "2j88": {
+    "chain": "A",
+    "organism": "APIS MELLIFERA",
+    "classification": "other"
+  },
+  "4cni": {
+    "chain": "D",
+    "organism": "HOMO SAPIENS",
+    "classification": "human"
+  },
+  "7cj2": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "3u30": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8qya": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "2arj": {
+    "chain": "R",
+    "organism": "Mus musculus",
+    "classification": "other"
+  },
+  "7df1": {
+    "chain": "D",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7dc8": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7sem": {
+    "chain": "F",
+    "organism": "Human metapneumovirus",
+    "classification": "viral"
+  },
+  "5f3b": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8w84": {
+    "chain": "C",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8f5i": {
+    "chain": "A",
+    "organism": "Rhodopseudomonas palustris",
+    "classification": "bacterial"
+  },
+  "5xxy": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6e63": {
+    "chain": "A",
+    "organism": "Plasmodium falciparum",
+    "classification": "other"
+  },
+  "3nps": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "8yx9": {
+    "chain": "J",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "7vux": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "5hys": {
+    "chain": "G",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "9ima": {
+    "chain": "A",
+    "organism": "Homo sapiens",
+    "classification": "human"
+  },
+  "6dfj": {
+    "chain": "E",
+    "organism": "Dengue virus 1",
+    "classification": "viral"
+  },
+  "6flb": {
+    "chain": "G",
+    "organism": "Dengue virus 2",
+    "classification": "viral"
+  },
+  "4f15": {
+    "chain": "J",
+    "organism": "Influenza A virus",
+    "classification": "viral"
+  }
+}
\ No newline at end of file
diff --git a/models/.DS_Store b/models/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..673896b92e3511dae8d114a142ddf2414558e133
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diff --git a/models/ReCEP/20250626_110438/best_mcc_model.bin b/models/ReCEP/20250626_110438/best_mcc_model.bin
new file mode 100644
index 0000000000000000000000000000000000000000..92c979a2cf8ddbae3a711390a5cd34936b6f8d14
--- /dev/null
+++ b/models/ReCEP/20250626_110438/best_mcc_model.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1358ed202985dce98a889affa264ff3065bfccc942d7b0043e6f3b0700eb8ea0
+size 19059594
diff --git a/models/ReCEP/20250626_110438/config.json b/models/ReCEP/20250626_110438/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..9e4db8016ddc08f1681165a266fd5124a844ecf4
--- /dev/null
+++ b/models/ReCEP/20250626_110438/config.json
@@ -0,0 +1,70 @@
+{
+  "model_config": {
+    "in_dim": 2560,
+    "rsa": true,
+    "dihedral": true,
+    "node_dims": [
+      512,
+      256,
+      256
+    ],
+    "edge_dim": 32,
+    "dropout": 0.4,
+    "activation": "gelu",
+    "residual": true,
+    "attention": true,
+    "normalize": true,
+    "coords_agg": "mean",
+    "ffn": true,
+    "batch_norm": true,
+    "concat": true,
+    "addition": false,
+    "pooling": "attention",
+    "fusion_type": "concat",
+    "node_gate": false,
+    "node_norm": true,
+    "node_layers": 2,
+    "out_dropout": 0.2,
+    "use_egnn": true,
+    "encoder": "esmc"
+  },
+  "training_config": {
+    "num_epoch": 120,
+    "batch_size": 64,
+    "lr": 5e-05,
+    "weight_decay": 1e-05,
+    "patience": 15,
+    "threshold": 0.5,
+    "mixed_precision": false,
+    "device_id": 0
+  },
+  "data_config": {
+    "radii": [
+      16,
+      18,
+      20
+    ],
+    "zero_ratio": 0.3,
+    "undersample": 0.5,
+    "seed": 42
+  },
+  "loss_config": {
+    "region_loss_type": "mse",
+    "reg_weight": 10.0,
+    "cls_type": "bce",
+    "gamma_high_cls": 2.0,
+    "regression_type": "smooth_l1",
+    "node_loss_type": "focal",
+    "alpha": 2.0,
+    "gamma": 2.0,
+    "pos_weight": 8.0,
+    "node_loss_weight": 0.5,
+    "region_weight": 1.0,
+    "consistency_weight": 0.3,
+    "consistency_type": "mse",
+    "label_smoothing": 0.1,
+    "gradnorm": true,
+    "gradnorm_alpha": 2.0,
+    "gradnorm_update_freq": 10
+  }
+}
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..950b02425c15ef23b27e5117d4fbf141b763345f
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,49 @@
+# requirements.txt - For Hugging Face Spaces
+numpy==1.26.4
+scipy==1.13.0
+pandas==2.1.4
+scikit-learn==1.5.2
+
+torch==2.5.0
+torchvision==0.20.0
+torchaudio==2.5.0
+
+torch-geometric==2.6.1
+--find-links https://data.pyg.org/whl/torch-2.5.0+cpu.html
+torch-scatter
+torch-sparse
+torch-cluster
+torch-spline-conv
+
+torchtext==0.18.0
+torchmetrics==1.6.0
+torch-optimi==0.2.1
+torch-optimizer==0.3.0
+
+esm==3.1.3
+
+biopython==1.85
+biotite==0.41.2
+
+h5py==3.9.0
+py3Dmol==2.4.2
+matplotlib==3.9.3
+seaborn==0.13.2
+tqdm==4.67.1
+prettytable==3.16.0
+
+gradio==4.44.1
+fastapi==0.104.1
+uvicorn==0.24.0
+python-multipart>=0.0.9
+jinja2==3.1.2
+aiofiles==23.2.1
+
+cloudpathlib==0.16.0
+requests==2.31.0
+pillow==10.1.0
+
+networkx>=2.8
+
+notebook==7.3.2
+ipykernel==6.29.5
\ No newline at end of file
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diff --git a/src/bce.egg-info/PKG-INFO b/src/bce.egg-info/PKG-INFO
new file mode 100644
index 0000000000000000000000000000000000000000..4c9381f1c4f6dce17218c6bb80e57e34ff4cc208
--- /dev/null
+++ b/src/bce.egg-info/PKG-INFO
@@ -0,0 +1,3 @@
+Metadata-Version: 2.4
+Name: bce
+Version: 0.1
diff --git a/src/bce.egg-info/SOURCES.txt b/src/bce.egg-info/SOURCES.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4de9add9d632a997f7440faa0d62d2e9092a7d7c
--- /dev/null
+++ b/src/bce.egg-info/SOURCES.txt
@@ -0,0 +1,43 @@
+README.md
+setup.py
+src/bce/__init__.py
+src/bce/check_surf.py
+src/bce/extract_embeddings.py
+src/bce/loss.py
+src/bce/prediction_tools.py
+src/bce.egg-info/PKG-INFO
+src/bce.egg-info/SOURCES.txt
+src/bce.egg-info/dependency_links.txt
+src/bce.egg-info/top_level.txt
+src/bce/antigen/__init__.py
+src/bce/antigen/antigen.py
+src/bce/antigen/pc.py
+src/bce/data/__init__.py
+src/bce/data/data.py
+src/bce/data/data_.py
+src/bce/data/raw_data_generation.py
+src/bce/data/utils.py
+src/bce/data/utils_.py
+src/bce/model/EGNN.py
+src/bce/model/ReGEP.py
+src/bce/model/__init__.py
+src/bce/model/activation.py
+src/bce/model/baseline.py
+src/bce/model/dihedral.py
+src/bce/model/pooling.py
+src/bce/model/scheduler.py
+src/bce/utils/__init__.py
+src/bce/utils/check_struct.py
+src/bce/utils/constants.py
+src/bce/utils/data_tools.py
+src/bce/utils/dssp.py
+src/bce/utils/egnn_trainer.py
+src/bce/utils/evaluatror.py
+src/bce/utils/loading.py
+src/bce/utils/metrics.py
+src/bce/utils/results.py
+src/bce/utils/sequence.py
+src/bce/utils/str_data_tools.py
+src/bce/utils/tools.py
+src/bce/utils/trainer.py
+src/bce/utils/training_tools.py
\ No newline at end of file
diff --git a/src/bce.egg-info/dependency_links.txt b/src/bce.egg-info/dependency_links.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/src/bce.egg-info/dependency_links.txt
@@ -0,0 +1 @@
+
diff --git a/src/bce.egg-info/top_level.txt b/src/bce.egg-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..baa5d672f3f7a656dd656ced84cf1dc109111631
--- /dev/null
+++ b/src/bce.egg-info/top_level.txt
@@ -0,0 +1 @@
+bce
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diff --git a/src/bce/antigen/__pycache__/protein_chain.cpython-310.pyc b/src/bce/antigen/__pycache__/protein_chain.cpython-310.pyc
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diff --git a/src/bce/antigen/antigen.py b/src/bce/antigen/antigen.py
new file mode 100644
index 0000000000000000000000000000000000000000..23a03e2791c66fd0d7abb75e6de25742cd09aefa
--- /dev/null
+++ b/src/bce/antigen/antigen.py
@@ -0,0 +1,2440 @@
+from __future__ import annotations
+
+import json
+import h5py
+import traceback
+import numpy as np
+import traceback
+from tqdm import tqdm
+from pathlib import Path
+from typing import Optional, Union, BinaryIO, TextIO
+from dataclasses import dataclass
+from scipy.spatial.distance import cdist
+
+import torch
+
+# ESM
+from esm.utils import residue_constants as RC
+from esm.utils.structure.protein_chain import ProteinChain
+
+# Biotite
+import biotite.structure as bs
+from biotite.database import rcsb
+from biotite.structure.io.pdb import PDBFile
+from biotite.structure import annotate_sse
+
+from cloudpathlib import CloudPath
+from Bio.Data import PDBData  # Ensure BioPython is imported.
+
+import py3Dmol
+
+# ReCEP Packages
+from ..utils.constants import BASE_DIR
+from ..utils.loading import load_epitopes_csv, load_epitopes_csv_single, load_species
+from .pc import AMINO_ACID_1TO3, AMINO_ACID_3TO1, MAX_ASA
+from ..model.ReCEP import ReCEP
+from ..data.utils import create_graph_data
+
+
+PathOrBuffer = Union[str, Path, BinaryIO, TextIO]
+
+@dataclass
+class AntigenChain(ProteinChain):
+    """
+    Extended ProteinChain class that adds additional functionalities,
+    such as computing surface residues based on SASA and maxASA constants.
+    """
+    def __post_init__(self, token: Optional[str] = "1mzAo8l1uxaU8UfVcGgV7B"):
+        super().__post_init__()  # Ensure parent class initialization
+        
+        # Map residue number to index
+        self.resnum_to_index = {int(rnum): i for i, rnum in enumerate(self.residue_index)}
+        
+        # Get epitopes as boolean array
+        self.epitopes = self.get_epitopes()  # Automatically get epitopes on initialization
+        
+        # Set token from parameter or environment variable
+        self.token = token
+        
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    @staticmethod
+    def convert_letter_1to3(letter: str) -> str:
+        """
+        Convert a one-letter amino acid code to its corresponding three-letter code.
+        
+        Args:
+            letter (str): A single-character amino acid code (e.g., "A").
+            
+        Returns:
+            str: The corresponding three-letter code (e.g., "ALA").
+                 Returns "UNK" if the code is not recognized.
+        """
+        return AMINO_ACID_1TO3.get(letter.upper(), "UNK")
+
+    @staticmethod
+    def convert_letter_3to1(three_letter: str) -> str:
+        """
+        Convert a three-letter amino acid code to its corresponding one-letter code.
+        
+        Args:
+            three_letter (str): A three-letter amino acid code (e.g., "ALA").
+            
+        Returns:
+            str: The corresponding one-letter code (e.g., "A").
+                 Returns "X" if the code is not recognized.
+        """
+        return AMINO_ACID_3TO1.get(three_letter.upper(), "X")
+    
+    def get_species(self) -> str:
+        """
+        Get the species of the antigen.
+        """
+        from ..utils.tools import get_chain_organism
+        
+        species_dict = load_species()
+        if self.id in species_dict:
+            species = species_dict[self.id]['classification']
+        else:
+            try:
+                species = get_chain_organism(self.id, self.chain_id)
+                species_dict[self.id] = {'classification': species}
+                
+                # Create directory if it doesn't exist
+                species_file_path = Path(f"{BASE_DIR}/data/species.json")
+                species_file_path.parent.mkdir(parents=True, exist_ok=True)
+                
+                with open(species_file_path, "w") as f:
+                    json.dump(species_dict, f, indent=2)
+            except Exception as e:
+                print(f"[ERROR] Failed to get species for {self.id}_{self.chain_id}: {str(e)}")
+                species = "Unknown"
+        return species
+    
+    def get_backbone_atoms(self) -> np.ndarray:
+        """
+        Get backbone atom coordinates in the order: CA, C, N.
+
+        Returns:
+            np.ndarray: [L, 3, 3] array where [:, 0] is CA, [:, 1] is C, [:, 2] is N.
+        """
+        file = Path(f"{BASE_DIR}/data/coords/{self.id}_{self.chain_id}.npy")
+        
+        if file.exists():
+            return np.load(file)
+        else:
+            idx_CA = RC.atom_order["CA"]
+            idx_C = RC.atom_order["C"]
+            idx_N = RC.atom_order["N"]
+
+            backbone_atoms = self.atom37_positions[:, [idx_N, idx_CA, idx_C], :]  # shape: [L, 3, 3]
+            
+            # Create directory if it doesn't exist
+            file.parent.mkdir(parents=True, exist_ok=True)
+            np.save(file, backbone_atoms)
+            return backbone_atoms
+        
+    def get_secondary_structure(self) -> np.ndarray:
+        """
+        Get secondary structure information using numpy operations.
+        """
+        try:
+            ss3_arr = annotate_sse(self.atom_array)
+            biotite_ss3_str = "".join(ss3_arr)
+            
+            if len(biotite_ss3_str) != len(self.sequence):
+                print(f"[WARNING] Secondary structure prediction length ({len(biotite_ss3_str)}) "
+                    f"doesn't match sequence length ({len(self.sequence)}) "
+                    f"for protein {self.id}_{self.chain_id}")
+                return None
+                
+            translation_table = str.maketrans({
+                "a": "H",  # alpha helix
+                "b": "E",  # beta sheet
+                "c": "C",  # coil
+            })
+            return biotite_ss3_str.translate(translation_table)
+            
+        except Exception as e:
+            print(f"[ERROR] Failed to predict secondary structure for "
+                f"{self.id}_{self.chain_id}: {str(e)}")
+            return None
+    
+    def get_ss_onehot(self) -> np.ndarray:
+        """
+        Get one-hot encoded secondary structure information using numpy operations.
+        Only encode H (helix) and E (sheet), as C (coil) can be inferred.
+        
+        Returns:
+            np.ndarray: One-hot encoded secondary structure array of shape (seq_len, 2)
+                       where 2 represents [H, E] (Helix, Sheet)
+        """
+        self.secondary_structure = self.get_secondary_structure()
+        seq_len = len(self.secondary_structure)
+        ss_onehot = np.zeros((seq_len, 2), dtype=np.float32)
+        
+        # Use boolean indexing for helix and sheet only
+        ss_array = np.array(list(self.secondary_structure))
+        ss_onehot[:, 0] = (ss_array == 'H')
+        ss_onehot[:, 1] = (ss_array == 'E')
+        
+        return ss_onehot
+
+    def get_rsa(self) -> np.ndarray:
+        """
+        Calculate relative solvent accessibility (RSA) for all residues.
+        RSA is the ratio of SASA to maximum ASA for each residue.
+        
+        Returns:
+            np.ndarray: An array of RSA values for each residue in the sequence.
+        """
+        
+        cache_file = Path(BASE_DIR) / "data" / "rsa" / f"{self.id}_{self.chain_id}.npy"
+        if cache_file.exists():
+            return np.load(cache_file)
+        
+        sasa_values = self.sasa()  # Get SASA values for all residues
+        rsa_values = np.zeros(len(self.sequence), dtype=np.float32)
+        
+        # Calculate RSA for each residue
+        for i, (letter, sasa) in enumerate(zip(self.sequence, sasa_values)):
+            three_letter = self.convert_letter_1to3(letter)
+            max_asa = MAX_ASA.get(three_letter)
+            if max_asa is not None and max_asa != 0:
+                rsa_values[i] = sasa / max_asa
+        
+        # Create directory if it doesn't exist
+        cache_file.parent.mkdir(parents=True, exist_ok=True)
+        np.save(cache_file, rsa_values)
+            
+        return rsa_values
+
+    def get_surface_residues(self, threshold: float = 0.25) -> list:
+        """
+        Identify surface-exposed residues using RSA values.
+        
+        A residue is considered surface-exposed if its RSA value
+        is at least `threshold`.
+        
+        Args:
+            threshold (float): The minimum RSA value required to consider 
+                             the residue as surface-exposed.
+        
+        Returns:
+            tuple: A tuple of two lists, where the first list contains residue numbers (from the PDB) that are surface-exposed,
+                   and the second list contains the indices of the surface residues in the sequence.
+        """
+        rsa_values = self.get_rsa()
+        surface_residue_numbers = []
+        surface_residue_indices = []
+        
+        # Identify surface residues based on RSA threshold
+        for idx, rsa in enumerate(rsa_values):
+            if rsa >= threshold:
+                surface_residue_numbers.append(int(self.residue_index[idx]))
+                surface_residue_indices.append(idx)
+                
+        return surface_residue_numbers, surface_residue_indices
+    
+    def get_epitopes(self, threshold: float = 0.25) -> np.ndarray:
+        """
+        Retrieve epitopes for this chain as a boolean array.
+        
+        Args:
+            threshold (float): SASA threshold for determining surface residues.
+        
+        Returns:
+            np.ndarray: A boolean array of length L (sequence length) where True indicates 
+                       epitope positions and False indicates non-epitope positions.
+                       Only surface-exposed residues can be True.
+        """
+        _, _, epitopes = load_epitopes_csv()
+
+        if f'{self.id}_{self.chain_id}' in epitopes:
+            binary_labels = epitopes.get(f'{self.id}_{self.chain_id}', [0] * len(self.sequence)) # default to 0 if not found
+        else:
+            print(f"[WARNING] Epitopes not found for {self.id}_{self.chain_id}. Use single epitopes.")
+            binary_labels = self.get_epitopes_single()
+            
+        # Initialize epitope array with False values
+        epitope_array = np.zeros(len(self.sequence), dtype=bool)
+        
+        # Check if we have binary labels - handle both list and numpy array cases
+        if binary_labels is not None and len(binary_labels) > 0:
+            # Ensure the binary labels match the sequence length
+            if len(binary_labels) == len(self.sequence):
+                epitope_array = np.array(binary_labels, dtype=bool)
+            else:
+                print(f"[WARNING] Binary labels length ({len(binary_labels)}) doesn't match "
+                      f"sequence length ({len(self.sequence)}) for {self.id}_{self.chain_id}")
+                return epitope_array
+            
+        if threshold == 0.0:
+            return epitope_array
+        
+        # Filter to ensure only surface residues can be epitopes
+        _, surface_indices = self.get_surface_residues(threshold=threshold)
+        
+        # Create surface mask: True for surface residues, False for buried residues
+        surface_mask = np.zeros(len(self.sequence), dtype=bool)
+        for res_idx in surface_indices:
+            if 0 <= res_idx < len(self.sequence):
+                surface_mask[res_idx] = True
+        
+        # Apply surface filter: epitopes can only be surface residues
+        epitope_array = epitope_array & surface_mask
+        
+        return epitope_array
+
+    def get_epitopes_single(self) -> np.ndarray:
+        """
+        Retrieve epitopes for this chain as a boolean array.
+        """
+        _, _, epitopes = load_epitopes_csv_single()
+        
+        # Try different key formats to find epitopes
+        possible_keys = [
+            f'{self.id.upper()}_{self.chain_id}',
+            f'{self.id}_{self.chain_id}',
+            f'{self.id.lower()}_{self.chain_id}'
+        ]
+        
+        epitopes_resnums = None
+        for key in possible_keys:
+            if key in epitopes:
+                epitopes_resnums = epitopes.get(key)
+                break
+        
+        if epitopes_resnums is not None:
+            epitope_array = np.zeros(len(self.sequence), dtype=int)
+            for resnum in epitopes_resnums:
+                if resnum in self.resnum_to_index:
+                    epitope_array[self.resnum_to_index[resnum]] = 1
+            return epitope_array
+        else:
+            print(f"[WARNING] Single Epitopes not found for {self.id}_{self.chain_id}. Use no epitopes.")
+            epitope_array = np.zeros(len(self.sequence), dtype=int)
+        
+        return epitope_array
+    
+    def get_epitope_residue_numbers(self) -> list:
+        """
+        Get epitope residue numbers from the boolean epitope array.
+        
+        Returns:
+            list: List of residue numbers that are epitopes.
+        """
+        epitope_indices = np.where(self.epitopes)[0]
+        epitope_residue_numbers = [int(self.residue_index[idx]) for idx in epitope_indices]
+        return epitope_residue_numbers
+
+    def get_embeddings(self, override: bool = False, encoder: str = "esmc") -> np.ndarray:
+        """
+        Retrieve or compute per-residue (full) ESM-C embeddings.
+
+        Returns:
+            np.ndarray: Array of shape (seq_len, embed_dim), dtype float32.
+        """
+        full_file = Path(BASE_DIR) / "data" / "embeddings" / f"{encoder}" / f"{self.id}_{self.chain_id}.h5"
+
+        if full_file.exists() and not override:
+            with h5py.File(full_file, "r") as h5f:
+                full_embedding = h5f["embedding"][:]
+        else:
+            if encoder == "esmc":
+                if self.token is None:
+                    raise ValueError("ESM token is not set. Please go to https://forge.evolutionaryscale.ai/ to get a token.")
+                
+                else:
+                    print(f"[INFO] Generating with ESM-C...")
+                    
+                    from esm.sdk.api import ESMProtein, LogitsConfig
+                    from esm.sdk.forge import ESM3ForgeInferenceClient
+
+                    token = self.token
+                    model = ESM3ForgeInferenceClient(
+                        model="esmc-6b-2024-12",
+                        url="https://forge.evolutionaryscale.ai",
+                        token=token
+                    )
+                    config = LogitsConfig(sequence=True, return_embeddings=True)
+
+                    sequence = self.sequence[:2046]  # truncate if too long
+                    protein = ESMProtein(sequence)
+                    protein_tensor = model.encode(protein)
+                    output = model.logits(protein_tensor, config)
+                    full_embedding = output.embeddings.squeeze(0)[1:-1, :].to(torch.float32).cpu().numpy()
+
+                    full_file.parent.mkdir(parents=True, exist_ok=True)
+                    with h5py.File(full_file, "w") as h5f:
+                        h5f.create_dataset("embedding", data=full_embedding, compression="gzip")
+            
+            elif encoder == "esm2":
+                model, alphabet = torch.hub.load("facebookresearch/esm:main", "esm2_t33_650M_UR50D")
+                batch_converter = alphabet.get_batch_converter()
+                model.eval()
+                data = [
+                        ("antigen", self.sequence[:2046])
+                    ]
+                batch_labels, batch_strs, batch_tokens = batch_converter(data)
+                batch_lens = (batch_tokens != alphabet.padding_idx).sum(1)
+                model.to(self.device)
+                batch_tokens = batch_tokens.to(self.device)
+                with torch.no_grad():
+                    results = model(batch_tokens, repr_layers=[33], return_contacts=True)
+                    token_representations = results["representations"][33]
+                    full_embedding = token_representations.squeeze(0)[1:-1, :].to(torch.float32).cpu().numpy()
+                        
+                    full_file.parent.mkdir(parents=True, exist_ok=True)
+                    with h5py.File(full_file, "w") as h5f:
+                        h5f.create_dataset("embedding", data=full_embedding, compression="gzip")
+
+        return full_embedding
+        
+    def _scan_surface_residues(self, radius: float, threshold: float = 0.25) -> tuple:
+        """
+        Helper function to compute the surface coverage for each surface residue.
+        For each surface residue, using its C_alpha coordinate as the center of a sphere with
+        radius `radius`, determine which surface residues are covered.
+        
+        Args:
+            radius (float): The radius of the sphere (in Ångstroms)
+            threshold (float): Fraction of maximum ASA to define a residue as surface-exposed
+        
+        Returns:
+            tuple:
+                - coverage (dict): Mapping from center residue index to:
+                    (list[int]): List of covered residue indices
+                    (list[int]): List of covered epitope residue indices
+                    (float): Precision
+                    (float): Recall
+                - max_recall_res (int): Center residue index with highest recall
+                - max_precision_res (int): Center residue index with highest precision
+        """
+        # Input validation
+        if radius <= 0:
+            raise ValueError("Radius must be positive")
+        if threshold < 0 or threshold > 1:
+            raise ValueError("Threshold must be between 0 and 1")
+
+        # Get surface residues number and indices
+        surface_res_nums, surface_indices = self.get_surface_residues(threshold=threshold)
+        
+        # Ensure indices are valid
+        valid_surface_indices = [
+            idx for idx in surface_indices 
+            if 0 <= idx < len(self.sequence)
+        ]
+        valid_surface_res_nums = [
+            surface_res_nums[surface_indices.index(idx)] 
+            for idx in valid_surface_indices
+        ]
+        
+        if not valid_surface_indices:
+            return {}, None, None
+
+        # Collect all atoms and their residue indices from surface residues
+        all_atoms = []
+        all_res_indices = []
+        for idx in valid_surface_indices:
+            mask = self.atom37_mask[idx]
+            coords = self.atom37_positions[idx][mask]
+            if len(coords) > 0:  # Ensure there are atoms
+                all_atoms.append(coords)
+                all_res_indices.extend([idx] * len(coords))
+        
+        if not all_atoms:  # No atoms to process
+            return {idx: ([], [], 0.0, 0.0) for idx in valid_surface_indices}, None, None
+        
+        all_atoms = np.vstack(all_atoms).astype(np.float32)  # shape: (total_atoms, 3)
+        all_res_indices = np.array(all_res_indices)
+        
+        # Collect C-alpha coordinates of surface residues
+        surface_ca = []
+        valid_center_indices = []
+        ca_idx = RC.atom_order["CA"]  # Get CA atom index from atom order
+        
+        for idx in valid_surface_indices:
+            # Get CA coordinates from atom37_positions
+            ca_coord = self.atom37_positions[idx, ca_idx, :]
+            if not np.any(np.isnan(ca_coord)) and self.atom37_mask[idx, ca_idx]:  # Ensure CA atom coordinates are valid and atom exists
+                surface_ca.append(ca_coord)
+                valid_center_indices.append(idx)
+        
+        if not surface_ca:  # No valid CA atoms
+            return {}, None, None
+        
+        surface_ca = np.array(surface_ca, dtype=np.float32)
+        surface_ca = surface_ca.reshape(-1, 3)  # Ensure shape is (n_residues, 3)
+        
+        # Compute distance matrix between each C-alpha and all atoms
+        try:
+            dist_matrix = cdist(surface_ca, all_atoms)
+        except ValueError as e:
+            print(f"Error in distance calculation: {e}")
+            print(f"surface_ca shape: {surface_ca.shape}")
+            print(f"all_atoms shape: {all_atoms.shape}")
+            return {}, None, None
+        
+        max_recall = -1
+        max_recall_res = None
+        max_precision = -1
+        max_precision_res = None
+
+        coverage = {}
+        epitope_indices = np.where(self.epitopes)[0]  # Get epitope indices directly
+        if len(epitope_indices) == 0:
+            print(f"No epitopes records for protein {self.id}_{self.chain_id}")
+        
+        for i, center_idx in enumerate(valid_center_indices):
+            within_radius = dist_matrix[i] < radius
+            covered_indices = np.unique(all_res_indices[within_radius])
+            covered_indices_list = covered_indices.tolist()
+            
+            # Find intersection with epitopes (using indices)
+            covered_epitope_indices = list(set(covered_indices_list).intersection(set(epitope_indices)))
+
+            # Calculate precision and recall
+            precision = len(covered_epitope_indices) / len(covered_indices_list) if covered_indices_list else 0.0
+            recall = len(covered_epitope_indices) / len(epitope_indices) if len(epitope_indices) > 0 else 0.0
+
+            if recall > max_recall:
+                max_recall = recall
+                max_recall_res = center_idx
+            if precision > max_precision:
+                max_precision = precision
+                max_precision_res = center_idx
+            
+            # Convert to native Python types for JSON compatibility
+            coverage[int(center_idx)] = (
+                [int(idx) for idx in covered_indices_list],
+                [int(idx) for idx in covered_epitope_indices], 
+                float(precision),
+                float(recall)
+            )
+
+        return coverage, max_recall_res, max_precision_res
+    
+    def get_surface_coverage(self, radius: float = 18, 
+                             threshold: float = 0.25, 
+                             index: bool = True,
+                             override: bool = False) -> tuple: 
+        """
+        Retrieve (or compute and cache) the coverage mapping for surface residues.
+        For each surface residue, using its C_alpha as the sphere center (with radius `radius`),
+        determine which surface residues are covered (i.e. if any atom falls within that sphere).
+        The result is cached to an HDF5 file for faster subsequent retrieval.
+        
+        The cache file is saved in BASE_DIR / "data/antigen_sphere", with the file name
+        "{self.id}_{self.chain_id}.h5", and radius as the first-level key.
+        
+        Args:
+            radius (float): The radius of the sphere (in Ångstroms).
+            threshold (float): Fraction of maximum ASA to define a residue as surface-exposed.
+            index (bool): If True, return indices instead of residue numbers for easier embeddings/coords access.
+            override (bool): If True, recompute even if cache exists.
+        
+        Returns:
+            tuple:
+                - coverage (dict): A dictionary mapping each surface residue to a tuple of:
+                    If index=True: center_index -> (list[int]): List of covered residue indices
+                                                 (list[int]): List of covered epitope residue indices  
+                                                 (float): Precision
+                                                 (float): Recall
+                    If index=False: center_residue_num -> (list[int]): List of covered residue numbers
+                                                         (list[int]): List of covered epitope residue numbers  
+                                                         (float): Precision
+                                                         (float): Recall
+                - max_recall_res (int): The surface residue number with the highest recall.
+                - max_precision_res (int): The surface residue number with the highest precision.
+        """
+        # Define the cache directory and file
+        cache_dir = BASE_DIR / "data" / "antigen_sphere"
+        cache_dir.mkdir(parents=True, exist_ok=True)
+        cache_filename = f"{self.id}_{self.chain_id}.h5"
+        cache_path = cache_dir / cache_filename
+        radius_key = f"r{radius}"
+        
+        # If the cache file exists and the radius key exists, load and return the cached result.
+        if cache_path.exists() and not override:
+            try:
+                with h5py.File(cache_path, "r") as h5f:
+                    if radius_key in h5f:
+                        # Load cached data for this radius
+                        radius_group = h5f[radius_key]
+                        
+                        if index:
+                            # Cache stores indices, so directly use them
+                            coverage = {}
+                            for center_idx_str in radius_group.keys():
+                                center_idx = int(center_idx_str)
+                                center_group = radius_group[center_idx_str]
+                                covered_indices = center_group['covered_indices'][:].tolist()
+                                covered_epitope_indices = center_group['covered_epitope_indices'][:].tolist()
+                                precision = float(center_group.attrs['precision'])
+                                recall = float(center_group.attrs['recall'])
+                                coverage[center_idx] = (covered_indices, covered_epitope_indices, precision, recall)
+                            return coverage, None, None 
+                        else:
+                            # Convert indices to residue numbers
+                            coverage = {}
+                            max_recall = -1
+                            max_recall_res = None
+                            max_precision = -1
+                            max_precision_res = None
+                            
+                            for center_idx_str in radius_group.keys():
+                                center_idx = int(center_idx_str)
+                                center_res_num = int(self.residue_index[center_idx])
+                                center_group = radius_group[center_idx_str]
+                                
+                                covered_indices = center_group['covered_indices'][:].tolist()
+                                covered_epitope_indices = center_group['covered_epitope_indices'][:].tolist()
+                                precision = float(center_group.attrs['precision'])
+                                recall = float(center_group.attrs['recall'])
+                                
+                                # Convert covered indices to residue numbers
+                                covered_res_nums = [int(self.residue_index[idx]) for idx in covered_indices if 0 <= idx < len(self.residue_index)]
+                                covered_epitope_res_nums = [int(self.residue_index[idx]) for idx in covered_epitope_indices if 0 <= idx < len(self.residue_index)]
+                                
+                                coverage[center_res_num] = (covered_res_nums, covered_epitope_res_nums, precision, recall)
+                                
+                                if recall > max_recall:
+                                    max_recall = recall
+                                    max_recall_res = center_res_num
+                                if precision > max_precision:
+                                    max_precision = precision
+                                    max_precision_res = center_res_num
+
+                            return coverage, max_recall_res, max_precision_res
+            except (OSError, KeyError, ValueError) as e:
+                print(f"[WARNING] Error reading cache file {cache_path}: {e}")
+                print(f"[INFO] Recomputing surface coverage...")
+        
+        # Otherwise, compute the coverage mapping (returns index-based results)
+        coverage, max_recall_res, max_precision_res = self._scan_surface_residues(radius, threshold)
+        
+        # Save the result to HDF5 file
+        # Create or open the HDF5 file and save data under the radius key
+        with h5py.File(cache_path, "a") as h5f:  # "a" mode: read/write if exists, create otherwise
+            # Create or overwrite the radius group
+            if radius_key in h5f:
+                del h5f[radius_key]  # Remove existing group if override or recompute
+            
+            radius_group = h5f.create_group(radius_key)
+            
+            # Save each center residue's data
+            for center_idx, (covered_indices, covered_epitope_indices, precision, recall) in coverage.items():
+                center_group = radius_group.create_group(str(center_idx))
+                center_group.create_dataset('covered_indices', data=np.array(covered_indices, dtype=np.int32), compression='gzip')
+                center_group.create_dataset('covered_epitope_indices', data=np.array(covered_epitope_indices, dtype=np.int32), compression='gzip')
+                center_group.attrs['precision'] = precision
+                center_group.attrs['recall'] = recall
+        
+        # Convert to residue numbers if index=False is requested
+        if not index:
+            coverage_resnums = {}
+            max_recall_res_num = None
+            max_precision_res_num = None
+            
+            if max_recall_res is not None:
+                max_recall_res_num = int(self.residue_index[max_recall_res])
+            if max_precision_res is not None:
+                max_precision_res_num = int(self.residue_index[max_precision_res])
+            
+            for center_idx, (covered_indices, covered_epitope_indices, precision, recall) in coverage.items():
+                center_res_num = int(self.residue_index[center_idx])
+                # Convert covered indices to residue numbers
+                covered_res_nums = [int(self.residue_index[idx]) for idx in covered_indices if 0 <= idx < len(self.residue_index)]
+                covered_epitope_res_nums = [int(self.residue_index[idx]) for idx in covered_epitope_indices if 0 <= idx < len(self.residue_index)]
+                coverage_resnums[center_res_num] = (covered_res_nums, covered_epitope_res_nums, precision, recall)
+            
+            return coverage_resnums, max_recall_res_num, max_precision_res_num
+        
+        return coverage, max_recall_res, max_precision_res
+        
+    def data_preparation(self, radius: float = None, encoder: str = "esmc", override: bool = False):
+        """
+        Retrieve or compute region embeddings for surface residues using spherical regions.
+
+        Args:
+            radius (float): Radius to define the neighborhood of each center residue.
+            threshold (float): Threshold to determine surface residues.
+            cover (bool): Whether to recompute and overwrite cached data.
+            verbose (bool): Whether to print progress information.
+
+        Returns:
+            tuple:
+                - embeddings (np.ndarray): Array of embeddings mean of the region. (num_regions, embedding_dim)
+                - center_residues (np.ndarray): Array of center residue numbers. (num_regions,)
+                - precisions (np.ndarray): Array of precision values for each center residue. (num_regions,)
+                - recalls (np.ndarray): Array of recall values for each center residue. (num_regions,)
+        """
+        embeddings = self.get_embeddings(encoder=encoder)
+        backbone_atoms = self.get_backbone_atoms()
+        rsa = self.get_rsa()
+        if radius is None:
+            # Used for creating data
+            for i in range(16,21,2):
+                _, _, _ = self.get_surface_coverage(radius=i, override=override)
+            return embeddings, backbone_atoms, rsa, None
+        else:
+            coverage_dict, _, _ = self.get_surface_coverage(radius=radius, override=override)
+            return embeddings, backbone_atoms, rsa, coverage_dict
+    
+    def evaluate(self, model_path: str = None, device_id: int = 1, radius: float = 19.0, k: int = 7, 
+                threshold: float = None, verbose: bool = True, encoder: str = "esmc", use_gpu: bool = True):
+        """
+        Evaluate epitopes using ReCEP model with spherical regions.
+        
+        Args:
+            model_path (str): Path to the trained ReCEP model
+            device_id (int): GPU device ID to use
+            radius (float): Radius for spherical regions
+            k (int): Number of top regions to select
+            threshold (float): Threshold for node-level epitope prediction
+            verbose (bool): Whether to print progress information
+            
+        Returns:
+            dict: Dictionary containing:
+                - 'predicted_epitopes': List of predicted epitope residue numbers
+                - 'true_epitopes': Set of true epitope residue numbers
+                - 'precision': Final prediction precision
+                - 'recall': Final prediction recall
+                - 'top_k_regions': Information about selected regions
+        """
+        # Set device
+        if use_gpu and torch.cuda.is_available() and device_id >= 0:
+            device = torch.device(f"cuda:{device_id}")
+        else:
+            device = torch.device("cpu")
+        if verbose:
+            print(f"[INFO] Using device: {device}")
+        
+        # Load ReCEP model
+        try:
+            if model_path is None:
+                model_path = f"{BASE_DIR}/models/ReCEP/20250626_110438/best_mcc_model.bin"
+                
+            if threshold is None:
+                model, threshold = ReCEP.load(model_path, device=device, strict=False, verbose=False)
+            else:
+                model, _ = ReCEP.load(model_path, device=device, strict=False, verbose=False)
+                
+            model.eval()
+            if verbose:
+                print(f"[INFO] Loaded ReCEP model from {model_path}")
+        except Exception as e:
+            if verbose:
+                print(f"[ERROR] Failed to load model: {str(e)}")
+            return {}
+        
+        # Get protein data using data_preparation
+        try:
+            embeddings, backbone_atoms, rsa, coverage_dict = self.data_preparation(radius=radius, encoder=encoder)
+            if verbose:
+                print(f"[INFO] Retrieved protein data for {len(coverage_dict)} surface regions")
+        except Exception as e:
+            if verbose:
+                print(f"[ERROR] Failed to prepare data: {str(e)}")
+                traceback.print_exc()
+            return {}
+        
+        if not coverage_dict:
+            if verbose:
+                print("[WARNING] No surface regions found")
+            return {}
+        
+        # Get epitope indices
+        epitope_indices = np.where(self.epitopes)[0].tolist()
+        
+        # Phase 1: Predict graph-level values for all regions
+        region_predictions = []
+        
+        with torch.no_grad():
+            for center_idx, (covered_indices, covered_epitope_indices, precision, recall) in tqdm(
+                coverage_dict.items(), desc="Predicting region values", disable=not verbose):
+                
+                if len(covered_indices) < 2:  # Skip regions with too few residues
+                    continue
+                
+                try:
+                    # Create graph data for this region
+                    graph_data = create_graph_data(
+                        center_idx=center_idx,
+                        covered_indices=covered_indices,
+                        covered_epitope_indices=covered_epitope_indices,
+                        embeddings=embeddings,
+                        backbone_atoms=backbone_atoms,
+                        rsa_values=rsa,
+                        epitope_indices=epitope_indices,
+                        recall=recall,
+                        precision=precision,
+                        pdb_id=self.id,
+                        chain_id=self.chain_id,
+                        verbose=True  # Enable verbose to see errors
+                    )
+                    
+                    if graph_data is None:
+                        if verbose:
+                            print(f"[WARNING] Failed to create graph data for region {center_idx}")
+                        continue
+                    
+                    # Move data to device
+                    graph_data = graph_data.to(device)
+                    
+                    # Create batch tensor for single graph - this is crucial!
+                    graph_data.batch = torch.zeros(graph_data.num_nodes, dtype=torch.long, device=device)
+                    
+                    # Predict using ReCEP model (following trainer.py pattern)
+                    outputs = model(graph_data)
+                    
+                    # Get graph-level prediction
+                    if 'global_pred' in outputs:
+                        graph_pred = torch.sigmoid(outputs['global_pred']).cpu().item()
+                    else:
+                        # Fallback: use mean of node predictions as graph prediction
+                        node_preds = torch.sigmoid(outputs['node_preds']).cpu().numpy()
+                        graph_pred = float(np.mean(node_preds))
+                    
+                    region_predictions.append({
+                        'center_idx': center_idx,
+                        'covered_indices': covered_indices,
+                        'covered_epitope_indices': covered_epitope_indices,
+                        'graph_pred': graph_pred,
+                        'true_recall': recall,
+                        'graph_data': graph_data
+                    })
+                    
+                except Exception as e:
+                    if verbose:
+                        print(f"[WARNING] Error processing region {center_idx}: {str(e)}")          
+                        traceback.print_exc()
+                    continue
+        
+        if not region_predictions:
+            if verbose:
+                print("[WARNING] No valid region predictions")
+            return {}
+        
+        # Phase 2: Select top-k regions based on graph predictions
+        region_predictions.sort(key=lambda x: x['graph_pred'], reverse=True)
+        top_k_regions = region_predictions[:k]
+        
+        if verbose:
+            print(f"[INFO] Selected top {len(top_k_regions)} regions:")
+            for i, region in enumerate(top_k_regions):
+                print(f"  Region {i+1}: center={region['center_idx']}, "
+                      f"predicted_value={region['graph_pred']:.3f}, "
+                      f"true_recall={region['true_recall']:.3f}")
+        
+        # Phase 3: Predict node-level epitopes for selected regions
+        residue_votes = {}  # residue_idx -> [list of binary predictions]
+        residue_probs = {}  # residue_idx -> [list of probabilities]
+        
+        with torch.no_grad():
+            for region in tqdm(top_k_regions, desc="Predicting node values", disable=not verbose):
+                try:
+                    graph_data = region['graph_data']
+                    
+                    # Ensure graph data has batch information - this is crucial!
+                    if not hasattr(graph_data, 'batch') or graph_data.batch is None:
+                        graph_data.batch = torch.zeros(graph_data.num_nodes, dtype=torch.long, device=device)
+                    
+                    # Predict using ReCEP model (following trainer.py pattern)
+                    outputs = model(graph_data)
+                    
+                    # Get node-level predictions
+                    node_preds = torch.sigmoid(outputs['node_preds']).cpu().numpy()
+                    
+                    # Store votes and probabilities for each residue
+                    for local_idx, residue_idx in enumerate(region['covered_indices']):
+                        if residue_idx not in residue_votes:
+                            residue_votes[residue_idx] = []
+                            residue_probs[residue_idx] = []
+                        
+                        # Store probability and binary vote
+                        prob = float(node_preds[local_idx])
+                        residue_probs[residue_idx].append(prob)
+                        
+                        # Binary vote based on threshold
+                        vote = 1 if prob >= threshold else 0
+                        residue_votes[residue_idx].append(vote)
+                        
+                except Exception as e:
+                    if verbose:
+                        print(f"[WARNING] Error in node prediction for region {region['center_idx']}: {str(e)}")
+                        traceback.print_exc()
+                    continue
+        
+        # Create predictions dictionary for all residues
+        all_residue_predictions = {}
+        for idx in range(len(self.residue_index)):
+            residue_num = int(self.residue_index[idx])
+            if idx in residue_probs:
+                # Calculate mean probability for residues in top-k regions
+                all_residue_predictions[residue_num] = float(np.mean(residue_probs[idx]))
+            else:
+                # Set probability to 1e-5 for residues not in any top-k region
+                all_residue_predictions[residue_num] = 1e-2
+        
+        # Phase 4a: Apply voting mechanism for voted_epitopes
+        voted_epitope_indices = []
+        for residue_idx, votes in residue_votes.items():
+            # If >= half of the votes are positive, predict as epitope
+            if sum(votes) >= len(votes) / 2:
+                voted_epitope_indices.append(residue_idx)
+        
+        # Convert indices to residue numbers for voted epitopes
+        voted_epitope_resnums = [int(self.residue_index[idx]) for idx in voted_epitope_indices 
+                               if 0 <= idx < len(self.residue_index)]
+        
+        # Phase 4b: Apply probability threshold for predicted_epitopes
+        predicted_epitope_resnums = []
+        for residue_num, prob in all_residue_predictions.items():
+            if prob >= threshold:
+                predicted_epitope_resnums.append(residue_num)
+        
+        # Get true epitopes
+        true_epitope_resnums = set(self.get_epitope_residue_numbers())
+        
+        # Calculate metrics for both prediction methods
+        # Metrics for voted epitopes
+        voted_tp = len(set(voted_epitope_resnums) & true_epitope_resnums)
+        voted_precision = voted_tp / len(voted_epitope_resnums) if voted_epitope_resnums else 0
+        voted_recall = voted_tp / len(true_epitope_resnums) if true_epitope_resnums else 0
+        
+        # Metrics for probability-based epitopes
+        predicted_tp = len(set(predicted_epitope_resnums) & true_epitope_resnums)
+        predicted_precision = predicted_tp / len(predicted_epitope_resnums) if predicted_epitope_resnums else 0
+        predicted_recall = predicted_tp / len(true_epitope_resnums) if true_epitope_resnums else 0
+        
+        if verbose:
+            print(f"\n[INFO] Final Results:")
+            print(f"  True epitopes: {len(true_epitope_resnums)}")
+            print(f"  Residues in top-k regions: {len(residue_probs)}/{len(self.residue_index)}")
+            print(f"\n  Voting-based prediction:")
+            print(f"    Voted epitopes: {len(voted_epitope_resnums)}")
+            print(f"    Voted precision: {voted_precision:.3f}")
+            print(f"    Voted recall: {voted_recall:.3f}")
+            print(f"\n  Probability-based prediction (threshold={threshold}):")
+            print(f"    Predicted epitopes: {len(predicted_epitope_resnums)}")
+            print(f"    Predicted precision: {predicted_precision:.3f}")
+            print(f"    Predicted recall: {predicted_recall:.3f}")
+        
+        return {
+            'predicted_epitopes': predicted_epitope_resnums,  # Based on probability threshold
+            'voted_epitopes': voted_epitope_resnums,          # Based on voting mechanism
+            'true_epitopes': true_epitope_resnums,
+            'predicted_precision': predicted_precision,       # Precision for probability-based
+            'predicted_recall': predicted_recall,             # Recall for probability-based
+            'voted_precision': voted_precision,               # Precision for voting-based
+            'voted_recall': voted_recall,                     # Recall for voting-based
+            'predictions': all_residue_predictions,           # All residue probabilities
+            'top_k_regions': [
+                {
+                    'center_residue': int(self.residue_index[region['center_idx']]),
+                    'center_idx': region['center_idx'],
+                    'predicted_value': region['graph_pred'],
+                    'true_recall': region['true_recall'],
+                    'covered_residues': [int(self.residue_index[idx]) for idx in region['covered_indices']]
+                }
+                for region in top_k_regions
+            ],
+            'residue_votes': {
+                int(self.residue_index[idx]): votes 
+                for idx, votes in residue_votes.items() 
+                if 0 <= idx < len(self.residue_index)
+            }
+        }
+        
+    def predict(self, model_path: str = None, device_id: int = 1, radius: float = 19.0, k: int = 7, 
+                threshold: float = None, verbose: bool = True, encoder: str = "esmc", use_gpu: bool = True, 
+                auto_cleanup: bool = False):
+        """
+        Predict epitopes using ReCEP model with spherical regions (for unknown true epitopes).
+        
+        Args:
+            model_path (str): Path to the trained ReCEP model
+            device_id (int): GPU device ID to use
+            radius (float): Radius for spherical regions
+            k (int): Number of top regions to select
+            threshold (float): Threshold for node-level epitope prediction
+            verbose (bool): Whether to print progress information
+            encoder (str): Encoder type for embeddings
+            use_gpu (bool): Whether to use GPU for computation
+            auto_cleanup (bool): Whether to automatically delete generated data files after prediction
+            
+        Returns:
+            dict: Dictionary containing:
+                - 'predicted_epitopes': List of predicted epitope residue numbers
+                - 'predictions': Dictionary of all residue probabilities {resnum: probability}
+                - 'top_k_centers': List of top-k center residue numbers
+                - 'top_k_region_residues': List of all residues covered by top-k regions (union)
+                - 'top_k_regions': Detailed information about selected regions
+        """
+        # Set device
+        if use_gpu and torch.cuda.is_available() and device_id >= 0:
+            device = torch.device(f"cuda:{device_id}")
+        else:
+            device = torch.device("cpu")
+        if verbose:
+            print(f"[INFO] Using device: {device}")
+        
+        # Load ReCEP model
+        try:
+            if model_path is None:
+                model_path = f"{BASE_DIR}/models/ReCEP/20250626_110438/best_mcc_model.bin"
+                
+            if threshold is None:
+                model, threshold = ReCEP.load(model_path, device=device, strict=False, verbose=False)
+            else:
+                model, _ = ReCEP.load(model_path, device=device, strict=False, verbose=False)
+                
+            model.eval()
+            if verbose:
+                print(f"[INFO] Loaded ReCEP model from {model_path}")
+        except Exception as e:
+            if verbose:
+                print(f"[ERROR] Failed to load model: {str(e)}")
+            return {}
+        
+        # Get protein data using data_preparation
+        try:
+            embeddings, backbone_atoms, rsa, coverage_dict = self.data_preparation(radius=radius, encoder=encoder)
+            if verbose:
+                print(f"[INFO] Retrieved protein data for {len(coverage_dict)} surface regions")
+        except Exception as e:
+            if verbose:
+                print(f"[ERROR] Failed to prepare data: {str(e)}")
+                traceback.print_exc()
+            return {}
+        
+        if not coverage_dict:
+            if verbose:
+                print("[WARNING] No surface regions found")
+            return {}
+        
+        # Phase 1: Predict graph-level values for all regions
+        region_predictions = []
+        
+        with torch.no_grad():
+            for center_idx, (covered_indices, covered_epitope_indices, precision, recall) in tqdm(
+                coverage_dict.items(), desc="Predicting region values", disable=not verbose):
+                
+                if len(covered_indices) < 2:  # Skip regions with too few residues
+                    continue
+                
+                try:
+                    # Create graph data for this region (without epitope information)
+                    graph_data = create_graph_data(
+                        center_idx=center_idx,
+                        covered_indices=covered_indices,
+                        covered_epitope_indices=[],  # No epitope information for prediction
+                        embeddings=embeddings,
+                        backbone_atoms=backbone_atoms,
+                        rsa_values=rsa,
+                        epitope_indices=[],  # No epitope information for prediction
+                        recall=0.0,  # No recall information
+                        precision=0.0,  # No precision information
+                        pdb_id=self.id,
+                        chain_id=self.chain_id,
+                        verbose=False
+                    )
+                    
+                    if graph_data is None:
+                        if verbose:
+                            print(f"[WARNING] Failed to create graph data for region {center_idx}")
+                        continue
+                    
+                    # Move data to device
+                    graph_data = graph_data.to(device)
+                    
+                    # Create batch tensor for single graph
+                    graph_data.batch = torch.zeros(graph_data.num_nodes, dtype=torch.long, device=device)
+                    
+                    # Predict using ReCEP model
+                    outputs = model(graph_data)
+                    
+                    # Get graph-level prediction
+                    if 'global_pred' in outputs:
+                        graph_pred = torch.sigmoid(outputs['global_pred']).cpu().item()
+                    else:
+                        # Fallback: use mean of node predictions as graph prediction
+                        node_preds = torch.sigmoid(outputs['node_preds']).cpu().numpy()
+                        graph_pred = float(np.mean(node_preds))
+                    
+                    region_predictions.append({
+                        'center_idx': center_idx,
+                        'covered_indices': covered_indices,
+                        'graph_pred': graph_pred,
+                        'graph_data': graph_data
+                    })
+                    
+                except Exception as e:
+                    if verbose:
+                        print(f"[WARNING] Error processing region {center_idx}: {str(e)}")          
+                        traceback.print_exc()
+                    continue
+        
+        if not region_predictions:
+            if verbose:
+                print("[WARNING] No valid region predictions")
+            return {}
+        
+        # Phase 2: Select top-k regions based on graph predictions
+        region_predictions.sort(key=lambda x: x['graph_pred'], reverse=True)
+        top_k_regions = region_predictions[:k]
+        
+        if verbose:
+            print(f"[INFO] Selected top {len(top_k_regions)} regions:")
+            for i, region in enumerate(top_k_regions):
+                print(f"  Region {i+1}: center={region['center_idx']}, "
+                      f"predicted_value={region['graph_pred']:.3f}")
+        
+        # Phase 3: Predict node-level epitopes for selected regions
+        residue_probs = {}  # residue_idx -> [list of probabilities]
+        
+        with torch.no_grad():
+            for region in tqdm(top_k_regions, desc="Predicting node values", disable=not verbose):
+                try:
+                    graph_data = region['graph_data']
+                    
+                    # Ensure graph data has batch information
+                    if not hasattr(graph_data, 'batch') or graph_data.batch is None:
+                        graph_data.batch = torch.zeros(graph_data.num_nodes, dtype=torch.long, device=device)
+                    
+                    # Predict using ReCEP model
+                    outputs = model(graph_data)
+                    
+                    # Get node-level predictions
+                    node_preds = torch.sigmoid(outputs['node_preds']).cpu().numpy()
+                    
+                    # Store probabilities for each residue
+                    for local_idx, residue_idx in enumerate(region['covered_indices']):
+                        if residue_idx not in residue_probs:
+                            residue_probs[residue_idx] = []
+                        
+                        # Store probability
+                        prob = float(node_preds[local_idx])
+                        residue_probs[residue_idx].append(prob)
+                        
+                except Exception as e:
+                    if verbose:
+                        print(f"[WARNING] Error in node prediction for region {region['center_idx']}: {str(e)}")
+                        traceback.print_exc()
+                    continue
+        
+        # Create predictions dictionary for all residues
+        all_residue_predictions = {}
+        for idx in range(len(self.residue_index)):
+            residue_num = int(self.residue_index[idx])
+            if idx in residue_probs:
+                # Calculate mean probability for residues in top-k regions
+                all_residue_predictions[residue_num] = float(np.mean(residue_probs[idx]))
+            else:
+                # Set probability to 0 for residues not in any top-k region
+                all_residue_predictions[residue_num] = 0.0
+        
+        # Apply probability threshold for predicted epitopes
+        predicted_epitope_resnums = []
+        node_mean = 0.0
+        for residue_num, prob in all_residue_predictions.items():
+            node_mean += prob
+            if prob >= threshold:
+                predicted_epitope_resnums.append(residue_num)
+        node_mean /= len(all_residue_predictions) if all_residue_predictions else 1
+        
+        # Get top-k center residue numbers
+        top_k_centers = [int(self.residue_index[region['center_idx']]) for region in top_k_regions]
+        
+        # Get union of all residues covered by top-k regions and mean graph predicted value
+        graph_mean = 0.0
+        all_covered_indices = set()
+        for region in top_k_regions:
+            all_covered_indices.update(region['covered_indices'])
+            graph_mean += region['graph_pred']
+        graph_mean /= len(top_k_regions)
+        
+        top_k_region_residues = [int(self.residue_index[idx]) for idx in all_covered_indices 
+                               if 0 <= idx < len(self.residue_index)]
+        
+        if verbose:
+            print(f"\n[INFO] Prediction Results:")
+            print(f"  Predicted epitopes: {len(predicted_epitope_resnums)}")
+            print(f"  Top-k centers: {top_k_centers}")
+            print(f"  Total residues in top-k regions: {len(top_k_region_residues)}")
+        
+        # Prepare return results
+        results = {
+            'predicted_epitopes': predicted_epitope_resnums,
+            'predictions': all_residue_predictions,
+            'top_k_centers': top_k_centers,
+            'top_k_region_residues': top_k_region_residues,
+            'top_k_regions': [
+                {
+                    'center_residue': int(self.residue_index[region['center_idx']]),
+                    'center_idx': region['center_idx'],
+                    'predicted_value': region['graph_pred'],
+                    'covered_residues': [int(self.residue_index[idx]) for idx in region['covered_indices']]
+                }
+                for region in top_k_regions
+            ],
+            'antigen_rate': graph_mean,
+            'epitope_rate': node_mean
+        }
+        
+        # Auto-cleanup generated data files if requested
+        if auto_cleanup:
+            self._cleanup_generated_data(encoder=encoder, verbose=verbose)
+        
+        return results
+    
+    def _cleanup_generated_data(self, encoder: str = "esmc", verbose: bool = True):
+        """
+        Clean up generated data files for this antigen chain.
+        
+        Args:
+            encoder (str): Encoder type used for embeddings
+            verbose (bool): Whether to print cleanup information
+        """
+        import os
+        
+        # List of files to delete
+        files_to_delete = [
+            # Embeddings file
+            Path(BASE_DIR) / "data" / "embeddings" / encoder / f"{self.id}_{self.chain_id}.h5",
+            # Backbone atoms file
+            Path(BASE_DIR) / "data" / "coords" / f"{self.id}_{self.chain_id}.npy",
+            # RSA file
+            Path(BASE_DIR) / "data" / "rsa" / f"{self.id}_{self.chain_id}.npy",
+            # Surface coverage file
+            Path(BASE_DIR) / "data" / "antigen_sphere" / f"{self.id}_{self.chain_id}.h5"
+        ]
+        
+        deleted_files = []
+        failed_deletions = []
+        total_size = 0
+        
+        for file_path in files_to_delete:
+            if file_path.exists():
+                try:
+                    # Get file size before deletion
+                    file_size = file_path.stat().st_size
+                    os.remove(file_path)
+                    deleted_files.append(file_path)
+                    total_size += file_size
+                    if verbose:
+                        print(f"[INFO] Deleted: {file_path}")
+                except Exception as e:
+                    failed_deletions.append((file_path, str(e)))
+                    if verbose:
+                        print(f"[WARNING] Failed to delete {file_path}: {str(e)}")
+            else:
+                if verbose:
+                    print(f"[INFO] File not found (already deleted or not generated): {file_path}")
+        
+        if verbose:
+            print(f"[INFO] Cleanup completed for {self.id}_{self.chain_id}")
+            print(f"  - Files deleted: {len(deleted_files)}")
+            print(f"  - Failed deletions: {len(failed_deletions)}")
+            if total_size > 0:
+                print(f"  - Total space freed: {total_size / (1024**2):.2f} MB")
+    
+    def visualize(self, 
+                  mode: str = 'normal',
+                  style: str = 'cartoon',
+                  predicted_epitopes: list = None,
+                  predict_results: dict = None,
+                  prediction_mode: str = 'residue',  # 'residue' or 'region'
+                  center_res: int = None,
+                  radius: float = None,
+                  region_index: int = None,  # Index of specific region to show (0-based)
+                  width: int = 800,
+                  height: int = 600,
+                  base_color: str = '#e6e6f7',
+                  true_epitope_color: str = '#f1b54c',  # True epitopes (deeper blue)
+                  false_positive_color: str = '#ef5331',  # False positives (deeper red)
+                  true_positive_color: str = '#a0d293',  # True positives (deeper green)
+                  coverage_color: str = '#9C6ADE',  # Coverage regions (purple)
+                  prediction_color: str = '#9C6ADE',  # Prediction color (purple)
+                  center_color: str = '#2C3E50',  # Center residue (dark gray)
+                  probability_colormap: str = 'RdYlBu_r',  # Colormap for probability visualization
+                  show_surface: bool = True,
+                  show_shape: bool = True,
+                  show_center: bool = True,
+                  center_radius: float = 0.7,
+                  n_points: int = 50,
+                  shape_opacity: float = 0.3,
+                  surface_opacity: float = 1.0,
+                  wireframe: bool = True,
+                  show_epitope: bool = True,
+                  show_coverage: bool = True,
+                  show_top_regions: bool = True,
+                  max_spheres: int = None,  # Maximum number of spheres to show
+                  prob_threshold: float = 0.5):
+        """
+        Visualize the protein chain with various modes and integration with predict results.
+        
+        Args:
+            mode (str): Visualization mode. Options:
+                - 'normal': Basic protein structure
+                - 'epitope': Show predicted epitopes vs true epitopes
+                - 'coverage': Show spherical coverage region
+                - 'evaluation': Show evaluation results from evaluate() function
+                - 'prediction': Show prediction results from predict() function
+                - 'probability': Show residue probabilities as color gradient
+                - 'top_regions': Show top-k regions from prediction
+                - 'comparison': Compare voted vs predicted epitopes
+            prediction_mode (str): Sub-mode for prediction visualization ('residue' or 'region')
+                - 'residue': Color predicted epitopes by probability (gradient purple)
+                - 'region': Color all residues in top-k regions uniformly
+            style (str): Protein representation style ('cartoon', 'stick', 'sphere', 'surface')
+            predicted_epitopes (list): List of predicted epitope residue numbers
+            predict_results (dict): Results dictionary from predict() function
+            center_res (int): Center residue number for coverage visualization
+            radius (float): Radius for spherical coverage
+            region_index (int): Index of specific region to show in probability mode (0-based)
+                              If None, shows all regions
+                              Each region uses a distinct color for shape visualization
+            probability_colormap (str): Colormap name for probability visualization
+            prob_threshold (float): Threshold for probability-based coloring
+            ... (other parameters as before)
+            
+        Returns:
+            py3Dmol.view: The molecular visualization view object
+        """
+        # Create view object and add basic structure
+        view = self._create_base_view(width, height)
+        
+        # Set basic style
+        style_dict = {
+            'cartoon': {'cartoon': {}},
+            'stick': {'stick': {}},
+            'sphere': {'sphere': {}},
+            'surface': {'surface': {}}
+        }
+        base_style = style_dict.get(style, {'cartoon': {}})
+        
+        # Visualization based on mode
+        if mode == 'epitope' and predicted_epitopes is not None:
+            self._add_epitope_visualization(
+                view, style, predicted_epitopes,
+                base_color, true_epitope_color, false_positive_color, 
+                true_positive_color, coverage_color,
+                show_surface, surface_opacity, show_coverage,
+                center_res, radius
+            )
+            
+            # Add shape visualization if needed
+            if show_shape and center_res is not None and radius is not None:
+                self._add_shape_visualization(
+                    view, center_res, radius,
+                    coverage_color, center_color,
+                    show_center, center_radius,
+                    shape_opacity, wireframe
+                )
+                
+        elif mode == 'coverage' and center_res is not None and radius is not None:
+            self._add_coverage_visualization(
+                view, style, center_res, radius,
+                base_color, coverage_color, true_positive_color, true_epitope_color,
+                show_surface, show_shape, show_center,
+                surface_opacity, shape_opacity, center_radius,
+                n_points, center_color, wireframe, show_epitope
+            )
+            
+        elif mode == 'evaluation' and predict_results is not None:
+            self._add_evaluation_visualization(
+                view, style, predict_results,
+                base_color, true_epitope_color, false_positive_color, 
+                true_positive_color, coverage_color,
+                show_surface, surface_opacity, show_shape, radius, max_spheres
+            )
+            
+        elif mode == 'prediction' and predict_results is not None:
+            self._add_prediction_visualization(
+                view, style, predict_results, prediction_mode,
+                base_color, prediction_color, show_surface, surface_opacity,
+                show_shape, shape_opacity, show_center, center_radius,
+                wireframe, radius, max_spheres
+            )
+            
+        elif mode == 'probability' and predict_results is not None:
+            self._add_probability_visualization(
+                view, style, predict_results,
+                base_color, probability_colormap, show_surface, surface_opacity, 
+                prob_threshold, region_index, radius, show_shape, shape_opacity, 
+                show_center, center_radius, wireframe, coverage_color, center_color
+            )
+            
+        elif mode == 'top_regions' and predict_results is not None:
+            self._add_top_regions_visualization(
+                view, style, predict_results,
+                base_color, coverage_color, center_color,
+                show_surface, show_shape, show_center,
+                surface_opacity, shape_opacity, center_radius,
+                wireframe, radius, max_spheres
+            )
+            
+        elif mode == 'comparison' and predict_results is not None:
+            self._add_comparison_visualization(
+                view, style, predict_results,
+                base_color, true_epitope_color, false_positive_color, 
+                true_positive_color, coverage_color, show_surface, surface_opacity
+            )
+            
+        else:
+            # Default mode: just show the basic structure
+            view.setStyle({'chain': self.chain_id}, base_style)
+        
+        # Adjust view
+        view.zoomTo()
+        return view
+    
+    def _add_prediction_visualization(self, view, style, predict_results, prediction_mode,
+                                  base_color, prediction_color, show_surface, surface_opacity,
+                                  show_shape, shape_opacity, show_center, center_radius,
+                                  wireframe, radius, max_spheres):
+        """Add visualization for prediction results"""
+        if prediction_mode == 'residue':
+            self._add_prediction_residue_mode(
+                view, style, predict_results, base_color, prediction_color, 
+                show_surface, surface_opacity
+            )
+        elif prediction_mode == 'region':
+            self._add_prediction_region_mode(
+                view, style, predict_results, base_color, prediction_color,
+                show_surface, surface_opacity, show_shape, shape_opacity,
+                show_center, center_radius, wireframe, radius, max_spheres
+            )
+    
+    def _add_prediction_residue_mode(self, view, style, predict_results, base_color, prediction_color, 
+                                 show_surface, surface_opacity):
+        """Add visualization for prediction results in residue mode"""
+        import matplotlib.pyplot as plt
+        import matplotlib.colors as mcolors
+        
+        # Get predictions dictionary
+        predictions = predict_results.get('predictions', {})
+        predicted_epitopes = predict_results.get('predicted_epitopes', [])
+        
+        # Get style configuration
+        style_dict = {
+            'cartoon': {'cartoon': {}},
+            'stick': {'stick': {}},
+            'sphere': {'sphere': {}},
+            'surface': {'surface': {}}
+        }
+        base_style = style_dict.get(style, {'cartoon': {}})
+        
+        if not predictions:
+            # Fallback to basic visualization
+            view.setStyle({'chain': self.chain_id}, {**base_style, 
+                          list(base_style.keys())[0]: {**list(base_style.values())[0], 'color': base_color}})
+            if show_surface:
+                view.addSurface(py3Dmol.VDW, {
+                    'opacity': surface_opacity * 0.9,  # Softer opacity for fallback
+                    'color': base_color
+                }, {'chain': self.chain_id})
+            return
+        
+        # Filter predictions to only include predicted epitopes
+        epitope_predictions = {res: prob for res, prob in predictions.items() 
+                             if res in predicted_epitopes}
+        
+        if not epitope_predictions:
+            # No predicted epitopes, show base style
+            view.setStyle({'chain': self.chain_id}, {**base_style, list(base_style.keys())[0]: {**list(base_style.values())[0], 'color': base_color}})
+            if show_surface:
+                view.addSurface(py3Dmol.VDW, {
+                    'opacity': surface_opacity * 0.9,  # Softer opacity for fallback
+                    'color': base_color
+                }, {'chain': self.chain_id})
+            return
+        
+        # Get probability range for predicted epitopes only
+        probs = list(epitope_predictions.values())
+        min_prob, max_prob = min(probs), max(probs)
+        
+        # Improved color scheme - use orange to red gradient for better contrast with gray
+        # This avoids confusion with gray background when probability is low
+        epitope_colors = [
+            '#FFE4B5',  # Light orange (moccasin)
+            '#FFD700',  # Gold
+            '#FFA500',  # Orange
+            '#FF8C00',  # Dark orange
+            '#FF6347',  # Tomato
+            '#FF4500',  # Orange red
+            '#DC143C'   # Crimson
+        ]
+        n_colors = len(epitope_colors)
+        
+        # Set base style for entire protein
+        view.setStyle({'chain': self.chain_id}, {**base_style, list(base_style.keys())[0]: {**list(base_style.values())[0], 'color': base_color}})
+        
+        # Color predicted epitopes based on probability with orange-red gradient
+        for residue_num, prob in epitope_predictions.items():
+            # Normalize probability to [0, 1] within the epitope range
+            if max_prob > min_prob:
+                norm_prob = (prob - min_prob) / (max_prob - min_prob)
+            else:
+                norm_prob = 0.5
+            
+            # Map to color index
+            color_idx = int(norm_prob * (n_colors - 1))
+            color_idx = max(0, min(color_idx, n_colors - 1))
+            color = epitope_colors[color_idx]
+            
+            # Add style for this residue with vivid color
+            style_name = list(base_style.keys())[0]
+            colored_style = {style_name: {'color': color}}
+            view.addStyle(
+                {'chain': self.chain_id, 'resi': residue_num},
+                colored_style
+            )
+        
+        # Add surface overlay if requested
+        if show_surface:
+            # Add base surface for non-epitope regions
+            all_residues = set(int(res) for res in self.residue_index)
+            non_epitope_residues = all_residues - set(predicted_epitopes)
+            
+            if non_epitope_residues:
+                view.addSurface(py3Dmol.VDW, {
+                    'opacity': surface_opacity * 0.9,  # Softer opacity for fallback
+                    'color': base_color
+                }, {'chain': self.chain_id, 'resi': list(non_epitope_residues)})
+            
+            # Add colored surfaces for predicted epitopes
+            for residue_num, prob in epitope_predictions.items():
+                # Normalize probability to [0, 1] within the epitope range
+                if max_prob > min_prob:
+                    norm_prob = (prob - min_prob) / (max_prob - min_prob)
+                else:
+                    norm_prob = 0.5
+                
+                # Map to color index
+                color_idx = int(norm_prob * (n_colors - 1))
+                color_idx = max(0, min(color_idx, n_colors - 1))
+                color = epitope_colors[color_idx]
+                
+                # Add surface for this residue
+                view.addSurface(py3Dmol.VDW, {
+                    'opacity': surface_opacity,
+                    'color': color
+                }, {'chain': self.chain_id, 'resi': residue_num})
+    
+    def _add_prediction_region_mode(self, view, style, predict_results, base_color, prediction_color,
+                                show_surface, surface_opacity, show_shape, shape_opacity,
+                                show_center, center_radius, wireframe, radius, max_spheres):
+        """Add visualization for prediction results in region mode"""
+        # Get top-k regions
+        top_k_regions = predict_results.get('top_k_regions', [])
+        top_k_region_residues = predict_results.get('top_k_region_residues', [])
+        
+        # Get style configuration
+        style_dict = {
+            'cartoon': {'cartoon': {}},
+            'stick': {'stick': {}},
+            'sphere': {'sphere': {}},
+            'surface': {'surface': {}}
+        }
+        base_style = style_dict.get(style, {'cartoon': {}})
+        
+        if not top_k_region_residues:
+            # No regions, show base style
+            view.setStyle({'chain': self.chain_id}, {**base_style, list(base_style.keys())[0]: {**list(base_style.values())[0], 'color': base_color}})
+            if show_surface:
+                view.addSurface(py3Dmol.VDW, {
+                    'opacity': surface_opacity * 0.9,  # Softer opacity for fallback
+                    'color': base_color
+                }, {'chain': self.chain_id})
+            return
+        
+        # Set base style for entire protein
+        view.setStyle({'chain': self.chain_id}, {**base_style, list(base_style.keys())[0]: {**list(base_style.values())[0], 'color': base_color}})
+        
+        # Color all residues in top-k regions with uniform purple
+        if top_k_region_residues:
+            style_name = list(base_style.keys())[0]
+            colored_style = {style_name: {'color': prediction_color}}
+            view.addStyle(
+                {'chain': self.chain_id, 'resi': top_k_region_residues},
+                colored_style
+            )
+        
+        # Add surface overlay if requested
+        if show_surface:
+            # Add base surface for non-region residues
+            all_residues = set(int(res) for res in self.residue_index)
+            non_region_residues = all_residues - set(top_k_region_residues)
+            
+            if non_region_residues:
+                view.addSurface(py3Dmol.VDW, {
+                    'opacity': surface_opacity * 0.9,  # Softer opacity for fallback
+                    'color': base_color
+                }, {'chain': self.chain_id, 'resi': list(non_region_residues)})
+            
+            # Color all residues in top-k regions with uniform purple surface
+            if top_k_region_residues:
+                view.addSurface(py3Dmol.VDW, {
+                    'opacity': surface_opacity,
+                    'color': prediction_color
+                }, {'chain': self.chain_id, 'resi': top_k_region_residues})
+        
+        # Add spherical regions if requested
+        if show_shape and top_k_regions:
+            self._add_multi_shape_visualization(
+                view, top_k_regions, radius, max_spheres,
+                show_center, center_radius, shape_opacity, wireframe
+            )
+    
+    def _add_evaluation_visualization(self, view, style, predict_results,
+                                  base_color, true_epitope_color, false_positive_color, 
+                                  true_positive_color, coverage_color,
+                                  show_surface, surface_opacity, show_shape, radius, max_spheres):
+        """Add visualization for evaluation results"""
+        # Get prediction results
+        predicted_epitopes = set(predict_results.get('predicted_epitopes', []))
+        true_epitopes = set(predict_results.get('true_epitopes', []))
+        
+        # Calculate different categories
+        true_positives = predicted_epitopes & true_epitopes
+        false_positives = predicted_epitopes - true_epitopes
+        false_negatives = true_epitopes - predicted_epitopes
+        
+        # Get style configuration
+        style_dict = {
+            'cartoon': {'cartoon': {}},
+            'stick': {'stick': {}},
+            'sphere': {'sphere': {}},
+            'surface': {'surface': {}}
+        }
+        base_style = style_dict.get(style, {'cartoon': {}})
+        
+        # Set base style for entire protein
+        view.setStyle({'chain': self.chain_id}, {**base_style, list(base_style.keys())[0]: {**list(base_style.values())[0], 'color': base_color}})
+        
+        # Add colored styles for specific categories with vivid colors
+        for residues, color in [
+            (true_positives, true_positive_color),
+            (false_positives, false_positive_color),
+            (false_negatives, true_epitope_color)
+        ]:
+            if residues:
+                # Create style with the specified color
+                style_name = list(base_style.keys())[0]
+                colored_style = {style_name: {'color': color}}
+                view.addStyle(
+                    {'chain': self.chain_id, 'resi': list(residues)},
+                    colored_style
+                )
+        
+        # Add surface overlay if requested (works with any base style)
+        if show_surface:
+            # Get all colored residues
+            all_colored_residues = true_positives | false_positives | false_negatives
+            
+            # Only add base surface for non-colored regions to avoid covering colored surfaces
+            if all_colored_residues:
+                all_residues = set(int(res) for res in self.residue_index)
+                non_colored_residues = all_residues - all_colored_residues
+                
+                if non_colored_residues:
+                    view.addSurface(py3Dmol.VDW, {
+                        'opacity': surface_opacity * 0.9,  # Softer opacity for fallback
+                        'color': base_color
+                    }, {'chain': self.chain_id, 'resi': list(non_colored_residues)})
+            else:
+                # If no colored residues, show entire surface in base color
+                view.addSurface(py3Dmol.VDW, {
+                    'opacity': surface_opacity * 0.9,  # Softer opacity for fallback
+                    'color': base_color
+                }, {'chain': self.chain_id})
+            
+            # Add colored surfaces for specific categories
+            for residues, color in [
+                (true_positives, true_positive_color),
+                (false_positives, false_positive_color),
+                (false_negatives, true_epitope_color)
+            ]:
+                if residues:
+                    view.addSurface(py3Dmol.VDW, {
+                        'opacity': surface_opacity,  # Full opacity for clear colors
+                        'color': color
+                    }, {'chain': self.chain_id, 'resi': list(residues)})
+        
+        # Show top regions with different colors if requested
+        if show_shape and 'top_k_regions' in predict_results:
+            top_regions = predict_results['top_k_regions']
+            self._add_multi_shape_visualization(
+                view, top_regions, radius, max_spheres,
+                True, 0.5, 0.2, True
+            )
+    
+    def _add_probability_visualization(self, view, style, predict_results,
+                                  base_color, colormap, show_surface, surface_opacity, threshold,
+                                  region_index, radius, show_shape, shape_opacity, 
+                                  show_center, center_radius, wireframe, coverage_color, center_color):
+        """
+        Add visualization based on prediction probabilities with enhanced support for 
+        specific region selection and surface rendering.
+        
+        Args:
+            view: py3Dmol view object
+            style (str): Protein representation style
+            predict_results (dict): Results from predict() function
+            base_color (str): Base color for non-highlighted residues
+            colormap (str): Colormap name for probability visualization
+            show_surface (bool): Whether to show surface
+            surface_opacity (float): Surface opacity
+            threshold (float): Probability threshold for coloring
+            region_index (int): Index of specific region to show (0-based), None for all
+                              Each region_index uses a distinct color for shape visualization
+            radius (float): Radius for spherical regions
+            show_shape (bool): Whether to show spherical shapes
+            shape_opacity (float): Shape opacity
+            show_center (bool): Whether to show center points
+            center_radius (float): Center point radius
+            wireframe (bool): Whether to show wireframe spheres
+            coverage_color (str): Color for coverage regions (not used when region_index is specified)
+            center_color (str): Color for center points
+        """
+        import matplotlib.pyplot as plt
+        import matplotlib.colors as mcolors
+        
+        # Get probability predictions and top-k regions
+        predictions = predict_results.get('predictions', {})
+        top_k_regions = predict_results.get('top_k_regions', [])
+        
+        # Get style configuration
+        style_dict = {
+            'cartoon': {'cartoon': {}},
+            'stick': {'stick': {}},
+            'sphere': {'sphere': {}},
+            'surface': {'surface': {}}
+        }
+        base_style = style_dict.get(style, {'cartoon': {}})
+        
+        if not predictions:
+            # Fallback to basic visualization
+            view.setStyle({'chain': self.chain_id}, {**base_style, 
+                          list(base_style.keys())[0]: {**list(base_style.values())[0], 'color': base_color}})
+            if show_surface:
+                view.addSurface(py3Dmol.VDW, {
+                    'opacity': surface_opacity * 0.9,  # Softer opacity for fallback
+                    'color': base_color
+                }, {'chain': self.chain_id})
+            return
+        
+        # Set base style for entire protein
+        view.setStyle({'chain': self.chain_id}, {**base_style, 
+                      list(base_style.keys())[0]: {**list(base_style.values())[0], 'color': base_color}})
+        
+        # Determine which residues to color based on region_index
+        target_residues = {}  # residue_num -> probability
+        selected_region = None
+        
+        if region_index is not None and 0 <= region_index < len(top_k_regions):
+            # Show only the selected region
+            selected_region = top_k_regions[region_index]
+            covered_residues = selected_region.get('covered_residues', [])
+            
+            # Get probabilities for residues in the selected region
+            for res_num in covered_residues:
+                if res_num in predictions:
+                    target_residues[res_num] = predictions[res_num]
+        else:
+            # Show all residues with probabilities above threshold
+            target_residues = {res: prob for res, prob in predictions.items() 
+                             if prob >= threshold}
+        
+        if not target_residues:
+            # No residues to color, show base style with surface
+            if show_surface:
+                view.addSurface(py3Dmol.VDW, {
+                    'opacity': surface_opacity * 0.9,  # Softer opacity for fallback
+                    'color': base_color
+                }, {'chain': self.chain_id})
+            return
+        
+        # Normalize probabilities for selected residues
+        probs = list(target_residues.values())
+        min_prob, max_prob = min(probs), max(probs)
+        
+        # Enhanced color scheme for better visibility on surface
+        if colormap in ['RdYlBu_r', 'coolwarm', 'RdBu_r']:
+            # Use predefined soft color scheme for better visual comfort
+            probability_colors = [
+                '#c6dbef',  # Very light blue (low probability)
+                '#9ecae1',  # Light blue
+                '#6baed6',  # Medium light blue
+                '#4292c6',  # Medium blue
+                '#2171b5',  # Medium blue
+                '#fcbba1',  # Light orange
+                '#fc9272',  # Medium light orange
+                '#fb6a4a',  # Medium orange
+                '#ef3b2c',  # Medium red
+                '#cb181d'   # Medium red (high probability)
+            ]
+            n_colors = len(probability_colors)
+        else:
+            # Use matplotlib colormap with reduced intensity
+            cmap = plt.cm.get_cmap(colormap)
+            probability_colors = []
+            n_colors = 10
+            for i in range(n_colors):
+                color_rgba = cmap(i / (n_colors - 1))
+                # Soften the colors by blending with white (0.3 factor)
+                softened_rgba = [
+                    color_rgba[0] * 0.7 + 0.3,  # Red channel
+                    color_rgba[1] * 0.7 + 0.3,  # Green channel
+                    color_rgba[2] * 0.7 + 0.3,  # Blue channel
+                ]
+                # Ensure values don't exceed 1.0
+                softened_rgba = [min(1.0, val) for val in softened_rgba]
+                probability_colors.append(mcolors.rgb2hex(softened_rgba))
+        
+        # Color residues based on normalized probability
+        colored_residues = []
+        for residue_num, prob in target_residues.items():
+            # Normalize probability to [0, 1] within the selected range
+            if max_prob > min_prob:
+                norm_prob = (prob - min_prob) / (max_prob - min_prob)
+            else:
+                norm_prob = 0.5
+            
+            # Map to color index
+            color_idx = int(norm_prob * (n_colors - 1))
+            color_idx = max(0, min(color_idx, n_colors - 1))
+            color = probability_colors[color_idx]
+            
+            # Add style for this residue with vivid color
+            style_name = list(base_style.keys())[0]
+            colored_style = {style_name: {'color': color}}
+            view.addStyle(
+                {'chain': self.chain_id, 'resi': residue_num},
+                colored_style
+            )
+            colored_residues.append(residue_num)
+        
+        # Add surface rendering with improved visibility
+        if show_surface:
+            # Add base surface for non-colored regions
+            all_residues = set(int(res) for res in self.residue_index)
+            non_colored_residues = all_residues - set(colored_residues)
+            
+            if non_colored_residues:
+                view.addSurface(py3Dmol.VDW, {
+                    'opacity': surface_opacity * 0.9,  # Further reduced opacity for softer appearance
+                    'color': base_color
+                }, {'chain': self.chain_id, 'resi': list(non_colored_residues)})
+            
+            # Add colored surfaces for probability residues with enhanced visibility
+            for residue_num, prob in target_residues.items():
+                # Normalize probability to [0, 1] within the selected range
+                if max_prob > min_prob:
+                    norm_prob = (prob - min_prob) / (max_prob - min_prob)
+                else:
+                    norm_prob = 0.5
+                
+                # Map to color index
+                color_idx = int(norm_prob * (n_colors - 1))
+                color_idx = max(0, min(color_idx, n_colors - 1))
+                color = probability_colors[color_idx]
+                
+                # Add surface for this residue with softer opacity for gentler visualization
+                view.addSurface(py3Dmol.VDW, {
+                    'opacity': surface_opacity * 0.9,  # Slightly reduced opacity for softer colors
+                    'color': color
+                }, {'chain': self.chain_id, 'resi': residue_num})
+        
+        # Add spherical region visualization if specific region is selected
+        if selected_region is not None and show_shape:
+            center_res = selected_region['center_residue']
+            
+            # Use radius from prediction results or provided radius
+            sphere_radius = radius or 19.0
+            
+            # Define distinct colors for different regions
+            region_colors = [
+                '#FF6B6B',  # Soft red
+                '#4ECDC4',  # Soft teal
+                '#45B7D1',  # Soft blue
+                '#96CEB4',  # Soft green
+                '#FFEAA7',  # Soft yellow
+                '#DDA0DD',  # Soft plum
+                '#87CEEB',  # Sky blue
+                '#F0E68C',  # Soft khaki
+                '#FFB6C1',  # Light pink
+                '#98FB98',  # Pale green
+                '#9C6ADE',  # Soft purple
+                '#FF9A8B'   # Soft coral
+            ]
+            
+            # Select color based on region_index
+            shape_color = region_colors[region_index % len(region_colors)]
+            
+            # Add sphere for the selected region with softer appearance and region-specific color
+            self._add_shape_visualization(
+                view, center_res, sphere_radius,
+                shape_color, center_color,  # Use region-specific color for shape
+                show_center, center_radius,
+                shape_opacity * 0.6,  # Reduced shape opacity for softer appearance
+                wireframe
+            )
+            
+            # Highlight center residue with softer color matching the region
+            view.addStyle(
+                {'chain': self.chain_id, 'resi': center_res},
+                {list(base_style.keys())[0]: {'color': shape_color}}  # Use region-specific color
+            )
+    
+    def _add_top_regions_visualization(self, view, style, predict_results,
+                                  base_color, coverage_color, center_color,
+                                  show_surface, show_shape, show_center,
+                                  surface_opacity, shape_opacity, center_radius,
+                                  wireframe, radius, max_spheres):
+        """Add visualization for top-k regions"""
+        # Set base style
+        view.setStyle({'chain': self.chain_id}, {style: {'color': base_color}})
+        
+        # Get top regions
+        top_regions = predict_results.get('top_k_regions', [])
+        
+        # Limit number of regions if max_spheres is specified
+        if max_spheres is not None:
+            top_regions = top_regions[:max_spheres]
+        
+        # Enhanced color scheme for different regions
+        region_colors = [
+            '#FF6B6B',  # Red
+            '#96CEB4',  # Green
+            '#4ECDC4',  # Teal
+            '#45B7D1',  # Blue
+            '#FFEAA7',  # Yellow
+            '#DDA0DD',  # Plum
+            '#87CEEB',  # Sky blue
+            '#F0E68C',  # Khaki
+            '#FFB6C1',  # Light pink
+            '#98FB98'   # Pale green
+        ]
+        
+        for i, region in enumerate(top_regions):
+            center_res = region['center_residue']
+            covered_residues = region.get('covered_residues', [])
+            region_color = region_colors[i % len(region_colors)]
+            
+            # Color covered residues
+            if covered_residues:
+                view.addStyle(
+                    {'chain': self.chain_id, 'resi': covered_residues},
+                    {style: {'color': region_color}}
+                )
+            
+            # Add spherical region
+            if show_shape:
+                self._add_shape_visualization(
+                    view, center_res, radius or 18.0,
+                    region_color, center_color,
+                    show_center, center_radius * 0.8,
+                    shape_opacity, wireframe
+                )
+        
+        # Add surface with balanced visibility
+        if show_surface:
+            # Base surface with good visibility
+            view.addSurface(py3Dmol.VDW, {
+                'opacity': surface_opacity * 0.9,  # Keep base surface visible
+                'color': base_color
+            })
+            
+            # Colored surface for covered residues
+            for i, region in enumerate(top_regions):
+                covered_residues = region.get('covered_residues', [])
+                region_color = region_colors[i % len(region_colors)]
+                
+                if covered_residues:
+                    view.addSurface(py3Dmol.VDW, {
+                        'opacity': surface_opacity,  # Full opacity for clear coloring
+                        'color': region_color
+                    }, {'resi': covered_residues})
+    
+    def _add_comparison_visualization(self, view, style, predict_results,
+                                 base_color, true_epitope_color, false_positive_color,
+                                 true_positive_color, coverage_color, show_surface, surface_opacity):
+        """Add visualization comparing voted vs predicted epitopes"""
+        # Set base style
+        view.setStyle({'chain': self.chain_id}, {style: {'color': base_color}})
+        
+        # Get different prediction sets
+        predicted_epitopes = set(predict_results.get('predicted_epitopes', []))
+        voted_epitopes = set(predict_results.get('voted_epitopes', []))
+        true_epitopes = set(predict_results.get('true_epitopes', []))
+        
+        # Calculate overlaps
+        both_methods = predicted_epitopes & voted_epitopes  # Agreed by both methods
+        only_predicted = predicted_epitopes - voted_epitopes  # Only by probability
+        only_voted = voted_epitopes - predicted_epitopes  # Only by voting
+        
+        # Further categorize by true epitopes
+        both_correct = both_methods & true_epitopes
+        both_incorrect = both_methods - true_epitopes
+        only_pred_correct = only_predicted & true_epitopes
+        only_pred_incorrect = only_predicted - true_epitopes
+        only_vote_correct = only_voted & true_epitopes
+        only_vote_incorrect = only_voted - true_epitopes
+        
+        # Assign colors and styles
+        color_assignments = [
+            (both_correct, '#00FF00'),      # Bright green: both correct
+            (both_incorrect, '#FF0000'),    # Red: both wrong
+            (only_pred_correct, '#90EE90'), # Light green: only predicted correct
+            (only_pred_incorrect, '#FFB6C1'), # Light red: only predicted wrong
+            (only_vote_correct, '#87CEEB'),  # Sky blue: only voted correct
+            (only_vote_incorrect, '#DDA0DD') # Plum: only voted wrong
+        ]
+        
+        for residues, color in color_assignments:
+            if residues:
+                view.addStyle(
+                    {'chain': self.chain_id, 'resi': list(residues)},
+                    {style: {'color': color}}
+                )
+        
+        # Add surface
+        if show_surface:
+            view.addSurface(py3Dmol.VDW, {
+                'opacity': surface_opacity,
+                'color': base_color
+            })
+            
+            for residues, color in color_assignments:
+                if residues:
+                    view.addSurface(py3Dmol.VDW, {
+                        'opacity': surface_opacity,
+                        'color': color
+                    }, {'resi': list(residues)})
+
+    def _create_base_view(self, width: int, height: int) -> py3Dmol.view:
+        """创建基本的py3Dmol视图并添加蛋白质结构"""
+        view = py3Dmol.view(width=width, height=height)
+        
+        # 构建PDB字符串
+        pdb_str = "MODEL        1\n"
+        atom_num = 1
+        for res_idx in range(len(self.sequence)):
+            one_letter = self.sequence[res_idx]
+            resname = self.convert_letter_1to3(one_letter)
+            resnum = self.residue_index[res_idx]
+            
+            mask = self.atom37_mask[res_idx]
+            coords = self.atom37_positions[res_idx][mask]
+            atoms = [name for name, exists in zip(RC.atom_types, mask) if exists]
+            
+            for atom_name, coord in zip(atoms, coords):
+                x, y, z = coord
+                pdb_str += (f"ATOM  {atom_num:5d}  {atom_name:<3s} {resname:>3s} {self.chain_id:1s}{resnum:4d}"
+                        f"    {x:8.3f}{y:8.3f}{z:8.3f}  1.00  0.00\n")
+                atom_num += 1
+        
+        pdb_str += "ENDMDL\n"
+        view.addModel(pdb_str, "pdb")
+        return view
+
+    def _add_epitope_visualization(self, view, style, predicted_epitopes,
+                             base_color, true_epitope_color, false_positive_color, true_positive_color, coverage_color,
+                             show_surface, surface_opacity, show_coverage,
+                             center_res=None, radius=None):
+        """添加表位可视化"""
+        # 设置基础颜色
+        view.setStyle({'chain': self.chain_id}, {style: {'color': base_color}})
+        
+        true_epitopes = set(self.get_epitope_residue_numbers())
+        true_positives = set(predicted_epitopes) & true_epitopes
+        false_positives = set(predicted_epitopes) - true_epitopes
+        false_negatives = true_epitopes - set(predicted_epitopes)
+        
+        # 如果提供了center_res和radius，获取覆盖区域
+        covered_residues = []
+        if center_res is not None and radius is not None:
+            coverage_dict, _, _ = self.get_surface_coverage(
+                radius=radius, threshold=0.25, index=False  # Use residue numbers for visualization
+            )
+            covered_res_list = coverage_dict.get(center_res, [[], [], 0, 0])[0]
+            covered_residues = covered_res_list
+            
+            # 计算覆盖区域内的True Negative (不是表位也没被预测为表位)
+            if covered_residues:
+                true_negatives = [res for res in covered_residues 
+                                 if res not in true_epitopes and res not in predicted_epitopes]
+                
+                # 为True Negative添加特殊颜色 (使用更明显的灰色)
+                true_negative_color = '#888888'  # 更深的灰色
+                
+                if true_negatives:
+                    view.addStyle(
+                        {'chain': self.chain_id, 'resi': true_negatives},
+                        {style: {'color': true_negative_color}}
+                    )
+        
+        # 添加样式 - 增加颜色的饱和度
+        for residues, color in [
+            (true_positives, true_positive_color),
+            (false_positives, false_positive_color),
+            (false_negatives, true_epitope_color)
+        ]:
+            if residues:
+                view.addStyle(
+                    {'chain': self.chain_id, 'resi': list(residues)},
+                    {style: {'color': color}}
+                )
+        
+        # 先添加基础表面
+        if show_surface:
+            # Base surface with good visibility for overall structure
+            view.addSurface(py3Dmol.VDW, {
+                'opacity': surface_opacity * 0.9,  # Keep base surface visible
+                'color': base_color
+            })
+            
+            # Colored surfaces for specific categories overlay on base surface
+            for residues, color in [
+                (true_positives, true_positive_color),
+                (false_positives, false_positive_color),
+                (false_negatives, true_epitope_color)
+            ]:
+                if residues:
+                    view.addSurface(py3Dmol.VDW, {
+                        'opacity': surface_opacity,  # Full opacity for clear coloring
+                        'color': color
+                    }, {'resi': list(residues)})
+        
+        # 为覆盖区域内的True Negative添加表面
+        if center_res is not None and radius is not None and covered_residues and show_coverage:
+            true_negatives = [res for res in covered_residues 
+                             if res not in true_epitopes and res not in predicted_epitopes]
+            
+            if true_negatives:
+                view.addSurface(py3Dmol.VDW, {
+                    'opacity': surface_opacity,
+                    'color': coverage_color
+                }, {'resi': true_negatives})
+
+    def _add_shape_visualization(self, view, center_res, radius,
+                            coverage_color, center_color,
+                            show_center, center_radius,
+                            shape_opacity, wireframe):
+        """添加球形可视化"""
+        center_idx = self.resnum_to_index.get(center_res)
+        if center_idx is None:
+            return
+            
+        ca_idx = RC.atom_order["CA"]  # Get CA atom index from atom order
+        center_coord = self.atom37_positions[center_idx, ca_idx, :]
+        
+        # 添加球形
+        sphere_params = {
+            'center': {'x': float(center_coord[0]), 
+                    'y': float(center_coord[1]), 
+                    'z': float(center_coord[2])},
+            'radius': float(radius),
+            'color': coverage_color
+        }
+        if wireframe:
+            sphere_params.update({'wireframe': True, 'linewidth': 1.5})  # 增加线宽
+        else:
+            sphere_params.update({'opacity': shape_opacity})
+        view.addSphere(sphere_params)
+        
+        # 添加中心点标记
+        if show_center:
+            view.addSphere({
+                'center': {'x': float(center_coord[0]), 
+                        'y': float(center_coord[1]), 
+                        'z': float(center_coord[2])},
+                'radius': float(center_radius),
+                'color': center_color,
+                'opacity': 1.0
+            })
+
+    def _add_coverage_visualization(self, view, style, center_res, radius,
+                              base_color, coverage_color, true_positive_color, true_epitope_color,
+                              show_surface, show_shape, show_center,
+                              surface_opacity, shape_opacity, center_radius,
+                              n_points, center_color, wireframe, show_epitope):
+        """添加覆盖区域可视化"""
+        # 首先设置基础样式和颜色
+        view.setStyle({'chain': self.chain_id}, {style: {'color': base_color}})
+        
+        # 获取覆盖区域
+        coverage_dict, _, _ = self.get_surface_coverage(
+            radius=radius, threshold=0.25, index=False  # Use residue numbers for visualization
+        )
+        
+        covered_res_list = coverage_dict.get(center_res, [[], [], 0, 0])[0]
+        covered_residues = covered_res_list
+
+        
+        if show_epitope:
+            true_epitopes = set(self.get_epitope_residue_numbers())
+        else:
+            true_epitopes = set()
+        
+        # 计算不同类别的残基
+        true_positives = set(covered_residues) & true_epitopes  # 被覆盖的表位
+        false_negatives = true_epitopes - set(covered_residues)  # 未被覆盖的表位
+        covered_non_epitopes = set(covered_residues) - true_epitopes  # 被覆盖的非表位
+        
+        # 添加表面渲染
+        if show_surface:
+            # 1. 添加基础表面，保持可见性
+            view.addSurface(py3Dmol.VDW, {
+                'opacity': surface_opacity * 1.0,  # Keep base surface visible
+                'color': base_color
+            })
+            
+            # 2. 添加未被覆盖的表位表面
+            if false_negatives:
+                view.addSurface(py3Dmol.VDW, {
+                    'opacity': surface_opacity,  # Full opacity for clear coloring
+                    'color': true_epitope_color
+                }, {'resi': list(false_negatives)})
+            
+            # 3. 添加被覆盖的表位表面
+            if true_positives:
+                view.addSurface(py3Dmol.VDW, {
+                    'opacity': surface_opacity,  # Full opacity for clear coloring
+                    'color': true_positive_color
+                }, {'resi': list(true_positives)})
+            
+            # 4. 添加被覆盖的非表位表面
+            if covered_non_epitopes:
+                view.addSurface(py3Dmol.VDW, {
+                    'opacity': surface_opacity * 0.9,  # Slightly reduced for distinction
+                    'color': coverage_color
+                }, {'resi': list(covered_non_epitopes)})
+            
+            # 添加样式
+            if false_negatives:
+                view.addStyle(
+                    {'chain': self.chain_id, 'resi': list(false_negatives)},
+                    {style: {'color': true_epitope_color}}
+                )
+            
+            if true_positives:
+                view.addStyle(
+                    {'chain': self.chain_id, 'resi': list(true_positives)},
+                    {style: {'color': true_positive_color}}
+                )
+            
+            if covered_non_epitopes:
+                view.addStyle(
+                    {'chain': self.chain_id, 'resi': list(covered_non_epitopes)},
+                    {style: {'color': coverage_color}}
+                )
+            
+            # 为中心残基添加黄色样式
+            view.addStyle(
+                {'chain': self.chain_id, 'resi': center_res},
+                {style: {'color': '#FFD700'}}  # 使用更鲜艳的黄色
+            )
+        
+        # 添加形状
+        if show_shape:
+            self._add_shape_visualization(
+                view, center_res, radius,
+                coverage_color,
+                center_color,
+                show_center, center_radius,
+                shape_opacity, wireframe
+            )
+
+    def _add_multi_shape_visualization(self, view, regions_data, radius, max_spheres, 
+                                   show_center, center_radius, shape_opacity, wireframe):
+        """Add multiple spherical regions with different colors"""
+        if not regions_data:
+            return
+            
+        # Limit number of spheres if max_spheres is specified
+        regions_to_show = regions_data[:max_spheres] if max_spheres else regions_data
+        
+        # Enhanced color scheme for different regions
+        sphere_colors = [
+            '#d671f1',  # Plum
+            '#7190f1',
+            '#FF6B6B',  # Red
+            '#96CEB4',  # Green
+            '#FFEAA7',  # Yellow
+            '#FFB6C1',  # Light pink
+            '#4ECDC4',  # Teal
+            '#87CEEB',  # Sky blue
+            '#F0E68C',  # Khaki
+            '#98FB98',  # Pale green,
+            '#45B7D1'   # Blue
+        ]
+        
+        for i, region_data in enumerate(regions_to_show):
+            if isinstance(region_data, dict):
+                # For prediction/evaluation results format
+                center_res = region_data['center_residue']
+            else:
+                # For simple center residue format
+                center_res = region_data
+                
+            sphere_color = sphere_colors[i % len(sphere_colors)]
+            self._add_shape_visualization(
+                view, center_res, radius or 18.0,
+                sphere_color, '#FFD700',  # Gold for center
+                show_center, center_radius, shape_opacity, wireframe
+            )
+
+    @classmethod
+    def from_pdb(
+        cls,
+        path: Optional[PathOrBuffer] = None,
+        chain_id: str = "detect",
+        id: Optional[str] = None,
+        is_predicted: bool = False,
+    ) -> "AntigenChain":
+        """
+        Return a AntigenChain object from a pdb file.
+
+        If `path` is not provided, the function will try multiple possible paths:
+            1. {id}_{chain_id}.pdb
+            2. {id}.pdb
+            3. {id.lower()}_{chain_id}.pdb
+            4. {id.upper()}_{chain_id}.pdb
+        If none of these paths exist, it will download the structure from RCSB PDB
+        and save it to the antigen_structs directory.
+
+        Args:
+            path (Optional[PathOrBuffer]): Path or buffer to read pdb file from. If None,
+                the default path is constructed from DATA_DIR.
+            chain_id (str, optional): Select a chain corresponding to (author) chain id.
+                "detect" uses the first detected chain.
+            id (Optional[str], optional): Protein identifier (pdb_id). If not provided and `path`
+                is given, the id will be inferred from the file name.
+            is_predicted (bool, optional): If True, reads b factor as the confidence readout.
+
+        Returns:
+            AntigenChain: The constructed antigen chain.
+        """
+        # If no path is provided, try multiple possible paths
+        id = id.lower()
+        
+        if path is None:
+            if id is None:
+                raise ValueError("Either 'path' or 'id' must be provided to locate the pdb file.")
+            
+            # Try multiple possible paths
+            possible_paths = [
+                Path(BASE_DIR) / "data" / "antigen_structs" / f"{id}_{chain_id}.pdb",
+                Path(BASE_DIR) / "data" / "antigen_structs" / f"{id}.pdb",
+                # Path(BASE_DIR) / "data" / "antigen_structs" / f"{id.lower()}_{chain_id}.pdb",
+                # Path(BASE_DIR) / "data" / "antigen_structs" / f"{id.upper()}_{chain_id}.pdb",
+                # Path(BASE_DIR) / "data" / "pdb" / f"{id.lower()}.pdb",
+                # Path(BASE_DIR) / "data" / "pdb" / f"{id.upper()}.pdb",
+            ]
+            
+            # Try each path
+            path = None
+            for p in possible_paths:
+                if p.exists():
+                    path = p
+                    print(f"Found pdb file at {path}")
+                    break
+            
+            # If no path exists, download from RCSB
+            if path is None:
+                try:
+                    # Create directory if it doesn't exist
+                    save_dir = Path(BASE_DIR) / "data" / "pdb"
+                    save_dir.mkdir(parents=True, exist_ok=True)
+                    
+                    # Download from RCSB
+                    rcsb.fetch(id, "pdb", target_path=save_dir)
+                    
+                    path = save_dir / f"{id}.pdb"
+                    print(f"No existing pdb file for {id}_{chain_id}, downloaded {id} complex pdb file to {path}")
+                    
+                except Exception as e:
+                    print(f"[ERROR] Failed to download pdb file for {id}: {str(e)}")
+                    return None
+        else:
+            path = Path(path)  # Ensure path is a Path object
+
+        # Determine the file_id from the provided id or from the path.
+        if id is not None:
+            file_id = id
+        else:
+            # Infer file_id from the file name if id is not provided.
+            file_id = path.with_suffix("").name
+
+        # Read the pdb file.
+        try:
+            atom_array = PDBFile.read(path).get_structure(model=1, extra_fields=["b_factor"])
+        except Exception as e:
+            print(f"[ERROR] Failed to read pdb file {path}: {str(e)}")
+            return None
+        
+        # If chain_id is "detect", use the first detected chain.
+        if chain_id == "detect":
+            chain_id = atom_array.chain_id[0]
+            print(f"[WARNING] No chain_id provided, using the first detected chain: {chain_id}")
+
+        # Filter the AtomArray: amino acids, non-hetero atoms, and matching chain.
+        atom_array = atom_array[
+            bs.filter_amino_acids(atom_array)
+            & ~atom_array.hetero
+            & (atom_array.chain_id == chain_id)
+        ]
+
+        # Set entity_id as 1 (not supplied in PDB files)
+        entity_id = 1
+
+        # Build the sequence by converting three-letter codes to one-letter codes.
+        sequence = "".join(
+            (
+                r if len((r := PDBData.protein_letters_3to1.get(monomer[0].res_name, "X"))) == 1 else "X"
+            )
+            for monomer in bs.residue_iter(atom_array)
+        )
+        num_res = len(sequence)
+
+        # Prepare arrays for atom coordinates, mask, residue indices, etc.
+        atom_positions = np.full([num_res, RC.atom_type_num, 3], np.nan, dtype=np.float32)
+        atom_mask = np.full([num_res, RC.atom_type_num], False, dtype=bool)
+        residue_index = np.full([num_res], -1, dtype=np.int64)
+        insertion_code = np.full([num_res], "", dtype="<U4")
+        confidence = np.ones([num_res], dtype=np.float32)
+
+        # Populate arrays from the pdb data.
+        for i, res in enumerate(bs.residue_iter(atom_array)):
+            for atom in res:
+                atom_name = atom.atom_name
+                if atom_name == "SE" and atom.res_name == "MSE":
+                    atom_name = "SD"
+                if atom_name in RC.atom_order:
+                    atom_positions[i, RC.atom_order[atom_name]] = atom.coord
+                    atom_mask[i, RC.atom_order[atom_name]] = True
+                    if is_predicted and atom_name == "CA":
+                        confidence[i] = atom.b_factor
+            residue_index[i] = res[0].res_id
+            insertion_code[i] = res[0].ins_code
+
+        # Ensure that sequence is valid.
+        assert all(sequence), "Some residue name was not specified correctly"
+
+        return cls(
+            id=file_id,
+            sequence=sequence,
+            chain_id=chain_id,
+            entity_id=entity_id,
+            atom37_positions=atom_positions,
+            atom37_mask=atom_mask,
+            residue_index=residue_index,
+            insertion_code=insertion_code,
+            confidence=confidence,
+        )
\ No newline at end of file
diff --git a/src/bce/antigen/pc.py b/src/bce/antigen/pc.py
new file mode 100644
index 0000000000000000000000000000000000000000..5756300546d61e3b171f299463a21c50bf7c9d85
--- /dev/null
+++ b/src/bce/antigen/pc.py
@@ -0,0 +1,37 @@
+# Amino acid conversion mappings
+AMINO_ACID_1TO3 = {
+    'A': 'ALA', 'R': 'ARG', 'N': 'ASN', 'D': 'ASP', 'C': 'CYS',
+    'Q': 'GLN', 'E': 'GLU', 'G': 'GLY', 'H': 'HIS', 'I': 'ILE',
+    'L': 'LEU', 'K': 'LYS', 'M': 'MET', 'F': 'PHE', 'P': 'PRO',
+    'S': 'SER', 'T': 'THR', 'W': 'TRP', 'Y': 'TYR', 'V': 'VAL'
+}
+
+AMINO_ACID_3TO1 = {
+    'ALA': 'A', 'ARG': 'R', 'ASN': 'N', 'ASP': 'D', 'CYS': 'C',
+    'GLN': 'Q', 'GLU': 'E', 'GLY': 'G', 'HIS': 'H', 'ILE': 'I',
+    'LEU': 'L', 'LYS': 'K', 'MET': 'M', 'PHE': 'F', 'PRO': 'P',
+    'SER': 'S', 'THR': 'T', 'TRP': 'W', 'TYR': 'Y', 'VAL': 'V'
+}
+
+MAX_ASA = {
+    'ALA': 129.0,
+    'ARG': 274.0,
+    'ASN': 195.0,
+    'ASP': 193.0,
+    'CYS': 167.0,
+    'GLN': 223.0,
+    'GLU': 225.0,
+    'GLY': 104.0,
+    'HIS': 224.0,
+    'ILE': 197.0,
+    'LEU': 201.0,
+    'LYS': 236.0,
+    'MET': 224.0,
+    'PHE': 240.0,
+    'PRO': 159.0,
+    'SER': 155.0,
+    'THR': 172.0,
+    'TRP': 285.0,
+    'TYR': 263.0,
+    'VAL': 174.0,
+}
\ No newline at end of file
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diff --git a/src/bce/data/data.py b/src/bce/data/data.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd4ccfd21b970f67bb01c9864635dae863084b9f
--- /dev/null
+++ b/src/bce/data/data.py
@@ -0,0 +1,1262 @@
+import os
+import h5py
+import torch
+import numpy as np
+import json
+import random
+from pathlib import Path
+from multiprocessing import Pool
+
+from typing import List, Tuple, Dict, Optional, Union
+from torch_geometric.data import Data, Dataset, Batch
+from torch_geometric.loader import DataLoader
+from tqdm import tqdm
+import pickle
+
+from ..utils.loading import load_epitopes_csv, load_data_split
+from ..utils.constants import BASE_DIR
+from .utils import create_graph_data, create_graph_data_full
+
+
+def apply_undersample(data_list: List, undersample_param: Union[int, float], seed: int = 42, verbose: bool = True):
+    """
+    Apply undersampling to a data list.
+    
+    Args:
+        data_list: List of data samples
+        undersample_param: If int, sample that many samples; if float (0-1), sample that fraction of data
+        seed: Random seed for reproducibility
+        verbose: Whether to print sampling information
+        
+    Returns:
+        Undersampled data list
+    """
+    if undersample_param is None:
+        return data_list
+    
+    original_size = len(data_list)
+    
+    if isinstance(undersample_param, float):
+        # Sample a fraction of the data
+        if not (0 < undersample_param <= 1.0):
+            raise ValueError(f"Float undersample must be between 0 and 1, got {undersample_param}")
+        target_size = int(len(data_list) * undersample_param)
+    elif isinstance(undersample_param, int):
+        # Sample a specific number of samples
+        if undersample_param <= 0:
+            raise ValueError(f"Int undersample must be positive, got {undersample_param}")
+        target_size = min(undersample_param, len(data_list))
+    else:
+        raise ValueError(f"Undersample must be int, float, or None, got {type(undersample_param)}")
+    
+    if target_size < len(data_list):
+        # Set random seed for reproducibility
+        random.seed(seed)
+        sampled_data = random.sample(data_list, target_size)
+        
+        if verbose:
+            print(f"Applied undersampling: {original_size} -> {target_size} samples")
+        
+        return sampled_data
+    elif verbose:
+        print(f"No undersampling applied: requested {target_size}, available {original_size}")
+    
+    return data_list
+
+class AntigenDataset(Dataset):
+    """
+    Dataset for antigen chains.
+    Each data point represents a complete protein as a graph, with nodes being residues
+    and edges based on spatial distance (< 18 Å).
+    """
+    def __init__(
+        self,
+        data_split: str = "train",
+        radius: float = 18,
+        threshold: float = 0.25,
+        num_posenc: int = 16,
+        num_rbf: int = 16,
+        undersample: Union[int, float, None] = None,
+        cache_dir: Optional[str] = None,
+        force_rebuild: bool = False,
+        verbose: bool = True,
+        seed: int = 42,
+        encoder: str = "esmc"
+    ):
+        """
+        Initialize the antigen dataset.
+        
+        Args:
+            data_split: Data split name ('train', 'val', 'test')
+            radius: Distance threshold for edge creation (Å)
+            threshold: SASA threshold for surface residues (not used in full protein)
+            num_posenc: Number of positional encoding features
+            num_rbf: Number of RBF features
+            undersample: Undersample parameter (int for count, float for ratio)
+            cache_dir: Directory to cache processed data
+            force_rebuild: Whether to force rebuild the dataset
+            verbose: Whether to print progress information
+            seed: Random seed for reproducibility
+            encoder: Encoder type ('esmc' or 'esm2')
+        """
+        self.data_split = data_split
+        self.radius = radius
+        self.threshold = threshold
+        self.num_posenc = num_posenc
+        self.num_rbf = num_rbf
+        self.undersample = undersample
+        self.verbose = verbose
+        self.seed = seed
+        self.encoder = encoder
+        
+        # Set cache directory
+        if cache_dir is None:
+            cache_dir = Path(f"{BASE_DIR}/data/full_region_cache/antigen_r{radius}")
+        self.cache_dir = Path(cache_dir)
+        self.cache_dir.mkdir(parents=True, exist_ok=True)
+        
+        # Cache file for this configuration
+        self.cache_file = self.cache_dir / f"{data_split}_antigen_dataset.h5"
+        
+        # Load data splits and epitope information
+        self.antigens = load_data_split(data_split, verbose=verbose)
+        _, _, self.epitope_dict = load_epitopes_csv()
+        
+        # Initialize data list
+        self.data_list = []
+        
+        # Load or build dataset
+        if self.cache_file.exists() and not force_rebuild:
+            if verbose:
+                print(f"Loading cached antigen dataset from {self.cache_file}")
+            self._load_cache()
+        else:
+            if verbose:
+                print(f"Building antigen dataset for {data_split} split...")
+            self._build_dataset()
+            self._save_cache()
+        
+        super().__init__()
+    
+    def _load_protein_data(self, pdb_id: str, chain_id: str) -> Optional[Dict]:
+        """
+        Load precomputed protein data from files.
+        
+        Args:
+            pdb_id: PDB ID
+            chain_id: Chain ID
+            
+        Returns:
+            Dictionary containing all protein data or None if loading fails
+        """
+        try:
+            protein_key = f"{pdb_id}_{chain_id}"
+            
+            # Load embeddings
+            embedding_file = Path(BASE_DIR) / "data" / "embeddings" / self.encoder / f"{protein_key}.h5"
+            if not embedding_file.exists():
+                if self.verbose:
+                    print(f"Embedding file not found: {embedding_file}")
+                return None
+            
+            with h5py.File(embedding_file, "r") as h5f:
+                embeddings = h5f["embedding"][:]
+            
+            # Load backbone atoms
+            coords_file = Path(BASE_DIR) / "data" / "coords" / f"{protein_key}.npy"
+            if not coords_file.exists():
+                if self.verbose:
+                    print(f"Coords file not found: {coords_file}")
+                return None
+            backbone_atoms = np.load(coords_file)
+            
+            # Load RSA values
+            rsa_file = Path(BASE_DIR) / "data" / "rsa" / f"{protein_key}.npy"
+            if not rsa_file.exists():
+                if self.verbose:
+                    print(f"RSA file not found: {rsa_file}")
+                return None
+            rsa_values = np.load(rsa_file)
+            
+            # Load epitope data from epitope_dict
+            binary_labels = self.epitope_dict.get(protein_key, [])
+            
+            # Create epitope indices from binary labels
+            epitope_indices = []
+            for idx, is_epitope in enumerate(binary_labels):
+                if is_epitope == 1:
+                    epitope_indices.append(idx)
+            
+            return {
+                'embeddings': embeddings,
+                'backbone_atoms': backbone_atoms,
+                'rsa_values': rsa_values,
+                'epitope_indices': epitope_indices,
+            }
+            
+        except Exception as e:
+            if self.verbose:
+                print(f"Error loading protein data for {pdb_id}_{chain_id}: {str(e)}")
+            return None
+    
+    def _build_dataset(self):
+        """Build the dataset from precomputed data files."""
+        failed_proteins = []
+        
+        for pdb_id, chain_id in tqdm(self.antigens, desc=f"Processing {self.data_split} antigens", 
+                                   disable=not self.verbose):
+            try:
+                # Load precomputed data
+                protein_data = self._load_protein_data(pdb_id, chain_id)
+                if protein_data is None:
+                    failed_proteins.append(f"{pdb_id}_{chain_id}")
+                    continue
+                
+                embeddings = protein_data['embeddings']
+                backbone_atoms = protein_data['backbone_atoms']
+                rsa_values = protein_data['rsa_values']
+                epitope_indices = protein_data['epitope_indices']
+                
+                # Create graph data for the full protein
+                graph_data = create_graph_data_full(
+                    embeddings=embeddings,
+                    backbone_atoms=backbone_atoms,
+                    rsa_values=rsa_values,
+                    epitope_indices=epitope_indices,
+                    pdb_id=pdb_id,
+                    chain_id=chain_id,
+                    num_rbf=self.num_rbf,
+                    num_posenc=self.num_posenc,
+                    radius=self.radius,
+                    verbose=self.verbose
+                )
+                
+                if graph_data is not None:
+                    self.data_list.append(graph_data)
+                else:
+                    failed_proteins.append(f"{pdb_id}_{chain_id}")
+                        
+            except Exception as e:
+                failed_proteins.append(f"{pdb_id}_{chain_id}")
+                if self.verbose:
+                    print(f"Error processing {pdb_id}_{chain_id}: {str(e)}")
+        
+        if failed_proteins and self.verbose:
+            print(f"Failed to process {len(failed_proteins)} proteins: {failed_proteins[:5]}...")
+        
+        # Apply undersampling if specified
+        if self.undersample is not None:
+            self.data_list = apply_undersample(
+                self.data_list,
+                self.undersample,
+                seed=self.seed,
+                verbose=self.verbose
+            )
+        
+        if self.verbose:
+            print(f"Successfully created {len(self.data_list)} protein graphs")
+    
+    def _save_cache(self):
+        """Save processed dataset to cache."""
+        try:
+            self._save_cache_hdf5()
+            if self.verbose:
+                print(f"Dataset cached to {self.cache_file}")
+        except Exception as e:
+            if self.verbose:
+                print(f"Failed to save cache: {str(e)}")
+    
+    def _load_cache(self):
+        """Load processed dataset from cache."""
+        try:
+            self._load_cache_hdf5()
+            if self.verbose:
+                print(f"Loaded {len(self.data_list)} samples from cache")
+        except Exception as e:
+            if self.verbose:
+                print(f"Failed to load cache: {str(e)}")
+            self.data_list = []
+    
+    def _save_cache_hdf5(self):
+        """Save dataset using HDF5 format."""
+        with h5py.File(self.cache_file, 'w') as f:
+            # Save metadata
+            f.attrs['num_samples'] = len(self.data_list)
+            f.attrs['radius'] = self.radius
+            f.attrs['threshold'] = self.threshold
+            f.attrs['data_split'] = self.data_split
+            f.attrs['encoder'] = self.encoder
+            f.attrs['dataset_type'] = 'antigen_full'
+            
+            # Save each protein as a separate group
+            for i, data in enumerate(tqdm(self.data_list, desc="Saving dataset...", disable=not self.verbose)):
+                group = f.create_group(f'protein_{i}')
+                
+                # Save tensors as datasets with compression
+                group.create_dataset('x', data=data.x.numpy(), compression='gzip', compression_opts=6)
+                group.create_dataset('pos', data=data.pos.numpy(), compression='gzip', compression_opts=6)
+                group.create_dataset('rsa', data=data.rsa.numpy(), compression='gzip', compression_opts=6)
+                group.create_dataset('edge_index', data=data.edge_index.numpy(), compression='gzip', compression_opts=6)
+                group.create_dataset('edge_attr', data=data.edge_attr.numpy(), compression='gzip', compression_opts=6)
+                group.create_dataset('y_node', data=data.y_node.numpy(), compression='gzip', compression_opts=6)
+                
+                # Save scalar and list attributes
+                group.attrs['pdb_id'] = data.pdb_id.encode('utf-8')
+                group.attrs['chain_id'] = data.chain_id.encode('utf-8')
+                group.attrs['num_nodes'] = data.num_nodes
+                group.attrs['num_epitopes'] = data.num_epitopes
+                group.attrs['epitope_ratio'] = data.epitope_ratio
+                group.attrs['radius'] = data.radius
+                
+                # Save epitope indices
+                group.create_dataset('epitope_indices', data=np.array(data.epitope_indices), compression='gzip', compression_opts=6)
+    
+    def _load_cache_hdf5(self):
+        """Load dataset from HDF5 cache."""
+        self.data_list = []
+        
+        with h5py.File(self.cache_file, 'r') as f:
+            total_samples = f.attrs['num_samples']
+            
+            for i in tqdm(range(total_samples), desc="Loading dataset...", disable=not self.verbose):
+                group = f[f'protein_{i}']
+                attrs = dict(group.attrs)
+                
+                # Safe string decoding
+                def safe_decode(attr):
+                    val = attrs[attr]
+                    return val.decode('utf-8') if isinstance(val, bytes) else str(val)
+                
+                data = Data(
+                    x=torch.tensor(group['x'][:]),
+                    pos=torch.tensor(group['pos'][:]),
+                    rsa=torch.tensor(group['rsa'][:]),
+                    edge_index=torch.tensor(group['edge_index'][:]),
+                    edge_attr=torch.tensor(group['edge_attr'][:]),
+                    y_node=torch.tensor(group['y_node'][:]),
+                    epitope_indices=group['epitope_indices'][:].tolist(),
+                    pdb_id=safe_decode('pdb_id'),
+                    chain_id=safe_decode('chain_id'),
+                    num_nodes=int(attrs['num_nodes']),
+                    num_epitopes=int(attrs['num_epitopes']),
+                    epitope_ratio=float(attrs['epitope_ratio']),
+                    radius=float(attrs['radius'])
+                )
+                self.data_list.append(data)
+        
+        # Apply undersampling if specified
+        if self.undersample is not None:
+            self.data_list = apply_undersample(
+                self.data_list,
+                self.undersample,
+                seed=self.seed,
+                verbose=self.verbose
+            )
+    
+    def len(self) -> int:
+        """Return the number of samples in the dataset."""
+        return len(self.data_list)
+    
+    def get(self, idx: int) -> Data:
+        """Get a sample by index."""
+        return self.data_list[idx]
+    
+    def get_stats(self) -> Dict:
+        """Get dataset statistics."""
+        if not self.data_list:
+            return {}
+        
+        # Collect statistics
+        num_nodes_list = [data.num_nodes for data in self.data_list]
+        num_edges_list = [data.edge_index.shape[1] for data in self.data_list]
+        num_epitopes_list = [data.num_epitopes for data in self.data_list]
+        epitope_ratio_list = [data.epitope_ratio for data in self.data_list]
+        
+        # Overall statistics
+        total_nodes = sum(num_nodes_list)
+        total_edges = sum(num_edges_list)
+        total_epitopes = sum(num_epitopes_list)
+        
+        stats = {
+            'num_proteins': len(self.data_list),
+            'avg_nodes_per_protein': np.mean(num_nodes_list),
+            'std_nodes_per_protein': np.std(num_nodes_list),
+            'min_nodes_per_protein': np.min(num_nodes_list),
+            'max_nodes_per_protein': np.max(num_nodes_list),
+            'avg_edges_per_protein': np.mean(num_edges_list),
+            'std_edges_per_protein': np.std(num_edges_list),
+            'total_nodes': total_nodes,
+            'total_edges': total_edges,
+            'total_epitopes': total_epitopes,
+            'avg_epitopes_per_protein': np.mean(num_epitopes_list),
+            'avg_epitope_ratio': np.mean(epitope_ratio_list),
+            'overall_epitope_ratio': total_epitopes / total_nodes if total_nodes > 0 else 0,
+        }
+        
+        return stats
+    
+    def print_stats(self):
+        """Print dataset statistics."""
+        stats = self.get_stats()
+        if not stats:
+            print("No statistics available (empty dataset)")
+            return
+        
+        print(f"\n=== {self.data_split.upper()} Antigen Dataset Statistics ===")
+        print(f"Number of proteins: {stats['num_proteins']:,}")
+        print(f"Average nodes per protein: {stats['avg_nodes_per_protein']:.1f} ± {stats['std_nodes_per_protein']:.1f}")
+        print(f"Nodes per protein range: [{stats['min_nodes_per_protein']}, {stats['max_nodes_per_protein']}]")
+        print(f"Average edges per protein: {stats['avg_edges_per_protein']:.1f} ± {stats['std_edges_per_protein']:.1f}")
+        print(f"Total nodes: {stats['total_nodes']:,}")
+        print(f"Total edges: {stats['total_edges']:,}")
+        print(f"Total epitope nodes: {stats['total_epitopes']:,}")
+        print(f"Average epitopes per protein: {stats['avg_epitopes_per_protein']:.1f}")
+        print(f"Average epitope ratio per protein: {stats['avg_epitope_ratio']:.3f}")
+        print(f"Overall epitope ratio: {stats['overall_epitope_ratio']:.3f}")
+        print("=" * 50)
+
+
+class SphereGraphDataset(Dataset):
+    """
+    Optimized graph dataset for training ReGEP model using spherical regions from antigen chains.
+    Each graph represents a spherical region centered on a surface residue.
+    
+    Optimizations:
+    - Only uses HDF5 format for caching
+    - Builds complete dataset without zero_ratio filtering
+    - Applies zero_ratio and undersample during loading
+    - Faster caching with optimized HDF5 structure
+    """
+    
+    def __init__(
+        self,
+        data_split: str = "train",
+        radius: int = 18,
+        threshold: float = 0.25,
+        num_posenc: int = 16,
+        num_rbf: int = 16,
+        zero_ratio: float = 0.1,
+        undersample: Union[int, float, None] = None,
+        cache_dir: Optional[str] = None,
+        force_rebuild: bool = False,
+        verbose: bool = True,
+        seed: int = 42,
+        use_embeddings2: bool = False
+    ):
+        """
+        Initialize the spherical graph dataset.
+        
+        Args:
+            data_split: Data split name ('train', 'val', 'test')
+            radius: Radius for spherical regions
+            threshold: SASA threshold for surface residues
+            num_posenc: Number of positional encoding features
+            num_rbf: Number of RBF features
+            zero_ratio: Ratio to downsample graphs with recall=0 (0.3 means keep 30%)
+            undersample: Undersample parameter (int for count, float for ratio)
+            cache_dir: Directory to cache processed data
+            force_rebuild: Whether to force rebuild the dataset
+            verbose: Whether to print progress information
+            seed: Random seed for reproducibility
+        """
+        self.data_split = data_split
+        self.radius = radius
+        self.threshold = threshold
+        self.num_posenc = num_posenc
+        self.num_rbf = num_rbf
+        self.zero_ratio = zero_ratio
+        self.undersample = undersample
+        self.verbose = verbose
+        self.seed = seed
+        self.use_embeddings2 = use_embeddings2
+        
+        # Set cache directory to large disk
+        if cache_dir is None:
+            cache_dir = Path(f"{BASE_DIR}/data/region_cache/sphere_r{radius}")
+        self.cache_dir = Path(cache_dir)
+        self.cache_dir.mkdir(parents=True, exist_ok=True)
+        
+        # Cache file for this configuration (only HDF5)
+        self.cache_file = self.cache_dir / f"{data_split}_dataset_complete.h5"
+        
+        # Load data splits
+        self.antigens = load_data_split(data_split, verbose=verbose)
+        
+        # Initialize data list
+        self.data_list = []
+        
+        # Load or build dataset
+        if self.cache_file.exists() and not force_rebuild:
+            if verbose:
+                print(f"Loading cached dataset with radius {self.radius} from {self.cache_file}")
+            self._load_cache()
+        else:
+            if verbose:
+                print(f"Building complete dataset with radius {self.radius} for {data_split} split...")
+            self._build_dataset()
+            self._save_cache()
+        
+        super().__init__()
+    
+    def _load_protein_data(self, pdb_id: str, chain_id: str) -> Optional[Dict]:
+        """
+        Load precomputed protein data from files.
+        
+        Args:
+            pdb_id: PDB ID
+            chain_id: Chain ID
+            
+        Returns:
+            Dictionary containing all protein data or None if loading fails
+        """
+        try:
+            protein_key = f"{pdb_id}_{chain_id}"
+            
+            # Load embeddings
+            embedding_file = Path(BASE_DIR) / "data" / "embeddings" / 'esmc' / f"{protein_key}.h5"
+            if not embedding_file.exists():
+                if self.verbose:
+                    print(f"Embedding file not found: {embedding_file}")
+                return None
+            
+            with h5py.File(embedding_file, "r") as h5f:
+                embeddings = h5f["embedding"][:]
+            
+            # Load other embeddings if available
+            esm2_file = Path(BASE_DIR) / "data" / "embeddings" / "esm2" / f"{protein_key}.h5"
+            if not esm2_file.exists():
+                if self.verbose:
+                    print(f"ESM2 file not found: {esm2_file}")
+                embeddings2 = None
+            else:
+                with h5py.File(esm2_file, "r") as h5f:
+                    embeddings2 = h5f["embedding"][:]
+            
+            # Load backbone atoms
+            coords_file = Path(BASE_DIR) / "data" / "coords" / f"{protein_key}.npy"
+            if not coords_file.exists():
+                if self.verbose:
+                    print(f"Coords file not found: {coords_file}")
+                return None
+            backbone_atoms = np.load(coords_file)
+            
+            # Load RSA values
+            rsa_file = Path(BASE_DIR) / "data" / "rsa" / f"{protein_key}.npy"
+            if not rsa_file.exists():
+                if self.verbose:
+                    print(f"RSA file not found: {rsa_file}")
+                return None
+            rsa_values = np.load(rsa_file)
+            
+            # Load surface coverage data
+            sphere_file = Path(BASE_DIR) / "data" / "antigen_sphere" / f"{protein_key}.h5"
+            radius_key = f"r{self.radius}"
+            
+            if not sphere_file.exists():
+                if self.verbose:
+                    print(f"Sphere file not found: {sphere_file}")
+                return None
+            
+            coverage_dict = {}
+            with h5py.File(sphere_file, "r") as h5f:
+                if radius_key not in h5f:
+                    if self.verbose:
+                        print(f"Radius {self.radius} not found in {sphere_file}")
+                    return None
+                
+                radius_group = h5f[radius_key]
+                for center_idx_str in radius_group.keys():
+                    center_idx = int(center_idx_str)
+                    center_group = radius_group[center_idx_str]
+                    covered_indices = center_group['covered_indices'][:].tolist()
+                    covered_epitope_indices = center_group['covered_epitope_indices'][:].tolist()
+                    precision = float(center_group.attrs['precision'])
+                    recall = float(center_group.attrs['recall'])
+                    coverage_dict[center_idx] = (covered_indices, covered_epitope_indices, precision, recall)
+            
+            # Load epitope data
+            _, _, epitopes = load_epitopes_csv()
+            binary_labels = epitopes.get(protein_key, [])
+            
+            # Create epitope indices
+            epitope_indices = []
+            for idx, is_epitope in enumerate(binary_labels):
+                if is_epitope == 1:
+                    epitope_indices.append(idx)
+            
+            return {
+                'embeddings': embeddings,
+                'backbone_atoms': backbone_atoms,
+                'rsa_values': rsa_values,
+                'coverage_dict': coverage_dict,
+                'epitope_indices': epitope_indices,
+                'embeddings2': embeddings2
+            }
+            
+        except Exception as e:
+            if self.verbose:
+                print(f"Error loading protein data for {pdb_id}_{chain_id}: {str(e)}")
+            return None
+
+    def _build_dataset(self):
+        """Build the complete dataset from precomputed data files (no zero_ratio filtering)."""
+        failed_proteins = []
+        
+        for pdb_id, chain_id in tqdm(self.antigens, desc=f"Processing {self.data_split} antigens", 
+                                   disable=not self.verbose):
+            try:
+                # Load precomputed data directly
+                protein_data = self._load_protein_data(pdb_id, chain_id)
+                if protein_data is None:
+                    if self.verbose:
+                        print(f"Failed to load data for {pdb_id}_{chain_id}")
+                    continue
+                
+                embeddings = protein_data['embeddings']
+                embeddings2 = protein_data['embeddings2']
+                backbone_atoms = protein_data['backbone_atoms']
+                rsa_values = protein_data['rsa_values']
+                coverage_dict = protein_data['coverage_dict']
+                epitope_indices = protein_data['epitope_indices']
+                
+                if not coverage_dict:
+                    if self.verbose:
+                        print(f"No surface regions found for {pdb_id}_{chain_id}")
+                    continue
+                
+                # Process each spherical region (no zero_ratio filtering here)
+                for center_idx, (covered_indices, covered_epitope_indices, precision, recall) in coverage_dict.items():
+                    if len(covered_indices) < 2:  # Skip regions with too few residues
+                        continue
+                    
+                    # Create graph data for this region (include all data)
+                    graph_data = create_graph_data(
+                        center_idx=center_idx,
+                        covered_indices=covered_indices,
+                        covered_epitope_indices=covered_epitope_indices,
+                        embeddings=embeddings,
+                        embeddings2=embeddings2,
+                        backbone_atoms=backbone_atoms,
+                        rsa_values=rsa_values,
+                        epitope_indices=epitope_indices,
+                        recall=recall,
+                        precision=precision,
+                        pdb_id=pdb_id,
+                        chain_id=chain_id,
+                        num_rbf=self.num_rbf,
+                        num_posenc=self.num_posenc,
+                        verbose=self.verbose
+                    )
+                    
+                    if graph_data is not None:
+                        self.data_list.append(graph_data)
+                        
+            except Exception as e:
+                failed_proteins.append(f"{pdb_id}_{chain_id}")
+                if self.verbose:
+                    print(f"Error processing {pdb_id}_{chain_id}: {str(e)}")
+        
+        if failed_proteins and self.verbose:
+            print(f"Failed to process {len(failed_proteins)} proteins: {failed_proteins[:5]}...")
+        
+        if self.verbose:
+            print(f"Successfully created {len(self.data_list)} graph samples (complete dataset)")
+    
+    def _save_cache(self):
+        """Save processed dataset to cache."""
+        try:
+            self._save_cache_hdf5()
+            if self.verbose:
+                print(f"Dataset cached to {self.cache_file}")
+        except Exception as e:
+            if self.verbose:
+                print(f"Failed to save cache: {str(e)}")
+    
+    def _load_cache(self):
+        """Load processed dataset from cache."""
+        try:
+            self._load_cache_hdf5()
+            if self.verbose:
+                print(f"Loaded {len(self.data_list)} samples from cache")
+        except Exception as e:
+            if self.verbose:
+                print(f"Failed to load cache: {str(e)}")
+            self.data_list = []
+    
+    def _save_cache_hdf5(self):
+        """Save dataset using optimized HDF5 format for faster loading."""
+        with h5py.File(self.cache_file, 'w') as f:
+            # Save metadata
+            f.attrs['num_samples'] = len(self.data_list)
+            f.attrs['radius'] = self.radius
+            f.attrs['threshold'] = self.threshold
+            f.attrs['data_split'] = self.data_split
+            f.attrs['complete_dataset'] = True  # Mark as complete dataset
+            
+            # Pre-allocate arrays for better performance
+            num_samples = len(self.data_list)
+            if num_samples == 0:
+                return
+            
+            # Collect all data first to determine max dimensions
+            all_x = []
+            all_pos = []
+            all_rsa = []
+            all_edge_index = []
+            all_edge_attr = []
+            all_y = []
+            all_y_node = []
+            all_center_idx = []
+            all_precision = []
+            all_pdb_ids = []
+            all_chain_ids = []
+            all_num_nodes = []
+            all_covered_indices = []
+            all_embeddings2 = []
+            
+            max_nodes = 0
+            max_edges = 0
+            
+            for data in self.data_list:
+                all_x.append(data.x.numpy())
+                all_pos.append(data.pos.numpy())
+                all_rsa.append(data.rsa.numpy())
+                all_edge_index.append(data.edge_index.numpy())
+                all_edge_attr.append(data.edge_attr.numpy())
+                all_y.append(data.y.numpy())
+                all_y_node.append(data.y_node.numpy())
+                all_center_idx.append(data.center_idx)
+                all_precision.append(data.precision)
+                all_pdb_ids.append(data.pdb_id.encode('utf-8'))
+                all_chain_ids.append(data.chain_id.encode('utf-8'))
+                all_num_nodes.append(data.num_nodes)
+                all_covered_indices.append(data.covered_indices)
+                
+                # Handle embeddings2 safely - it could be None or numpy array
+                if hasattr(data, 'embeddings2') and data.embeddings2 is not None:
+                    if isinstance(data.embeddings2, np.ndarray):
+                        all_embeddings2.append(data.embeddings2)
+                    else:
+                        # It's a torch tensor
+                        all_embeddings2.append(data.embeddings2.numpy())
+                else:
+                    # No embeddings2 available, use zeros as placeholder
+                    all_embeddings2.append(np.zeros((data.num_nodes, 1280), dtype=np.float32))  # ESM2 dim
+                
+                max_nodes = max(max_nodes, data.num_nodes)
+                max_edges = max(max_edges, data.edge_index.shape[1])
+            
+            # Save each graph as a separate group with compression
+            progress_bar = tqdm(enumerate(self.data_list), total=num_samples, desc="Saving dataset...", disable=not self.verbose)
+            
+            for i, data in progress_bar:
+                group = f.create_group(f'graph_{i}')
+                
+                # Save tensors as datasets with compression
+                group.create_dataset('x', data=all_x[i], compression='gzip', compression_opts=6)
+                group.create_dataset('pos', data=all_pos[i], compression='gzip', compression_opts=6)
+                group.create_dataset('rsa', data=all_rsa[i], compression='gzip', compression_opts=6)
+                group.create_dataset('edge_index', data=all_edge_index[i], compression='gzip', compression_opts=6)
+                group.create_dataset('edge_attr', data=all_edge_attr[i], compression='gzip', compression_opts=6)
+                group.create_dataset('y', data=all_y[i], compression='gzip', compression_opts=6)
+                group.create_dataset('y_node', data=all_y_node[i], compression='gzip', compression_opts=6)
+                group.create_dataset('embeddings2', data=all_embeddings2[i], compression='gzip', compression_opts=6)
+                
+                # Save scalar attributes
+                group.attrs['center_idx'] = all_center_idx[i]
+                group.attrs['precision'] = all_precision[i]
+                group.attrs['pdb_id'] = all_pdb_ids[i]
+                group.attrs['chain_id'] = all_chain_ids[i]
+                group.attrs['num_nodes'] = all_num_nodes[i]
+                
+                # Save list attributes as datasets with compression
+                group.create_dataset('covered_indices', data=np.array(all_covered_indices[i]), compression='gzip', compression_opts=6)
+                
+    def _load_cache_hdf5(self):
+        """Optimized cache loader with robust string handling."""
+        self.data_list = []
+        
+        with h5py.File(self.cache_file, 'r') as f:
+            # PHASE 1: Rapid metadata scan
+            zero_recall_indices = []
+            non_zero_recall_indices = []
+            total_samples = f.attrs['num_samples']
+            
+            if self.verbose:
+                print(f"Scanning {total_samples} samples for recall values...")
+            
+            for i in range(total_samples):
+                recall = f[f'graph_{i}/y'][0].item()
+                if recall == 0.0:
+                    zero_recall_indices.append(i)
+                else:
+                    non_zero_recall_indices.append(i)
+            
+            # PHASE 2: Apply zero_ratio filtering
+            selected_indices = non_zero_recall_indices.copy()
+            
+            if isinstance(self.zero_ratio, (int, float)) and 0 <= self.zero_ratio <= 1:
+                if self.zero_ratio < 1.0 and zero_recall_indices:
+                    random.seed(self.seed)
+                    target_count = int(len(zero_recall_indices) * self.zero_ratio)
+                    selected_zero_indices = random.sample(zero_recall_indices, target_count)
+                    selected_indices.extend(selected_zero_indices)
+                    
+                    if self.verbose:
+                        kept = len(selected_zero_indices)
+                        total = len(zero_recall_indices)
+                        print(f"Zero-recall filtering: kept {kept}/{total} samples (ratio={self.zero_ratio})")
+                else:
+                    selected_indices.extend(zero_recall_indices)
+            
+            # PHASE 3: Selective data loading with safe string handling
+            if self.verbose:
+                print(f"Loading {len(selected_indices)} selected samples...")
+            
+            for idx in tqdm(selected_indices, disable=not self.verbose):
+                group = f[f'graph_{idx}']
+                attrs = dict(group.attrs)
+                
+                # Safe string decoding
+                def safe_decode(attr):
+                    val = attrs[attr]
+                    return val.decode('utf-8') if isinstance(val, bytes) else str(val)
+                
+                # Load embeddings2 if available and use_embeddings2 is True
+                if 'embeddings2' in group and self.use_embeddings2:
+                    if group['embeddings2'] is not None:
+                        emb = torch.tensor(group['embeddings2'][:])
+                    else:
+                        emb = torch.tensor(group['x'][:])
+                else:
+                    emb = torch.tensor(group['x'][:])
+                
+                data = Data(
+                    x=emb,
+                    pos=torch.tensor(group['pos'][:]),
+                    rsa=torch.tensor(group['rsa'][:]),
+                    edge_index=torch.tensor(group['edge_index'][:]),
+                    edge_attr=torch.tensor(group['edge_attr'][:]),
+                    y=torch.tensor(group['y'][:]),
+                    y_node=torch.tensor(group['y_node'][:]),
+                    center_idx=int(attrs['center_idx']),
+                    covered_indices=group['covered_indices'][:].tolist(),
+                    precision=float(attrs['precision']),
+                    pdb_id=safe_decode('pdb_id'),
+                    chain_id=safe_decode('chain_id'),
+                    num_nodes=int(attrs['num_nodes'])
+                )
+                
+                self.data_list.append(data)
+        
+        # PHASE 4: Apply undersampling
+        if self.undersample is not None:
+            self.data_list = apply_undersample(
+                self.data_list,
+                self.undersample,
+                seed=self.seed,
+                verbose=self.verbose
+            )
+        
+        if self.verbose:
+            print(f"Loaded {len(self.data_list)} samples (optimized loader)")
+    
+    def len(self) -> int:
+        """Return the number of samples in the dataset."""
+        return len(self.data_list)
+    
+    def get(self, idx: int) -> Data:
+        """Get a sample by index."""
+        return self.data_list[idx]
+    
+    def apply_filters(self, zero_ratio: Optional[float] = None, undersample: Union[int, float, None] = None, seed: int = None):
+        """
+        Apply filtering to the already loaded dataset (for compatibility).
+        Note: It's more efficient to set these parameters during initialization.
+        
+        Args:
+            zero_ratio: Ratio to downsample graphs with recall=0
+            undersample: Undersample parameter
+            seed: Random seed for reproducibility
+        """
+        if seed is None:
+            seed = self.seed
+            
+        # Update instance parameters and re-filter
+        if zero_ratio is not None:
+            self.zero_ratio = zero_ratio
+        if undersample is not None:
+            self.undersample = undersample
+        if seed is not None:
+            self.seed = seed
+            
+        # Reload from cache with new parameters
+        if self.cache_file.exists():
+            if self.verbose:
+                print("Re-applying filters to cached dataset...")
+            self._load_cache_hdf5()
+        else:
+            if self.verbose:
+                print("Warning: No cache file found, filters cannot be applied")
+    
+    def get_stats(self) -> Dict:
+        """Get dataset statistics."""
+        if not self.data_list:
+            return {}
+        
+        # Collect statistics
+        num_nodes_list = [data.num_nodes for data in self.data_list]
+        recall_list = [data.y.item() for data in self.data_list]
+        precision_list = [data.precision for data in self.data_list]
+        
+        # Node-level statistics
+        total_nodes = sum(num_nodes_list)
+        total_epitopes = sum([data.y_node.sum().item() for data in self.data_list])
+        num_zero_recall = sum([1 for data in self.data_list if data.y.item() == 0])
+        
+        stats = {
+            'num_graphs': len(self.data_list),
+            'avg_nodes_per_graph': np.mean(num_nodes_list),
+            'std_nodes_per_graph': np.std(num_nodes_list),
+            'min_nodes_per_graph': np.min(num_nodes_list),
+            'max_nodes_per_graph': np.max(num_nodes_list),
+            'total_nodes': total_nodes,
+            'total_epitopes': total_epitopes,
+            'epitope_ratio': total_epitopes / total_nodes if total_nodes > 0 else 0,
+            'avg_recall': np.mean(recall_list),
+            'std_recall': np.std(recall_list),
+            'avg_precision': np.mean(precision_list),
+            'std_precision': np.std(precision_list),
+            'num_zero_recall': num_zero_recall,
+        }
+        
+        return stats
+    
+    def print_stats(self):
+        """Print dataset statistics."""
+        stats = self.get_stats()
+        if not stats:
+            print("No statistics available (empty dataset)")
+            return
+        
+        print(f"\n=== {self.data_split.upper()} Dataset Statistics ===")
+        print(f"Number of graphs: {stats['num_graphs']:,}")
+        print(f"Average nodes per graph: {stats['avg_nodes_per_graph']:.1f} ± {stats['std_nodes_per_graph']:.1f}")
+        print(f"Nodes per graph range: [{stats['min_nodes_per_graph']}, {stats['max_nodes_per_graph']}]")
+        print(f"Total nodes: {stats['total_nodes']:,}")
+        print(f"Total epitope nodes: {stats['total_epitopes']:,}")
+        print(f"Epitope ratio: {stats['epitope_ratio']:.3f}")
+        print(f"Average recall: {stats['avg_recall']:.3f} ± {stats['std_recall']:.3f}")
+        print(f"Average precision: {stats['avg_precision']:.3f} ± {stats['std_precision']:.3f}")
+        print(f"Number of graphs with zero recall: {stats['num_zero_recall']:,}")
+        print("=" * 40)
+
+
+class MultiRadiusGraphDataset(Dataset):
+    """
+    Dataset that combines multiple radius datasets for multi-scale training.
+    """
+    
+    def __init__(
+        self,
+        data_split: str = "train",
+        radii: List[int] = [16, 18, 20],
+        threshold: float = 0.25,
+        num_posenc: int = 16,
+        num_rbf: int = 16,
+        zero_ratio: float = 0.1,
+        undersample: Union[int, float, None] = None,
+        cache_dir: Optional[str] = None,
+        force_rebuild: bool = False,
+        verbose: bool = True,
+        use_embeddings2: bool = False
+    ):
+        """
+        Initialize multi-radius dataset.
+        
+        Args:
+            data_split: Data split name
+            radii: List of radii to use
+            threshold: SASA threshold for surface residues
+            num_posenc: Number of positional encoding features
+            num_rbf: Number of RBF features
+            zero_ratio: Ratio to downsample graphs with recall=0
+            undersample: Undersample parameter (int for count, float for ratio)
+            cache_dir: Directory to cache processed data
+            force_rebuild: Whether to force rebuild the dataset
+            verbose: Whether to print progress information
+        """
+        self.data_split = data_split
+        self.radii = radii
+        self.verbose = verbose
+        
+        # Create individual datasets
+        self.datasets = []
+        for radius in radii:
+            dataset = SphereGraphDataset(
+                data_split=data_split,
+                radius=radius,
+                threshold=threshold,
+                num_posenc=num_posenc,
+                num_rbf=num_rbf,
+                zero_ratio=zero_ratio,
+                undersample=undersample,
+                cache_dir=cache_dir,
+                force_rebuild=force_rebuild,
+                verbose=verbose,
+                use_embeddings2=use_embeddings2
+            )
+            self.datasets.append(dataset)
+        
+        # Combine all data
+        self.data_list = []
+        for dataset in self.datasets:
+            self.data_list.extend(dataset.data_list)
+        
+        if verbose:
+            print(f"Combined {len(self.datasets)} datasets with {len(self.data_list)} total samples")
+        
+        super().__init__()
+    
+    def len(self) -> int:
+        return len(self.data_list)
+    
+    def get(self, idx: int) -> Data:
+        return self.data_list[idx]
+    
+    def apply_filters(self, undersample: Union[int, float, None] = None, seed: int = 42):
+        """
+        Apply filtering to the loaded multi-radius dataset.
+        
+        Args:
+            undersample: Undersample parameter (int for count, float for ratio)
+            seed: Random seed for reproducibility
+        """
+        if undersample is not None:
+            original_size = len(self.data_list)
+            self.data_list = apply_undersample(self.data_list, undersample, seed=seed, verbose=True)
+    
+    def get_stats(self) -> Dict:
+        """Get combined dataset statistics."""
+        if not self.data_list:
+            return {}
+        
+        # Collect statistics
+        num_nodes_list = [data.num_nodes for data in self.data_list]
+        recall_list = [data.y.item() for data in self.data_list]
+        
+        # Node-level statistics
+        total_nodes = sum(num_nodes_list)
+        total_epitopes = sum([data.y_node.sum().item() for data in self.data_list])
+        
+        stats = {
+            'num_graphs': len(self.data_list),
+            'num_radii': len(self.radii),
+            'radii': self.radii,
+            'avg_nodes_per_graph': np.mean(num_nodes_list),
+            'std_nodes_per_graph': np.std(num_nodes_list),
+            'min_nodes_per_graph': np.min(num_nodes_list),
+            'max_nodes_per_graph': np.max(num_nodes_list),
+            'total_nodes': total_nodes,
+            'total_epitopes': total_epitopes,
+            'epitope_ratio': total_epitopes / total_nodes if total_nodes > 0 else 0,
+            'avg_recall': np.mean(recall_list),
+        }
+        
+        return stats
+
+    def print_stats(self):
+        """Print dataset statistics."""
+        stats = self.get_stats()
+        if not stats:
+            print("No statistics available (empty dataset)")
+            return
+        
+        print(f"\n=== {self.data_split.upper()} Dataset Statistics ===")
+        print(f"Number of graphs: {stats['num_graphs']:,}")
+        print(f"Average nodes per graph: {stats['avg_nodes_per_graph']:.1f} ± {stats['std_nodes_per_graph']:.1f}")
+        print(f"Nodes per graph range: [{stats['min_nodes_per_graph']}, {stats['max_nodes_per_graph']}]")
+        print(f"Total nodes: {stats['total_nodes']:,}")
+        print(f"Total epitope nodes: {stats['total_epitopes']:,}")
+        print(f"Epitope ratio: {stats['epitope_ratio']:.3f}")
+        print(f"Average recall: {stats['avg_recall']:.3f} ± {stats['std_recall']:.3f}")
+        print(f"Average precision: {stats['avg_precision']:.3f} ± {stats['std_precision']:.3f}")
+        print("=" * 40)
+
+
+
+# Utility functions for dataset creation and management
+def create_datasets(
+    radii: List[int] = [16, 18, 20],
+    splits: List[str] = ["train", "test"],
+    threshold: float = 0.25,
+    zero_ratio: float = None,
+    undersample: Union[int, float, None] = None,
+    cache_dir: Optional[str] = None,
+    force_rebuild: bool = False,
+    verbose: bool = False,
+    seed: int = 42,
+    use_embeddings2: bool = False,
+) -> Dict[str, SphereGraphDataset]:
+    """
+    Create optimized datasets for all splits and radii.
+    
+    Args:
+        radii: List of radii to use
+        splits: List of data splits to create
+        threshold: SASA threshold for surface residues
+        zero_ratio: Ratio to downsample graphs with recall=0
+        undersample: Undersample parameter (int for count, float for ratio)
+        cache_dir: Directory to cache processed data
+        force_rebuild: Whether to force rebuild datasets
+        verbose: Whether to print progress information
+        seed: Random seed for reproducibility
+        
+    Returns:
+        Dictionary mapping split names to datasets
+    """
+    datasets = {}
+    
+    for split in splits:
+        if len(radii) == 1:
+            # Single radius dataset
+            dataset = SphereGraphDataset(
+                data_split=split,
+                radius=radii[0],
+                threshold=threshold,
+                zero_ratio=zero_ratio,
+                undersample=undersample,
+                cache_dir=cache_dir,
+                force_rebuild=force_rebuild,
+                verbose=verbose,
+                seed=seed,
+                use_embeddings2=use_embeddings2
+            )
+            if verbose:
+                dataset.print_stats()
+        else:
+            # Multi-radius dataset
+            dataset = MultiRadiusGraphDataset(
+                data_split=split,
+                radii=radii,
+                threshold=threshold,
+                zero_ratio=zero_ratio,
+                undersample=undersample,
+                cache_dir=cache_dir,
+                force_rebuild=force_rebuild,
+                verbose=verbose,
+                use_embeddings2=use_embeddings2
+            )
+        
+        datasets[split] = dataset
+    
+    return datasets
+
+
+def custom_collate_fn(batch):
+    """
+    Custom collate function for ReGEP model.
+    Converts PyG Data objects to the format expected by ReGEP.
+    """
+    # Use PyG's default batching
+    batched_data = Batch.from_data_list(batch)
+    
+    # ReGEP expects the input features to be concatenated
+    # x: [N_total, embed_dim], rsa: [N_total], ss: [N_total, 2]
+    # The model will concatenate them internally: [x, rsa, ss] -> [N_total, embed_dim + 3]
+    
+    return batched_data
+
+
+class ReGEPDataLoader(DataLoader):
+    """
+    Custom DataLoader for ReGEP model that handles the specific input format.
+    Supports undersampling at the DataLoader level.
+    """
+    
+    def __init__(self, dataset, batch_size=32, shuffle=True, **kwargs):
+        """
+        Initialize ReGEP DataLoader with optional undersampling.
+        
+        Args:
+            dataset: The dataset to load from
+            batch_size: Batch size
+            shuffle: Whether to shuffle the data
+            **kwargs: Additional arguments for DataLoader
+        """
+        # Set default collate_fn if not provided
+        if 'collate_fn' not in kwargs:
+            kwargs['collate_fn'] = custom_collate_fn
+            
+        super().__init__(
+            dataset=dataset,
+            batch_size=batch_size,
+            shuffle=shuffle,
+            **kwargs
+        )
+
+def create_data_loader(
+    radii=[16, 18, 20],
+    batch_size=32,
+    zero_ratio=0.1,
+    undersample=0.5,
+    seed=42,
+    verbose=False,
+    use_embeddings2=False,
+    **kwargs
+):
+    """
+    Create train and test data loaders.
+    
+    Args:
+        radii (list): List of radii for data processing
+        batch_size (int): Batch size for training
+        zero_ratio (float): Ratio of zero samples for training
+        undersample (float): Undersampling ratio for training
+        seed (int): Random seed
+        verbose (bool): Whether to print verbose information
+        **kwargs: Additional arguments for data loader
+    
+    Returns:
+        tuple: (train_loader, test_loader)
+    """
+    train_dataset = create_datasets(
+            radii=radii,
+            splits=["train"],
+            threshold=0.25,
+            undersample=undersample,
+            zero_ratio=zero_ratio,
+            cache_dir=None,
+            seed=seed,
+            verbose=verbose,
+            use_embeddings2=use_embeddings2
+        )["train"]
+    
+    test_dataset = create_datasets(
+            radii=radii,
+            splits=["test"],
+            threshold=0.25,
+            undersample=None,
+            zero_ratio=None,
+            cache_dir=None,
+            verbose=verbose,
+            use_embeddings2=use_embeddings2
+        )["test"]
+    
+    train_loader = ReGEPDataLoader(
+        train_dataset,
+        batch_size=batch_size,
+        shuffle=True,
+        collate_fn=custom_collate_fn,
+        **kwargs
+    )
+    
+    test_loader = ReGEPDataLoader(
+        test_dataset,
+        batch_size=batch_size*4,
+        shuffle=False,
+        **kwargs
+    )
+    
+    return train_loader, test_loader
+    
\ No newline at end of file
diff --git a/src/bce/data/utils.py b/src/bce/data/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..349c11269426a54bcc3f61b63b52d468e4a168b8
--- /dev/null
+++ b/src/bce/data/utils.py
@@ -0,0 +1,352 @@
+"""
+Simplified graph utilities for SphereGraphDataset.
+Contains only the essential functions needed without external dependencies.
+"""
+
+import numpy as np
+from pathlib import Path
+from typing import List, Optional, Tuple
+
+import torch
+from torch_geometric.data import Data
+from torch_geometric.nn import radius_graph
+
+def parse_binding_site_txt(txt_path: Path) -> Tuple[List[str], List[str], List[torch.Tensor]]:
+    """
+    Parse a fasta-like txt file with 3 lines per entry: >id, sequence, label
+    Returns: (rna_ids, rna_seqs, labels)
+    """
+    rna_ids, rna_seqs, labels = [], [], []
+    with open(txt_path, 'r') as f:
+        lines = [line.strip() for line in f if line.strip()]
+    for i in range(0, len(lines), 3):
+        rna_id = lines[i][1:] if lines[i].startswith('>') else lines[i]
+        seq = lines[i+1]
+        label_str = lines[i+2]
+        label = torch.tensor([int(x) for x in label_str], dtype=torch.float32)
+        rna_ids.append(rna_id)
+        rna_seqs.append(seq)
+        labels.append(label)
+    return rna_ids, rna_seqs, labels
+
+def create_graph_data_full(
+    embeddings: np.ndarray,
+    backbone_atoms: np.ndarray,
+    rsa_values: np.ndarray,
+    epitope_indices: List[int],
+    pdb_id: str,
+    chain_id: str,
+    num_rbf: int = 16,
+    num_posenc: int = 16,
+    radius: float = 18.0,
+    verbose: bool = True
+    ) -> Optional[Data]:
+    """
+    Create a PyTorch Geometric Data object for a full protein graph.
+    
+    Args:
+        embeddings: Full protein embeddings [seq_len, embed_dim]
+        backbone_atoms: Full protein backbone atoms [seq_len, 3, 3] (N, CA, C)
+        rsa_values: Full protein RSA values [seq_len]
+        epitope_indices: List of epitope residue indices
+        pdb_id: PDB ID
+        chain_id: Chain ID
+        num_rbf: Number of RBF features
+        num_posenc: Number of positional encoding features
+        radius: Distance threshold for edge creation (default: 18.0 Å)
+        verbose: Whether to print debug information
+        
+    Returns:
+        PyTorch Geometric Data object or None if creation fails
+    """
+    try:
+        # Validate input dimensions
+        seq_len = len(embeddings)
+        if len(backbone_atoms) != seq_len or len(rsa_values) != seq_len:
+            if verbose:
+                print(f"[WARNING] Dimension mismatch for {pdb_id}_{chain_id}: "
+                      f"embeddings={len(embeddings)}, backbone={len(backbone_atoms)}, "
+                      f"rsa={len(rsa_values)}")
+            return None
+        
+        if seq_len == 0:
+            if verbose:
+                print(f"[WARNING] Empty protein {pdb_id}_{chain_id}")
+            return None
+        
+        # Create node labels (binary epitope classification)
+        node_labels = np.zeros(seq_len, dtype=np.float32)
+        if epitope_indices:
+            # Filter epitope_indices to ensure they are within bounds
+            valid_epitope_indices = [idx for idx in epitope_indices if 0 <= idx < seq_len]
+            if valid_epitope_indices:
+                node_labels[valid_epitope_indices] = 1.0
+            
+            if verbose and len(valid_epitope_indices) != len(epitope_indices):
+                print(f"[WARNING] Some epitope indices out of bounds for {pdb_id}_{chain_id}: "
+                      f"filtered {len(epitope_indices)} -> {len(valid_epitope_indices)}")
+        
+        # Extract CA coordinates for distance calculation
+        ca_coords = backbone_atoms[:, 1, :]  # CA is the second atom [seq_len, 3]
+        
+        # Validate CA coordinates
+        if ca_coords.shape[0] == 0:
+            if verbose:
+                print(f"[WARNING] Empty CA coordinates for {pdb_id}_{chain_id}")
+            return None
+        
+        # Check for NaN or infinite values
+        if np.any(np.isnan(ca_coords)) or np.any(np.isinf(ca_coords)):
+            if verbose:
+                print(f"[WARNING] Invalid CA coordinates (NaN/Inf) for {pdb_id}_{chain_id}")
+            return None
+        
+        # Create edges based on distance threshold using radius_graph
+        ca_coords_tensor = torch.tensor(ca_coords, dtype=torch.float32)
+        
+        # Additional safety check for tensor
+        if ca_coords_tensor.numel() == 0:
+            if verbose:
+                print(f"[WARNING] Empty CA coordinates tensor for {pdb_id}_{chain_id}")
+            return None
+        
+        edge_index = radius_graph(ca_coords_tensor, r=radius, loop=False, max_num_neighbors=32)
+        
+        if edge_index.shape[1] == 0:
+            if verbose:
+                print(f"[WARNING] No edges found for {pdb_id}_{chain_id} with radius {radius}")
+            # Create a minimal graph with self-loops to avoid empty graph
+            edge_index = torch.stack([torch.arange(seq_len), torch.arange(seq_len)], dim=0)
+        
+        # Compute edge features
+        edge_features = compute_edge_features(ca_coords, edge_index, num_rbf=num_rbf, num_posenc=num_posenc)
+        
+        # Convert to tensors
+        x = torch.tensor(embeddings, dtype=torch.float32)  # [seq_len, embed_dim]
+        pos = torch.tensor(backbone_atoms, dtype=torch.float32)  # [seq_len, 3, 3]
+        rsa = torch.tensor(rsa_values, dtype=torch.float32)  # [seq_len]
+        
+        # Node-level labels
+        y_node = torch.tensor(node_labels, dtype=torch.float32)  # [seq_len]
+        
+        # Additional protein-level statistics
+        num_epitopes = int(node_labels.sum())
+        epitope_ratio = num_epitopes / seq_len if seq_len > 0 else 0.0
+        
+        # Create Data object
+        data = Data(
+            x=x,  # Node embeddings [seq_len, embed_dim]
+            pos=pos,  # Backbone coordinates [seq_len, 3, 3]
+            rsa=rsa,  # RSA values [seq_len]
+            edge_index=edge_index,  # Edge connectivity [2, n_edges]
+            edge_attr=edge_features,  # Edge features [n_edges, edge_dim]
+            y_node=y_node,  # Node-level labels [seq_len]
+            epitope_indices=epitope_indices,  # Original epitope indices
+            pdb_id=pdb_id,  # PDB ID
+            chain_id=chain_id,  # Chain ID
+            num_nodes=seq_len,  # Number of nodes (residues)
+            num_epitopes=num_epitopes,  # Number of epitope residues
+            epitope_ratio=epitope_ratio,  # Ratio of epitope residues
+            radius=radius  # Distance threshold used for edges
+        )
+        
+        if verbose:
+            print(f"[INFO] Created full protein graph for {pdb_id}_{chain_id}: "
+                  f"{seq_len} nodes, {edge_index.shape[1]} edges, {num_epitopes} epitopes")
+        
+        return data
+        
+    except Exception as e:
+        if verbose:
+            print(f"[ERROR] Failed to create full protein graph for {pdb_id}_{chain_id}: {str(e)}")
+        return None
+
+def create_graph_data(
+    center_idx: int,
+    covered_indices: List[int],
+    covered_epitope_indices: List[int],
+    embeddings: np.ndarray,
+    backbone_atoms: np.ndarray,
+    rsa_values: np.ndarray,
+    epitope_indices: List[int],
+    recall: float,
+    precision: float,
+    pdb_id: str,
+    chain_id: str,
+    embeddings2: np.ndarray = None,
+    num_rbf: int = 16,
+    num_posenc: int = 16,
+    verbose: bool = True
+    ) -> Optional[Data]:
+    """
+    Create a PyTorch Geometric Data object for a spherical region.
+        
+    Args:
+        center_idx: Index of the center residue
+        covered_indices: List of residue indices in the region
+        covered_epitope_indices: List of epitope residue indices in the region
+        embeddings: Full protein embeddings
+        backbone_atoms: Full protein backbone atoms [seq_len, 3, 3]
+        rsa_values: Full protein RSA values
+        epitope_indices: List of all epitope indices in the protein (if available)
+        recall: Region recall value (if available)
+        precision: Region precision value (if available)
+        pdb_id: PDB ID
+        chain_id: Chain ID
+            
+        Returns:
+            PyTorch Geometric Data object or None if creation fails
+        """
+    try:
+        # Validate indices first
+        if not covered_indices:
+            if verbose:
+                print(f"[WARNING] Empty covered_indices for center {center_idx}")
+            return None
+        
+        # Check if indices are within bounds
+        max_idx = max(covered_indices)
+        if max_idx >= len(embeddings) or max_idx >= len(backbone_atoms) or max_idx >= len(rsa_values):
+            if verbose:
+                print(f"[WARNING] Index out of bounds: max_idx={max_idx}, "
+                      f"embeddings_len={len(embeddings)}, backbone_len={len(backbone_atoms)}, "
+                      f"rsa_len={len(rsa_values)}")
+            return None
+        
+        # Extract node features for covered residues
+        node_embeddings = embeddings[covered_indices]  # [n_nodes, embed_dim]
+        node_backbone = backbone_atoms[covered_indices]  # [n_nodes, 3, 3]  
+        node_rsa = rsa_values[covered_indices]  # [n_nodes]
+        
+        if embeddings2 is not None:
+            node_embeddings2 = embeddings2[covered_indices]  # [n_nodes, embed_dim]
+        else:
+            node_embeddings2 = None
+            
+        # Create node labels (binary epitope classification)
+        node_labels = np.zeros(len(covered_indices), dtype=np.float32)
+        # Use the epitope_indices from the loaded data if available
+        epitope_mask = np.isin(covered_indices, epitope_indices)
+        node_labels[epitope_mask] = 1.0
+            
+        # Create fully connected edge index (no self-loops)
+        n_nodes = len(covered_indices)
+        edge_index = get_edges(n_nodes)
+        edge_index = torch.tensor(edge_index, dtype=torch.long)
+        
+        # Compute edge features using CA coordinates
+        ca_coords = node_backbone[:, 1, :]  # Extract CA coordinates [n_nodes, 3]
+        edge_features = compute_edge_features(ca_coords, edge_index, num_rbf=num_rbf, num_posenc=num_posenc)
+        
+        # Convert to tensors
+        x = torch.tensor(node_embeddings, dtype=torch.float32)
+        pos = torch.tensor(node_backbone, dtype=torch.float32)  # [n_nodes, 3, 3]
+        rsa = torch.tensor(node_rsa, dtype=torch.float32)
+        
+        # Graph-level label (recall)
+        y_graph = torch.tensor([recall], dtype=torch.float32)
+        
+        # Node-level labels
+        y_node = torch.tensor(node_labels, dtype=torch.float32)
+        
+        # Create Data object
+        data = Data(
+            x=x,  # Node embeddings [n_nodes, embed_dim]
+            pos=pos,  # Backbone coordinates [n_nodes, 3, 3]
+            rsa=rsa,  # RSA values [n_nodes]
+            edge_index=edge_index,  # Edge connectivity [2, n_edges]
+            edge_attr=edge_features,  # Edge features [n_edges, edge_dim]
+            y=y_graph,  # Graph-level label (recall)
+            y_node=y_node,  # Node-level labels [n_nodes]
+            center_idx=center_idx,  # Center residue index
+            covered_indices=covered_indices,  # All covered residue indices
+            precision=precision,  # Region precision
+            pdb_id=pdb_id,  # PDB ID
+            chain_id=chain_id,  # Chain ID
+            num_nodes=n_nodes,  # Number of nodes
+            embeddings2=node_embeddings2,  # other embeddings [n_nodes, embed_dim] - region-specific
+        )
+        
+        return data
+        
+    except Exception as e:
+        if verbose:
+            print(f"Error creating graph data for {pdb_id}_{chain_id} center {center_idx}: {str(e)}")
+        return None
+    
+def compute_edge_features(coords: np.ndarray, edge_index: torch.Tensor, num_rbf: int = 16, num_posenc: int = 16) -> torch.Tensor:
+    """
+    Compute edge features including RBF and positional encoding.
+        
+    Args:
+        coords: Node coordinates [n_nodes, 3]
+        edge_index: Edge connectivity [2, n_edges]
+        num_rbf: Number of RBF features
+        num_posenc: Number of positional encoding features
+        
+    Returns:
+        Edge features [n_edges, edge_dim]
+        """
+    # Convert to torch tensors
+    coords_tensor = torch.tensor(coords, dtype=torch.float32)
+        
+    # Compute edge vectors and distances
+    edge_vectors = coords_tensor[edge_index[0]] - coords_tensor[edge_index[1]]  # [n_edges, 3]
+    edge_distances = torch.norm(edge_vectors, dim=-1)  # [n_edges]
+        
+    # RBF features
+    edge_rbf = rbf(edge_distances, D_count=num_rbf)  # [n_edges, num_rbf]
+        
+    # Positional encoding
+    edge_posenc = get_posenc(edge_index, num_posenc=num_posenc)  # [n_edges, num_posenc]
+        
+    # Concatenate edge features
+    edge_features = torch.cat([edge_rbf, edge_posenc], dim=-1)  # [n_edges, num_rbf + num_posenc]
+        
+    return edge_features
+
+
+def get_edges(n_nodes):
+    """Generate fully connected edge indices (no self-loops)"""
+    rows, cols = [], []
+    for i in range(n_nodes):
+        for j in range(n_nodes):
+            if i != j:
+                rows.append(i)
+                cols.append(j)
+    return [rows, cols]
+
+
+def get_posenc(edge_index, num_posenc=16):
+    """
+    Generate positional encoding for edges.
+    From https://github.com/jingraham/neurips19-graph-protein-design
+    """
+    d = edge_index[0] - edge_index[1]
+
+    frequency = torch.exp(
+        torch.arange(0, num_posenc, 2, dtype=torch.float32, device=d.device)
+        * -(np.log(10000.0) / num_posenc)
+    )
+
+    angles = d.unsqueeze(-1) * frequency
+    E = torch.cat((torch.cos(angles), torch.sin(angles)), -1)
+    return E
+
+
+def rbf(D, D_min=0., D_max=20., D_count=16):
+    """
+    Radial Basis Function (RBF) encoding for distances.
+    From https://github.com/jingraham/neurips19-graph-protein-design
+
+    Returns an RBF embedding of `torch.Tensor` `D` along a new axis=-1.
+    That is, if `D` has shape [...dims], then the returned tensor will have
+    shape [...dims, D_count].
+    """
+    D_mu = torch.linspace(D_min, D_max, D_count, device=D.device)
+    D_mu = D_mu.view([1, -1])
+    D_sigma = (D_max - D_min) / D_count
+    D_expand = torch.unsqueeze(D, -1)
+
+    RBF = torch.exp(-((D_expand - D_mu) / D_sigma) ** 2)
+    return RBF 
\ No newline at end of file
diff --git a/src/bce/model/EGNN.py b/src/bce/model/EGNN.py
new file mode 100644
index 0000000000000000000000000000000000000000..3edf4cc6037097fd1f269f5ae805855b8a29763e
--- /dev/null
+++ b/src/bce/model/EGNN.py
@@ -0,0 +1,315 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch_scatter import scatter
+from torch_geometric.nn import InstanceNorm
+
+class EGNNLayer(nn.Module):
+    """
+    EGNN layer with optional feed forward network and batch normalization.
+    
+    Args:
+        input_nf: Number of input node features
+        output_nf: Number of output node features
+        hidden_nf: Number of hidden features
+        edges_in_d: Number of input edge features
+        act_fn: Activation function
+        residual: Whether to use residual connections
+        attention: Whether to use attention mechanism for edge features
+        normalize: Whether to normalize coordinates
+        coords_agg: Aggregation method for coordinates (mean, sum, max, min)
+        tanh: Whether to use tanh activation for coordinate updates
+        dropout: Dropout rate
+        ffn: Whether to use feed forward network
+        batch_norm: Whether to use batch normalization
+    """
+    def __init__(self, input_nf, output_nf, hidden_nf,
+                 edges_in_d=0, act_fn=nn.SiLU(),
+                 residual=True, attention=False, normalize=False,
+                 coords_agg='mean', tanh=False, dropout=0.0,
+                 ffn=False, batch_norm=True):
+        super().__init__()
+        self.input_nf = input_nf
+        self.output_nf = output_nf
+        self.hidden_nf = hidden_nf
+        self.residual = residual
+        self.attention = attention
+        self.normalize = normalize
+        self.coords_agg = coords_agg
+        self.tanh = tanh
+        self.epsilon = 1e-8
+        self.dropout = dropout
+        self.ffn = ffn
+        self.batch_norm = batch_norm
+
+        # Edge MLP
+        in_edge = input_nf*2 + 1 + edges_in_d
+        self.edge_mlp = nn.Sequential(
+            nn.Linear(in_edge, hidden_nf),
+            act_fn, nn.Dropout(dropout),
+            nn.Linear(hidden_nf, hidden_nf),
+            act_fn, nn.Dropout(dropout),
+        )
+        if attention:
+            self.att_mlp = nn.Sequential(nn.Linear(hidden_nf,1), nn.Sigmoid())
+
+        # Coord MLP
+        layer = nn.Linear(hidden_nf,1, bias=False)
+        nn.init.xavier_uniform_(layer.weight, gain=0.001)
+        coord_blocks = [nn.Linear(hidden_nf, hidden_nf), act_fn,
+                        nn.Dropout(dropout), layer]
+        if tanh: coord_blocks.append(nn.Tanh())
+        self.coord_mlp = nn.Sequential(*coord_blocks)
+
+        # Node MLP
+        self.node_mlp = nn.Sequential(
+            nn.Linear(hidden_nf + input_nf, hidden_nf),
+            act_fn, nn.Dropout(dropout),
+            nn.Linear(hidden_nf, output_nf),
+        )
+
+        # per-graph normalization
+        if batch_norm:
+            self.norm_node = InstanceNorm(output_nf, affine=True)
+            self.norm_coord = InstanceNorm(3, affine=True)
+
+        # FFN
+        if ffn:
+            self.ff1 = nn.Linear(output_nf, output_nf*2)
+            self.ff2 = nn.Linear(output_nf*2, output_nf)
+            self.act_ff = act_fn
+            self.drop_ff = nn.Dropout(dropout)
+            if batch_norm:
+                self.norm_ff1 = InstanceNorm(output_nf, affine=True)
+                self.norm_ff2 = InstanceNorm(output_nf, affine=True)
+
+    def coord2radial(self, edge_index, coord):
+        row, col = edge_index
+        diff = coord[row] - coord[col]
+        dist2 = (diff**2).sum(dim=-1, keepdim=True)
+        
+        # Clamp distance to prevent extreme values
+        dist2 = torch.clamp(dist2, min=self.epsilon, max=100.0)
+        
+        if self.normalize:
+            norm = (dist2.sqrt().detach() + self.epsilon)
+            diff = diff / norm
+            # Check for NaN/Inf in normalized diff
+            diff = torch.where(torch.isfinite(diff), diff, torch.zeros_like(diff))
+        return dist2, diff
+
+    def _ff_block(self, x):
+        """Feed Forward block.
+        """
+        x = self.drop_ff(self.act_ff(self.ff1(x)))
+        return self.ff2(x)
+    
+    def forward(self, h, coord, edge_index, batch, edge_attr=None, node_attr=None):
+        row, col = edge_index
+        radial, coord_diff = self.coord2radial(edge_index, coord)
+
+        # -- edge features --
+        e_in = [h[row], h[col], radial]
+        if edge_attr is not None: e_in.append(edge_attr)
+        e = torch.cat(e_in, dim=-1)
+        e = self.edge_mlp(e)
+        if self.attention:
+            att = self.att_mlp(e)
+            e = e * att
+
+        # -- coordinate update --
+        coord_update = self.coord_mlp(e)  # [E,1]
+        # Clamp coordinate updates to prevent explosion
+        coord_update = torch.clamp(coord_update, -1.0, 1.0)
+        trans = coord_diff * coord_update  # [E,3]
+        
+        # Check for NaN/Inf in coordinate updates
+        trans = torch.where(torch.isfinite(trans), trans, torch.zeros_like(trans))
+        
+        agg_coord = scatter(trans, row, dim=0,
+                            dim_size=coord.size(0),
+                            reduce=self.coords_agg)
+        coord = coord + agg_coord
+        
+        # Check for NaN/Inf in final coordinates
+        coord = torch.where(torch.isfinite(coord), coord, torch.zeros_like(coord))
+        
+        if self.batch_norm:
+            coord = self.norm_coord(coord, batch)
+
+        # -- node update --
+        agg_node = scatter(e, row, dim=0,
+                           dim_size=h.size(0), reduce='sum')
+        x_in = torch.cat([h, agg_node], dim=-1)
+        if node_attr is not None:
+            x_in = torch.cat([x_in, node_attr], dim=-1)
+        h_new = self.node_mlp(x_in)
+        if self.batch_norm:
+            h_new = self.norm_node(h_new, batch)
+        if self.residual and h_new.shape[-1] == h.shape[-1]:
+            h_new = h + h_new
+
+        # -- optional FFN --
+        if self.ffn:
+            if self.batch_norm:
+                h_new = self.norm_ff1(h_new, batch)
+            h_new = h_new + self._ff_block(h_new)
+            if self.batch_norm:
+                h_new = self.norm_ff2(h_new, batch)
+
+        return h_new, coord, e
+
+class EGNNLayer2(nn.Module):
+    """
+    EGNN layer with optional feed forward network and batch normalization.
+    
+    Args:
+        input_nf: Number of input node features
+        output_nf: Number of output node features
+        hidden_nf: Number of hidden features
+        edges_in_d: Number of input edge features
+        act_fn: Activation function
+        residual: Whether to use residual connections
+        attention: Whether to use attention mechanism for edge features
+        normalize: Whether to normalize coordinates
+        coords_agg: Aggregation method for coordinates (mean, sum, max, min)
+        tanh: Whether to use tanh activation for coordinate updates
+        dropout: Dropout rate
+        ffn: Whether to use feed forward network
+        batch_norm: Whether to use batch normalization
+    """
+    def __init__(self, input_nf, output_nf, hidden_nf,
+                 edges_in_d=0, act_fn=nn.SiLU(),
+                 residual=True, attention=False, normalize=False,
+                 coords_agg='mean', tanh=False, dropout=0.0,
+                 ffn=False, batch_norm=True):
+        super().__init__()
+        self.input_nf = input_nf
+        self.output_nf = output_nf
+        self.hidden_nf = hidden_nf
+        self.residual = residual
+        self.attention = attention
+        self.normalize = normalize
+        self.coords_agg = coords_agg
+        self.tanh = tanh
+        self.epsilon = 1e-8
+        self.dropout = dropout
+        self.ffn = ffn
+        self.batch_norm = batch_norm
+
+        # Edge MLP
+        in_edge = input_nf*2 + 1 + edges_in_d
+        self.edge_mlp = nn.Sequential(
+            nn.Linear(in_edge, hidden_nf),
+            act_fn, nn.Dropout(dropout),
+            nn.Linear(hidden_nf, hidden_nf),
+            act_fn, nn.Dropout(dropout),
+        )
+        if attention:
+            self.att_mlp = nn.Sequential(nn.Linear(hidden_nf,1), nn.Sigmoid())
+
+        # Coord MLP
+        layer = nn.Linear(hidden_nf,1, bias=False)
+        nn.init.xavier_uniform_(layer.weight, gain=0.001)
+        coord_blocks = [nn.Linear(hidden_nf, hidden_nf), act_fn,
+                        nn.Dropout(dropout), layer]
+        if tanh: coord_blocks.append(nn.Tanh())
+        self.coord_mlp = nn.Sequential(*coord_blocks)
+
+        # Node MLP
+        self.node_mlp = nn.Sequential(
+            nn.Linear(hidden_nf + input_nf, hidden_nf),
+            act_fn, nn.Dropout(dropout),
+            nn.Linear(hidden_nf, output_nf),
+        )
+
+        # per-graph normalization
+        if batch_norm:
+            self.norm_node = InstanceNorm(output_nf, affine=True)
+            self.norm_coord = InstanceNorm(3, affine=True)
+
+        # FFN
+        if ffn:
+            self.ff1 = nn.Linear(output_nf, output_nf*2)
+            self.ff2 = nn.Linear(output_nf*2, output_nf)
+            self.act_ff = act_fn
+            self.drop_ff = nn.Dropout(dropout)
+            if batch_norm:
+                self.norm_ff1 = InstanceNorm(output_nf, affine=True)
+                self.norm_ff2 = InstanceNorm(output_nf, affine=True)
+
+    def coord2radial(self, edge_index, coord):
+        row, col = edge_index
+        diff = coord[row] - coord[col]
+        dist2 = (diff**2).sum(dim=-1, keepdim=True)
+        
+        # Clamp distance to prevent extreme values
+        dist2 = torch.clamp(dist2, min=self.epsilon, max=100.0)
+        
+        if self.normalize:
+            norm = (dist2.sqrt().detach() + self.epsilon)
+            diff = diff / norm
+            # Check for NaN/Inf in normalized diff
+            diff = torch.where(torch.isfinite(diff), diff, torch.zeros_like(diff))
+        return dist2, diff
+
+    def _ff_block(self, x):
+        """Feed Forward block.
+        """
+        x = self.drop_ff(self.act_ff(self.ff1(x)))
+        return self.ff2(x)
+    
+    def forward(self, h, coord, edge_index, batch, edge_attr=None, node_attr=None):
+        row, col = edge_index
+        radial, coord_diff = self.coord2radial(edge_index, coord)
+
+        # -- edge features --
+        e_in = [h[row], h[col], radial]
+        if edge_attr is not None: e_in.append(edge_attr)
+        e = torch.cat(e_in, dim=-1)
+        e = self.edge_mlp(e)
+        if self.attention:
+            att = self.att_mlp(e)
+            e = e * att
+
+        # -- coordinate update --
+        coord_update = self.coord_mlp(e)  # [E,1]
+        # Clamp coordinate updates to prevent explosion
+        coord_update = torch.clamp(coord_update, -1.0, 1.0)
+        trans = coord_diff * coord_update  # [E,3]
+        
+        # Check for NaN/Inf in coordinate updates
+        trans = torch.where(torch.isfinite(trans), trans, torch.zeros_like(trans))
+        
+        agg_coord = scatter(trans, row, dim=0,
+                            dim_size=coord.size(0),
+                            reduce=self.coords_agg)
+        coord = coord + agg_coord
+        
+        # Check for NaN/Inf in final coordinates
+        coord = torch.where(torch.isfinite(coord), coord, torch.zeros_like(coord))
+        
+        if self.batch_norm:
+            coord = self.norm_coord(coord, batch)
+
+        # -- node update --
+        agg_node = scatter(e, row, dim=0,
+                           dim_size=h.size(0), reduce='sum')
+        x_in = torch.cat([h, agg_node], dim=-1)
+        if node_attr is not None:
+            x_in = torch.cat([x_in, node_attr], dim=-1)
+        h_new = self.node_mlp(x_in)
+        if self.batch_norm:
+            h_new = self.norm_node(h_new, batch)
+        if self.residual and h_new.shape[-1] == h.shape[-1]:
+            h_new = h + h_new
+
+        # -- optional FFN --
+        if self.ffn:
+            if self.batch_norm:
+                h_new = self.norm_ff1(h_new, batch)
+            h_new = h_new + self._ff_block(h_new)
+            if self.batch_norm:
+                h_new = self.norm_ff2(h_new, batch)
+
+        return h_new, coord, e
\ No newline at end of file
diff --git a/src/bce/model/ReCEP.py b/src/bce/model/ReCEP.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa6e2e25f215a3131de21b1675e1caf656ac3bf6
--- /dev/null
+++ b/src/bce/model/ReCEP.py
@@ -0,0 +1,481 @@
+from pathlib import Path
+from prettytable import PrettyTable
+
+import torch
+import torch.nn as nn
+from torch_scatter import scatter_softmax, scatter_sum
+from torch_geometric.data import Data, Batch
+
+from .dihedral import DihedralFeatures
+from .EGNN import EGNNLayer
+from .pooling import AttentionPooling, AddPooling
+from .activation import get_activation
+# from .baseline import EP
+
+class ReCEP(nn.Module):
+    """
+    Refined Graph Epitope Predictor with optional EGNN layer skipping for ablation.
+    """
+    def __init__(
+        self,
+        in_dim: int = 2560,
+        rsa: bool = True,
+        dihedral: bool = True,
+        node_dims: list = [512, 256, 256],
+        edge_dim: int = 32,
+        dropout: float = 0.3,
+        activation: str = "gelu",
+        residual: bool = True,
+        attention: bool = True,
+        normalize: bool = True,
+        coords_agg: str = 'mean',
+        ffn: bool = True,
+        batch_norm: bool = True,
+        concat: bool = False,
+        addition: bool = False,
+        # Global predictor
+        pooling: str = 'attention',
+        # Node classifier
+        fusion_type: str = 'concat',
+        node_gate: bool = False,
+        node_norm: bool = False,
+        node_layers: int = 2,
+        out_dropout: float = 0.2,
+        use_egnn: bool = True,  # NEW: toggle for EGNN layer usage
+        encoder: str = 'esmc',
+    ):
+        super().__init__()
+        self.use_egnn = use_egnn
+        self.in_dim = in_dim
+        self.rsa = rsa
+        self.dihedral = dihedral
+        self.original_node_dims = node_dims.copy()
+        self.edge_dim = edge_dim
+        self.dropout = dropout
+        self.activation = activation
+        self.residual = residual
+        self.attention = attention
+        self.normalize = normalize
+        self.ffn = ffn
+        self.batch_norm = batch_norm
+        self.coords_agg = coords_agg
+        self.concat = concat
+        self.addition = addition
+        self.fusion_type = fusion_type
+        self.node_gate = node_gate
+        self.node_norm = node_norm
+        self.node_layers = node_layers
+        self.out_dropout = out_dropout
+        self.pooling = pooling
+
+        self.base_node_dim = node_dims[0]
+        self.node_dims = node_dims.copy()
+        self.node_dims[0] += 1 if rsa else 0
+        
+        self.node_dims[-1] = self.node_dims[0] if addition else self.node_dims[-1]
+        
+        # Modify input dimension based on encoder
+        self.encoder = encoder
+        if encoder == 'esmc':
+            self.in_dim = 2560
+        elif encoder == 'esm2':
+            self.in_dim = 1280
+        else:
+            self.in_dim = in_dim
+        
+        # Calculate actual final node dimension based on whether EGNN is used
+        if self.use_egnn:
+            self.final_node_dim = self.node_dims[-1]
+        else:
+            self.final_node_dim = self.node_dims[0]
+            self.concat = False
+            self.addition = False
+
+        self.proj_layer = nn.Sequential(
+            nn.Linear(self.in_dim, self.base_node_dim),
+            get_activation(activation),
+            nn.Dropout(dropout),
+        )
+
+        if dihedral:
+            try:
+                self.dihedral_features = DihedralFeatures(self.base_node_dim)
+            except:
+                print("Warning: DihedralFeatures not found, skipping dihedral features")
+                self.dihedral = False
+
+        self.egnn_layers = nn.ModuleList()
+        if self.use_egnn:
+            for i in range(len(self.node_dims) - 1):
+                self.egnn_layers.append(
+                    EGNNLayer(
+                        input_nf=self.node_dims[i],
+                        output_nf=self.node_dims[i+1],
+                        hidden_nf=self.node_dims[i+1],
+                        edges_in_d=edge_dim,
+                        act_fn=get_activation(activation),
+                        residual=residual,
+                        attention=attention,
+                        normalize=normalize,
+                        coords_agg=coords_agg,
+                        dropout=dropout,
+                        ffn=ffn,
+                        batch_norm=batch_norm
+                    )
+                )
+
+        if concat and self.use_egnn:
+            self.final_node_dim += self.node_dims[0]
+            
+        if addition and self.use_egnn:
+            assert self.node_dims[0] == self.node_dims[-1], "Node dimension mismatch for addition"
+            self.final_node_dim = self.node_dims[0]
+            
+        # Calculate node classifier input dimension based on fusion type
+        if fusion_type == 'concat':
+            self.node_classifier_input_dim = self.final_node_dim * 2
+        elif fusion_type == 'add':
+            self.node_classifier_input_dim = self.final_node_dim
+        else:
+            raise ValueError(f"Unsupported fusion type: {fusion_type}")
+        
+        # Calculate node gate input dimension
+        if node_gate:
+            if fusion_type == 'concat':
+                self.node_gate_input_dim = self.final_node_dim * 2
+            elif fusion_type == 'add':
+                self.node_gate_input_dim = self.final_node_dim
+            else:
+                raise ValueError(f"Unsupported fusion type: {fusion_type}")
+
+        if pooling == 'attention':
+            self.graph_pool = AttentionPooling(
+                input_dim=self.final_node_dim,
+                dropout=dropout,
+                activation=activation
+            )
+        elif pooling == 'add':
+            self.graph_pool = AddPooling(
+                input_dim=self.final_node_dim,
+                dropout=dropout
+            )
+        else:
+            raise ValueError(f"Unsupported pooling method: {pooling}")
+
+        self.global_predictor = nn.Sequential(
+            nn.Linear(self.final_node_dim, self.final_node_dim // 2),
+            get_activation(activation),
+            nn.Dropout(out_dropout),
+            nn.Linear(self.final_node_dim // 2, 1)
+        )
+
+        if node_gate:
+            self.node_gate = nn.Sequential(
+                nn.Linear(self.node_gate_input_dim, self.final_node_dim),
+                get_activation(activation),
+                nn.LayerNorm(self.final_node_dim),
+                nn.Linear(self.final_node_dim, self.final_node_dim),
+                nn.Sigmoid()
+            )
+
+        self.node_classifier = self._build_node_classifier()
+
+        self._param_printed = False
+        self.apply(self._init_weights)
+
+    def _build_node_classifier(self):
+        layers = []
+        input_dim = self.node_classifier_input_dim
+        current_dim = input_dim
+        for i in range(self.node_layers):
+            output_dim = 1 if i == self.node_layers - 1 else max(current_dim // 2, 32)
+            layers.append(nn.Linear(current_dim, output_dim))
+            if self.node_norm and i < self.node_layers - 1:
+                layers.append(nn.LayerNorm(output_dim))
+            if i < self.node_layers - 1:
+                layers.append(get_activation(self.activation))
+                layers.append(nn.Dropout(self.out_dropout))
+            current_dim = output_dim
+        return nn.Sequential(*layers)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            nn.init.xavier_normal_(module.weight)
+            if module.bias is not None:
+                nn.init.constant_(module.bias, 0.0 if module.out_features == 1 else 0.01)
+        elif isinstance(module, nn.LayerNorm):
+            nn.init.ones_(module.weight)
+            nn.init.zeros_(module.bias)
+
+    def forward(self, data: Batch) -> dict:
+        if self.training and not self._param_printed:
+            print(f"ReCEP total params: {sum(p.numel() for p in self.parameters()):,}")
+            self._param_printed = True
+
+        x = data.x
+    
+        coords = data.pos
+        batch = data.batch
+        e_attr = data.edge_attr
+        coords_C = coords[:, 1].clone()
+
+        x = self.proj_layer(x)
+        if self.dihedral and coords is not None:
+            x = x + self.dihedral_features(coords)
+        if self.rsa and data.rsa is not None:
+            rsa = data.rsa.unsqueeze(-1)
+            x = torch.cat([x, rsa], dim=-1)
+
+        h = x
+        assert h.shape[1] == self.node_dims[0], f"[ReCEP] Node feature dim mismatch: got {h.shape[1]}, expected {self.node_dims[0]}"
+
+        if self.use_egnn:
+            for layer in self.egnn_layers:
+                h, coords_C, _ = layer(h, coords_C, data.edge_index, batch, edge_attr=e_attr)
+
+        if self.concat and self.use_egnn:
+            h = torch.cat([x, h], dim=-1)
+        elif self.addition and self.use_egnn:
+            h = h + x
+
+        graph_feats = self.graph_pool(h, batch)
+        global_pred = self.global_predictor(graph_feats).squeeze(-1)
+
+        context = graph_feats[batch]
+        if self.node_gate and hasattr(self, 'node_gate'):
+            if self.fusion_type == 'concat':
+                gate_input = torch.cat([h, context], dim=-1)
+            elif self.fusion_type == 'add':
+                gate_input = h + context
+            else:
+                raise ValueError(f"Unsupported fusion type: {self.fusion_type}")
+            gate = self.node_gate(gate_input)
+            gated_h = h + gate * h
+        else:
+            gated_h = h
+
+        if self.fusion_type == 'concat':
+            cat = torch.cat([gated_h, context], dim=-1)
+        elif self.fusion_type == 'add':
+            # Ensure dimensions match for addition
+            assert gated_h.shape[-1] == context.shape[-1], f"[ReCEP] Dimension mismatch for add fusion: gated_h {gated_h.shape[-1]} vs context {context.shape[-1]}"
+            cat = gated_h + context
+        else:
+            raise ValueError(f"Unsupported fusion type: {self.fusion_type}")
+        
+        # Verify input dimension matches node classifier expectation
+        expected_dim = self.node_classifier_input_dim
+        actual_dim = cat.shape[-1]
+        assert actual_dim == expected_dim, f"[ReCEP] Node classifier input dim mismatch: got {actual_dim}, expected {expected_dim}"
+        
+        node_preds = self.node_classifier(cat).squeeze(-1)
+
+        return {"global_pred": global_pred, "node_preds": node_preds}
+    
+    def print_param_count(self):
+        """Print a summary table of parameter counts"""
+        table = PrettyTable()
+        table.field_names = ["Layer Name", "Type", "Parameters", "Trainable"]
+        total_params = 0
+        trainable_params = 0
+
+        for name, module in self.named_modules():
+            if not list(module.children()):  # Only leaf nodes
+                params = sum(p.numel() for p in module.parameters())
+                is_trainable = any(p.requires_grad for p in module.parameters())
+                
+                if params > 0:
+                    total_params += params
+                    trainable_params += params if is_trainable else 0
+                    
+                    table.add_row([
+                        name,
+                        module.__class__.__name__,
+                        f"{params:,}",
+                        "✓" if is_trainable else "✗"
+                    ])
+
+        table.add_row(["", "", "", ""], divider=True)
+        table.add_row([
+            "TOTAL", 
+            "", 
+            f"{total_params:,}",
+            f"Trainable: {trainable_params:,}"
+        ])
+
+        print("\nReCEP Model Parameter Summary:")
+        print(table)
+        print(f"Parameter Density: {trainable_params/total_params:.1%}\n")
+    
+    def save(self, path, threshold: float = 0.5):
+        """Save model with configuration"""
+        path = Path(path)
+        try:
+            path.parent.mkdir(parents=True, exist_ok=True)
+            save_path = path.with_suffix('.bin')
+            
+            config = self.get_config()
+            # config = {
+            #     'in_dim': self.in_dim,
+            #     'rsa': self.rsa,
+            #     'dihedral': self.dihedral,
+            #     'node_dims': self.original_node_dims,  # Use original node_dims
+            #     'edge_dim': self.edge_dim,
+            #     'dropout': self.dropout,
+            #     'activation': self.activation,
+            #     'residual': self.residual,
+            #     'attention': self.attention,
+            #     'normalize': self.normalize,
+            #     'coords_agg': self.coords_agg,
+            #     'ffn': self.ffn,
+            #     'batch_norm': self.batch_norm,
+            #     'concat': self.concat,
+            #     'node_norm': self.node_norm,
+            #     'node_layers': self.node_layers,
+            #     'node_gate': self.node_gate,
+            #     'out_dropout': self.out_dropout
+            # }
+            
+            torch.save({
+                'model_state': self.state_dict(),
+                'config': config,
+                'model_class': self.__class__.__name__,
+                'version': '1.0',
+                'threshold': threshold
+            }, save_path)
+            print(f"ReCEP model saved to {save_path}")
+        except Exception as e:
+            print(f"Save failed: {str(e)}")
+            raise
+    
+    @classmethod
+    def load(cls, path, device='cpu', strict=True, verbose=True):
+        """Load model with configuration"""
+        path = Path(path)
+        if not path.exists():
+            raise FileNotFoundError(f"Model file {path} not found")
+
+        try:
+            if isinstance(device, str):
+                device = torch.device(device)
+            elif isinstance(device, int):
+                if device >= 0 and torch.cuda.is_available():
+                    device = torch.device(f'cuda:{device}')
+                else:
+                    device = torch.device('cpu')
+            elif not isinstance(device, torch.device):
+                raise ValueError(f"Unsupported device type: {type(device)}")
+            
+            checkpoint = torch.load(
+                path,
+                map_location=device,
+                weights_only=False
+            )
+        except RuntimeError:
+            print("Warning: Using unsafe load due to weights_only restriction")
+            checkpoint = torch.load(path, map_location=device)
+
+        # Version compatibility check
+        if 'version' not in checkpoint:
+            print("Warning: Loading legacy model without version info")
+        
+        # Rebuild configuration
+        config = checkpoint.get('config', {})
+        model = cls(**config)
+        
+        # Load state dict
+        model_state = checkpoint['model_state']
+        current_state = model.state_dict()
+        
+        # Auto-match parameters
+        matched_state = {}
+        for name, param in model_state.items():
+            if name in current_state:
+                if param.shape == current_state[name].shape:
+                    matched_state[name] = param
+                else:
+                    print(f"Size mismatch: {name} (load {param.shape} vs model {current_state[name].shape})")
+            else:
+                print(f"Parameter not found: {name}")
+        
+        current_state.update(matched_state)
+        model.load_state_dict(current_state, strict=strict)
+        
+        if verbose:
+            print(f"Successfully loaded {len(matched_state)}/{len(model_state)} parameters")
+        
+        return model.to(device), checkpoint.get('threshold', 0.5)
+    
+    def get_config(self):
+        """Get model configuration"""
+        return {
+            'in_dim': self.in_dim,
+            'rsa': self.rsa,
+            'dihedral': self.dihedral,
+            'node_dims': self.original_node_dims,
+            'edge_dim': self.edge_dim,
+            'dropout': self.dropout,
+            'activation': self.activation,
+            'residual': self.residual,
+            'attention': self.attention,
+            'normalize': self.normalize,
+            'coords_agg': self.coords_agg,
+            'ffn': self.ffn,
+            'batch_norm': self.batch_norm,
+            'concat': self.concat,
+            'addition': self.addition,
+            'pooling': self.pooling,
+            'fusion_type': self.fusion_type,
+            'node_gate': self.node_gate,
+            'node_norm': self.node_norm,
+            'node_layers': self.node_layers,
+            'out_dropout': self.out_dropout,
+            'use_egnn': self.use_egnn,
+            'encoder': self.encoder
+        }
+        
+        
+model_registry = {
+    "ReCEP": ReCEP,
+}
+
+def get_model(configs):
+    """
+    Flexible model loader. Accepts either an argparse.Namespace or a dict.
+    Returns an instance of the selected model.
+    """
+    # Support both argparse.Namespace and dict
+    if hasattr(configs, '__dict__'):
+        args = vars(configs)
+    else:
+        args = configs
+    
+    # Default to ReCEP if no model specified
+    model_name = args.get('model', 'ReCEP')
+    
+    if model_name not in model_registry:
+        valid_models = list(model_registry.keys())
+        raise ValueError(f"Invalid model type: {model_name}. Must be one of: {valid_models}")
+    
+    model_class = model_registry[model_name]
+    
+    # Use inspect to get the model's __init__ parameters
+    import inspect
+    init_signature = inspect.signature(model_class.__init__)
+    parameters = init_signature.parameters
+    
+    # Build model configuration from args
+    model_config = {}
+    for param_name, param in parameters.items():
+        if param_name == 'self':
+            continue
+        if param_name in args:
+            model_config[param_name] = args[param_name]
+        elif param.default is not param.empty:
+            model_config[param_name] = param.default
+        else:
+            print(f"[WARNING] Required parameter '{param_name}' not found in args and has no default value")
+    
+    # print(f"[INFO] Creating {model_name} model with config: {list(model_config.keys())}")
+    model = model_class(**model_config)
+    return model
\ No newline at end of file
diff --git a/src/bce/model/__init__.py b/src/bce/model/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..25801aafb7c297812149a6c9ba435fbf2f1a73e2
--- /dev/null
+++ b/src/bce/model/__init__.py
@@ -0,0 +1,11 @@
+"""
+BCE Model Module
+
+This module contains various neural network models for B-cell epitope prediction.
+"""
+
+# from .ReCEP import ReCEP
+# from .EGNN import EGNNLayer
+# from .dihedral import DihedralFeatures
+
+# __all__ = ['ReCEP', 'EGNNLayer', 'DihedralFeatures'] 
\ No newline at end of file
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diff --git a/src/bce/model/__pycache__/train_model.cpython-310.pyc b/src/bce/model/__pycache__/train_model.cpython-310.pyc
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diff --git a/src/bce/model/activation.py b/src/bce/model/activation.py
new file mode 100644
index 0000000000000000000000000000000000000000..be9dac0647897a41e7736abcd3d4c47235527aef
--- /dev/null
+++ b/src/bce/model/activation.py
@@ -0,0 +1,16 @@
+import torch.nn as nn
+
+def get_activation(activation: str) -> nn.Module:
+    """Get activation function by name."""
+    if activation == "relu":
+        return nn.ReLU()
+    elif activation == "leaky_relu":
+        return nn.LeakyReLU(negative_slope=0.2)
+    elif activation == "gelu":
+        return nn.GELU()
+    elif activation == "silu":
+        return nn.SiLU()
+    elif activation == "tanh":
+        return nn.Tanh()
+    else:
+        raise ValueError(f"Unsupported activation function: {activation}")
\ No newline at end of file
diff --git a/src/bce/model/baseline.py b/src/bce/model/baseline.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/bce/model/dihedral.py b/src/bce/model/dihedral.py
new file mode 100644
index 0000000000000000000000000000000000000000..bde66c138023530ba521ebb5b7967e1e02d746f1
--- /dev/null
+++ b/src/bce/model/dihedral.py
@@ -0,0 +1,75 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class DihedralFeatures(nn.Module):
+    def __init__(self, node_embed_dim):
+        """ Embed dihedral angle features. """
+        super(DihedralFeatures, self).__init__()
+        # 3 dihedral angles; sin and cos of each angle
+        node_in = 6
+        # Normalization and embedding
+        self.node_embedding = nn.Linear(node_in,  node_embed_dim, bias=True)
+        self.norm_nodes = Normalize(node_embed_dim)
+
+    def forward(self, X):
+        """ Featurize coordinates as an attributed graph """
+        with torch.no_grad():
+            V = self._dihedrals(X)
+        V = V.squeeze(1)
+        V = self.node_embedding(V)
+        V = self.norm_nodes(V)
+        return V
+
+    @staticmethod
+    def _dihedrals(X, eps=1e-7,):
+        # First 3 coordinates are [N, CA, C] / [C4', C1', N1/N9]
+        if len(X.shape) == 4:
+            X = X[..., :3, :].reshape(X.shape[0], 3*X.shape[1], 3)
+        else:
+            X = X[:, :3, :]
+
+        # Shifted slices of unit vectors
+        dX = X[:,1:,:] - X[:,:-1,:]
+        U = F.normalize(dX, dim=-1)
+        u_2 = U[:,:-2,:]
+        u_1 = U[:,1:-1,:]
+        u_0 = U[:,2:,:]
+        # Backbone normals
+        n_2 = F.normalize(torch.cross(u_2, u_1, dim=-1), dim=-1)
+        n_1 = F.normalize(torch.cross(u_1, u_0, dim=-1), dim=-1)
+
+        # Angle between normals
+        cosD = (n_2 * n_1).sum(-1)
+        cosD = torch.clamp(cosD, -1+eps, 1-eps)
+        D = torch.sign((u_2 * n_1).sum(-1)) * torch.acos(cosD)
+
+        # This scheme will remove phi[0], psi[-1], omega[-1]
+        D = F.pad(D, (1,2), 'constant', 0)
+        D = D.view((D.size(0), int(D.size(1)/3), 3))
+
+        # Lift angle representations to the circle
+        D_features = torch.cat((torch.cos(D), torch.sin(D)), 2)
+        return D_features
+
+
+class Normalize(nn.Module):
+    def __init__(self, features, epsilon=1e-6):
+        super(Normalize, self).__init__()
+        self.gain = nn.Parameter(torch.ones(features))
+        self.bias = nn.Parameter(torch.zeros(features))
+        self.epsilon = epsilon
+
+    def forward(self, x, dim=-1):
+        mu = x.mean(dim, keepdim=True)
+        sigma = torch.sqrt(x.var(dim, keepdim=True) + self.epsilon)
+        gain = self.gain
+        bias = self.bias
+        # Reshape
+        if dim != -1:
+            shape = [1] * len(mu.size())
+            shape[dim] = self.gain.size()[0]
+            gain = gain.view(shape)
+            bias = bias.view(shape)
+        return gain * (x - mu) / (sigma + self.epsilon) + bias
\ No newline at end of file
diff --git a/src/bce/model/pooling.py b/src/bce/model/pooling.py
new file mode 100644
index 0000000000000000000000000000000000000000..22d79141be9b64ebb43d32372c307117d20c2a14
--- /dev/null
+++ b/src/bce/model/pooling.py
@@ -0,0 +1,48 @@
+from torch_scatter import scatter_softmax, scatter_sum
+import torch
+import torch.nn as nn
+
+from .activation import get_activation
+
+class AttentionPooling(nn.Module):
+    """Attention-based pooling layer supporting batched graphs."""
+    def __init__(self, input_dim: int, dropout: float = 0.2, activation: str = 'gelu'):
+        super().__init__()
+        self.attention = nn.Sequential(
+            nn.Linear(input_dim, input_dim // 2),
+            get_activation(activation),
+            nn.Dropout(dropout),
+            nn.Linear(input_dim // 2, 1)
+        )
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x: torch.Tensor, batch: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            x:     (N, input_dim) Node features from multiple graphs
+            batch: (N,) Graph ID per node
+        Returns:
+            (num_graphs, input_dim) Pooled graph features
+        """
+        attn_logits = self.attention(x).squeeze(-1)       # (N,)
+        attn_weights = scatter_softmax(attn_logits, batch)  # (N,)
+        weighted_x = x * attn_weights.unsqueeze(-1)       # (N, D)
+        pooled = scatter_sum(weighted_x, batch, dim=0)    # (num_graphs, D)
+        return self.dropout(pooled)
+
+class AddPooling(nn.Module):
+    """Simple addition-based pooling layer supporting batched graphs."""
+    def __init__(self, input_dim: int, dropout: float = 0.2):
+        super().__init__()
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x: torch.Tensor, batch: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            x:     (N, input_dim) Node features from multiple graphs
+            batch: (N,) Graph ID per node
+        Returns:
+            (num_graphs, input_dim) Pooled graph features
+        """
+        pooled = scatter_sum(x, batch, dim=0)    # (num_graphs, input_dim)
+        return self.dropout(pooled)
\ No newline at end of file
diff --git a/src/bce/model/scheduler.py b/src/bce/model/scheduler.py
new file mode 100644
index 0000000000000000000000000000000000000000..558841eacdd42125f68722fbc8af2f0f384c7e69
--- /dev/null
+++ b/src/bce/model/scheduler.py
@@ -0,0 +1,187 @@
+import math
+from typing import Dict, Any, Union, Optional
+from torch.optim import Optimizer
+from torch.optim.lr_scheduler import _LRScheduler, CosineAnnealingLR, StepLR, ExponentialLR, CosineAnnealingWarmRestarts, OneCycleLR
+
+
+class AutoScheduler(_LRScheduler):
+    """
+    Automatic learning rate scheduler with warmup and configurable main schedule.
+    """
+    
+    # Default parameters for different scheduler types
+    DEFAULT_PARAMS = {
+        "cosine": {"eta_min": 1e-6},
+        "cosine_restart": {"T_mult": 2, "eta_min": 1e-6, "rounds": 5},
+        "step": {"gamma": 0.5, "decay_steps": 3},
+        "exponential": {"gamma": 0.95},
+        "one_cycle": {"lr_mult": 10.0, "div_factor": 25.0, "final_div_factor": 1e4}
+    }
+    
+    def __init__(
+        self,
+        optimizer: Optimizer,
+        total_steps: int,
+        scheduler_type: str = "cosine_restart",
+        warmup_ratio: float = 0.1,
+        warmup_type: str = "linear",
+        **kwargs
+    ):
+        self.scheduler_type = scheduler_type
+        self.warmup_type = warmup_type
+        self.warmup_ratio = warmup_ratio
+        self.total_steps = total_steps
+        self.warmup_steps = max(1, int(total_steps * warmup_ratio))
+        self.current_step = 0
+        self._is_warmup = True
+        
+        # Merge default parameters with user-provided kwargs
+        self.params = self._get_merged_params(kwargs)
+        
+        # Validate parameters
+        self._validate_parameters()
+        
+        # Create the main scheduler BEFORE calling super().__init__
+        # This is needed because super().__init__ will call step() immediately
+        self.after_scheduler = None  # Initialize as None first
+        
+        # Initialize parent class
+        super().__init__(optimizer)
+        
+        # Now create the main scheduler
+        self.after_scheduler = self._create_main_scheduler()
+
+    def _get_merged_params(self, user_kwargs: Dict) -> Dict:
+        """Merge default parameters with user-provided parameters."""
+        defaults = self.DEFAULT_PARAMS.get(self.scheduler_type, {}).copy()
+        defaults.update(user_kwargs)
+        return defaults
+
+    def _validate_parameters(self):
+        """Validate scheduler parameters."""
+        if self.warmup_type not in ["linear", "exponential"]:
+            raise ValueError(f"Invalid warmup type: {self.warmup_type}")
+        if self.scheduler_type not in self.DEFAULT_PARAMS:
+            raise ValueError(f"Unsupported scheduler type: {self.scheduler_type}")
+
+    def _create_main_scheduler(self) -> _LRScheduler:
+        """Create the main scheduler after warmup."""
+        remaining_steps = self.total_steps - self.warmup_steps
+        
+        if self.scheduler_type == "cosine":
+            return CosineAnnealingLR(
+                self.optimizer, 
+                T_max=remaining_steps,
+                eta_min=self.params["eta_min"]
+            )
+        
+        elif self.scheduler_type == "cosine_restart":
+            T_0 = max(1, remaining_steps // self.params["rounds"])
+            return CosineAnnealingWarmRestarts(
+                self.optimizer,
+                T_0=T_0,
+                T_mult=self.params["T_mult"],
+                eta_min=self.params["eta_min"]
+            )
+        
+        elif self.scheduler_type == "step":
+            step_size = max(1, remaining_steps // self.params["decay_steps"])
+            return StepLR(
+                self.optimizer,
+                step_size=step_size,
+                gamma=self.params["gamma"]
+            )
+        
+        elif self.scheduler_type == "exponential":
+            return ExponentialLR(
+                self.optimizer,
+                gamma=self.params["gamma"]
+            )
+        
+        elif self.scheduler_type == "one_cycle":
+            # Get base learning rates safely
+            base_lrs = getattr(self, 'base_lrs', [group['lr'] for group in self.optimizer.param_groups])
+            return OneCycleLR(
+                self.optimizer,
+                max_lr=[base_lr * self.params["lr_mult"] for base_lr in base_lrs],
+                total_steps=self.total_steps,
+                pct_start=self.warmup_ratio,
+                anneal_strategy='cos',
+                div_factor=self.params["div_factor"],
+                final_div_factor=self.params["final_div_factor"]
+            )
+
+    def get_lr(self):
+        """Get current learning rate."""
+        if self._is_warmup:
+            progress = min(1.0, self.current_step / self.warmup_steps)
+            if self.warmup_type == "linear":
+                factor = progress
+            else:  # exponential
+                factor = math.exp(progress * math.log(100)) / 100
+            return [base_lr * factor for base_lr in self.base_lrs]
+        
+        # Return base learning rates if after_scheduler is not yet created
+        if self.after_scheduler is None:
+            return self.base_lrs
+        
+        return self.after_scheduler.get_last_lr()
+
+    def step(self):
+        """Step the scheduler."""
+        self.current_step += 1
+        if self._is_warmup and self.current_step >= self.warmup_steps:
+            self._is_warmup = False
+        
+        if self._is_warmup:
+            super().step()
+        else:
+            # Only step the after_scheduler if it's been created
+            if self.after_scheduler is not None:
+                self.after_scheduler.step()
+
+
+def get_scheduler(args, optimizer, num_samples):
+    """
+    Create a learning rate scheduler from training arguments.
+    
+    Args:
+        args: Training arguments object containing scheduler configuration
+              Expected attributes:
+              - batch_size: Training batch size
+              - num_epoch: Number of training epochs
+              - scheduler_type: Type of scheduler (default: 'cosine_restart')
+              - warmup_ratio: Warmup ratio (default: 0.1)
+              - warmup_type: Warmup type (default: 'linear')
+              - eta_min, T_mult, rounds, gamma, decay_steps: Optional scheduler-specific params
+        optimizer: PyTorch optimizer
+        num_samples: Number of training samples
+        
+    Returns:
+        AutoScheduler instance
+        
+    Example:
+        # In any trainer class:
+        self.optimizer = optim.AdamW(model.parameters(), lr=args.lr)
+        self.scheduler = get_scheduler(args, self.optimizer, len(dataset))
+        
+        # During training:
+        self.scheduler.step()
+    """
+    # Extract scheduler-specific parameters from args
+    scheduler_kwargs = {}
+    for param in ['eta_min', 'T_mult', 'rounds', 'gamma', 'decay_steps']:
+        if hasattr(args, param):
+            scheduler_kwargs[param] = getattr(args, param)
+    
+    # Calculate total steps and create scheduler
+    total_steps = math.ceil(num_samples / args.batch_size) * args.num_epoch
+    
+    return AutoScheduler(
+        optimizer=optimizer,
+        total_steps=total_steps,
+        scheduler_type=args.scheduler_type,
+        warmup_ratio=getattr(args, 'warmup_ratio', 0.1),
+        warmup_type=getattr(args, 'warmup_type', 'linear'),
+        **scheduler_kwargs
+    )
\ No newline at end of file
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diff --git a/src/bce/utils/constants.py b/src/bce/utils/constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..85330121dbdab8554d61ddd0fd56306dbce8ca6a
--- /dev/null
+++ b/src/bce/utils/constants.py
@@ -0,0 +1,7 @@
+from pathlib import Path
+
+BASE_DIR = Path(__file__).resolve().parents[3]  # Root directory of the project
+EMBEDDING_DIR = BASE_DIR / "PDB_db/embeddings"
+
+DISK_DIR = Path("/disk18T3/Yitao/project_1")
+FULL_EMBEDDING_DIR = DISK_DIR / "embeddings/protein"
diff --git a/src/bce/utils/data_tools.py b/src/bce/utils/data_tools.py
new file mode 100644
index 0000000000000000000000000000000000000000..61bbfcc1a044cc58abc0ab36486831c0a635f0d6
--- /dev/null
+++ b/src/bce/utils/data_tools.py
@@ -0,0 +1,2134 @@
+import os
+import subprocess
+
+from scipy.spatial import distance
+import numpy as np
+import pandas as pd
+
+from Bio.Blast import NCBIXML
+from pathlib import Path
+import sys
+from Bio import SeqIO
+from Bio.PDB import PDBParser, PDBIO, PPBuilder, Select
+from Bio.PDB.Polypeptide import is_aa
+from Bio.PDB.PDBExceptions import PDBConstructionWarning
+from biopandas.pdb import PandasPdb
+
+import warnings
+from tqdm import tqdm
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from datetime import datetime
+import re
+import requests
+import pickle
+import tempfile
+
+from collections import defaultdict
+import shutil
+
+
+
+# Suppress warnings from the PDB parser
+warnings.simplefilter('ignore', PDBConstructionWarning)
+
+# 0. Some tools for data processing
+def get_pdb_release_date_and_resolution(pdb_id, dir="PDB_db", verbose=True):
+    """
+    Retrieve PDB resolution and release date, prioritizing local file data if available.
+
+    Parameters:
+        pdb_id (str): The PDB ID (e.g., '101m').
+        pdb_path (str): Optional path to the local PDB file.
+
+    Returns:
+        tuple: (release_date, resolution) where:
+            - release_date (str or None): Release date in 'YYYY-MM-DD' format or None.
+            - resolution (float or None): Resolution in Ångströms or None.
+    """
+    release_date = None
+    resolution = None
+
+    # Try to get the data from the local PDB file
+    pdb_path = f"{dir}/structs/{pdb_id.upper()}.pdb"
+    try:
+        with open(pdb_path, "r") as file:
+            for line in file:
+                    # Extract release date from the HEADER line
+                if line.startswith("HEADER"):
+                    date_str = line[50:59].strip()  # Extract the date (e.g., '15-FEB-94')
+                    release_date = pd.to_datetime(date_str, format='%d-%b-%y').strftime('%Y-%m-%d')
+
+                # Extract resolution from the REMARK line
+                if line.startswith("REMARK   2 RESOLUTION."):
+                    resolution = float(line.split()[3])
+
+                # Stop reading if both resolution and release date are found
+                if release_date and resolution and verbose:
+                    print(f"Data found locally: Release Date = {release_date}, Resolution = {resolution} Å")
+                    return release_date, resolution
+    except Exception as e:
+        print(f"Error reading data from local file {pdb_path}: {e}")
+
+    # If not available locally, fetch the data from the RCSB PDB API
+    url = f"https://data.rcsb.org/rest/v1/core/entry/{pdb_id}"
+    try:
+        response = requests.get(url)
+        response.raise_for_status()
+        data = response.json()
+
+        # Extract release date and resolution from API response
+        release_date = data.get('rcsb_accession_info', {}).get('initial_release_date', None)
+        resolution = data.get('rcsb_entry_info', {}).get('resolution_combined', [None])[0]
+
+        # Format release date if available
+        release_date = release_date.split("T")[0] if release_date else None
+
+        if verbose and release_date and resolution:
+            print(f"Data fetched from RCSB API: Release Date = {release_date}, Resolution = {resolution} Å")
+        return release_date, resolution
+
+    except requests.exceptions.RequestException as e:
+        print(f"Error fetching data for {pdb_id}: {e}")
+        return None, None
+    
+def getxyz(df):
+    """Extracts the (x, y, z) coordinates from a DataFrame."""
+    return np.array([df["x_coord"], df["y_coord"], df["z_coord"]]).T
+
+def aa_3_to_1(resn):
+    """Convert three-letter amino acid codes to one-letter codes."""
+    d = {'CYS': 'C', 'ASP': 'D', 'SER': 'S', 'GLN': 'Q', 'LYS': 'K',
+     'ILE': 'I', 'PRO': 'P', 'THR': 'T', 'PHE': 'F', 'ASN': 'N',
+     'GLY': 'G', 'HIS': 'H', 'LEU': 'L', 'ARG': 'R', 'TRP': 'W',
+     'ALA': 'A', 'VAL':'V', 'GLU': 'E', 'TYR': 'Y', 'MET': 'M'}
+    return d[resn]
+
+
+def kalign(seq1, seq2, dir="PDB_db"):
+    """
+    Align two sequences using kalign, ensuring unique temporary files for multithreaded safety.
+    """
+    if not seq1 or not seq2:
+        print("Error: One or both sequences are empty.")
+        return "", ""
+    if len(seq1) < 5 or len(seq2) < 5:
+        print("Error: Sequences are too short for meaningful alignment.")
+        return "", ""
+
+    try:
+        # Create unique temporary FASTA file
+        with tempfile.NamedTemporaryFile(mode="w", dir=dir, delete=False, suffix=".fasta") as input_fasta:
+            input_fasta.write(f">1\n{seq1}\n>2\n{seq2}\n")
+            input_fasta_path = input_fasta.name
+
+        # Run kalign and capture output
+        result = subprocess.check_output(f"cat {input_fasta_path} | kalign -f fasta", shell=True)
+        alignment = result.decode("UTF-8").split("\n")
+
+        aligned_seqs = {}
+        current_id = None
+        for line in alignment:
+            if line.startswith(">"):
+                current_id = line[1:]
+                aligned_seqs[current_id] = []
+            elif current_id:
+                aligned_seqs[current_id].append(line.strip())
+
+        seq1_aligned = "".join(aligned_seqs.get("1", []))
+        seq2_aligned = "".join(aligned_seqs.get("2", []))
+
+        if not seq1_aligned or not seq2_aligned:
+            print("Error: Kalign alignment failed. Check input sequences.")
+            return "", ""
+
+        return seq1_aligned, seq2_aligned
+    except subprocess.CalledProcessError as e:
+        print(f"Error running kalign: {e}")
+        return "", ""
+    finally:
+        # Clean up temporary file
+        if os.path.exists(input_fasta_path):
+            os.remove(input_fasta_path)
+
+def mafft_align(s1, s2, strict=True):
+    """Align two sequences using MAFFT."""
+    with open("m.fasta", 'w') as fo:
+        fo.write(f">1\n{s1}\n>2\n{s2}\n")
+    if not strict:
+        d = subprocess.check_output("mafft --anysymbol --op 0.1 m.fasta", shell=True)
+    else:
+        d = subprocess.check_output("mafft --anysymbol --auto m.fasta", shell=True)
+
+    res_ = d.decode("UTF-8").split("\n")
+    res = []
+    for l in res_:
+        if len(l) == 0:
+            continue
+        if l[0] == ">":
+            res.append("")
+            continue
+        res[-1] += l.rstrip()
+    return res
+
+def filter_chains_by_resolution(input_csv, output, dir="PDB_db", resolution_threshold=3.0):
+    """
+    Filters chains based on PDB resolution, saves chains with resolution < threshold to a text file,
+    saves a CSV of rows passing the filter, and returns the list of remaining chains.
+
+    Args:
+        input_csv (str): Path to the input CSV file.
+        output_txt (str): Path to save chains with resolution < threshold.
+        dir (str): Directory containing PDB files.
+        resolution_threshold (float): Resolution threshold (default: 3.0 Å).
+
+    Returns:
+        list: List of chains with resolution < threshold.
+    """
+    input_csv_path = Path(f"{dir}/{input_csv}")
+    data = pd.read_csv(input_csv_path)
+
+    # Store resolution for each row
+    resolutions = []
+    for _, row in tqdm(data.iterrows(), total=len(data), desc="Filtering by Resolution"):
+        pdb_chain = row["antigen_chain"]
+        pdb_id = pdb_chain.split("_")[0]
+        res = get_pdb_release_date_and_resolution(pdb_id, dir)[1]
+        resolutions.append(res)
+
+    data["Resolution"] = resolutions
+    filtered_data = data[(data["Resolution"].notnull()) & (data["Resolution"] <= resolution_threshold)]
+    remaining_chains = filtered_data["antigen_chain"].tolist()
+
+    # Save filtered chains to txt
+    output_txt_path = Path(dir) / f"{output}_{resolution_threshold}.txt"
+    with open(output_txt_path, mode="w") as txtfile:
+        txtfile.write("\n".join(remaining_chains))
+    print(f"Chains with resolution < {resolution_threshold} Å saved to {output_txt_path}.")
+
+    # Also save filtered CSV
+    filtered_csv_path = Path(dir) / f"{output}_{resolution_threshold}.csv"
+    filtered_data.to_csv(filtered_csv_path, index=False)
+    print(f"Filtered CSV saved to {filtered_csv_path}.")
+
+    print(f"Remaining chains: {len(remaining_chains)} with resolution < {resolution_threshold} Å.\n")
+    return remaining_chains
+
+
+## 1. Fetch PDB sequences and generate blast DB (Download data in 2024/11/04)
+# Remark: it is very time-consuming to filter the data. 
+def fetch_seq_pdb_data(output_dir="PDB_db", date_filter=None):
+    """
+    Fetches PDB sequences and prepares a BLAST database. Optionally filters the dataset by date.
+
+    Parameters:
+        output_dir (str): The directory to store PDB files.
+        date_filter (str): Optional. Include only entries published after this date (format: YYYY-MM-DD).
+    """
+    if not os.path.exists(output_dir):
+        os.mkdir(output_dir)
+
+    pdb_seqres_path = os.path.join(output_dir, "pdb_seqres.txt")
+
+    # Download the PDB sequence database if not already downloaded
+    if not os.path.exists(pdb_seqres_path):
+        print("Downloading pdb_seqres.txt...")
+        subprocess.call("wget https://files.wwpdb.org/pub/pdb/derived_data/pdb_seqres.txt.gz", shell=True)
+        subprocess.call("gzip -d pdb_seqres.txt.gz", shell=True)
+        subprocess.call(f"mv pdb_seqres.txt {output_dir}/", shell=True)
+    else:
+        print("PDB sequence database already exists. Skipping download...")
+
+    # Create a BLAST database if not already prepared
+    if not os.path.exists(os.path.join(output_dir, "pdb_seqres.txt.psq")):
+        print("Creating BLAST database...")
+        subprocess.call(f"makeblastdb -in pdb_seqres.txt -dbtype prot -title pdb", shell=True, cwd=output_dir)
+
+    return pdb_seqres_path
+
+## 2. Preliminary screen for proteins in the PDB database with homology to fragment antigen-binding region
+def parse_blast_output(input_path, save_path="fab_hits.txt"):
+        """
+        Parse the BLAST XML output to extract PDB IDs with high alignment.
+
+        Parameters:
+            input_path (str): Path to the BLAST XML output file.
+
+        Returns:
+            set: A set of PDB IDs with high alignment scores.
+        """
+        print(f"Parsing BLAST output: {input_path}...")
+        with open(input_path, "r") as result:
+            records = NCBIXML.parse(result)
+            item = next(records)  # Retrieve the first BLAST record from the results
+            pdb_fabs = set()      # Set to store full PDB IDs with chain information
+            pdb_fabs_ = set()     # Set to store only PDB IDs without chain information
+            for alignment in item.alignments:
+                for hsp in alignment.hsps:
+                    # Extract PDB ID and chain from the alignment title
+                    pdb_id = alignment.title.split()[1]
+                    pdb_id_id = pdb_id.split("_")[0]  # Extract PDB ID without chain identifier
+                    pdb_fabs.add(pdb_id)               # Add full PDB ID with chain
+                    pdb_fabs_.add(pdb_id_id)           # Add PDB ID without chain
+            print(f"Found {len(pdb_fabs)} hits in {input_path}.\n")
+            return pdb_fabs
+        
+def screen_fab_sequences(output_dir = "PDB_db"):
+    """
+    Screen the PDB database for sequences homologous to fragment antigen-binding regions.
+
+    Writes light and heavy chain sequences to separate files, runs BLAST searches if needed, and parses the results.
+
+    Returns:
+        set: A combined set of PDB IDs matching light and heavy chains.
+    
+    Example: {"1ABC_A", "2XYZ_B", "1DEF_C"}
+    """
+    if not os.path.exists(output_dir):
+        os.mkdir(output_dir)
+    
+    save_path = os.path.join(output_dir, "fab_hits.txt")
+    if os.path.exists(save_path):
+        print(f"Loading existing PDB hits from {save_path}...")
+        with open(save_path, "r") as f:
+            pdb_fab_hits = {line.strip() for line in f}
+        print(f"Loaded {len(pdb_fab_hits)} PDB hits.\n")
+        return pdb_fab_hits
+
+    light = "DILLTQSPVILSVSPGERVSFSCRASQSIGTNIHWYQQRTNGSPRLLIKYASESISGIPSRFSGSGSGTDFTLSINSVESEDIADYYCQQNNNWPTTFGAGTKLELK"
+    print("Writing light chain sequence to fab_light.fasta...")
+    with open(os.path.join(output_dir, "fab_light.fasta"), 'w') as fo:
+        fo.write(">input_light\n")
+        fo.write(light)
+
+    heavy = "QVQLKQSGPGLVQPSQSLSITCTVSGFSLTNYGVHWVRQSPGKGLEWLGVIWSGGNTDYNTPFTSRLSINKDNSKSQVFFKMNSLQSNDTAIYYCARALTYYDYEFAYWGQGTLVTVSA"
+    print("Writing heavy chain sequence to fab_heavy.fasta...")
+    with open(os.path.join(output_dir, "fab_heavy.fasta"), 'w') as fo:
+        fo.write(">input_heavy\n")
+        fo.write(heavy)
+
+    # Run BLAST for light chain if results do not already exist
+    if not os.path.exists(os.path.join(output_dir, "hits_fabs_light.xml")):
+        print("Running BLAST search for light chain...")
+        subprocess.call("blastp -db pdb_seqres.txt -num_alignments 99999 -evalue 1e-9 -query fab_light.fasta -out hits_fabs_light.xml -outfmt 5", shell=True, cwd=output_dir)
+    else:
+        print("BLAST results for light chain already exist. Skipping search...")
+
+    # Run BLAST for heavy chain if results do not already exist
+    if not os.path.exists(os.path.join(output_dir, "hits_fabs_heavy.xml")):
+        print("Running BLAST search for heavy chain...")
+        subprocess.call("blastp -db pdb_seqres.txt -num_alignments 99999 -evalue 1e-9 -query fab_heavy.fasta -out hits_fabs_heavy.xml -outfmt 5", shell=True, cwd=output_dir)
+    else:
+        print("BLAST results for heavy chain already exist. Skipping search...")
+    
+    # Parse BLAST results for light and heavy chains
+    print("Parsing BLAST results for light chain...")
+    pdb_fab_hits_1 = parse_blast_output(os.path.join(output_dir, "hits_fabs_light.xml"))
+    print("Parsing BLAST results for heavy chain...")
+    pdb_fab_hits_2 = parse_blast_output(os.path.join(output_dir, "hits_fabs_heavy.xml"))
+
+    # Combine results from light and heavy chain BLAST searches
+    pdb_fab_hits = pdb_fab_hits_1 | pdb_fab_hits_2
+    print(f"Total unique PDB hits: {len(pdb_fab_hits)}")
+
+    if not os.path.exists(save_path):
+
+        with open(save_path, "w") as outfile:
+            for pdb_id in pdb_fab_hits:
+                outfile.write(f"{pdb_id}\n")
+        print(f"Saved PDB hits to {save_path}")
+    
+    return pdb_fab_hits
+
+## 3. Screen for heavy and light fab chains using ANARCI
+def load_fasta(path):
+        """
+        Load sequences from a FASTA file.
+
+        Parameters:
+            path (str): Path to the FASTA file.
+
+        Returns:
+            list: A list of [header, sequence] pairs.
+        """
+        print(f"Loading FASTA file: {path}...")
+        r = []
+        with open(path) as f:
+            for line in f:
+                if line[0] == ">":
+                    r.append([])
+                r[-1].append(line.rstrip())
+        r = [[r_[0], "".join(r_[1:])] for r_ in r]
+        print(f"Loaded {len(r)} sequences from {path}.")
+        return r
+
+def process_fab_chains(pdb_fab_hits, pdb_seqres_path="PDB_db/pdb_seqres.txt", output_dir="PDB_db"):
+    """
+    Process heavy and light Fab chains using ANARCI and filter sequences based on BLAST hits.
+
+    Parameters:
+        pdb_fab_hits (set): Set of PDB IDs matching Fab chains.
+        pdb_seqres_path (str): Path to the PDB sequence file.
+        output_dir (str): Directory for output files.
+
+    Returns:
+        None
+    """
+    # Load all PDB sequences
+    print("Loading PDB sequences...")
+    with open(pdb_seqres_path) as f:
+        r = []
+        for line in f:
+            if line[0] == ">":
+                r.append([])
+            r[-1].append(line)
+
+    # Filter sequences based on BLAST hits
+    print("Filtering sequences based on BLAST hits...")
+    rfabs = []
+    for r_ in r:
+        title = r_[0].split(" ")[0][1:]
+        if title not in pdb_fab_hits:
+            continue
+        rfabs.append([r_[0].split(" ")[0][1:], r_[1]])
+    print(f"Filtered {len(rfabs)} sequences matching BLAST hits.")
+
+    # Save filtered FAB sequences to a new FASTA file
+    filtered_fasta_path = os.path.join(output_dir, "putative_fabs.fasta")
+    print(f"Saving filtered sequences to {filtered_fasta_path}...")
+    if not os.path.exists(filtered_fasta_path):
+        with open(filtered_fasta_path, 'w') as fo:
+            for r in rfabs:
+                fo.write("".join([">" + r[0] + "\n", r[1]]) + "\n")
+    else:
+        print(f"{filtered_fasta_path} already exists. Skipping save.")
+
+    # Run ANARCI for heavy chains if not already done
+    heavy_anarci_path = os.path.join(output_dir, "all_fabs_heavy.anarci")
+    if not os.path.exists(heavy_anarci_path):
+        print("Running ANARCI for heavy chains...")
+        anarci_command = f"ANARCI -i putative_fabs.fasta -o all_fabs_heavy.anarci -s chothia -r ig --ncpu 8 --bit_score_threshold 100 --restrict heavy"
+        subprocess.call(anarci_command, shell=True, cwd=output_dir)
+    else:
+        print("ANARCI results for heavy chains already exist. Skipping ANARCI run...")
+
+    # Run ANARCI for light chains if not already done
+    light_anarci_path = os.path.join(output_dir, "all_fabs_light.anarci")
+    if not os.path.exists(light_anarci_path):
+        print("Running ANARCI for light chains...")
+        anarci_command = f"ANARCI -i putative_fabs.fasta -o all_fabs_light.anarci -s chothia -r ig --ncpu 8 --bit_score_threshold 100 --restrict light"
+        subprocess.call(anarci_command, shell=True, cwd=output_dir)
+    else:
+        print("ANARCI results for light chains already exist. Skipping ANARCI run...\n")
+
+## 4. Parse ANARCI output
+def parse_anarci_annotation(path="light.anarci", n=108, save_path=None):
+    """
+    Parse ANARCI annotation output to extract aligned amino acid sequences.
+
+    Parameters:
+        path (str): Path to the ANARCI output file.
+        n (int): Maximum sequence length for alignment positions.
+        save_path (str): Optional. Path to save the parsed results as a file.
+
+    Returns:
+        dict: Parsed alignment data where keys are sequence names and values are aligned residues.
+    """
+    # Check if the save_path exists and load it directly if it does
+    if save_path and os.path.exists(save_path):
+        print(f"Loading existing parsed results from {save_path}...")
+        out_ = {}
+        with open(save_path, "r") as f:
+            current_name = None
+            for line in f:
+                if line.startswith(">"):
+                    current_name = line[1:].strip()
+                    out_[current_name] = []
+                elif line.startswith("Position"):
+                    position_residue = line.split(": ")[-1].strip()
+                    out_[current_name].append(position_residue)
+        print(f"{len(out_)} sequences from ANARCI output.\n")
+        return out_
+
+    print(f"Parsing ANARCI output file: {path}...")
+    seqs = []
+    seqs.append([[] for _ in range(n)])
+    used = set()
+    data = {}
+
+    with open(path) as f:
+        w = f.readlines()
+        data = [[]]
+        for line in w:
+            data[-1].append(line)
+            if line[0] == "/":
+                data.append([])
+
+    out = {}
+    for d in data:
+        if len(d) == 0:
+            continue
+        name = d[0].rstrip().split()[-1]
+        if name in out:
+            continue
+        out[name] = [[] for _ in range(n)]
+        for d_ in d:
+            if d_[0] == "#" or d_[0] == "/":
+                continue
+            id_ = d_.split()[1]
+            id_ = int(id_)
+            if d_[10] == "-":
+                continue
+            out[name][id_].append(d_[10])
+
+    out_ = {name: ["".join(c) for c in alignment if c] for name, alignment in out.items() if any(alignment)}
+
+    # Save the parsed results to a file if required
+    if save_path:
+        with open(save_path, "w") as f:
+            for name, alignment in out_.items():
+                f.write(f">{name}\n")
+                for pos, residues in enumerate(alignment):
+                    if residues:
+                        f.write(f"Position {pos + 1}: {residues}\n")
+                f.write("\n")
+
+    print(f"Parsed {len(out_)} sequences from ANARCI output.")
+    return out_
+
+## 5. Fetch PDB structures and metadata
+def standardize_date_format(date_str):
+    """
+    Convert dates into a standard 'YYYY-MM-DD' format.
+
+    Parameters:
+        date_str (str): Date string in formats like '11-APR-22' or '2023-10-25'.
+
+    Returns:
+        str: Date in 'YYYY-MM-DD' format or None if invalid.
+    """
+    if not isinstance(date_str, str) or not date_str.strip():
+        return None  # Handle non-string or empty input
+    try:
+        if re.match(r"\d{2}-[A-Z]{3}-\d{2}", date_str):
+            return datetime.strptime(date_str, "%d-%b-%y").strftime("%Y-%m-%d")
+        elif re.match(r"\d{4}-\d{2}-\d{2}", date_str):
+            return datetime.strptime(date_str, "%Y-%m-%d").strftime("%Y-%m-%d")
+    except ValueError:
+        pass
+    return None
+
+def fetch_pdb_structures(pdb_fab_hits, anarci_list_light, anarci_list_heavy, dir="PDB_db"):
+    """
+    Fetch all PDB structures containing light and heavy chains for Fab complexes,
+    and store additional metadata (release date and resolution).
+
+    Parameters:
+        pdb_fab_hits (set): Set of PDB IDs matching Fab chains.
+        anarci_list_light (list): List of light chain IDs from ANARCI.
+        anarci_list_heavy (list): List of heavy chain IDs from ANARCI.
+        output_dir (str): Directory to store downloaded PDB structures.
+
+    Returns:
+        dict: A dictionary containing PDB metadata (release date, resolution, and chain details).
+    """
+
+    output_dir = os.path.join(dir, "structs")
+    save_metadata_path = os.path.join(dir, "metadata.csv")
+
+    if os.path.exists(save_metadata_path):
+        print(f"Structure data has been downloaded! Loading existing metadata from {save_metadata_path}...")
+        metadata_df = pd.read_csv(save_metadata_path, index_col=0)
+        metadata_df['release_date'] = metadata_df['release_date'].apply(standardize_date_format)
+        metadata_df['resolution'] = pd.to_numeric(metadata_df['resolution'], errors='coerce')
+        pdb_3 = metadata_df.to_dict(orient="index")
+        print(f"Loaded metadata for {len(pdb_3)} PDB entries.")
+        return pdb_3
+    
+    # Create a dictionary to store light and heavy chain information
+    pdb_3 = {r[:4]: {"light": [], "heavy": [], "release_date": None, "resolution": None} for r in pdb_fab_hits}
+
+    # Combine and populate light and heavy chains in a single loop
+    for chain_dict, chain_type in [(anarci_list_light, "light"), (anarci_list_heavy, "heavy")]:
+        for h in chain_dict.keys():  # Use the keys of the dictionary
+            h4 = h[:4]
+            if h4 in pdb_3:
+                pdb_3[h4][chain_type].append(h)
+
+    # Ensure the output directory exists
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    def download_and_extract_metadata(pdb_id):
+        """
+        Download a PDB structure and extract its metadata.
+
+        Parameters:
+            pdb_id (str): The PDB ID to process.
+
+        Returns:
+            tuple: The updated metadata for the PDB ID.
+        """
+        pdb_name = pdb_id.upper() + ".pdb.gz"
+        pdb_path = os.path.join(output_dir, pdb_name)
+        unzipped_path = pdb_path.rstrip(".gz")
+
+        # Skip if already downloaded and processed
+        if os.path.exists(pdb_path) or os.path.exists(unzipped_path):
+            release_date, resolution = get_pdb_release_date_and_resolution(pdb_id)
+            return pdb_id, standardize_date_format(release_date), resolution
+
+        with open(os.devnull, 'w') as devnull:
+            subprocess.call(f"wget https://files.rcsb.org/download/{pdb_name}",
+                            shell=True, cwd=output_dir, stdout=devnull, stderr=devnull)
+
+        # Decompress the file if downloaded
+        if os.path.exists(pdb_path):
+            with open(os.devnull, 'w') as devnull:
+                subprocess.call(f"gzip -d {pdb_name}", shell=True, cwd=output_dir, stdout=devnull, stderr=devnull)
+
+        # Extract metadata from the file and select the first model
+        if os.path.exists(pdb_path):
+            release_date, resolution = None, None
+            pdb_data = []
+            with open(pdb_path, "r") as f:
+                for line in f:
+                    pdb_data.append(line)
+                    if line.startswith("HEADER"):
+                        release_date = line[50:59].strip()
+                    if line.startswith("REMARK   2") and "RESOLUTION." in line:
+                        resolution = line.split("RESOLUTION.")[1].split()[0].strip()
+                    if line.startswith("ENDMDL"):
+                        break
+
+            # Write back only the first model
+            with open(pdb_path, "w") as fo:
+                fo.writelines(pdb_data)
+
+        return pdb_id, standardize_date_format(release_date), resolution
+
+    # Use multithreading to speed up downloads and metadata extraction
+    print("Fetching PDB structures and metadata...")
+    total_pdbs = len(pdb_3)
+    with ThreadPoolExecutor(max_workers=8) as executor:
+        results = list(tqdm(executor.map(download_and_extract_metadata, pdb_3.keys()), total=total_pdbs, desc="Processing PDBs"))
+
+    # Update the metadata in pdb_3
+    for i, (pdb_id, release_date, resolution) in enumerate(results):
+        if pdb_id in pdb_3:
+            pdb_3[pdb_id]["release_date"] = release_date
+            pdb_3[pdb_id]["resolution"] = resolution
+        if (i + 1) % (total_pdbs // 20) == 0:  # Print progress every 5%
+            print(f"Processed {i + 1}/{total_pdbs} PDB structures...")
+
+    # Save metadata to a CSV file
+    if save_metadata_path:
+        metadata_df = pd.DataFrame.from_dict(pdb_3, orient="index")
+        metadata_df.to_csv(save_metadata_path)
+        print(f"Metadata saved to {save_metadata_path}")
+
+    print("PDB structure processing complete.\n")
+    return pdb_3
+
+def filter_structures_by_resolution_and_date(pdb_3, resolution_threshold=3.0, date_threshold=None):
+    """
+    Filter PDB structures based on resolution and release date.
+
+    Parameters:
+        pdb_3 (dict): Dictionary containing PDB metadata.
+        resolution_threshold (float): Maximum resolution to include.
+        date_threshold (str): Minimum release date to include (format: 'YYYY-MM-DD').
+
+    Returns:
+        dict: Filtered dictionary of PDB structures.
+    """
+    filtered_pdbs = {}
+    date_threshold = datetime.strptime(date_threshold, "%Y-%m-%d") if date_threshold else None
+
+    for pdb_id, metadata in pdb_3.items():
+        resolution = metadata.get("resolution")
+        release_date = metadata.get("release_date")
+        
+        if resolution and resolution <= resolution_threshold:
+            if date_threshold:
+                if release_date:
+                    release_date = datetime.strptime(release_date, "%Y-%m-%d")
+                    if release_date >= date_threshold:
+                        filtered_pdbs[pdb_id] = metadata
+            else:
+                filtered_pdbs[pdb_id] = metadata
+
+    print(f"Filtered PDBs count: {len(filtered_pdbs)}")
+    if date_threshold:
+        print(f"Data all published after: {date_threshold.strftime('%Y-%m-%d')}")
+    print(f"Resolution threshold: {resolution_threshold}\n")
+    return filtered_pdbs
+
+## 6. Prepare PDB dataframes and align full sequence (from pdb seq-res) on sequence of resloved protein (may contain some gaps)
+def remove_alternative_conformations(pdb_dataframe):
+    """Remove alternative conformations from a PDB DataFrame."""
+    return pdb_dataframe[(pdb_dataframe["alt_loc"] == "A") | (pdb_dataframe["alt_loc"] == " ") | (pdb_dataframe["alt_loc"] == "")]
+
+def remove_unk(pdb_dataframe):
+    """Remove unknown residues (UNK) from a PDB DataFrame."""
+    return pdb_dataframe[pdb_dataframe["residue_name"] != "UNK"]
+
+def consider_insertions(pdb_dataframe):
+    """Generate residue keys considering insertions in a PDB DataFrame."""
+    pdb_dataframe["residue_key"] = list(zip(pdb_dataframe["residue_number"], 
+                                             pdb_dataframe["insertion"], 
+                                             pdb_dataframe["chain_id"], 
+                                             pdb_dataframe["residue_name"]))
+    return pdb_dataframe
+
+def put_full_sequence(pdb_dataframe, full_seq):
+    """Align PDB sequence with full sequence and combine data."""
+    pdb_dataframe = remove_alternative_conformations(pdb_dataframe)
+    pdb_dataframe = remove_unk(pdb_dataframe)
+    pdb_dataframe = consider_insertions(pdb_dataframe)
+
+    if pdb_dataframe.empty:
+        print("Empty PDB DataFrame.")
+        return None
+    pdb_ca = pdb_dataframe[pdb_dataframe["atom_name"] == "CA"]
+    residue_numbers = []
+    residue_seq = []
+    used = set()
+
+    for _, row in pdb_ca.iterrows():
+        one_letter = aa_3_to_1(row["residue_name"])
+        if not one_letter:
+            print(f"Unknown residue: {row['residue_name']}. Skipping...")
+            continue
+        residue_number = row["residue_key"]
+        if residue_number in used:
+            continue
+        residue_numbers.append(residue_number)
+        residue_seq.append(one_letter)
+
+    pdb_seq = "".join(residue_seq)
+
+    if len(pdb_seq) <= 5:
+        print("PDB sequence is too short.")
+        return None
+
+    pdb_seq_aligned, full_seq_aligned = kalign(pdb_seq, full_seq)
+    print(f"PDB-aligned sequence: {pdb_seq_aligned}")
+    print(f"Full-aligned sequence: {full_seq_aligned}")
+
+
+    assert full_seq_aligned.replace("-", "") == full_seq
+
+    n_pdb = -1
+    n_pdb_map = []
+
+    for a_pdb, a_fullseq in zip(pdb_seq_aligned, full_seq_aligned):
+        if a_pdb != '-':
+            n_pdb += 1
+        n_pdb_map.append({"resi": None if a_pdb == "-" else residue_numbers[n_pdb],
+                          "a_pdb": a_pdb if a_pdb != "-" else None,
+                          "a_full": a_fullseq})
+
+    full_df = []
+    for mapping in n_pdb_map:
+        if mapping["resi"] is None:
+            empty_row = pd.DataFrame(np.nan, index=[0], columns=pdb_ca.columns)
+            empty_row["atom_name"] = "CA"
+            empty_row["seqres"] = mapping["a_full"]
+            full_df.append(empty_row)
+            continue
+
+        pdb_residue = pdb_dataframe[pdb_dataframe["residue_key"] == mapping["resi"]].copy()
+        pdb_residue.loc[:, "seqres"] = mapping["a_full"]
+        pdb_residue.loc[:, "aa"] = mapping["a_pdb"]
+        full_df.append(pdb_residue)
+    return pd.concat(full_df, axis=0, ignore_index=True)
+
+def get_PDBDataFrame(pdb_id, chains, dir="PDB_db"):
+    """Process PDB chains into DataFrames and save to .pkl files."""
+    pdb_path = f"{dir}/structs/{pdb_id.upper()}.pdb"
+    if not os.path.exists(pdb_path):
+        print(f"Error: PDB file {pdb_id} not found in {pdb_path}.")
+        return
+
+    pdb_structure = PandasPdb().read_pdb(pdb_path).df["ATOM"]
+    if pdb_structure.empty:
+        print(f"Error: No ATOM records found in PDB file {pdb_id}.")
+        return
+
+    sequences = {}
+    output_dir = f"{dir}/structs_per_chain/"
+    os.makedirs(output_dir, exist_ok=True)
+
+    # Extract sequences
+    for record in SeqIO.parse(pdb_path, "pdb-seqres"):
+        chain = record.id[-1]
+        sequences[chain] = record.seq
+
+    # Process chains
+    for chain in chains:
+        if chain not in sequences or not sequences[chain]:
+            print(f"Warning: No sequence data for chain {chain} in PDB {pdb_id}. Skipping...")
+            continue
+
+        pdb_chain = pdb_structure[pdb_structure["chain_id"] == chain]
+        if pdb_chain.empty:
+            print(f"Warning: No ATOM data for chain {chain} in PDB {pdb_id}. Skipping...")
+            continue
+
+        output_file = f"{output_dir}/{pdb_id}_{chain}.pkl"
+        if os.path.exists(output_file):
+            print(f"Output file already exists: {output_file}. Skipping...")
+            continue
+
+        print(f"Processing {pdb_id}, chain {chain}.")
+        full_df = put_full_sequence(pdb_chain, sequences[chain])
+        if full_df is not None:
+            pickle.dump(full_df, open(output_file, "wb"))
+            print(f"Saved processed DataFrame for {pdb_id}, chain {chain} to {output_file}.")
+        else:
+            print(f"Failed to process chain {chain} in PDB {pdb_id}.")
+
+def get_tasks(dir="PDB_db"):
+    """Identify unprocessed chains from PDB files."""
+    tasks_file = f"{dir}/all_pdbids_and_chains.txt"
+    structs_dir = Path(f"{dir}/structs/")
+    processed_dir = Path(f"{dir}/structs_per_chain/")
+
+    if not os.path.exists(tasks_file):
+        all_chains = set()
+        for pdb_file in structs_dir.glob("*.pdb"):
+            with open(pdb_file, "r") as f:
+                for line in f:
+                    if line.startswith("ATOM") and len(line) > 21 and line[13:15] == "CA":
+                        all_chains.add(f"{pdb_file.stem}_{line[21]}")
+        with open(tasks_file, "w") as f:
+            f.write("\n".join(all_chains))
+        print(f"Created tasks file with {len(all_chains)} chains.")
+
+    all_chains = {line.strip() for line in open(tasks_file).readlines()}
+    processed_chains = {file.stem for file in processed_dir.glob("*.pkl")}
+
+    tasks = {}
+    for chain in all_chains - processed_chains:
+        pdb_id, chain_id = chain.split("_")
+        tasks.setdefault(pdb_id, set()).add(chain_id)
+
+    print(f"Found {len(processed_chains)} processed PDB files.")
+    print(f"Found {len(tasks)} unprocessed PDB files.")
+    return tasks
+
+
+def process_pdb_task(pdb_id, chains, dir="PDB_db"):
+    """Process a single PDB ID with its chains."""
+    try:
+        print(f"Processing {pdb_id} with chains: {', '.join(chains)}")
+        get_PDBDataFrame(pdb_id, chains, dir)
+    except Exception as e:
+        print(f"Error processing {pdb_id}: {e}")
+
+
+def run_parallel_tasks(dir="PDB_db"):
+    """Run PDB processing tasks in parallel with progress tracking."""
+    jobs = get_tasks()  # Get all unprocessed tasks
+    print(f"Found {len(jobs)} PDB files to process.")
+
+    from os import cpu_count
+    max_workers = min(16, cpu_count())  # Dynamically determine workers based on system resources
+
+    with ThreadPoolExecutor(max_workers=max_workers) as executor:
+        futures = {
+            executor.submit(process_pdb_task, pdb_id, jobs[pdb_id], dir): pdb_id
+            for pdb_id in jobs
+        }
+
+        for future in tqdm(futures, desc="Processing PDB Files", total=len(futures)):
+            pdb_id = futures[future]
+            try:
+                future.result()  # Wait for the task to complete
+            except Exception as e:
+                print(f"Error processing {pdb_id}: {e}")
+    
+    jobs = get_tasks()  # Get all unprocessed tasks
+    print(f"Found {len(jobs)} PDB files unprocessed.")
+
+## 7. Put ANARCI annotation into antibodies dataframes prepared in the previous step
+def realign_sequences(pdb_seq, anarci_, firstLetterException = False):
+    seq_aa = []
+    seq_i  = []
+    for i,s_ in enumerate(anarci_):
+        if len(s_)==0:
+            continue
+        seq_aa+=s_
+        seq_i +=[i for i_ in range(len(s_))]
+    al = kalign("".join(seq_aa),"".join(pdb_seq))
+    
+    n_anarci = 0
+    n_pdb    = 0
+    pdb_anarci_map = [None for i in pdb_seq]
+ 
+    for i,[a_anarci,a_pdb] in enumerate(zip(*al)):
+        if a_anarci!="-" and a_pdb!="-":#i!=0:
+            pdb_anarci_map[n_pdb] = i
+            if n_anarci == 0 and firstLetterException:
+                n_pdb+=1
+                n_anarci+=1
+                continue
+            if a_pdb!=a_anarci:
+                return None
+        if a_pdb!="-":
+            n_pdb+=1
+        if a_anarci!="-":            
+            n_anarci+=1
+
+    return pdb_anarci_map
+
+def put_anarci_annotation(pdb_dataframe, fab_id, heavy_list, light_list, firstLetterException = False):
+    pdb_id,chain,fab_type = fab_id
+    if fab_type == "light":
+        anarci_seq     = light_list[pdb_id.lower()+"_"+chain]
+    else:
+        anarci_seq     = heavy_list[pdb_id.lower()+"_"+chain]
+    pdb_ca        = pdb_dataframe[pdb_dataframe["atom_name"] == "CA"]#["seqres"]
+    pdb_seq = "".join(pdb_ca["seqres"])
+    pdb_anarci_map = realign_sequences(pdb_seq, anarci_seq, firstLetterException)
+    
+    if pdb_anarci_map is None:
+        return None
+    
+    pdb_anarci_map = [fab_type[0].upper()+str(i)  if i is not None else None for i in pdb_anarci_map]    
+    pdb_dataframe["anarci"] = None
+    
+    for anarci_id, residue_number in zip(pdb_anarci_map, pdb_ca["residue_key"]):
+        ids = pdb_dataframe["residue_key"] == residue_number
+        pdb_dataframe.loc[ids,"anarci"] = anarci_id     
+        
+    return pdb_dataframe
+
+def collect_jobs(anarci_list_heavy, anarci_list_light):
+    """Collect jobs for heavy and light chain annotations."""
+    jobs = []
+    for anarci_id, _ in anarci_list_heavy.items():
+        jobs.append((anarci_id[:4].upper(), anarci_id[-1], "heavy"))
+    for anarci_id, _ in anarci_list_light.items():
+        jobs.append((anarci_id[:4].upper(), anarci_id[-1], "light"))
+    return jobs
+
+def process_antibody_chain(pdb_id, chain, fab_type, input_dir, output_dir, heavy_list, light_list, strange_error_list, firstLetterException=True):
+    """Process and annotate a single antibody chain."""
+    pdb_path = f"{input_dir}/{pdb_id}_{chain}.pkl"
+    out_path = f"{output_dir}/{pdb_id}_{chain}_{fab_type}.pkl"
+
+    # Skip if the input file does not exist or the output file already exists
+    if not os.path.exists(pdb_path):
+        #print(f"Input file does not exist: {pdb_path}")
+        return False
+    if os.path.exists(out_path):
+        #print(f"Output file already exists: {out_path}")
+        return True
+
+    # Load the PDB data
+    fab = pickle.load(open(pdb_path, 'rb'))
+
+    # Annotate the data
+    fab_annotated = put_anarci_annotation(fab, (pdb_id, chain, fab_type), heavy_list, light_list, firstLetterException)
+
+    # If annotation failed, add to error list and return False
+    if fab_annotated is None:
+        #print(f"Annotation failed for {pdb_id} chain {chain} ({fab_type})")
+        strange_error_list.add((pdb_id, chain, fab_type))
+        return False
+
+    # Save the annotated data
+    # print(f"Saving annotated data to {out_path}")
+    pickle.dump(fab_annotated, open(out_path, 'wb'))
+    return True
+
+def annotate_antibody_chains(anarci_list_heavy, anarci_list_light, dir = "PDB_db"):
+    """Annotate antibody chains and save the results."""
+    # Ensure the output directory exists
+    
+    input_dir = os.path.join(dir, "structs_per_chain")
+    output_dir = os.path.join(dir, "structs_antibodies")
+
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    # Collect all jobs
+    jobs = collect_jobs(anarci_list_heavy, anarci_list_light)
+
+    # Initialize the error list
+    strange_error_list = set()
+    input_file_not_exists_count = 0
+
+    # Process each job
+    for pdb_id, chain, fab_type in tqdm(jobs, desc="Processing Antibody Chains", unit="job", leave=True, dynamic_ncols=True):
+        if not process_antibody_chain(pdb_id, chain, fab_type, input_dir, output_dir, anarci_list_heavy, anarci_list_light , strange_error_list):
+            input_file_not_exists_count += 1
+
+    print(f"Number of input files that do not exist or annotation failed: {input_file_not_exists_count}")
+
+    # Return the list of errors
+    return strange_error_list
+
+## 8. Find heavy/light chain fab pairs
+def get_pdb_list(dir = "PDB_db"):
+    list = [p.name[:4] for p in Path(f"{dir}/structs_antibodies/").glob(f"*.pkl")]
+    print(f"Found {len(list)} PDB IDs.")
+    return list
+    
+def get_fabs_pdbid(pdb_id = "1LK3", dir = "PDB_db"):
+    fab_path = Path(f"{dir}/structs_antibodies/").glob(f"{pdb_id}*.pkl")
+    fab_ids  = {"heavy":[],"light":[]}
+    for struct_id in fab_path:
+        pdb_id, _, fab_type = struct_id.name.rstrip(".pkl").split("_")
+        fab_ids[fab_type].append(struct_id)
+    return fab_ids
+
+def get_pair_interface(path_light, path_heavy, threshold = 4.5):
+    pdb_light = pickle.load(open(path_light,'rb'))
+    pdb_heavy = pickle.load(open(path_heavy,'rb'))
+    
+    ### interface residues of heavy and light fab chains
+    heavy_interface = list(range(32,39)) + list(range(44,50)) + list(range(85,95))
+    light_interface = list(range(34,39)) + list(range(45,51)) + list(range(90,108))
+
+    heavy_ids = ["H"+str(i) for i in heavy_interface]
+    light_ids = ["L"+str(i) for i in light_interface]
+    
+    heavy_interface = pdb_heavy[pdb_heavy["anarci"].isin(heavy_ids)]
+    light_interface = pdb_light[pdb_light["anarci"].isin(light_ids)]
+    
+    xyz_heavy = getxyz(heavy_interface)
+    xyz_light = getxyz(light_interface)
+    
+    cd = distance.cdist(xyz_heavy,xyz_light)
+    ids = np.where(cd<threshold)
+    
+    return len(set(ids[0]))+len(set(ids[1]))
+    
+def screen_fab_pairs(pdb_id, threshold = 4.5, dir = "PDB_db"):
+    fab_path = get_fabs_pdbid(pdb_id, dir)
+    contacts = {}
+    for heavy_path in fab_path["heavy"]:
+        for light_path in fab_path["light"]:
+            n = get_pair_interface(path_light = light_path, path_heavy = heavy_path, threshold = threshold)
+            ### cut off to select interface that interact with each other
+            if n > 3:# 10:
+                contacts[(light_path.name.rstrip(".pkl"), heavy_path.name.rstrip(".pkl"))] = n
+    return contacts
+
+def process_pdb_id(pdb_id, dir="PDB_db"):
+    """
+    Process a single PDB ID to identify heavy-light chain pairs.
+    
+    Parameters:
+        pdb_id (str): The PDB ID to process.
+        dir (str): Directory containing structure files.
+    
+    Returns:
+        tuple: A tuple containing the PDB ID and its interacting pairs.
+    """
+    return pdb_id, screen_fab_pairs(pdb_id, dir=dir)
+
+def find_fab_pairs(dir="PDB_db"):
+    """
+    Find and save all heavy-light chain pairs for all PDB IDs.
+    
+    Parameters:
+        dir (str): Directory containing PDB structure files.
+        output_file (str): File path to save the results.
+    """
+    output_file = os.path.join(dir, "fab_pairs.pkl")
+    temp_file = os.path.join(dir, "fab_pairs_temp.pkl")
+
+    if os.path.exists(output_file):
+        print(f"Results already exist at {output_file}. Skipping processing.\n")
+        return pickle.load(open(output_file, 'rb'))
+
+    # Get all PDB IDs
+    pdb_list = get_pdb_list(dir)
+
+    # Initialize result dictionary
+    fab_contacts = {}
+
+    # Load intermediate results if available
+    if os.path.exists(temp_file):
+        print(f"Resuming from temporary file: {temp_file}")
+        with open(temp_file, 'rb') as f:
+            fab_contacts = pickle.load(f)
+        # Remove already processed PDB IDs
+        pdb_list = [pdb for pdb in pdb_list if pdb not in fab_contacts]
+
+    # Use ThreadPoolExecutor for parallel processing
+    try:
+        with ThreadPoolExecutor() as executor:
+            for pdb_id, contacts in tqdm(
+                executor.map(lambda pdb_id: process_pdb_id(pdb_id, dir), pdb_list),
+                total=len(pdb_list),
+                desc="Processing PDB IDs",
+                unit="PDB",
+                dynamic_ncols=True, leave=True
+            ):
+                fab_contacts[pdb_id] = contacts
+                # Save progress to temp file
+                with open(temp_file, 'wb') as f:
+                    pickle.dump(fab_contacts, f)
+    except KeyboardInterrupt:
+        print("Process interrupted. Progress saved to temporary file.")
+
+    # Save the final results
+    with open(output_file, 'wb') as f:
+        pickle.dump(fab_contacts, f)
+    print(f"Saved fab pairs to {output_file}\n")
+
+    # Remove the temporary file after completion
+    if os.path.exists(temp_file):
+        os.remove(temp_file)
+        print(f"Temporary file {temp_file} removed.\n")
+    
+    return fab_contacts
+
+## 9. Find antigens and corresponding interacting antibodies
+def get_all_antigens_list(anarci_list_heavy=None, anarci_list_light=None, dir="PDB_db"):
+    save_path = os.path.join(dir, "antigens_path_list.pkl")
+    pdbid_save_path = os.path.join(dir, "antigen_pdbids.pkl")
+
+    # Check if the antigens list already exists and load it
+    if os.path.exists(save_path):
+        #print(f"Loading existing antigens list from {save_path}...")
+        with open(save_path, "rb") as f:
+            pdb_ids = pickle.load(f)
+        #print(f"Loaded {len(pdb_ids)} antigen_chains.")
+        
+        # Load PDB IDs if available
+        if os.path.exists(pdbid_save_path):
+            with open(pdbid_save_path, "rb") as f:
+                antigen_pdbids = pickle.load(f)
+            #print(f"Loaded {len(antigen_pdbids)} antigen PDB IDs.")
+            return pdb_ids, antigen_pdbids
+        return pdb_ids, []
+
+    # Generate the list of antigen_chains
+    fab_ids = set(anarci_list_heavy) | set(anarci_list_light)
+    fab_ids = {f[:4].upper() + "_" + f[-1] for f in fab_ids}
+    print(fab_ids)
+    print(f"Found {len(fab_ids)} antibody chains.")
+    
+    # Extract first 4 letters from fab_ids for comparison
+    fab_prefixes = {p[:4] for p in fab_ids}
+
+    if "8H64" in fab_prefixes:
+        print("8H64 found in fab_prefixes")
+
+    # Store chains whose first 4 letters exist in fab_ids but first 6 is different
+    pdb_ids = [p for p in Path(f"{dir}/structs_per_chain/").glob("*.pkl") 
+               if p.name[:4] in fab_prefixes 
+               and p.name[:6] not in fab_ids]
+    antigen_pdbids = list({p.name[:4] for p in pdb_ids})
+    print(f"Found {len(pdb_ids)} antigen chains.")
+    
+    # Save the list of antigen chains
+    with open(save_path, "wb") as f:
+        pickle.dump(pdb_ids, f)
+    print(f"Saved antigens list to {save_path}.")
+
+    # Save the list of antigen PDB IDs
+    with open(pdbid_save_path, "wb") as f:
+        pickle.dump(antigen_pdbids, f)
+    print(f"Saved antigen PDB IDs to {pdbid_save_path}.")
+
+    return pdb_ids, antigen_pdbids
+
+def get_antigens_PDBID(pdb_id, anarci_list_heavy=None, anarci_list_light=None):
+    all_antigens, _ = get_all_antigens_list(anarci_list_heavy, anarci_list_light, dir="PDB_db")
+    list = [a for a in all_antigens if a.name[:4] == pdb_id]
+    print(f"Found {len(list)} chains for PDB ID {pdb_id}.")
+    return list
+
+def find_antigen_contacts(pdb_id, dir="PDB_db", threshold=4.0, cdr=True, fab_contacts=None):
+    """
+    Find antigen contacts with all antibody chains.
+
+    Parameters:
+        pdb_id (str): PDB ID to process.
+        dir (str): Directory containing structure files.
+        threshold (float): Distance threshold for defining contacts.
+
+    Returns:
+        dict: A dictionary with contact results for each antigen chain.
+    """
+    # Collect all antibody chains with the same PDB ID prefix
+    antibody_dir = Path(f"{dir}/structs_antibodies")
+    all_antibody_chains = [
+        fab_path for fab_path in antibody_dir.glob("*.pkl")
+        if fab_path.name.startswith(pdb_id)
+    ]
+
+    if all_antibody_chains == []:
+        print(f"No antibody chains found for PDB ID {pdb_id}.")
+        return {}
+    #print(f"Found {len(all_antibody_chains)} antibody chains for PDB ID {pdb_id}.")
+
+    # Initialize results
+    results = {}
+
+    # Get antigen file paths
+    antigens = get_antigens_PDBID(pdb_id, dir)
+
+    if fab_contacts is None:
+        fab_contacts = pickle.load(open(f"{dir}/fab_pairs.pkl", 'rb'))
+    fab_pairs = fab_contacts[pdb_id]
+
+    for antigen in antigens:
+        antigen_path = Path(antigen)
+
+        antigen_df = pickle.load(antigen_path.open('rb'))
+        total_antigen_residues = antigen_df["residue_number"].nunique()
+        if total_antigen_residues < 25:
+            print(f"Skipping antigen {antigen_path.stem} with no more than 25 residues.")
+            continue
+
+        #print(f"Processing antigen chain {antigen_path.name}...")
+        antigen_results = {"all_chain": set(), "light": set(), "heavy": set(), "pair": set()}
+        chain_contacts = {}
+
+        # Iterate through antibody chains
+        for fab_path in all_antibody_chains:
+            contact_data = test_contacts(antigen_path, fab_path, threshold, cdr)
+
+            if contact_data["n_contacts"] > 0:
+                residues = contact_data["contacting_residues"]
+                antigen_results["all_chain"].update(residues)
+                if contact_data["fab_type"] == "light":
+                    antigen_results["light"].update(residues)
+                elif contact_data["fab_type"] == "heavy":
+                    antigen_results["heavy"].update(residues)
+                chain_contacts[fab_path.stem] = residues
+
+        # Check for heavy-light pairs
+        combined_chains = set()
+        for (light_chain, heavy_chain), _ in fab_pairs.items():
+            combined_chains.add(light_chain)
+            combined_chains.add(heavy_chain)
+        
+        for chain in combined_chains:
+            if chain in chain_contacts:
+                antigen_results["pair"].update(chain_contacts[chain])
+
+        results[antigen.stem] = {
+            "all_chain": sorted(antigen_results["all_chain"]),
+            "light": sorted(antigen_results["light"]),
+            "heavy": sorted(antigen_results["heavy"]),
+            "pair": sorted(antigen_results["pair"])
+        }
+
+    return results
+
+
+def test_contacts(antigen_path, fab_path, threshold=4.0, use_cdr=True):
+    """
+    Test contacts between antigen and antibody chain with optional ANARCI filtering.
+
+    Parameters:
+        antigen_path (Path): Path to the antigen pickle file.
+        fab_path (Path): Path to the antibody chain pickle file.
+        threshold (float): Distance threshold for defining contacts.
+        use_cdr (bool): Whether to filter antibody residues using CDR (ANARCI regions).
+
+    Returns:
+        dict: A dictionary with contact information:
+            - "n_contacts": Number of contacting residues.
+            - "contacting_residues": List of contacting antigen residues (e.g., 131_GLU).
+            - "fab_type": Type of the FAB chain ("light" or "heavy").
+    """
+    # Load antigen and antibody data
+    antigen_df = pickle.load(antigen_path.open('rb'))
+    if antigen_df is None:
+        return {"n_contacts": 0, "contacting_residues": []}
+
+    fab_df = pickle.load(fab_path.open('rb'))
+
+    # Determine antibody type and ANARCI-defined interface residues
+    fab_type = None
+    if fab_path.name.split("_")[-1] == "light.pkl":
+        fab_type = "light"
+        if use_cdr:  # Only filter if use_cdr is True
+            interface = ["L" + str(i) for i in list(range(23, 35)) + list(range(66, 72)) + list(range(89, 98))]
+        else:
+            interface = []
+    elif fab_path.name.split("_")[-1] == "heavy.pkl":
+        fab_type = "heavy"
+        if use_cdr:  # Only filter if use_cdr is True
+            interface = ["H" + str(i) for i in list(range(23, 35)) + list(range(51, 57)) + list(range(93, 102))]
+        else:
+            interface = []
+    else:
+        interface = []
+
+    # Filter antibody residues based on ANARCI interface
+    if interface:
+        fab_df = fab_df[fab_df["anarci"].isin(interface)]
+
+    # Get antibody coordinates
+    xyz_fab = getxyz(fab_df)
+    if xyz_fab.size == 0:
+        return {"n_contacts": 0, "contacting_residues": [], "fab_type": fab_type}
+
+    # Initialize set for contacting residues
+    contacting_residues = set()
+
+    # Group antigen residues by residue_number and check for contacts
+    for residue_number, residue_df in antigen_df.groupby("residue_number"):
+        xyz_residue = getxyz(residue_df)  # Get residue coordinates
+        if np.any(distance.cdist(xyz_residue, xyz_fab) < threshold):
+            # Combine residue_number and residue_name (e.g., 131_GLU)
+            residue_name = residue_df['residue_name'].iloc[0]
+            contacting_residues.add(f"{int(residue_number)}_{residue_name}")
+
+    # Return the results
+    return {
+        "n_contacts": len(contacting_residues),
+        "contacting_residues": sorted(contacting_residues),
+        "fab_type": fab_type
+    }
+
+
+def store_antigen_contacts_csv(results, dir="PDB_db", threshold=4, cdr = True):
+    """
+    Save antigen contacts into four separate CSV files.
+
+    Parameters:
+        results (dict): Dictionary with antigen contact results.
+        dir (str): Directory where CSV files will be stored.
+    """
+    import pandas as pd
+
+    csv_results = {
+        "pair_contacts": [],
+        "all_chain_contacts": [],
+        "light_chain_contacts": [],
+        "heavy_chain_contacts": []
+    }
+
+    for antigen, data in results.items():
+        # Save all-chain results
+        if "all_chain" in data and data["all_chain"]:
+            csv_results["all_chain_contacts"].append((antigen, ", ".join(data["all_chain"])))
+        # Save light-chain results
+        if "light" in data and data["light"]:
+            csv_results["light_chain_contacts"].append((antigen, ", ".join(data["light"])))
+        # Save heavy-chain results
+        if "heavy" in data and data["heavy"]:
+            csv_results["heavy_chain_contacts"].append((antigen, ", ".join(data["heavy"])))
+        # Save pair results
+        if "pair" in data and data["pair"]:
+            csv_results["pair_contacts"].append((antigen, ", ".join(data["pair"])))
+
+    # Write to CSV files
+    for key, content in csv_results.items():
+        if content:
+            df = pd.DataFrame(content, columns=["antigen_chain", "Epitopes (resi_resn)"])
+            if cdr:
+                output_dir = Path(f"{dir}/epitopes_cdr")
+            else:
+                output_dir = Path(f"{dir}/epitopes_no_cdr")
+            if not os.path.exists(output_dir):
+                os.makedirs(output_dir)
+            output_path = output_dir / f"{key}_{threshold}.csv"
+
+            df.to_csv(output_path, index=False)
+            print(f"Saved {key} to {output_path}")
+            print(f"Number of antigens with epitopes for {key}: {len(df) - 1}\n")
+
+
+
+def process_pdb_id(pdb_id, dir="PDB_db", threshold=4.0, cdr = True):
+    """
+    Wrapper function to process a single PDB ID and find antigen contacts.
+    """
+    try:
+        #print(f"Processing PDB ID: {pdb_id}")
+        return pdb_id, find_antigen_contacts(pdb_id, dir, threshold, cdr)
+    except Exception as e:
+        print(f"Error processing {pdb_id}: {e}")
+        return pdb_id, None
+
+def process_all_pdb_ids(pdb_ids=None, dir="PDB_db", threshold=4.0, cdr = True, max_workers=16):
+    """
+    Process all PDB IDs in parallel and store results into CSV files.
+
+    Parameters:
+        pdb_ids (list): List of PDB IDs to process.
+        dir (str): Directory containing structure files.
+        threshold (float): Distance threshold for defining contacts.
+        max_workers (int): Maximum number of worker threads to use.
+    """
+
+    results = {}
+
+    if pdb_ids is None:
+        ag, pdb_ids = get_all_antigens_list(dir=dir)
+
+    print(f"Processing {len(pdb_ids)} PDB IDs.")
+    print(f"Processing {len(ag)} antigens.")
+    print(f"Using distance threshold of {threshold} Å.\n")
+    if cdr:
+        print("Processing epitopes with considering CDRs.")
+        epitopes_dir = Path(f"{dir}/epitopes_cdr/")
+    else:
+        print("Processing epitopes without considering CDRs.")
+        epitopes_dir = Path(f"{dir}/epitopes_no_cdr/")
+
+    if epitopes_dir.exists() and all(
+        (epitopes_dir / f"{key}_{threshold}.csv").exists() for key in [
+            "pair_contacts", "all_chain_contacts", "light_chain_contacts", "heavy_chain_contacts"
+        ]
+    ):
+        print("All epitope CSV files already exist. Skipping processing.\n")
+        
+        # Load and print the number of antigens for each CSV
+        for key in ["pair_contacts", "all_chain_contacts", "light_chain_contacts", "heavy_chain_contacts"]:
+            csv_path = epitopes_dir / f"{key}_{threshold}.csv"
+            df = pd.read_csv(csv_path)
+            print(f"Number of antigens with epitopes for {key}: {len(df) - 1}")
+        print("\n")
+        return
+    
+
+    # Parallelize processing of PDB IDs
+    with ThreadPoolExecutor(max_workers=max_workers) as executor:
+        futures = {executor.submit(process_pdb_id, pdb_id, dir, threshold, cdr): pdb_id for pdb_id in pdb_ids}
+        for future in tqdm(as_completed(futures), total=len(futures), desc="Processing PDB IDs", unit="PDB"):
+            pdb_id, result = future.result()
+            if result:
+                results.update(result)
+            else:
+                print(f"Skipping PDB ID {pdb_id} due to an error.")
+
+    # Store results in CSV files
+    print("Storing results into CSV files...")
+    store_antigen_contacts_csv(results, dir=dir, threshold=threshold, cdr=cdr)
+    print("All results stored successfully!")
+
+# Modified version of find_antigen_contacts to include heavy-light pairs
+def find_antigen_contacts_v2(pdb_id, dir="PDB_db", threshold=4.0, cdr=False, fab_contacts=None):
+    """
+    Find antigen contacts with antibody heavy-light pairs.
+
+    Parameters:
+        pdb_id (str): PDB ID to process.
+        dir (str): Directory containing structure files.
+        threshold (float): Distance threshold for defining contacts.
+        cdr (bool): Whether to filter antibody residues by CDR in test_contacts.
+        fab_contacts (dict): Pre-loaded dictionary of fab_pairs. If None, loads from pickled file.
+
+    Returns:
+        dict: 
+            Nested dict with structure:
+                {
+                    antigen_chain1: {
+                        (light_chain, heavy_chain): {
+                            "light": [...],
+                            "heavy": [...],
+                            "pair": [...]
+                        },
+                        ...
+                    },
+                    antigen_chain2: { ... },
+                    ...
+                }
+    """
+
+    # Directory with antibody chain .pkl files
+    antibody_dir = Path(f"{dir}/structs_antibodies")
+    
+    # Collect .pkl files that match this PDB ID
+    all_antibody_chains = [
+        fab_path for fab_path in antibody_dir.glob("*.pkl")
+        if fab_path.name.startswith(pdb_id)
+    ]
+
+    if not all_antibody_chains:
+        print(f"[WARNING] No antibody chains found for PDB ID {pdb_id}.")
+        return {}
+
+    # Get antigen file paths
+    antigens = get_antigens_PDBID(pdb_id, dir)
+    if not antigens:
+        print(f"[WARNING] No antigen chains found for PDB ID {pdb_id}.")
+        return {}
+
+    # Load fab_pairs if not provided
+    if fab_contacts is None:
+        with open(f"{dir}/fab_pairs.pkl", "rb") as f:
+            fab_contacts = pickle.load(f)
+
+    fab_pairs = fab_contacts.get(pdb_id, {})
+    # Ensure fab_pairs is not empty
+    if not fab_pairs:
+        print(f"[WARNING] No Fab pairs found for PDB ID {pdb_id}.")
+        return {}
+
+    # Prepare the results data structure
+    results = {}  # {antigen_stem: {(light_chain, heavy_chain): {"light":[], "heavy":[], "pair":[]}}}
+
+    for antigen_path_str in antigens:
+        antigen_path = Path(antigen_path_str)
+        antigen_stem = antigen_path.stem  # e.g. '1A4J_A'
+        
+        # Load antigen
+        antigen_df = pickle.load(antigen_path.open('rb'))
+        if antigen_df is None or antigen_df.empty:
+            print(f"[INFO] Empty or missing antigen data for {antigen_stem}. Skipping.")
+            continue
+        
+        # Quick residue count check
+        total_antigen_residues = antigen_df["residue_number"].nunique()
+        if total_antigen_residues < 25:
+            print(f"[INFO] Skipping antigen {antigen_stem} with <= 25 residues.")
+            continue
+        
+        # Dictionary for this antigen's results
+        results_for_antigen = {}
+
+        # Process each (light, heavy) pair in fab_pairs
+        for (light_chain, heavy_chain), _ in fab_pairs.items():
+            # Find the corresponding .pkl files for the light and heavy chains
+            light_path = next((p for p in all_antibody_chains if light_chain in p.name), None)
+            heavy_path = next((p for p in all_antibody_chains if heavy_chain in p.name), None)
+
+            if (light_path is None) or (heavy_path is None):
+                print(f"[WARNING] Missing chain file for pair ({light_chain}, {heavy_chain}). Skipping.")
+                continue
+
+            # Calculate contacts for the light and heavy chains
+            light_data = test_contacts(antigen_path, light_path, threshold=threshold, use_cdr=cdr)
+            heavy_data = test_contacts(antigen_path, heavy_path, threshold=threshold, use_cdr=cdr)
+
+            # Build the combined "pair" contact set (union of light + heavy)
+            pair_contacts = set(light_data["contacting_residues"]) | set(heavy_data["contacting_residues"])
+
+            results_for_antigen[(light_chain, heavy_chain)] = {
+                "light": sorted(light_data["contacting_residues"]),
+                "heavy": sorted(heavy_data["contacting_residues"]),
+                "pair": sorted(pair_contacts)
+            }
+
+        # Add to final dictionary
+        results[antigen_stem] = results_for_antigen
+
+    return results
+
+def store_antigen_contacts_csv_v2(results, dir="PDB_db", threshold=4, cdr=True):
+    """
+    Save antigen contacts into three separate CSV files: pair, heavy, and light.
+
+    Expected structure of `results`:
+        {
+            antigen_chain1: {
+                (light_chain, heavy_chain): {
+                    "light": [...],
+                    "heavy": [...],
+                    "pair": [...]
+                },
+                ...
+            },
+            ...
+        }
+
+    Columns in CSV:
+        - antigen_chain
+        - antibody_chains (either a single chain or the (light, heavy) tuple)
+        - Epitopes (resi_resn)
+    """
+    import pandas as pd
+
+    # Prepare lists to accumulate rows for each output CSV
+    pair_contacts = []
+    light_contacts = []
+    heavy_contacts = []
+
+    # Iterate over each antigen chain in results
+    for antigen_chain, pairs_dict in results.items():
+        # pairs_dict: { (light_chain, heavy_chain): {"light": [...], "heavy": [...], "pair": [...]} }
+        for (light_chain, heavy_chain), contact_data in pairs_dict.items():
+            # contact_data has keys: "light", "heavy", "pair"
+
+            # 1) Light CSV
+            if contact_data["light"]:
+                # Example row: (antigen_chain, "12E8_L_light", "123_GLU, 124_ARG, ...")
+                light_contacts.append(
+                    (
+                        antigen_chain,
+                        light_chain,  # store the name of the light chain
+                        ", ".join(contact_data["light"])
+                    )
+                )
+
+            # 2) Heavy CSV
+            if contact_data["heavy"]:
+                heavy_contacts.append(
+                    (
+                        antigen_chain,
+                        heavy_chain,  # store the name of the heavy chain
+                        ", ".join(contact_data["heavy"])
+                    )
+                )
+
+            # 3) Pair CSV
+            if contact_data["pair"]:
+                # For 'antibody_chains', we can combine the pair into a single string
+                pair_name = f"{light_chain}+{heavy_chain}"
+                pair_contacts.append(
+                    (
+                        antigen_chain,
+                        pair_name,
+                        ", ".join(contact_data["pair"])
+                    )
+                )
+
+    # Now create DataFrames and save to CSV
+    # Decide on output directory: cdr vs no_cdr
+    if cdr:
+        output_dir = Path(f"{dir}/epitopes_cdr")
+    else:
+        output_dir = Path(f"{dir}/epitopes_no_cdr")
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    # 1) Pair CSV
+    if pair_contacts:
+        df_pair = pd.DataFrame(pair_contacts,
+                               columns=["antigen_chain", "antibody_chains", "Epitopes (resi_resn)"])
+        output_path = output_dir / f"pair_contacts_{threshold}.csv"
+        df_pair.to_csv(output_path, index=False)
+        print(f"[INFO] Saved pair contacts to: {output_path}")
+        print(f"Number of rows in pair CSV: {len(df_pair)}\n")
+
+    # 2) Light CSV
+    if light_contacts:
+        df_light = pd.DataFrame(light_contacts,
+                                columns=["antigen_chain", "antibody_chains", "Epitopes (resi_resn)"])
+        output_path = output_dir / f"light_chain_contacts_{threshold}.csv"
+        df_light.to_csv(output_path, index=False)
+        print(f"[INFO] Saved light contacts to: {output_path}")
+        print(f"Number of rows in light CSV: {len(df_light)}\n")
+
+    # 3) Heavy CSV
+    if heavy_contacts:
+        df_heavy = pd.DataFrame(heavy_contacts,
+                                columns=["antigen_chain", "antibody_chains", "Epitopes (resi_resn)"])
+        output_path = output_dir / f"heavy_chain_contacts_{threshold}.csv"
+        df_heavy.to_csv(output_path, index=False)
+        print(f"[INFO] Saved heavy contacts to: {output_path}")
+        print(f"Number of rows in heavy CSV: {len(df_heavy)}\n")
+
+def process_pdb_id_v2(pdb_id, dir="PDB_db", threshold=4.0, cdr=False):
+    """
+    Wrapper function to process a single PDB ID and find antigen contacts.
+
+    Parameters:
+        pdb_id (str): PDB ID to process.
+        dir (str): Directory containing structure files.
+        threshold (float): Distance threshold for defining contacts.
+        cdr (bool): Whether to filter antibody residues using CDR.
+
+    Returns:
+        tuple: (pdb_id, results_dict) or (pdb_id, None) in case of an error.
+    """
+    try:
+        print(f"Processing PDB ID: {pdb_id}")
+        results = find_antigen_contacts_v2(pdb_id, dir=dir, threshold=threshold, cdr=cdr)
+        return pdb_id, results
+    except Exception as e:
+        print(f"Error processing {pdb_id}: {e}")
+        return pdb_id, None
+
+
+def process_all_pdb_ids_v2(
+    pdb_ids=None, dir="PDB_db", threshold=4.0, cdr=False, max_workers=16
+):
+    """
+    Process all PDB IDs in parallel and store results into CSV files.
+
+    Parameters:
+        pdb_ids (list): List of PDB IDs to process.
+        dir (str): Directory containing structure files.
+        threshold (float): Distance threshold for defining contacts.
+        cdr (bool): Whether to filter antibody residues using CDR.
+        max_workers (int): Maximum number of worker threads to use.
+
+    Returns:
+        None
+    """
+    results = {}
+
+    # If no PDB IDs are provided, get them from the database
+    if pdb_ids is None:
+        ag, pdb_ids = get_all_antigens_list(dir=dir)
+
+    print(f"Processing {len(pdb_ids)} PDB IDs.")
+    if cdr:
+        print("Processing epitopes considering CDR regions.")
+        epitopes_dir = Path(f"{dir}/epitopes_cdr/")
+    else:
+        print("Processing epitopes without considering CDR regions.")
+        epitopes_dir = Path(f"{dir}/epitopes_no_cdr/")
+
+    # Check if CSVs already exist
+    csv_keys = ["pair_contacts", "light_chain_contacts", "heavy_chain_contacts"]
+    if epitopes_dir.exists() and all(
+        (epitopes_dir / f"{key}_{threshold}.csv").exists() for key in csv_keys
+    ):
+        print("All epitope CSV files already exist. Skipping processing.\n")
+        for key in csv_keys:
+            csv_path = epitopes_dir / f"{key}_{threshold}.csv"
+            df = pd.read_csv(csv_path)
+            print(f"Number of antigens with epitopes for {key}: {len(df)}")
+        return
+
+    # Parallel processing of PDB IDs
+    with ThreadPoolExecutor(max_workers=max_workers) as executor:
+        futures = {
+            executor.submit(process_pdb_id_v2, pdb_id, dir, threshold, cdr): pdb_id
+            for pdb_id in pdb_ids
+        }
+        for future in tqdm(
+            as_completed(futures),
+            total=len(futures),
+            desc="Processing PDB IDs",
+            unit="PDB",
+        ):
+            pdb_id, result = future.result()
+            if result:
+                results.update(result)
+            else:
+                print(f"Skipping PDB ID {pdb_id} due to an error.")
+
+    # Store results into CSV files
+    print("Storing results into CSV files...")
+    store_antigen_contacts_csv_v2(results, dir=dir, threshold=threshold, cdr=cdr)
+    print("All results stored successfully!")
+
+
+## 10. Epitope Processing
+def process_epitope_data(dir="PDB_db", cdr=False, fasta = False):
+    """
+    Process all epitope CSV data files in the directory to:
+    - Count total epitopes, total antigens, and unique residues.
+    - Extract sequences in FASTA format after filtering unique residues.
+    - Remove rows with less than 5 epitopes and save the updated CSV.
+
+    Parameters:
+        dir (str): Directory containing antigen PDB pickle files and epitope CSV files.
+    """
+    if cdr:
+        epitope_dir = Path(f"{dir}/epitopes_cdr")
+        processed_dir = Path(f"{dir}/processed_epitopes_cdr")
+        processed_dir.mkdir(parents=True, exist_ok=True)
+    else:
+        epitope_dir = Path(f"{dir}/epitopes_no_cdr")
+        processed_dir = Path(f"{dir}/processed_epitopes_no_cdr")
+        processed_dir.mkdir(parents=True, exist_ok=True)
+
+    for epitope_csv in epitope_dir.glob("*.csv"):
+        csv_name = epitope_csv.stem
+        processed_csv_path = processed_dir / f"{csv_name}.csv"
+
+        print(f"Processing file: {epitope_csv}")
+
+        # Load the epitope CSV file
+        df = pd.read_csv(epitope_csv)
+
+        # Filter out rows with less than 5 epitopes
+        df['Epitope Count'] = df['Epitopes (resi_resn)'].apply(lambda x: len(x.split(", ")))
+        df_filtered = df[df['Epitope Count'] >= 5].drop(columns=['Epitope Count'])
+
+        # Print original and filtered number of antigens
+        original_antigens = df['antigen_chain'].nunique()
+        filtered_antigens = df_filtered['antigen_chain'].nunique()
+        unique_epitopes = df['Epitopes (resi_resn)'].nunique()
+        print(f"Original number of antigens: {original_antigens}")
+        print("Filter the antigens with less than 5 epitopes.")
+        print(f"Filtered number of antigens: {filtered_antigens}")
+        print(f"Number of antigens with unique epitopes: {unique_epitopes}\n")
+
+        if fasta:
+            # Initialize counters
+            total_epitopes = 0
+            total_residues = 0
+            total_antigens = df_filtered['antigen_chain'].nunique()  # Count unique antigen chains
+            sequences = set()
+
+            # Prepare FASTA content
+            fasta_output = ""
+
+            # Iterate through each antigen chain
+            for _, row in tqdm(df_filtered.iterrows(), total=len(df_filtered), desc="Processing Epitopes", unit="epitope"):
+                pdb_chain = row['antigen_chain']  # e.g., "7XCZ_A"
+                epitopes = row['Epitopes (resi_resn)'].split(", ")  # List of residue labels
+
+                pdb_id = pdb_chain.split("_")[0]  # Extract PDB ID
+                chain_id = pdb_chain.split("_")[1]  # Extract chain ID
+
+                # Locate the corresponding antigen .pkl file
+                antigen_file = Path(dir) / f"structs_per_chain/{pdb_id}_{chain_id}.pkl"
+
+                if not antigen_file.exists():
+                    print(f"Warning: Antigen file not found for {pdb_chain}")
+                    continue
+
+                # Load the antigen DataFrame
+                with open(antigen_file, 'rb') as f:
+                    antigen_df = pickle.load(f)
+
+                # Filter unique residues based on 'residue_number'
+                antigen_df_unique = antigen_df.drop_duplicates(subset=['residue_number'])
+
+                # Extract and concatenate the sequence from the 'seqres' column
+                sequence = "".join(antigen_df_unique['seqres'].dropna().values)
+
+                # Add sequence to the set of unique sequences
+                sequences.add(sequence)
+
+                # Add to FASTA output
+                fasta_output += f">{pdb_chain}\n{sequence}\n"
+
+                total_residues += len(sequence)
+                total_epitopes += len(epitopes)
+
+            # Print counts
+            print(f"Total Antigens: {total_antigens}")
+            print(f"Total Epitopes: {total_epitopes}")
+            print(f"Total Residues: {total_residues}")
+            print(f"Total Number of Unique Sequences: {len(sequences)}\n")
+
+            # Save the FASTA file
+            if cdr:
+                fasta_dir = Path(f"{dir}/antigen_sequences_cdr")
+            else:
+                fasta_dir = Path(f"{dir}/antigen_sequences_no_cdr")
+            fasta_dir.mkdir(parents=True, exist_ok=True)
+            fasta_path = fasta_dir / f"{csv_name}_sequences.fasta"
+
+            if fasta_path.exists():
+                print(f"FASTA file already exists in : {fasta_path}")
+            else:
+                with open(fasta_path, "w") as fasta_file:
+                    fasta_file.write(fasta_output)
+                print(f"FASTA sequences saved to {fasta_path}")
+
+        # Save the filtered CSV file
+        if processed_csv_path.exists():
+            print(f"Processed file already exist in: {processed_csv_path}")
+        else:
+            df_filtered.to_csv(processed_csv_path, index=False)
+            print(f"Filtered CSV saved to {processed_csv_path}")
+        print("\n")
+
+# 12. Combine Epitope Sequences
+def combine_epitope_sequences(
+    input_csv,
+    output_csv,
+    fasta_filename="sequences.fasta",
+    dir="PDB_db"
+):
+    """
+    Combine the epitopes for the antigens with same antigen sequences.
+    
+    1. Read `input_csv` from the `dir` folder into a dataframe.
+    2. For each row, load the chain's sequence from {pdb_id}_{chain_id}.pkl using multithreading.
+    3. Group by 'sequence'.
+       - Keep only the first PDB chain in each group.
+       - Merge (union) all epitopes across that sequence.
+    4. Save CSV with columns: ['PDB chain', 'Epitopes (resi_resn)'].
+    5. Also save each unique sequence in FASTA format to 'fasta_filename'.
+    """
+
+    # -------------------------------------------------------------------
+    # Step 0: Read the input CSV
+    # -------------------------------------------------------------------
+    csv_path = Path(dir) / input_csv
+    df_filtered = pd.read_csv(csv_path)
+
+    # Check necessary columns
+    required_cols = {"antigen_chain", "Epitopes (resi_resn)"}
+    if not required_cols.issubset(df_filtered.columns):
+        raise ValueError(f"CSV must contain columns {required_cols}")
+
+    # We'll add a new column "sequence" (initially empty)
+    df_filtered["sequence"] = None
+
+    # -------------------------------------------------------------------
+    # Step 1: Function to process each row in parallel
+    # -------------------------------------------------------------------
+    def load_sequence(idx_and_row):
+        """
+        Given (idx, row), load the .pkl file corresponding to that chain,
+        extract the chain's sequence, and return (idx, sequence).
+        """
+        idx, row = idx_and_row
+        pdb_chain = row["antigen_chain"]  # e.g., "7XCZ_A"
+
+        # Split PDB id and chain id
+        parts = pdb_chain.split("_")
+        if len(parts) != 2:
+            # If parsing fails, return None
+            return (idx, None)
+        pdb_id, chain_id = parts
+
+        # Path to the .pkl file
+        antigen_file = Path(dir) / f"structs_per_chain/{pdb_id}_{chain_id}.pkl"
+        if not antigen_file.exists():
+            print(f"Warning: Antigen file not found for {pdb_chain}")
+            return (idx, None)
+
+        # Load the antigen DataFrame and extract sequence
+        try:
+            with open(antigen_file, "rb") as f:
+                antigen_df = pickle.load(f)
+        except Exception as e:
+            print(f"Error reading {antigen_file}: {e}")
+            return (idx, None)
+
+        # Remove duplicate residue entries
+        antigen_df_unique = antigen_df.drop_duplicates(subset=["residue_number"])
+        # Concatenate the sequence
+        sequence = "".join(antigen_df_unique["seqres"].dropna().values)
+
+        return (idx, sequence)
+
+    # -------------------------------------------------------------------
+    # Step 2: Use ThreadPoolExecutor to load sequences in parallel
+    # -------------------------------------------------------------------
+    futures = []
+    with ThreadPoolExecutor() as executor:
+        for idx, row in df_filtered.iterrows():
+            futures.append(executor.submit(load_sequence, (idx, row)))
+
+        for future in tqdm(as_completed(futures), total=len(futures), desc="Loading sequences", unit="chain"):
+            idx, seq = future.result()
+            df_filtered.at[idx, "sequence"] = seq
+
+    # -------------------------------------------------------------------
+    # Step 3: Group by 'sequence' (union epitopes, choose first PDB)
+    # -------------------------------------------------------------------
+    def combine_epitopes(epitope_series):
+        """
+        Given a series of strings like:
+            ["448_ASN, 449_TYR", "452_LEU, 455_LEU", ...]
+        parse and merge them uniquely.
+        """
+        all_epitopes = []
+        for e in epitope_series:
+            if not isinstance(e, str):
+                continue
+            # Split by comma
+            splitted = [item.strip() for item in e.split(",")]
+            all_epitopes.extend(splitted)
+
+        # Remove duplicates and empty entries
+        all_epitopes = list(filter(None, all_epitopes))
+        unique_epitopes = sorted(set(all_epitopes))
+        return ", ".join(unique_epitopes)
+
+    # - We pick the 'first' PDB chain from each unique sequence
+    # - We combine all epitopes from that sequence
+    # - The 'sequence' column is kept so we can still write FASTA
+    df_combined = (
+        df_filtered
+        .groupby("sequence", dropna=False, as_index=False)
+        .agg({
+            "antigen_chain": "first",  # pick just one
+            "Epitopes (resi_resn)": combine_epitopes,
+        })
+    )
+
+    # -------------------------------------------------------------------
+    # Step 4: Save the final CSV with only the required columns
+    # -------------------------------------------------------------------
+    # The user wants ONLY 'PDB chain' and 'Epitopes (resi_resn)'
+    # (We have them in df_combined; 'sequence' is still there though.)
+    final_df = df_combined[["antigen_chain", "Epitopes (resi_resn)"]].copy()
+    output_csv_path = Path(dir) / output_csv
+    final_df.to_csv(output_csv_path, index=False)
+
+    print(f"\nFinal CSV saved to: {output_csv_path}")
+    print(f"Number of unique sequences: {df_combined.shape[0]}")
+
+    # -------------------------------------------------------------------
+    # Step 5: Write each unique sequence to a FASTA file
+    # -------------------------------------------------------------------
+    fasta_path = Path(dir) / fasta_filename
+
+    with open(fasta_path, "w") as fasta_file:
+        for _, row in df_combined.iterrows():
+            seq = row["sequence"]
+            pdb_chain = row["antigen_chain"]
+            # Skip empty or None sequences
+            if not seq:
+                continue
+
+            # Write in FASTA format
+            # Example:
+            # >7XCZ_A
+            # MKKLLLLVVAVSV...
+            fasta_file.write(f">{pdb_chain}\n{seq}\n")
+
+    print(f"FASTA file with unique sequences saved to: {fasta_path}")
+
+def prepare_antigen_structures_from_csv(csv_path, dir="PDB_db"):
+    """
+    Extracts specific chains from PDB files and saves them into a new directory,
+    along with their sequences in FASTA format. Skips already processed chains.
+    """
+    # File paths
+    base_dir = Path(dir)
+    csv_path = base_dir / csv_path
+    source_dir = base_dir / "structs"
+    dest_dir_pdb = base_dir / "antigen_structs"
+
+    # Ensure destination directories exist
+    os.makedirs(dest_dir_pdb, exist_ok=True)
+
+    # Load PDB chain list from the CSV
+    epitopes_df = pd.read_csv(csv_path)
+    pdb_chain_list = epitopes_df.iloc[:, 0].unique()  # Unique PDB chains (e.g., '8YJ8_A')
+
+    # Filter out already processed chains
+    unprocessed_chains = [
+        pdb_chain for pdb_chain in pdb_chain_list 
+        if not (dest_dir_pdb / f"{pdb_chain}.pdb").exists()
+    ]
+
+    if not unprocessed_chains:
+        print("All chains have already been processed. Nothing to do.")
+        return
+
+    # PDB Parser
+    parser = PDBParser(QUIET=True)
+
+    class ChainSelect(Select):
+        """Custom PDBIO Select class for extracting a single chain."""
+        def __init__(self, chain_id):
+            self.chain_id = chain_id
+
+        def accept_chain(self, chain):
+            return chain.id == self.chain_id
+
+    # Process each unprocessed PDB chain with tqdm progress bar
+    for pdb_chain in tqdm(unprocessed_chains, desc="Processing PDB Chains", unit="chain"):
+        if "_" not in pdb_chain:
+            print(f"Invalid PDB chain format: {pdb_chain}")
+            continue
+
+        pdb_id, chain_id = pdb_chain.split("_")  # Split into PDB ID and chain
+        source_path = source_dir / f"{pdb_id}.pdb"
+        dest_path = dest_dir_pdb / f"{pdb_chain}.pdb"
+
+        if source_path.exists():
+            try:
+                # Parse PDB file
+                structure = parser.get_structure(pdb_id, str(source_path))
+
+                # Check if the chain exists
+                if chain_id not in [chain.id for chain in structure[0]]:
+                    print(f"Chain {chain_id} not found in {pdb_id}")
+                    continue
+
+                # Save the selected chain
+                io = PDBIO()
+                io.set_structure(structure)
+                io.save(str(dest_path), select=ChainSelect(chain_id))
+                print(f"Extracted and saved: {pdb_chain}.pdb")
+            except Exception as e:
+                print(f"Error processing {pdb_chain}: {e}")
+        else:
+            print(f"Warning: PDB file not found for {pdb_id} at {source_path}")
+
+    print(f"\nCompleted. Chain-specific PDB files saved to: {dest_dir_pdb}.")
+    print(f"Processed {len(unprocessed_chains)} new chains.")
+    print(f"Skipped {len(pdb_chain_list) - len(unprocessed_chains)} already existing chains.")
+
+## 11. Cluster Representatives
+# Step 1: Parse BLAST Results
+def parse_blast_results(blast_result_path):
+    """
+    Parse BLAST results and map query-target pairs with alignment information.
+    """
+    print("Parsing BLAST results...")
+    blast_mappings = defaultdict(list)
+    with open(blast_result_path, "r") as file:
+        for line in file:
+            query, target, *rest = line.strip().split("\t")
+            blast_mappings[query].append(target)
+    print(f"Parsed {len(blast_mappings)} representative mappings from BLAST.")
+    return blast_mappings
+
+# Step 2: Map Epitopes to Representatives
+def map_epitopes_to_representatives(epitope_csv_path, blast_mappings):
+    """
+    Map epitopes from the original antigen sequences to their cluster representatives.
+    """
+    print("Mapping epitopes to cluster representatives...")
+    epitopes_df = pd.read_csv(epitope_csv_path)
+    epitopes_dict = {
+        row["antigen_chain"]: set(row["Epitopes (resi_resn)"].split(", "))
+        for _, row in epitopes_df.iterrows()
+    }
+
+    mapped_epitopes = defaultdict(set)
+    for rep, queries in blast_mappings.items():
+        for query in queries:
+            if query in epitopes_dict:
+                mapped_epitopes[rep].update(epitopes_dict[query])
+
+    print(f"Mapped epitopes to {len(mapped_epitopes)} representatives.")
+    return mapped_epitopes
+
+# Step 3: Prepare Final DataFrame
+def prepare_final_dataframe(mapped_epitopes):
+    """
+    Prepare the final DataFrame for saving.
+    """
+    print("Preparing final DataFrame...")
+    rows = []
+    for rep, epitopes in mapped_epitopes.items():
+        epitope_list = ", ".join(sorted(epitopes, key=lambda r: int(r.split("_")[0])))
+        rows.append({"antigen_chain": rep, "Epitopes (resi_resn)": epitope_list})
+
+    final_df = pd.DataFrame(rows)
+    print(f"Final DataFrame prepared with {final_df.shape[0]} rows.")
+    return final_df
+
+# Step 4: Verify Row Count and Save
+def save_final_epitopes(final_df, representative_fasta_path, final_output_path):
+    """
+    Verify the row count and save the final DataFrame.
+    """
+    print("Verifying row count and saving final CSV...")
+    rep_count = sum(1 for line in open(representative_fasta_path) if line.startswith(">"))
+    print(f"Number of representatives in FASTA: {rep_count}")
+    print(f"Number of rows in final DataFrame: {final_df.shape[0]}")
+
+    if rep_count != final_df.shape[0]:
+        print("Warning: Mismatch between representative count and DataFrame rows!")
+
+    final_df.to_csv(final_output_path, index=False)
+    print(f"Final representative epitope file saved to: {final_output_path}")
+
+# Main Workflow
+
+def process_representative_epitopes(dir="PDB_db"):
+    """
+    End-to-end process for mapping epitopes and saving the final representative CSV.
+    """
+    blast_result_path = f"{dir}/antigen_sequences_no_cdr/cluster/blastp_results.tsv"
+    epitope_csv_path = f"{dir}/processed_epitopes_no_cdr/pair_contacts_4.0.csv"
+    representative_fasta_path = f"{dir}/antigen_sequences_no_cdr/cluster/DB_clu_rep.fasta"
+    final_output_path = f"{dir}/processed_epitopes_no_cdr/representative_pair_contacts_4.0.csv"
+
+    blast_mappings = parse_blast_results(blast_result_path)
+    mapped_epitopes = map_epitopes_to_representatives(epitope_csv_path, blast_mappings)
+    final_df = prepare_final_dataframe(mapped_epitopes)
+    save_final_epitopes(final_df, representative_fasta_path, final_output_path)
\ No newline at end of file
diff --git a/src/bce/utils/evaluatror.py b/src/bce/utils/evaluatror.py
new file mode 100644
index 0000000000000000000000000000000000000000..fcdc07b7e6feac245347346f61201a90bdf727a1
--- /dev/null
+++ b/src/bce/utils/evaluatror.py
@@ -0,0 +1,41 @@
+import os
+import json
+import numpy as np
+from tqdm import tqdm
+from pathlib import Path
+from datetime import datetime
+import matplotlib.pyplot as plt
+from collections import defaultdict
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+from torch.amp import autocast, GradScaler
+from torch.utils.data import DataLoader, WeightedRandomSampler, Subset
+from torch.optim.lr_scheduler import CosineAnnealingLR, SequentialLR, LinearLR
+
+from scipy.stats import pearsonr
+from sklearn.metrics import (
+    mean_squared_error, mean_absolute_error, r2_score, 
+    average_precision_score, roc_auc_score, f1_score, 
+    precision_score, recall_score, matthews_corrcoef,
+    accuracy_score
+)
+
+from ..loss import CLoss
+from .constants import DISK_DIR, BASE_DIR
+from ..data.data import create_data_loader
+from ..model.ReGEP import ReGEP
+from ..model.scheduler import get_scheduler
+
+torch.set_num_threads(12)
+
+class Evaluator:
+    def __init__(self, args):
+        self.device = torch.device(f"cuda:{args.device_id}" if torch.cuda.is_available() else "cpu")
+        self.model = ReGEP.load(args.model_path, device=self.device, strict=False)
+        
+        self.model.eval()
+        
+        
\ No newline at end of file
diff --git a/src/bce/utils/loading.py b/src/bce/utils/loading.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3ab143a105a3e3d5b574eb179465ca805fccd6a
--- /dev/null
+++ b/src/bce/utils/loading.py
@@ -0,0 +1,321 @@
+import os
+import h5py 
+from pathlib import Path
+import yaml
+import pickle
+
+import concurrent.futures
+from tqdm import tqdm
+import json
+
+import pandas as pd
+import numpy as np
+from typing import Dict, List, Tuple, Optional
+
+import torch
+
+from .constants import *
+
+# load combined epitopes csv
+def load_epitopes_csv(csv_name: str = "epitopes.csv") -> pd.DataFrame:
+    epitopes_csv = Path(BASE_DIR) / "data" / 'epitopes' / csv_name
+    if not epitopes_csv.exists():
+        raise FileNotFoundError(f"[Error] Epitopes CSV not found at {epitopes_csv}")
+    df = pd.read_csv(epitopes_csv)
+    if df.empty:
+        print(f"[Warning] The CSV {epitopes_csv} is empty.")
+        return None
+    
+    unique_protein_chains = set()
+    epitope_dict = {}
+
+    for _, row in df.iterrows():
+        antigen_name = row.get("antigen_name", "N/A")
+        binary_label = row.get("binary_label", "")
+
+        # Split antigen name into pdb and chain
+        if "_" in antigen_name:
+            pdb, chain = antigen_name.split("_", 1)
+        else:
+            pdb, chain = antigen_name, "A"  # Default to chain A if no underscore
+
+        unique_protein_chains.add((pdb, chain))
+
+        # Convert binary_label string to list of integers
+        if isinstance(binary_label, str):
+            binary_list = [int(char) for char in binary_label if char in ['0', '1']]
+        else:
+            binary_list = []
+        
+        epitope_dict[antigen_name] = binary_list
+
+    return df, list(unique_protein_chains), epitope_dict
+
+def load_epitopes_csv_single(
+    csv_path: str = None
+) -> pd.DataFrame:
+    if csv_path is None:
+        epitopes_csv = Path(BASE_DIR)/ "data" / "epitopes" / "epitopes_13.csv"
+    else:
+        epitopes_csv = Path(csv_path)
+    if not epitopes_csv.exists():
+        raise FileNotFoundError(f"[Error] Epitopes CSV not found at {epitopes_csv}")
+    df = pd.read_csv(epitopes_csv)
+    if df.empty:
+        print(f"[Warning] The CSV {epitopes_csv} is empty.")
+        return None
+    
+    unique_protein_chains = set()
+    epitope_dict = {}
+
+    for _, row in df.iterrows():
+        antigen = row.get("antigen_chain", "N/A")
+        epitopes = row.get("epitopes", "")
+
+        pdb, chain = antigen.split("_")
+
+        unique_protein_chains.add((pdb, chain))
+
+        # Parse out epitope residue numbers
+        epitope_nums = []
+        if isinstance(epitopes, str):
+            for e in epitopes.split(","):
+                e = e.strip()
+                if "_" in e:
+                    parts = e.split("_", 1)
+                    try:
+                        ep_num = int(parts[0])
+                        epitope_nums.append(ep_num)
+                    except ValueError:
+                        pass
+        
+        epitope_dict[antigen] = epitope_nums
+
+    return df, list(unique_protein_chains), epitope_dict
+
+def load_species(species_path: str = f"{BASE_DIR}/data/species.json") -> Dict[str, Dict[str, str]]:
+    with open(species_path, "r") as f:
+        species = json.load(f)
+    return species
+
+def load_data_split(data_split: str, verbose: bool = True) -> List[Tuple[str, str]]:
+    """
+    Load the antigens for the specified data split.
+    
+    Args:
+        data_split: Data split name ('train', 'val', 'test')
+        
+    Returns:
+        List of (pdb_id, chain_id) tuples for the split
+    """
+    splits_file = Path(BASE_DIR) / "data" / "epitopes" / "data_splits.json"
+    
+    if not splits_file.exists():
+        if verbose:
+            print(f"Data splits file not found: {splits_file}")
+            print("Using all antigens from load_epitopes_csv()")
+        _, antigens, _ = load_epitopes_csv()
+        return antigens
+    
+    try:
+        with open(splits_file, 'r') as f:
+            splits = json.load(f)
+        
+        if data_split not in splits:
+            raise KeyError(f"Split '{data_split}' not found in splits file")
+        
+        antigens = [(item[0], item[1]) for item in splits[data_split]]
+        
+        if verbose:
+            print(f"Loaded {len(antigens)} antigens for {data_split} split")
+        
+        return antigens
+        
+    except Exception as e:
+        if verbose:
+            print(f"Error loading data splits: {str(e)}")
+            print("Falling back to load_epitopes_csv()")
+        _, antigens, _ = load_epitopes_csv()
+        return antigens
+
+def load_split_antigens(base_dir=DISK_DIR / "data", split="train"):
+    """
+    Load the antigen list for a specific data split.
+    
+    Args:
+        base_dir (str): Base directory where split files are stored
+        split (str): One of "train", "val", or "test"
+        
+    Returns:
+        list: List of (pdb_id, chain_id) tuples for the specified split
+    """
+    
+    base_dir = Path(base_dir)
+    pickle_path = base_dir / f"{split}_antigens.pkl"
+    
+    if not pickle_path.exists():
+        raise FileNotFoundError(f"Split file not found: {pickle_path}")
+    
+    with open(pickle_path, "rb") as f:
+        antigens = pickle.load(f)
+    
+    print(f"[INFO] Loaded {len(antigens)} antigens for {split} split")
+    return antigens
+
+# load protein embedding extracted by ESM-C
+def load_protein_embedding(
+    pdb_chain: Tuple[str, str],
+    embedding_dir: Optional[str] = None,
+    mode: str = "full"
+) -> np.ndarray:
+    """
+    Retrieve either full or mean embeddings for a given (PDB ID, Chain ID) pair.
+
+    Args:
+        pdb_chain (Tuple[str, str]): Protein identifier (pdb_id, chain_id).
+        embedding_dir (Optional[str]): Directory where embeddings are stored.
+        mode (str): "mean" to retrieve mean embedding (from .npy) or
+                    "full" to retrieve full embedding (from .h5).
+
+    Returns:
+        np.ndarray: The requested embedding (mean: (embed_dim,), full: (seq_len, embed_dim)).
+    """
+    pdb_id, chain_id = pdb_chain
+    
+    # Handle default embedding directory selection
+    if embedding_dir is None:
+        embedding_dir = EMBEDDING_DIR if mode == "mean" else FULL_EMBEDDING_DIR
+        embedding_dir = Path(embedding_dir) / "esmc-6b"
+
+    embedding_dir = Path(embedding_dir)  # Ensure it's a Path object
+
+    if mode == "mean":
+        mean_file = embedding_dir / f"{pdb_id}_{chain_id}.npy"
+        if not mean_file.exists():
+            raise FileNotFoundError(f"Mean embedding file not found: {mean_file}")
+        return np.load(mean_file)  # Shape: (embed_dim,)
+
+    elif mode == "full":
+        full_file = embedding_dir / f"{pdb_id}_{chain_id}.h5"
+        if not full_file.exists():
+            raise FileNotFoundError(f"Full embedding file not found: {full_file}")
+        with h5py.File(full_file, "r") as h5f:
+            return h5f["embedding"][:]  # Shape: (seq_len, embed_dim)
+
+    else:
+        raise ValueError("Invalid mode. Use 'mean' or 'full'.")
+
+# load EGNN model
+def load_egnn_model(model_name: str = "best_large_egnn.bin", layer_type: str = "EGNNWithAngleLayer", model_type: str = "Angle",
+                    verbose: bool = True):
+    """
+    Load the best trained EGNN model from a checkpoint.
+    """
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    
+    model_path = BASE_DIR / "models" / model_name
+    model_path = Path(model_path)
+    
+    if 'small' in model_name:
+        hidden_dim = 640
+        n_layers = 1
+    else:
+        hidden_dim = 1280
+        n_layers = 2
+        
+    # Load the trained EGNN model
+    if model_type == "Angle":
+        model = EGNNWithAngle(in_dim=2560, hidden_dim=hidden_dim, n_layers=n_layers, layer_type=layer_type).to(device)
+    elif model_type == "Attention":
+        model = EGNNWithAttention(in_dim=2560, hidden_dim=hidden_dim, n_layers=n_layers).to(device)
+        
+    model.load_state_dict(torch.load(model_path, map_location=device))
+    model.eval()
+    
+    if verbose:
+        print(f"[INFO] Successfully loaded EGNN model from {model_path}")
+
+    return model, device
+
+# load SBEP model
+def load_sbep(model_name: str = "best_model.bin", timestamp: str = None, version: int = 3, device_id: int = 1, verbose: bool = True):
+    """
+    Load the trained SBEP model from a checkpoint with enhanced architecture mismatch handling.
+
+    Args:
+        model_name (str): Name of the model file (default: "best_model.bin")
+        timestamp (str): Timestamp of the model run (e.g., "20250326_084627")
+        verbose (bool): Whether to print loading information
+
+    Returns:
+        model (SBEP): The trained model ready for inference
+        device (torch.device): The device (CPU/GPU) used for inference
+    """
+    from bce.model.model import SBEP  # Import here to avoid circular imports
+    import torch
+    from pathlib import Path
+    from bce.utils.constants import BASE_DIR
+    
+    # Detect device
+    # Check available GPUs and their memory
+    if torch.cuda.is_available() and device_id >= 0:
+        device = torch.device(f"cuda:{device_id}")
+    else:
+        device = torch.device("cpu")
+    
+    # Construct model path
+    if timestamp:
+        model_path = BASE_DIR / "results" / "sbep" / timestamp / model_name.replace('.bin', '')
+    else:
+        # If no timestamp provided, use the latest model
+        results_dir = BASE_DIR / "results" / "sbep"
+        if not results_dir.exists():
+            raise FileNotFoundError(f"Results directory not found: {results_dir}")
+            
+        # Get the latest timestamp directory
+        timestamps = [d for d in results_dir.iterdir() if d.is_dir()]
+        if not timestamps:
+            raise FileNotFoundError("No model runs found")
+            
+        latest_dir = max(timestamps, key=lambda x: x.name)
+        model_path = latest_dir / model_name.replace('.bin', '')
+        if verbose:
+            print(f"Using latest model from: {latest_dir.name}")
+    
+    # Check if either the path or path with .bin extension exists
+    if not model_path.exists():
+        model_path = model_path.with_suffix('.bin')
+        if not model_path.exists():
+            raise FileNotFoundError(f"Model file not found at {model_path}")
+    
+    # Load model with architecture mismatch handling
+    try:
+        if verbose:
+            print(f"Loading model from: {model_path}")
+        
+        # First try loading with strict=False to handle size mismatches
+        if version == 1:
+            model = SBEP.load(model_path, device=device, strict=False, verbose=verbose)
+        elif version == 2:
+            model = SBEP_v2.load(model_path, device=device, strict=False, verbose=verbose)
+        elif version == 3:
+            model = SBEP_v3.load(model_path, device=device, strict=False, verbose=verbose)
+        elif version == 4:
+            model = SBEP_v4.load(model_path, device=device, strict=False, verbose=verbose)
+        else:
+            raise ValueError(f"Invalid version: {version}")
+        
+        model.eval()
+        
+        if verbose:
+            print(f"[INFO] Model loaded (some parameters may not match current architecture)")
+            print(f"[INFO] Model architecture: {model.__class__.__name__}")
+            print(f"[INFO] Model device: {next(model.parameters()).device}")
+        
+        return model, device
+        
+    except Exception as e:
+        error_msg = f"Failed to load model from {model_path}: {str(e)}"
+        if verbose:
+            print(f"[ERROR] {error_msg}")
+        raise RuntimeError(error_msg)
\ No newline at end of file
diff --git a/src/bce/utils/metrics.py b/src/bce/utils/metrics.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1cf69927dc615e82b56baa33980e5625c60bccc
--- /dev/null
+++ b/src/bce/utils/metrics.py
@@ -0,0 +1,183 @@
+import numpy as np
+
+from sklearn.metrics import (
+    mean_squared_error, mean_absolute_error, r2_score, 
+    average_precision_score, roc_auc_score, f1_score, 
+    precision_score, recall_score, matthews_corrcoef,
+    accuracy_score, confusion_matrix, roc_curve, precision_recall_curve
+)
+
+def calculate_graph_metrics(preds, labels, threshold=0.5):
+    """
+    Calculate graph-level metrics for recall prediction.
+    
+    Args:
+        preds: Predicted recall values (numpy array)
+        labels: True recall values (numpy array)  
+        threshold: Threshold for binary classification (default: 0.5, was 0.7)
+        
+    Returns:
+        Dictionary of metrics
+    """
+    # Check for NaN values and replace with zeros
+    preds = np.nan_to_num(preds, nan=0.0, posinf=1.0, neginf=0.0)
+    labels = np.nan_to_num(labels, nan=0.0, posinf=1.0, neginf=0.0)
+    
+    # Convert predictions to binary for classification metrics
+    pred_binary = (preds > threshold).astype(int)
+    label_binary = (labels > threshold).astype(int)
+    
+    metrics = {}
+    
+    # Classification metrics
+    if len(np.unique(label_binary)) > 1:  # Check if both classes exist
+        metrics['recall'] = recall_score(label_binary, pred_binary, zero_division=0)
+        metrics['precision'] = precision_score(label_binary, pred_binary, zero_division=0)
+        metrics['mcc'] = matthews_corrcoef(label_binary, pred_binary)
+        metrics['f1'] = f1_score(label_binary, pred_binary, zero_division=0)
+        metrics['accuracy'] = accuracy_score(label_binary, pred_binary)
+    else:
+        metrics['recall'] = 0.0
+        metrics['precision'] = 0.0
+        metrics['mcc'] = 0.0
+        metrics['f1'] = 0.0
+        metrics['accuracy'] = 0.0
+    
+    # Regression metrics
+    metrics['mse'] = mean_squared_error(labels, preds)
+    metrics['mae'] = mean_absolute_error(labels, preds)
+    metrics['r2'] = r2_score(labels, preds)
+    
+    return metrics
+
+def calculate_node_metrics(preds, labels, find_threshold=False, include_curves=False):
+    """
+    Calculate node-level metrics for epitope prediction.
+    
+    Args:
+        preds: Predicted probabilities (numpy array)
+        labels: True binary labels (numpy array)
+        find_threshold: If True, find the threshold that maximizes F1 score
+        include_curves: If True, include PR and ROC curves for visualization
+        
+    Returns:
+        Dictionary of metrics including optimal threshold if find_threshold=True
+    """
+    # Check for NaN values and replace with zeros
+    preds = np.nan_to_num(preds, nan=0.0, posinf=1.0, neginf=0.0)
+    labels = np.nan_to_num(labels, nan=0.0, posinf=1.0, neginf=0.0)
+    
+    metrics = {}
+    
+    # Check if both classes exist
+    if len(np.unique(labels)) > 1:
+        # AUROC and AUPRC (threshold-independent metrics)
+        try:
+            metrics['auroc'] = roc_auc_score(labels, preds)
+            metrics['auprc'] = average_precision_score(labels, preds)
+            
+            # Include curves for visualization if requested
+            if include_curves:
+                # Calculate PR curve
+                precision_curve, recall_curve, _ = precision_recall_curve(labels, preds)
+                metrics['pr_curve'] = {
+                    'precision': precision_curve,
+                    'recall': recall_curve
+                }
+                
+                # Calculate ROC curve
+                fpr, tpr, _ = roc_curve(labels, preds)
+                metrics['roc_curve'] = {
+                    'fpr': fpr,
+                    'tpr': tpr
+                }
+            else:
+                metrics['pr_curve'] = None
+                metrics['roc_curve'] = None
+                
+        except:
+            metrics['auroc'] = 0.0
+            metrics['auprc'] = 0.0
+            metrics['pr_curve'] = None
+            metrics['roc_curve'] = None
+        
+        # Find optimal threshold if requested
+        if find_threshold:
+            best_threshold, best_mcc = find_optimal_threshold(preds, labels)
+            metrics['best_threshold'] = best_threshold
+            threshold = best_threshold
+        else:
+            threshold = 0.5
+            metrics['best_threshold'] = 0.5
+        
+        # Binary classification metrics using the determined threshold
+        pred_binary = (preds > threshold).astype(int)
+        metrics['f1'] = f1_score(labels, pred_binary, zero_division=0)
+        metrics['mcc'] = matthews_corrcoef(labels, pred_binary)
+        metrics['precision'] = precision_score(labels, pred_binary, zero_division=0)
+        metrics['recall'] = recall_score(labels, pred_binary, zero_division=0)
+        metrics['accuracy'] = accuracy_score(labels, pred_binary)
+        
+        # Confusion matrix components
+        try:
+            tn, fp, fn, tp = confusion_matrix(labels, pred_binary).ravel()
+            metrics['true_positives'] = int(tp)
+            metrics['false_positives'] = int(fp)
+            metrics['true_negatives'] = int(tn)
+            metrics['false_negatives'] = int(fn)
+        except:
+            metrics['true_positives'] = 0
+            metrics['false_positives'] = 0
+            metrics['true_negatives'] = 0
+            metrics['false_negatives'] = 0
+        
+        # Store the threshold used for these metrics
+        metrics['threshold_used'] = threshold
+        
+    else:
+        # All metrics are 0 if only one class exists
+        metrics['auroc'] = 0.0
+        metrics['auprc'] = 0.0
+        metrics['f1'] = 0.0
+        metrics['mcc'] = 0.0
+        metrics['precision'] = 0.0
+        metrics['recall'] = 0.0
+        metrics['accuracy'] = 0.0
+        metrics['best_threshold'] = 0.5
+        metrics['threshold_used'] = 0.5
+        metrics['true_positives'] = 0
+        metrics['false_positives'] = 0
+        metrics['true_negatives'] = 0
+        metrics['false_negatives'] = 0
+        metrics['pr_curve'] = None
+        metrics['roc_curve'] = None
+    
+    return metrics
+
+def find_optimal_threshold(preds, labels, num_thresholds=100):
+    """
+    Find the threshold that maximizes F1 score.
+    
+    Args:
+        preds: Predicted probabilities (numpy array)
+        labels: True binary labels (numpy array)
+        num_thresholds: Number of thresholds to test
+        
+    Returns:
+        Tuple of (best_threshold, best_f1_score)
+    """
+    # Generate threshold candidates
+    thresholds = np.linspace(0.01, 0.99, num_thresholds)
+    
+    best_mcc = 0.0
+    best_threshold = 0.5
+    
+    for threshold in thresholds:
+        pred_binary = (preds > threshold).astype(int)
+        mcc = matthews_corrcoef(labels, pred_binary)
+        
+        if mcc > best_mcc:
+            best_mcc = mcc
+            best_threshold = threshold
+    
+    return best_threshold, best_mcc
diff --git a/src/bce/utils/results.py b/src/bce/utils/results.py
new file mode 100644
index 0000000000000000000000000000000000000000..2813554c8eaba1737e61a55f7f0d514146e3ace6
--- /dev/null
+++ b/src/bce/utils/results.py
@@ -0,0 +1,304 @@
+import numpy as np
+import pandas as pd
+from tqdm import tqdm
+from sklearn.metrics import roc_curve, auc, precision_recall_curve
+
+from .constants import BASE_DIR
+from .loading import load_data_split
+from ..antigen.antigen import AntigenChain
+from .metrics import find_optimal_threshold, calculate_node_metrics
+
+def evaluate_ReCEP(model_path, device_id=0, radius=18.0, threshold=0.5, k=5, 
+                   verbose=True, split="test", save_results=True, output_dir=None, encoder="esmc"):
+    """
+    Evaluate ReCEP model on a dataset split using both probability-based and voting-based predictions.
+    
+    Args:
+        model_path: Path to the trained ReCEP model
+        device_id: GPU device ID
+        radius: Radius for spherical regions
+        threshold: Threshold for probability-based predictions
+        k: Number of top regions to select
+        verbose: Whether to print progress
+        split: Dataset split to evaluate ('test', 'val', 'train')
+        save_results: Whether to save detailed results to files
+        output_dir: Directory to save results (if save_results=True)
+        
+    Returns:
+        Dictionary containing evaluation metrics for both prediction methods
+    """
+    print(f"[INFO] Evaluating ReCEP model from {model_path}")
+    print(f"[INFO] Settings:")
+    print(f"  Radius: {radius}")
+    print(f"  K: {k}")
+    print(f"  Split: {split}\n")
+
+    antigens = load_data_split(split, verbose=verbose)
+    
+    # Collect data for all proteins
+    all_true_labels = []
+    all_predicted_probs = []
+    all_voted_labels = []
+    all_predicted_binary = []
+    
+    protein_results = []
+    
+    for pdb_id, chain_id in tqdm(antigens, desc=f"Evaluating ReCEP on {split} set", disable=not verbose):
+        try:
+            antigen_chain = AntigenChain.from_pdb(chain_id=chain_id, id=pdb_id)
+            results = antigen_chain.evaluate(
+                model_path=model_path,
+                device_id=device_id,
+                radius=radius,
+                threshold=threshold,
+                k=k,
+                verbose=False,
+                encoder=encoder
+            )
+            
+            # Get true epitope labels as binary array
+            true_epitopes = antigen_chain.get_epitope_residue_numbers()
+            true_binary = []
+            predicted_probs = []
+            voted_binary = []
+            predicted_binary = []
+            
+            # Convert to aligned arrays based on residue numbers
+            for idx in range(len(antigen_chain.residue_index)):
+                residue_num = int(antigen_chain.residue_index[idx])
+                
+                # True label
+                true_binary.append(1 if residue_num in true_epitopes else 0)
+                
+                # Predicted probability
+                predicted_probs.append(results['predictions'].get(residue_num, 0))
+                
+                # Voted prediction
+                voted_binary.append(1 if residue_num in results['voted_epitopes'] else 0)
+                
+                # Probability-based prediction
+                predicted_binary.append(1 if residue_num in results['predicted_epitopes'] else 0)
+            
+            # Store for overall evaluation
+            all_true_labels.extend(true_binary)
+            all_predicted_probs.extend(predicted_probs)
+            all_voted_labels.extend(voted_binary)
+            all_predicted_binary.extend(predicted_binary)
+            
+            length = len(antigen_chain.sequence)
+            species = antigen_chain.get_species()
+            precision = results['predicted_precision']
+            recall = results['predicted_recall']
+            f1 = 2 * precision * recall / (precision + recall + 1e-10)
+            
+            # Calculate PR-AUC using true_binary and predicted_probs
+            if len(set(true_binary)) > 1:  # Check if there are both positive and negative samples
+                pr_precision, pr_recall, _ = precision_recall_curve(true_binary, predicted_probs)
+                pr_auc = auc(pr_recall, pr_precision)
+            else:
+                pr_auc = 0.0  # Default value when all labels are the same
+            
+            # Store individual protein results
+            protein_results.append({
+                'pdb_id': pdb_id,
+                'chain_id': chain_id,
+                'length': length,
+                'species': species,
+                'predicted_precision': precision,
+                'predicted_recall': recall,
+                'predicted_f1': f1,
+                'pr_auc': pr_auc,
+                'voted_precision': results['voted_precision'],
+                'voted_recall': results['voted_recall'],
+                'num_residues': len(true_binary),
+                'num_true_epitopes': sum(true_binary),
+                'num_predicted_epitopes': sum(predicted_binary),
+                'num_voted_epitopes': sum(voted_binary),
+                'true_epitopes': true_binary,
+                'predicted_probabilities': predicted_probs
+            })
+            
+        except Exception as e:
+            if verbose:
+                print(f"[WARNING] Failed to evaluate {pdb_id}_{chain_id}: {str(e)}")
+            continue
+    
+    # Convert to numpy arrays
+    all_true_labels = np.array(all_true_labels)
+    all_predicted_probs = np.array(all_predicted_probs)
+    all_voted_labels = np.array(all_voted_labels)
+    all_predicted_binary = np.array(all_predicted_binary)
+    
+    # Calculate metrics for probability-based predictions (includes both probability and binary metrics)
+    prob_metrics = calculate_node_metrics(all_predicted_probs, all_true_labels, find_threshold=True, include_curves=True)
+    
+    # Calculate metrics for voting-based predictions (binary only)
+    vote_metrics = calculate_node_metrics(all_voted_labels.astype(float), all_true_labels, find_threshold=False)
+    
+    # Calculate metrics for probability-based binary predictions using original threshold
+    pred_metrics = calculate_node_metrics(all_predicted_binary.astype(float), all_true_labels, find_threshold=False)
+    
+    # Additional statistics for comprehensive evaluation
+    prediction_stats = {
+        'prob_based': {
+            'total_predicted_positive': int(np.sum(all_predicted_binary)),
+            'prediction_rate': float(np.mean(all_predicted_binary))
+        },
+        'vote_based': {
+            'total_predicted_positive': int(np.sum(all_voted_labels)),
+            'prediction_rate': float(np.mean(all_voted_labels))
+        }
+    }
+    
+    # Overall statistics
+    overall_stats = {
+        'num_proteins': len(protein_results),
+        'total_residues': len(all_true_labels),
+        'total_true_epitopes': int(np.sum(all_true_labels)),
+        'epitope_ratio': float(np.mean(all_true_labels)),
+        'avg_protein_size': np.mean([p['num_residues'] for p in protein_results]),
+        'avg_epitopes_per_protein': np.mean([p['num_true_epitopes'] for p in protein_results]),
+        'prediction_stats': prediction_stats
+    }
+    
+    if verbose:
+        print_evaluation_results(prob_metrics, vote_metrics, pred_metrics, overall_stats, threshold)
+    
+    # Prepare results dictionary
+    results = {
+        'probability_metrics': prob_metrics,
+        'voted_metrics': vote_metrics,
+        'predicted_metrics': pred_metrics,
+        'overall_stats': overall_stats,
+        'protein_results': protein_results,
+        'threshold': threshold
+    }
+    
+    if save_results:
+        if output_dir is None:
+            # Handle both string and Path objects
+            from pathlib import Path
+            model_path_obj = Path(model_path)
+            timestamp = model_path_obj.parent.name
+            model_name = model_path_obj.name.split("_")[1]
+            output_dir = BASE_DIR / "results" / "ReCEP" / timestamp
+        save_evaluation_results(results, output_dir, model_name)
+    
+    return results
+
+
+
+def print_evaluation_results(prob_metrics, vote_metrics, pred_metrics, overall_stats, threshold):
+    """Print formatted evaluation results for both prediction modes."""
+    print(f"\n{'='*80}")
+    print(f"ReCEP MODEL EVALUATION RESULTS")
+    print(f"{'='*80}")
+    
+    print(f"\nOverall Statistics:")
+    print(f"  Number of proteins: {overall_stats['num_proteins']}")
+    print(f"  Total residues: {overall_stats['total_residues']:,}")
+    print(f"  Total true epitopes: {overall_stats['total_true_epitopes']:,}")
+    print(f"  Epitope ratio: {overall_stats['epitope_ratio']:.3f}")
+    print(f"  Average protein size: {overall_stats['avg_protein_size']:.1f}")
+    print(f"  Average epitopes per protein: {overall_stats['avg_epitopes_per_protein']:.1f}")
+    
+    print(f"\n{'-'*40}")
+    print(f"PROBABILITY-BASED PREDICTIONS")
+    print(f"{'-'*40}")
+    print(f"Threshold: {prob_metrics['best_threshold']}")
+    print(f"\nProbability Metrics:")
+    print(f"  AUPRC: {prob_metrics['auprc']:.4f}")
+    print(f"  AUROC: {prob_metrics['auroc']:.4f}")
+    print(f"\nBinary Classification Metrics:")
+    print(f"  Accuracy:  {prob_metrics['accuracy']:.4f}")
+    print(f"  Precision: {prob_metrics['precision']:.4f}")
+    print(f"  Recall:    {prob_metrics['recall']:.4f}")
+    print(f"  F1-Score:  {prob_metrics['f1']:.4f}")
+    print(f"  MCC:       {prob_metrics['mcc']:.4f}")
+    print(f"\nConfusion Matrix:")
+    print(f"  True Pos:  {prob_metrics['true_positives']:>6} | False Pos: {prob_metrics['false_positives']:>6}")
+    print(f"  False Neg: {prob_metrics['false_negatives']:>6} | True Neg:  {prob_metrics['true_negatives']:>6}")
+    
+    print(f"\n{'-'*40}")
+    print(f"VOTING-BASED PREDICTIONS")
+    print(f"{'-'*40}")
+    print(f"Binary Classification Metrics:")
+    print(f"  Accuracy:  {vote_metrics['accuracy']:.4f}")
+    print(f"  Precision: {vote_metrics['precision']:.4f}")
+    print(f"  Recall:    {vote_metrics['recall']:.4f}")
+    print(f"  F1-Score:  {vote_metrics['f1']:.4f}")
+    print(f"  MCC:       {vote_metrics['mcc']:.4f}")
+    print(f"\nConfusion Matrix:")
+    print(f"  True Pos:  {vote_metrics['true_positives']:>6} | False Pos: {vote_metrics['false_positives']:>6}")
+    print(f"  False Neg: {vote_metrics['false_negatives']:>6} | True Neg:  {vote_metrics['true_negatives']:>6}")
+    
+    print(f"\n{'-'*40}")
+    print(f"COMPARISON SUMMARY")
+    print(f"{'-'*40}")
+    print(f"{'Metric':<12} {'Probability':<12} {'Voting':<12} {'Difference':<12}")
+    print(f"{'-'*48}")
+    print(f"{'Accuracy':<12} {prob_metrics['accuracy']:<12.4f} {vote_metrics['accuracy']:<12.4f} {prob_metrics['accuracy']-vote_metrics['accuracy']:<12.4f}")
+    print(f"{'Precision':<12} {prob_metrics['precision']:<12.4f} {vote_metrics['precision']:<12.4f} {prob_metrics['precision']-vote_metrics['precision']:<12.4f}")
+    print(f"{'Recall':<12} {prob_metrics['recall']:<12.4f} {vote_metrics['recall']:<12.4f} {prob_metrics['recall']-vote_metrics['recall']:<12.4f}")
+    print(f"{'F1-Score':<12} {prob_metrics['f1']:<12.4f} {vote_metrics['f1']:<12.4f} {prob_metrics['f1']-vote_metrics['f1']:<12.4f}")
+    print(f"{'MCC':<12} {prob_metrics['mcc']:<12.4f} {vote_metrics['mcc']:<12.4f} {prob_metrics['mcc']-vote_metrics['mcc']:<12.4f}")
+    
+    print(f"\n{'='*80}")
+    
+
+def save_evaluation_results(results, output_dir=None, prefix="evaluation"):
+    """
+    Save detailed evaluation results to files for further analysis.
+    
+    Args:
+        results: Dictionary containing all evaluation results
+        output_dir: Directory to save results
+        prefix: Prefix for output files
+    """
+    import os
+    import json
+    
+    if output_dir is None:
+        output_dir = BASE_DIR / "results" / "evaluation"
+    
+    # Create output directory if it doesn't exist
+    os.makedirs(output_dir, exist_ok=True)
+    
+    # Save overall results as JSON
+    results_to_save = {
+        'probability_metrics': results['probability_metrics'],
+        'voted_metrics': results['voted_metrics'], 
+        'predicted_metrics': results['predicted_metrics'],
+        'overall_stats': results['overall_stats'],
+        'threshold': results['threshold']
+    }
+    
+    # Remove non-serializable items (curves)
+    if 'pr_curve' in results_to_save['probability_metrics']:
+        if results_to_save['probability_metrics']['pr_curve'] is not None:
+            # Convert numpy arrays to lists for JSON serialization
+            results_to_save['probability_metrics']['pr_curve'] = {
+                'precision': results_to_save['probability_metrics']['pr_curve']['precision'].tolist(),
+                'recall': results_to_save['probability_metrics']['pr_curve']['recall'].tolist()
+            }
+    
+    if 'roc_curve' in results_to_save['probability_metrics']:
+        if results_to_save['probability_metrics']['roc_curve'] is not None:
+            results_to_save['probability_metrics']['roc_curve'] = {
+                'fpr': results_to_save['probability_metrics']['roc_curve']['fpr'].tolist(),
+                'tpr': results_to_save['probability_metrics']['roc_curve']['tpr'].tolist()
+            }
+    
+    # Save main results
+    with open(os.path.join(output_dir, f"{prefix}_results.json"), 'w') as f:
+        json.dump(results_to_save, f, indent=2)
+    
+    # Save protein-level results as CSV
+    if 'protein_results' in results:
+        df = pd.DataFrame(results['protein_results'])
+        df.to_csv(os.path.join(output_dir, f"{prefix}_protein_results.csv"), index=False)
+    
+    print(f"\nResults saved to {output_dir}/")
+    print(f"  - {prefix}_results.json: Overall metrics")
+    print(f"  - {prefix}_protein_results.csv: Per-protein results")
+    
\ No newline at end of file
diff --git a/src/bce/utils/tools.py b/src/bce/utils/tools.py
new file mode 100644
index 0000000000000000000000000000000000000000..4481b9c519be166127a2e37e8b1ba17bd9c6743b
--- /dev/null
+++ b/src/bce/utils/tools.py
@@ -0,0 +1,47 @@
+import torch
+
+def set_seed(seed=42):
+    """Set random seed for reproducibility."""
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed)
+        
+def get_chain_organism(pdb_id, chain_id):
+    """
+    Use RCSB PDB API to get the organism of the chain.
+    """
+    import requests
+    entry_url = f"https://data.rcsb.org/rest/v1/core/entry/{pdb_id.lower()}"
+    res = requests.get(entry_url)
+    if res.status_code != 200:
+        return "Unknown"
+    entry_data = res.json()
+    
+    # Find the polymer_entity_id of the chain
+    chain_to_entity = {}
+    for entity_id in entry_data.get("rcsb_entry_container_identifiers", {}).get("polymer_entity_ids", []):
+        entity_url = f"https://data.rcsb.org/rest/v1/core/polymer_entity/{pdb_id.lower()}/{entity_id}"
+        entity_res = requests.get(entity_url)
+        if entity_res.status_code != 200:
+            continue
+        entity_data = entity_res.json()
+        chains = entity_data.get("rcsb_polymer_entity_container_identifiers", {}).get("auth_asym_ids", [])
+        for c in chains:
+            chain_to_entity[c] = entity_id
+        if chain_id in chains:
+            organism = entity_data.get("rcsb_entity_source_organism", [{}])[0].get("scientific_name", "Unknown")
+            return organism
+    return "Unknown"
+
+def classify_antigen(organisms):
+    for org in organisms:
+        if "virus" in org.lower() or "coronavirus" in org.lower():
+            return "viral"
+        elif "homo sapiens" in org.lower():
+            return "human"
+        elif "bacteria" in org.lower() or "bacillus" in org.lower():
+            return "bacterial"
+        elif "tumor" in org.lower() or "cancer" in org.lower():
+            return "tumor"
+    return "other"
\ No newline at end of file
diff --git a/src/bce/utils/trainer.py b/src/bce/utils/trainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..fcb3496dad32e76c8ca5d7098bd5f37597618e87
--- /dev/null
+++ b/src/bce/utils/trainer.py
@@ -0,0 +1,1037 @@
+import os
+import json
+import time
+import numpy as np
+from tqdm import tqdm
+from pathlib import Path
+from datetime import datetime
+import matplotlib.pyplot as plt
+from collections import defaultdict
+
+import torch
+from torch.amp import autocast, GradScaler
+
+from ..loss import CLoss
+from .metrics import calculate_graph_metrics, calculate_node_metrics
+from .results import evaluate_ReCEP
+from .constants import BASE_DIR
+from ..data.data import create_data_loader
+from ..model.ReCEP import get_model, ReCEP
+from ..model.scheduler import get_scheduler
+
+torch.set_num_threads(12)
+
+def check_for_nan_inf(tensor, name="tensor"):
+    """Check for NaN or Inf values in tensor and raise error if found."""
+    if torch.isnan(tensor).any():
+        raise ValueError(f"NaN detected in {name}")
+    if torch.isinf(tensor).any():
+        raise ValueError(f"Inf detected in {name}")
+
+def format_loss_info(loss_dict, prefix=""):
+    """
+    Format loss information from loss_dict for display.
+    
+    Args:
+        loss_dict: Dictionary containing loss components
+        prefix: Prefix for the output string
+        
+    Returns:
+        Formatted string with loss breakdown
+    """
+    main_info = f"{prefix}Total: {loss_dict['loss/total']:.4f}, " \
+                f"Region: {loss_dict['loss/region']:.4f}, " \
+                f"Node: {loss_dict['loss/node']:.4f}"
+    
+    additional_info = []
+    if loss_dict.get('loss/cls', 0) > 0:
+        additional_info.append(f"Cls: {loss_dict['loss/cls']:.4f}, Reg: {loss_dict['loss/reg']:.4f}")
+    if loss_dict.get('loss/consistency', 0) > 0:
+        additional_info.append(f"Consistency: {loss_dict['loss/consistency']:.4f}")
+    
+    if additional_info:
+        return main_info + "\n" + prefix + "  " + ", ".join(additional_info)
+    return main_info
+
+class Trainer:
+    """
+    Trainer class for ReCEP model with comprehensive training and evaluation.
+    
+    Features:
+    - Early stopping with patience
+    - Model checkpointing (best AUPRC and best F1)
+    - Mixed precision training
+    - Comprehensive metrics calculation
+    - Learning rate scheduling
+    """
+
+    @staticmethod
+    def convert_to_serializable(obj):
+        """Convert numpy/torch types to Python native types for JSON serialization."""
+        if isinstance(obj, dict):
+            return {k: Trainer.convert_to_serializable(v) for k, v in obj.items()}
+        elif isinstance(obj, list):
+            return [Trainer.convert_to_serializable(item) for item in obj]
+        elif isinstance(obj, (np.float32, np.float64)):
+            return float(obj)
+        elif isinstance(obj, (np.int32, np.int64)):
+            return int(obj)
+        elif hasattr(obj, 'item'):  # For torch tensors and numpy scalars
+            return obj.item()
+        else:
+            return obj
+
+    def __init__(self, args):
+        """
+        Initialize Trainer with datasets, model, and hyperparameters.
+        
+        Args:
+            args: Argument namespace containing all training parameters
+        """
+        self.args = args
+        
+        # Set device
+        self.device = torch.device(f"cuda:{args.device_id}" if torch.cuda.is_available() else "cpu")
+        print(f"[INFO] Using device: {self.device}")
+        
+        # Create data loaders
+        use_embeddings2 = False if args.encoder == "esmc" else True
+        start_time = time.time()
+        self.train_loader, self.test_loader = create_data_loader(
+            radii=args.radii,
+            batch_size=args.batch_size,
+            undersample=args.undersample,
+            zero_ratio=args.zero_ratio,
+            seed=args.seed,
+            use_embeddings2=use_embeddings2
+        )
+        self.val_loader = self.test_loader
+        end_time = time.time()
+        print(f"[INFO] Data loading time: {end_time - start_time:.2f} seconds")
+        
+        print(f"[INFO] Train samples: {len(self.train_loader.dataset)}")
+        print(f"[INFO] Test samples: {len(self.test_loader.dataset)}")
+
+        # Create directories
+        timestamp = getattr(args, 'timestamp', None)
+        if timestamp is None:
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        
+        self.results_dir = Path(f"{BASE_DIR}/results/ReCEP/{timestamp}")
+        self.results_dir.mkdir(parents=True, exist_ok=True)
+        self.model_dir = Path(f"{BASE_DIR}/models/ReCEP/{timestamp}")
+        self.model_dir.mkdir(parents=True, exist_ok=True)
+        
+        # Model save paths
+        self.best_auprc_model_path = Path(self.model_dir / "best_auprc_model.bin")
+        self.best_mcc_model_path = Path(self.model_dir / "best_mcc_model.bin")
+        self.last_model_path = Path(self.model_dir / "last_model.bin")
+        
+        # Initialize model using the flexible model loader
+        self.model = get_model(args)
+        
+        # Store finetune configuration for later use
+        self.is_finetune_mode = getattr(args, 'mode', 'train') == 'finetune'
+        if self.is_finetune_mode:
+            print("[INFO] Finetune mode enabled - will load pretrained model and freeze most parameters")
+        
+        # Initialize optimizer
+        self.optimizer = torch.optim.AdamW(
+            self.model.parameters(), 
+            lr=args.lr, 
+            weight_decay=args.weight_decay
+        )
+        
+        # Initialize scheduler
+        self.scheduler = get_scheduler(
+            args=args,
+            optimizer=self.optimizer,
+            num_samples=len(self.train_loader.dataset)
+        )
+        
+        # Initialize loss function
+        self.criterion = CLoss(
+            region_loss_type=args.region_loss_type,
+            region_weight=args.region_weight,
+            node_loss_type=args.node_loss_type,
+            node_loss_weight=args.node_loss_weight,
+            consistency_weight=args.consistency_weight,
+            consistency_type=args.consistency_type,
+            threshold=args.threshold,
+            label_smoothing=args.label_smoothing,
+            gradnorm=getattr(args, 'gradnorm', False),
+            gradnorm_alpha=getattr(args, 'gradnorm_alpha', 1.5),
+            **{k: v for k, v in vars(args).items() if k in ['alpha', 'pos_weight', 'reg_weight', 'gamma_high_cls', 'cls_type', 'regression_type', 'weight_mode']}
+        )
+        
+        # GradNorm parameters
+        self.gradnorm_enabled = getattr(args, 'gradnorm', False)
+        self.gradnorm_update_freq = getattr(args, 'gradnorm_update_freq', 10)
+        self.gradnorm_step_counter = 0
+        
+        if self.gradnorm_enabled:
+            print(f"[INFO] GradNorm enabled with alpha={getattr(args, 'gradnorm_alpha', 1.5)}, update frequency={self.gradnorm_update_freq}")
+            # Initialize optimizer for task weights
+            if hasattr(self.criterion, 'log_w_region') and hasattr(self.criterion, 'log_w_node'):
+                self.task_weight_optimizer = torch.optim.Adam([
+                    self.criterion.log_w_region,
+                    self.criterion.log_w_node
+                ], lr=0.025)  # Higher learning rate for task weights
+                print(f"[INFO] GradNorm task weight optimizer initialized")
+            else:
+                print(f"[WARNING] GradNorm enabled but task weight parameters not found in criterion")
+                self.gradnorm_enabled = False
+        
+        # Mixed precision setup
+        self.mixed_precision = args.mixed_precision
+        if self.mixed_precision:
+            self.scaler = GradScaler('cuda')
+            print("[INFO] Using mixed precision training")
+        else:
+            self.scaler = None
+            print("[INFO] Using full precision training")
+        
+        # Training parameters
+        self.patience = args.patience
+        self.threshold = args.threshold
+        self.log_steps = 50
+        self.max_grad_norm = getattr(args, 'max_grad_norm', 1.0)  # Gradient clipping threshold
+                 
+        # Training history
+        self.train_history = []
+        self.val_history = []
+        
+        # Save configuration
+        if not self.is_finetune_mode:
+            self.save_config()
+            
+        self.best_threshold = None
+
+    def train(self):
+        """Train the model with early stopping and checkpointing."""
+        print("[INFO] Starting training...")
+        
+        self.model.to(self.device)
+        # Also move loss function to device (important for GradNorm parameters)
+        self.criterion.to(self.device)
+                
+        # Print model info
+        self.model.print_param_count()
+        
+        # Initialize tracking variables
+        best_val_auprc = 0.0
+        best_val_mcc = 0.0
+        patience_counter = -5 # 5 epochs avoid stopping too early
+        start_time = time.time()
+        
+        for epoch in range(self.args.num_epoch):
+            print(f"\n=== Epoch {epoch + 1}/{self.args.num_epoch} ===")
+            
+            # Training phase
+            train_metrics = self._train_epoch(epoch)
+            self.train_history.append(train_metrics)
+            
+            # Validation phase
+            if self.val_loader:
+                val_metrics = self._validate_epoch(epoch)
+                self.val_history.append(val_metrics)
+                
+                # Check for improvement
+                current_auprc = val_metrics['node_auprc']
+                current_mcc = val_metrics['graph_mcc']
+                
+                improved = False
+                
+                # Save best AUPRC model
+                if current_auprc > best_val_auprc:
+                    best_val_auprc = current_auprc
+                    print(f"[INFO] New best AUPRC: {best_val_auprc:.4f} - Model saved")
+                    self.model.save(self.best_auprc_model_path, threshold=val_metrics['threshold'])
+                    improved = True
+                
+                # Save best F1 model
+                if current_mcc > best_val_mcc:
+                    best_val_mcc = current_mcc
+                    print(f"[INFO] New best MCC: {best_val_mcc:.4f} - Model saved")
+                    self.model.save(self.best_mcc_model_path, threshold=val_metrics['threshold'])
+                    self.best_threshold = val_metrics['threshold']
+                    improved = True
+                
+                # Early stopping logic
+                if improved:
+                    patience_counter = 0
+                else:
+                    patience_counter += 1
+                    print(f"[INFO] No improvement for {patience_counter}/{self.patience} epochs")
+                
+                if patience_counter >= self.patience:
+                    print(f"[INFO] Early stopping triggered after {epoch + 1} epochs")
+                    break
+            
+            # Save last model
+            self.model.save(self.last_model_path)
+        
+        end_time = time.time()
+        print(f"\n[INFO] Training completed for {end_time - start_time:.2f} seconds")
+        print(f"[INFO] Best validation AUPRC: {best_val_auprc:.4f}")
+        print(f"[INFO] Best validation MCC: {best_val_mcc:.4f}")
+        
+        # Save training history
+        self._save_training_history()
+        
+        # Evaluate the model
+        print("\n" + "="*80)
+        print("[INFO] Evaluating best AUPRC model...")
+        results = evaluate_ReCEP(
+            model_path=self.best_auprc_model_path,
+            device_id=self.args.device_id,
+            radius=self.args.radius,
+            threshold=self.best_threshold,
+            k=self.args.k,
+            verbose=True,
+            split="test",
+            encoder=self.args.encoder
+        )
+        
+        print("="*80)
+        print("\n[INFO] Evaluating best MCC model...")
+        results = evaluate_ReCEP(
+            model_path=self.best_mcc_model_path,
+            device_id=self.args.device_id,
+            radius=self.args.radius,
+            threshold=self.best_threshold,
+            k=self.args.k,
+            verbose=True,
+            split="test",
+            encoder=self.args.encoder
+        )
+    
+    def finetune(self, pretrained_model_path=None):
+        """
+        Finetune the node prediction parameters of the model.
+        
+        Args:
+            pretrained_model_path: Path to pretrained model. If None, uses best_auprc_model_path
+        """
+        print("[INFO] Starting finetuning...")
+        
+        # Load pretrained model
+        if pretrained_model_path is None:
+            # Prefer AUPRC model, fallback to MCC model
+            if self.best_mcc_model_path.exists():
+                pretrained_model_path = self.best_mcc_model_path
+                print(f"[INFO] Using best MCC model for finetuning")
+            elif self.best_auprc_model_path.exists():
+                pretrained_model_path = self.best_auprc_model_path
+                print(f"[INFO] Using best AUPRC model for finetuning")
+            else:
+                raise FileNotFoundError(
+                    f"No pretrained model found. Please train the model first or provide a pretrained_model_path.\n"
+                    f"Expected paths: {self.best_auprc_model_path} or {self.best_mcc_model_path}"
+                )
+        else:
+            # Convert string path to Path object if needed
+            pretrained_model_path = Path(pretrained_model_path)
+        
+        if not pretrained_model_path.exists():
+            raise FileNotFoundError(f"Pretrained model not found at {pretrained_model_path}")
+        
+        print(f"[INFO] Loading pretrained model from {pretrained_model_path}")
+        self.model, loaded_threshold = ReCEP.load(pretrained_model_path, device=self.device)
+        print(f"[INFO] Loaded model with threshold: {loaded_threshold}")
+        
+        # Freeze all parameters first
+        for param in self.model.parameters():
+            param.requires_grad = False
+        
+        # Unfreeze specific modules for node prediction
+        trainable_modules = ['node_gate', 'node_classifier']  # Correct module names from ReCEP model
+        trainable_params = []
+        frozen_params = []
+        
+        print("[INFO] Setting trainable parameters:")
+        for name, param in self.model.named_parameters():
+            is_trainable = False
+            for module_name in trainable_modules:
+                if module_name in name:
+                    param.requires_grad = True
+                    is_trainable = True
+                    trainable_params.append(param)
+                    print(f"  - {name} (trainable)")
+                    break
+            
+            if not is_trainable:
+                frozen_params.append(param)
+        
+        print(f"[INFO] Trainable parameters: {len(trainable_params)}")
+        print(f"[INFO] Frozen parameters: {len(frozen_params)}")
+                
+        # Print model info
+        self.model.print_param_count()
+        
+        if len(trainable_params) == 0:
+            raise ValueError("No trainable parameters found! Check trainable_modules list.")
+        
+        # Reinitialize optimizer with only trainable parameters
+        self.optimizer = torch.optim.AdamW(
+            trainable_params,
+            lr=self.args.lr * 0.1,  # Use smaller learning rate for finetuning
+            weight_decay=self.args.weight_decay
+        )
+        
+        # Reinitialize scheduler
+        self.args.schedule_type = "cosine"
+        self.args.warmup_ratio = 0.0
+        self.scheduler = get_scheduler(
+            args=self.args,
+            optimizer=self.optimizer,
+            num_samples=len(self.train_loader.dataset)
+        )
+        
+        # Adjust loss function weights for node-focused training
+        print("[INFO] Adjusting loss weights for node-focused training")
+        self.criterion.region_weight = 0.0
+        self.criterion.node_loss_weight = 1.0
+        self.gradnorm_enabled = False
+        
+        # Reset training history for finetuning
+        self.train_history = []
+        self.val_history = []
+        
+        # Update model save paths for finetuning
+        self.best_finetuned_model_path = self.model_dir / "best_finetuned_model.bin"
+        self.last_finetuned_model_path = self.model_dir / "last_finetuned_model.bin"
+        
+        print("[INFO] Finetune setup completed. Use train() method to start finetuning.")
+        
+        # Start finetuning training loop
+        self._finetune_train()
+        
+        print("\n" + "="*80)
+        print("[INFO] Evaluating finetuned model...")
+        results = evaluate_ReCEP(
+            model_path=self.best_finetuned_model_path,
+            device_id=self.args.device_id,
+            radius=self.args.radius,
+            k=self.args.k,
+            verbose=True,
+            split="test"
+        )
+
+    def _finetune_train(self):
+        """Training loop specifically for finetuning."""
+        print("[INFO] Starting finetune training loop...")
+        
+        self.model.to(self.device)
+        # Also move loss function to device (important for GradNorm parameters)
+        self.criterion.to(self.device)
+        
+        # Initialize tracking variables
+        best_val_auprc = 0.0
+        patience_counter = 0  # No initial patience for finetuning
+        start_time = time.time()
+        
+        # Use fewer epochs for finetuning
+        finetune_epochs = self.args.num_epoch  # Max 60 epochs for finetuning
+        
+        for epoch in range(finetune_epochs):
+            print(f"\n=== Finetune Epoch {epoch + 1}/{finetune_epochs} ===")
+            
+            # Training phase
+            train_metrics = self._train_epoch(epoch)
+            self.train_history.append(train_metrics)
+            
+            # Validation phase
+            if self.val_loader:
+                val_metrics = self._validate_epoch(epoch)
+                self.val_history.append(val_metrics)
+                
+                # Check for improvement (focus on node metrics)
+                current_auprc = val_metrics['node_auprc']
+                
+                improved = False
+                
+                # Save best AUPRC model
+                if current_auprc > best_val_auprc:
+                    best_val_auprc = current_auprc
+                    print(f"[INFO] New best AUPRC: {best_val_auprc:.4f} - Finetuned model saved")
+                    self.model.save(self.best_finetuned_model_path, threshold=val_metrics['threshold'])
+                    improved = True
+                
+                # Early stopping logic (shorter patience for finetuning)
+                if improved:
+                    patience_counter = 0
+                else:
+                    patience_counter += 1
+                    print(f"[INFO] No improvement for {patience_counter}/{min(self.patience, 10)} epochs")
+                
+                if patience_counter >= min(self.patience, 10):  # Shorter patience
+                    print(f"[INFO] Early stopping triggered after {epoch + 1} epochs")
+                    break
+            
+            # Save last model
+            self.model.save(self.last_finetuned_model_path)
+        
+        end_time = time.time()
+        print(f"\n[INFO] Finetuning completed in {end_time - start_time:.2f} seconds")
+        print(f"[INFO] Best validation AUPRC: {best_val_auprc:.4f}")
+        
+        # Save training history
+        self._save_training_history()
+
+    def _train_epoch(self, epoch):
+        """Train for one epoch."""
+        self.model.train()
+        
+        # Initialize accumulators for loss components and predictions
+        loss_accumulators = defaultdict(float)
+        all_global_preds = []
+        all_global_labels = []
+        all_node_preds = []
+        all_node_labels = []
+        
+        pbar = tqdm(self.train_loader, desc=f"Training Epoch {epoch + 1}")
+        
+        for batch_idx, batch in enumerate(pbar):
+            batch = batch.to(self.device)
+            
+            # Prepare targets
+            targets = {
+                'y': batch.y,
+                'y_node': batch.y_node
+            }
+            
+            # Forward pass
+            if self.mixed_precision:
+                with autocast('cuda'):
+                    outputs = self.model(batch)
+                    if self.gradnorm_enabled:
+                        loss, loss_dict, individual_losses = self.criterion(outputs, targets, batch.batch, return_individual_losses=True)
+                    else:
+                        loss, loss_dict = self.criterion(outputs, targets, batch.batch)
+                
+                # Check for NaN/Inf in loss
+                if torch.isnan(loss) or torch.isinf(loss):
+                    # print(f"[WARNING] NaN/Inf loss detected at batch {batch_idx}, skipping batch")
+                    continue
+                
+                # Backward pass and GradNorm update
+                self.optimizer.zero_grad()
+                
+                # GradNorm update BEFORE main backward to preserve computation graph  
+                if self.gradnorm_enabled and self.gradnorm_step_counter % self.gradnorm_update_freq == 0:
+                    # Update GradNorm weights first
+                    self.task_weight_optimizer.zero_grad()
+                    gradnorm_info = self.criterion.update_gradnorm_weights(individual_losses, self.model)
+                    if gradnorm_info:
+                        loss_dict.update(gradnorm_info)
+                    self.task_weight_optimizer.step()
+                
+                # Main backward pass
+                self.scaler.scale(loss).backward()
+                
+                # Gradient clipping
+                self.scaler.unscale_(self.optimizer)
+                torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)
+                
+                self.scaler.step(self.optimizer)
+                self.scaler.update()
+            else:
+                outputs = self.model(batch)
+                if self.gradnorm_enabled:
+                    loss, loss_dict, individual_losses = self.criterion(outputs, targets, batch.batch, return_individual_losses=True)
+                else:
+                    loss, loss_dict = self.criterion(outputs, targets, batch.batch)
+                
+                # Check for NaN/Inf in loss
+                if torch.isnan(loss) or torch.isinf(loss):
+                    # print(f"[WARNING] NaN/Inf loss detected at batch {batch_idx}, skipping batch")
+                    continue
+                
+                # Backward pass and GradNorm update
+                self.optimizer.zero_grad()
+                loss.backward(retain_graph=self.gradnorm_enabled)
+                
+                # GradNorm update BEFORE main backward to preserve computation graph
+                if self.gradnorm_enabled and self.gradnorm_step_counter % self.gradnorm_update_freq == 0:
+                    # Update GradNorm weights first
+                    self.task_weight_optimizer.zero_grad()
+                    gradnorm_info = self.criterion.update_gradnorm_weights(individual_losses, self.model)
+                    if gradnorm_info:
+                        loss_dict.update(gradnorm_info)
+                    self.task_weight_optimizer.step()
+                
+                # Gradient clipping
+                torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)
+                
+                self.optimizer.step()
+                if hasattr(self.scheduler, 'step'):
+                    self.scheduler.step()
+            
+            # Update GradNorm step counter
+            if self.gradnorm_enabled:
+                self.gradnorm_step_counter += 1
+            
+            # Accumulate loss components from loss_dict
+            for key, value in loss_dict.items():
+                if key.startswith('loss/'):
+                    loss_accumulators[key] += value
+            
+            # Extract predictions from loss_dict (already detached)
+            global_preds = torch.sigmoid(loss_dict['logits/global']).cpu().numpy()
+            global_labels = batch.y.detach().cpu().numpy()
+            node_preds = torch.sigmoid(loss_dict['logits/node']).cpu().numpy()
+            node_labels = batch.y_node.detach().cpu().numpy()
+            
+            all_global_preds.extend(global_preds)
+            all_global_labels.extend(global_labels)
+            all_node_preds.extend(node_preds)
+            all_node_labels.extend(node_labels)
+            
+            # Update progress bar with detailed loss info
+            pbar.set_postfix({
+                'Total': f"{loss_dict['loss/total']:.4f}",
+                'Region': f"{loss_dict['loss/region']:.4f}",
+                'Node': f"{loss_dict['loss/node']:.4f}",
+                'LR': f"{self.optimizer.param_groups[0]['lr']:.2e}",
+                'RW': f"{loss_dict['loss/region_weight']:.3f}" if self.gradnorm_enabled else "",
+                'NW': f"{loss_dict['loss/node_weight']:.3f}" if self.gradnorm_enabled else ""
+            })
+        
+        # Calculate average loss components
+        num_batches = len(self.train_loader)
+        avg_losses = {key: value / num_batches for key, value in loss_accumulators.items()}
+        
+        # Convert to numpy arrays
+        all_global_preds = np.array(all_global_preds)
+        all_global_labels = np.array(all_global_labels)
+        all_node_preds = np.array(all_node_preds)
+        all_node_labels = np.array(all_node_labels)
+        
+        # Calculate metrics
+        graph_metrics = calculate_graph_metrics(all_global_preds, all_global_labels, self.threshold)
+        node_metrics = calculate_node_metrics(all_node_preds, all_node_labels, find_threshold=False)
+        
+        # Combine all metrics
+        metrics = {
+            'lr': self.optimizer.param_groups[0]['lr'],
+            **avg_losses,  # Include all loss components
+        }
+        
+        # Add prefixed metrics
+        for k, v in graph_metrics.items():
+            metrics[f'graph_{k}'] = v
+        for k, v in node_metrics.items():
+            metrics[f'node_{k}'] = v
+        
+        # Print epoch summary with detailed loss breakdown
+        print(format_loss_info(avg_losses, "Train Loss - "))
+        print(f"Train Graph MCC: {graph_metrics['mcc']:.4f}, F1: {graph_metrics['f1']:.4f}, Recall: {graph_metrics['recall']:.4f}, Precision: {graph_metrics['precision']:.4f}")
+        print(f"Train Node AUPRC: {node_metrics['auprc']:.4f}, AUROC: {node_metrics['auroc']:.4f}, F1: {node_metrics['f1']:.4f} (threshold: {node_metrics['threshold_used']:.3f})")
+        
+        # Print GradNorm info if enabled
+        if self.gradnorm_enabled:
+            print(f"GradNorm Weights - Region: {avg_losses.get('loss/region_weight', 1.0):.3f}, Node: {avg_losses.get('loss/node_weight', 1.0):.3f}")
+            if 'gradnorm/region_grad_norm' in avg_losses:
+                print(f"GradNorm Info - Region GradNorm: {avg_losses.get('gradnorm/region_grad_norm', 0):.4f}, Node GradNorm: {avg_losses.get('gradnorm/node_grad_norm', 0):.4f}")
+        
+        return metrics
+
+    def _validate_epoch(self, epoch):
+        """Validate for one epoch."""
+        self.model.eval()
+        
+        # Initialize accumulators for loss components and predictions
+        loss_accumulators = defaultdict(float)
+        all_global_preds = []
+        all_global_labels = []
+        all_node_preds = []
+        all_node_labels = []
+        
+        with torch.no_grad():
+            pbar = tqdm(self.val_loader, desc=f"Validation Epoch {epoch + 1}")
+            
+            for batch in pbar:
+                batch = batch.to(self.device)
+                
+                # Prepare targets
+                targets = {
+                    'y': batch.y,
+                    'y_node': batch.y_node
+                }
+                
+                # Forward pass
+                if self.mixed_precision:
+                    with autocast('cuda'):
+                        outputs = self.model(batch)
+                        loss, loss_dict = self.criterion(outputs, targets, batch.batch)
+                else:
+                    outputs = self.model(batch)
+                    loss, loss_dict = self.criterion(outputs, targets, batch.batch)
+                
+                # Accumulate loss components from loss_dict
+                for key, value in loss_dict.items():
+                    if key.startswith('loss/'):
+                        loss_accumulators[key] += value
+                
+                # Extract predictions from loss_dict (already detached)
+                global_preds = torch.sigmoid(loss_dict['logits/global']).cpu().numpy()
+                global_labels = batch.y.detach().cpu().numpy()
+                node_preds = torch.sigmoid(loss_dict['logits/node']).cpu().numpy()
+                node_labels = batch.y_node.detach().cpu().numpy()
+                
+                all_global_preds.extend(global_preds)
+                all_global_labels.extend(global_labels)
+                all_node_preds.extend(node_preds)
+                all_node_labels.extend(node_labels)
+                
+                # Update progress bar with detailed loss info
+                pbar.set_postfix({
+                    'Total': f"{loss_dict['loss/total']:.4f}",
+                    'Region': f"{loss_dict['loss/region']:.4f}",
+                    'Node': f"{loss_dict['loss/node']:.4f}"
+                })
+        
+        # Calculate average loss components
+        num_batches = len(self.val_loader)
+        avg_losses = {key: value / num_batches for key, value in loss_accumulators.items()}
+        
+        # Convert to numpy arrays
+        all_global_preds = np.array(all_global_preds)
+        all_global_labels = np.array(all_global_labels)
+        all_node_preds = np.array(all_node_preds)
+        all_node_labels = np.array(all_node_labels)
+        
+        # Calculate metrics
+        graph_metrics = calculate_graph_metrics(all_global_preds, all_global_labels, self.threshold)
+        node_metrics = calculate_node_metrics(all_node_preds, all_node_labels, find_threshold=True)
+        
+        # Combine all metrics
+        metrics = {
+            'epoch': epoch,
+            **avg_losses,  # Include all loss components
+        }
+        
+        # Add prefixed metrics
+        for k, v in graph_metrics.items():
+            metrics[f'graph_{k}'] = v
+        for k, v in node_metrics.items():
+            metrics[f'node_{k}'] = v
+        metrics['threshold'] = node_metrics['threshold_used']
+        
+        # Print epoch summary with detailed loss breakdown
+        print(format_loss_info(avg_losses, "Val Loss - "))
+        print(f"Val Graph MCC: {graph_metrics['mcc']:.4f}, F1: {graph_metrics['f1']:.4f}, Recall: {graph_metrics['recall']:.4f}, Precision: {graph_metrics['precision']:.4f}")
+        print(f"Val Node AUPRC: {node_metrics['auprc']:.4f}, AUROC: {node_metrics['auroc']:.4f}, F1: {node_metrics['f1']:.4f}, MCC: {node_metrics['mcc']:.4f}, Precision: {node_metrics['precision']:.4f}, Recall: {node_metrics['recall']:.4f} (threshold: {node_metrics['threshold_used']:.3f})")
+        
+        return metrics
+
+    def evaluate(self, model_path=None, split='test'):
+        """
+        Evaluate the model on test set.
+        
+        Args:
+            model_path: Path to model checkpoint (if None, uses current model)
+            split: Which split to evaluate ('test', 'val', 'train')
+        """
+        print(f"[INFO] Evaluating on {split} set...")
+        
+        # Load model if path provided
+        if model_path:
+            print(f"[INFO] Loading model from {model_path}")
+            self.model, _ = ReCEP.load(model_path, device=self.device)
+        
+        # Select data loader
+        if split == 'test':
+            data_loader = self.test_loader
+        elif split == 'val' and self.val_loader:
+            data_loader = self.val_loader
+        elif split == 'train':
+            data_loader = self.train_loader
+        else:
+            raise ValueError(f"Invalid split: {split}")
+        
+        self.model.eval()
+        
+        # Initialize accumulators for loss components and predictions
+        loss_accumulators = defaultdict(float)
+        all_global_preds = []
+        all_global_labels = []
+        all_node_preds = []
+        all_node_labels = []
+        
+        with torch.no_grad():
+            pbar = tqdm(data_loader, desc=f"Evaluating {split}")
+            
+            for batch in pbar:
+                batch = batch.to(self.device)
+                
+                # Prepare targets
+                targets = {
+                    'y': batch.y,
+                    'y_node': batch.y_node
+                }
+                
+                # Forward pass
+                if self.mixed_precision:
+                    with autocast('cuda'):
+                        outputs = self.model(batch)
+                        loss, loss_dict = self.criterion(outputs, targets, batch.batch)
+                else:
+                    outputs = self.model(batch)
+                    loss, loss_dict = self.criterion(outputs, targets, batch.batch)
+                
+                # Accumulate loss components from loss_dict
+                for key, value in loss_dict.items():
+                    if key.startswith('loss/'):
+                        loss_accumulators[key] += value
+                
+                # Extract predictions from loss_dict (already detached)
+                global_preds = torch.sigmoid(loss_dict['logits/global']).cpu().numpy()
+                global_labels = batch.y.detach().cpu().numpy()
+                node_preds = torch.sigmoid(loss_dict['logits/node']).cpu().numpy()
+                node_labels = batch.y_node.detach().cpu().numpy()
+                
+                all_global_preds.extend(global_preds)
+                all_global_labels.extend(global_labels)
+                all_node_preds.extend(node_preds)
+                all_node_labels.extend(node_labels)
+        
+        # Calculate average loss components
+        num_batches = len(data_loader)
+        avg_losses = {key: value / num_batches for key, value in loss_accumulators.items()}
+        
+        # Convert to numpy arrays
+        all_global_preds = np.array(all_global_preds)
+        all_global_labels = np.array(all_global_labels)
+        all_node_preds = np.array(all_node_preds)
+        all_node_labels = np.array(all_node_labels)
+        
+        # Calculate metrics (use optimal threshold for evaluation)
+        graph_metrics = calculate_graph_metrics(all_global_preds, all_global_labels, self.threshold)
+        node_metrics = calculate_node_metrics(all_node_preds, all_node_labels, find_threshold=True)
+        
+        # Print results with detailed loss breakdown
+        print(f"\n=== {split.upper()} RESULTS ===")
+        print(format_loss_info(avg_losses, "Loss - "))
+        
+        print(f"\nGraph-level Metrics (Recall Prediction):")
+        print(f"  MCC: {graph_metrics['mcc']:.4f}")
+        print(f"  F1: {graph_metrics['f1']:.4f}")
+        print(f"  Recall: {graph_metrics['recall']:.4f}")
+        print(f"  Precision: {graph_metrics['precision']:.4f}")
+        print(f"  MSE: {graph_metrics['mse']:.4f}")
+        print(f"  MAE: {graph_metrics['mae']:.4f}")
+        print(f"  R²: {graph_metrics['r2']:.4f}")
+        print(f"  Pearson r: {graph_metrics['pearson_r']:.4f}")
+        
+        print(f"\nNode-level Metrics (Epitope Prediction):")
+        print(f"  AUPRC: {node_metrics['auprc']:.4f}")
+        print(f"  AUROC: {node_metrics['auroc']:.4f}")
+        print(f"  F1: {node_metrics['f1']:.4f} (optimal threshold: {node_metrics['best_threshold']:.3f})")
+        print(f"  MCC: {node_metrics['mcc']:.4f}")
+        print(f"  Precision: {node_metrics['precision']:.4f}")
+        print(f"  Recall: {node_metrics['recall']:.4f}")
+        print("=" * 40)
+        
+        # Save results with detailed loss components
+        results = {
+            'split': split,
+            'loss_components': self.convert_to_serializable(avg_losses),
+            'graph_metrics': self.convert_to_serializable(graph_metrics),
+            'node_metrics': self.convert_to_serializable(node_metrics),
+            'model_path': str(model_path) if model_path else None
+        }
+        
+        results_file = self.results_dir / f"{split}_results.json"
+        with open(results_file, 'w') as f:
+            json.dump(results, f, indent=2)
+        
+        return results
+
+    def _save_training_history(self):
+        """Save training history to file and generate loss plots."""
+        history = {
+            'train_history': self.convert_to_serializable(self.train_history),
+            'val_history': self.convert_to_serializable(self.val_history)
+        }
+        
+        history_file = self.results_dir / "training_history.json"
+        with open(history_file, 'w') as f:
+            json.dump(history, f, indent=2)
+        
+        print(f"[INFO] Training history saved to {history_file}")
+        
+        # Generate loss plots
+        self._plot_training_curves()
+
+    def _plot_training_curves(self):
+        """Generate and save training curves for losses."""
+        if not self.train_history:
+            print("[WARNING] No training history to plot")
+            return
+        
+        # Extract epochs and loss data
+        train_epochs = list(range(1, len(self.train_history) + 1))
+        train_total_loss = [h.get('loss/total', 0) for h in self.train_history]
+        train_region_loss = [h.get('loss/region', 0) for h in self.train_history]
+        train_node_loss = [h.get('loss/node', 0) for h in self.train_history]
+        
+        # Create subplots
+        fig, axes = plt.subplots(2, 2, figsize=(15, 10))
+        fig.suptitle('Training and Validation Loss Curves', fontsize=16, fontweight='bold')
+        
+        # Plot total loss
+        axes[0, 0].plot(train_epochs, train_total_loss, 'b-', label='Train', linewidth=2, marker='o', markersize=3)
+        if self.val_history:
+            val_epochs = list(range(1, len(self.val_history) + 1))
+            val_total_loss = [h.get('loss/total', 0) for h in self.val_history]
+            axes[0, 0].plot(val_epochs, val_total_loss, 'r-', label='Validation', linewidth=2, marker='s', markersize=3)
+        axes[0, 0].set_title('Total Loss', fontweight='bold')
+        axes[0, 0].set_xlabel('Epoch')
+        axes[0, 0].set_ylabel('Loss')
+        axes[0, 0].legend()
+        axes[0, 0].grid(True, alpha=0.3)
+        
+        # Plot region loss (global loss)
+        axes[0, 1].plot(train_epochs, train_region_loss, 'b-', label='Train', linewidth=2, marker='o', markersize=3)
+        if self.val_history:
+            val_region_loss = [h.get('loss/region', 0) for h in self.val_history]
+            axes[0, 1].plot(val_epochs, val_region_loss, 'r-', label='Validation', linewidth=2, marker='s', markersize=3)
+        axes[0, 1].set_title('Region Loss (Global)', fontweight='bold')
+        axes[0, 1].set_xlabel('Epoch')
+        axes[0, 1].set_ylabel('Loss')
+        axes[0, 1].legend()
+        axes[0, 1].grid(True, alpha=0.3)
+        
+        # Plot node loss
+        axes[1, 0].plot(train_epochs, train_node_loss, 'b-', label='Train', linewidth=2, marker='o', markersize=3)
+        if self.val_history:
+            val_node_loss = [h.get('loss/node', 0) for h in self.val_history]
+            axes[1, 0].plot(val_epochs, val_node_loss, 'r-', label='Validation', linewidth=2, marker='s', markersize=3)
+        axes[1, 0].set_title('Node Loss', fontweight='bold')
+        axes[1, 0].set_xlabel('Epoch')
+        axes[1, 0].set_ylabel('Loss')
+        axes[1, 0].legend()
+        axes[1, 0].grid(True, alpha=0.3)
+        
+        # Plot combined comparison
+        axes[1, 1].plot(train_epochs, train_total_loss, 'b-', label='Train Total', linewidth=2, alpha=0.8)
+        axes[1, 1].plot(train_epochs, train_region_loss, 'g-', label='Train Region', linewidth=2, alpha=0.8)
+        axes[1, 1].plot(train_epochs, train_node_loss, 'orange', label='Train Node', linewidth=2, alpha=0.8)
+        if self.val_history:
+            axes[1, 1].plot(val_epochs, val_total_loss, 'r--', label='Val Total', linewidth=2, alpha=0.8)
+            axes[1, 1].plot(val_epochs, val_region_loss, 'cyan', linestyle='--', label='Val Region', linewidth=2, alpha=0.8)
+            axes[1, 1].plot(val_epochs, val_node_loss, 'magenta', linestyle='--', label='Val Node', linewidth=2, alpha=0.8)
+        axes[1, 1].set_title('All Losses Comparison', fontweight='bold')
+        axes[1, 1].set_xlabel('Epoch')
+        axes[1, 1].set_ylabel('Loss')
+        axes[1, 1].legend()
+        axes[1, 1].grid(True, alpha=0.3)
+        
+        # Adjust layout
+        plt.tight_layout()
+        
+        # Save the plot
+        plot_file = self.results_dir / "training_loss_curves.png"
+        plt.savefig(plot_file, dpi=300, bbox_inches='tight')
+        plt.close()
+        
+        print(f"[INFO] Training loss curves saved to {plot_file}")
+        
+        # Also create individual plots for better readability
+        self._plot_individual_loss_curves()
+
+    def _plot_individual_loss_curves(self):
+        """Generate individual loss curve plots for each loss type."""
+        if not self.train_history:
+            return
+        
+        # Extract epochs and loss data
+        train_epochs = list(range(1, len(self.train_history) + 1))
+        loss_types = [
+            ('total', 'Total Loss'),
+            ('region', 'Region Loss (Global)'),
+            ('node', 'Node Loss')
+        ]
+        
+        for loss_key, loss_title in loss_types:
+            fig, ax = plt.subplots(1, 1, figsize=(10, 6))
+            
+            # Training loss
+            train_loss = [h.get(f'loss/{loss_key}', 0) for h in self.train_history]
+            ax.plot(train_epochs, train_loss, 'b-', label='Training', linewidth=2.5, marker='o', markersize=4)
+            
+            # Validation loss
+            if self.val_history:
+                val_epochs = list(range(1, len(self.val_history) + 1))
+                val_loss = [h.get(f'loss/{loss_key}', 0) for h in self.val_history]
+                ax.plot(val_epochs, val_loss, 'r-', label='Validation', linewidth=2.5, marker='s', markersize=4)
+            
+            # Formatting
+            ax.set_title(f'{loss_title} Over Training', fontsize=14, fontweight='bold')
+            ax.set_xlabel('Epoch', fontsize=12)
+            ax.set_ylabel('Loss', fontsize=12)
+            ax.legend(fontsize=11)
+            ax.grid(True, alpha=0.3)
+            
+            # Add some styling
+            ax.spines['top'].set_visible(False)
+            ax.spines['right'].set_visible(False)
+            ax.spines['left'].set_linewidth(0.5)
+            ax.spines['bottom'].set_linewidth(0.5)
+            
+            # Save individual plot
+            plot_file = self.results_dir / f"{loss_key}_loss_curve.png"
+            plt.tight_layout()
+            plt.savefig(plot_file, dpi=300, bbox_inches='tight')
+            plt.close()
+            
+            print(f"[INFO] {loss_title} curve saved to {plot_file}")
+
+    def save_config(self):
+        """Save training configuration to JSON file."""
+        config = {
+            'model_config': self.model.get_config(),  # Use model's get_config method
+            'training_config': {
+                'num_epoch': self.args.num_epoch,
+                'batch_size': self.args.batch_size,
+                'lr': self.args.lr,
+                'weight_decay': self.args.weight_decay,
+                'patience': self.args.patience,
+                'threshold': self.args.threshold,
+                'mixed_precision': self.args.mixed_precision,
+                'device_id': self.args.device_id
+            },
+            'data_config': {
+                'radii': self.args.radii,
+                'zero_ratio': self.args.zero_ratio,
+                'undersample': self.args.undersample,
+                'seed': self.args.seed
+            },
+            'loss_config': {
+                'region_loss_type': self.args.region_loss_type,
+                'reg_weight': self.args.reg_weight,
+                'cls_type': self.args.cls_type,
+                'gamma_high_cls': self.args.gamma_high_cls,
+                'regression_type': self.args.regression_type,
+                'node_loss_type': self.args.node_loss_type,
+                'alpha': self.args.alpha,
+                'gamma': self.args.gamma,
+                'pos_weight': self.args.pos_weight,
+                'node_loss_weight': self.args.node_loss_weight,
+                'region_weight': self.args.region_weight,
+                'consistency_weight': self.args.consistency_weight,
+                'consistency_type': self.args.consistency_type,
+                'label_smoothing': self.args.label_smoothing,
+                'gradnorm': getattr(self.args, 'gradnorm', False),
+                'gradnorm_alpha': getattr(self.args, 'gradnorm_alpha', 1.5),
+                'gradnorm_update_freq': getattr(self.args, 'gradnorm_update_freq', 10)
+            }
+        }
+        
+        config_file = self.model_dir / "config.json"
+        with open(config_file, 'w') as f:
+            json.dump(config, f, indent=2)
+        
+        print(f"[INFO] Configuration saved to {config_file}")
diff --git a/src/bce/utils/training_tools.py b/src/bce/utils/training_tools.py
new file mode 100644
index 0000000000000000000000000000000000000000..d728ee7cc67b817d67bced7dcb0a6c83066d3852
--- /dev/null
+++ b/src/bce/utils/training_tools.py
@@ -0,0 +1,56 @@
+import random
+import numpy as np
+
+import torch
+
+def parse_range(range_str):
+    """Parse range string in format 'start:end:step' into a list of values"""
+    try:
+        start, end, step = map(float, range_str.split(':'))
+        # Add a small epsilon to ensure end value is included
+        epsilon = step / 100
+        return list(np.arange(start, end + epsilon, step))
+    except ValueError:
+        # If only one value, return a list containing that value
+        try:
+            value = float(range_str)
+            return [value]
+        except ValueError:
+            raise ValueError(f"Invalid range format: {range_str}. Use 'start:end:step' or single value.")
+
+def setup_device(device_id=1):
+    """Setup and verify CUDA device."""
+    if device_id >= 0 and torch.cuda.is_available():
+    device = torch.device(f"cuda:{device_id}")
+    if not hasattr(setup_device, '_printed'):
+        print(f"[INFO] Using device: {device}")
+        print(f"[INFO] CUDA device: {torch.cuda.get_device_name(0)}")
+        setup_device._printed = True
+    return device_id
+    else:
+        if not hasattr(setup_device, '_printed'):
+            print(f"[INFO] Using device: cpu")
+            setup_device._printed = True
+        return -1  # Return -1 to indicate CPU usage
+
+def set_seed(seed, deterministic=False):
+    """
+    Set random seed for reproducibility across all libraries
+    
+    Args:
+        seed (int): Random seed value
+        deterministic (bool): Whether to enable deterministic mode in PyTorch
+    """
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    # torch.cuda.manual_seed_all(seed)  # For multi-GPU setups
+    
+    if deterministic:
+        # These settings may impact performance but ensure reproducibility
+        torch.backends.cudnn.deterministic = True
+        torch.backends.cudnn.benchmark = False
+        print(f"[INFO] Deterministic mode enabled (may impact performance)")
+    
+    print(f"[INFO] Random seed set to {seed} for reproducibility")
\ No newline at end of file