inference_app.py

import random
import time
from pathlib import Path

import numpy as np
from biotite.structure.atoms import AtomArrayStack
from scipy.spatial.transform import Rotation as R
from pinder.core.structure.atoms import atom_array_from_pdb_file, normalize_orientation, write_pdb
from pinder.core.structure.contacts import get_stack_contacts

import gradio as gr

from gradio_molecule3d import Molecule3D


def predict(
    receptor_pdb: Path, 
    ligand_pdb: Path, 
    receptor_fasta: Path | None = None, 
    ligand_fasta: Path | None = None,
) -> tuple[str, float]:
    start_time = time.time()
    # Do inference here
    # return an output pdb file with the protein and two chains R and L.  
    receptor = atom_array_from_pdb_file(receptor_pdb, extra_fields=["b_factor"])
    ligand = atom_array_from_pdb_file(ligand_pdb, extra_fields=["b_factor"])
    receptor = normalize_orientation(receptor)
    ligand = normalize_orientation(ligand)
    
    # Number of random poses to generate
    M = 50
    # Inititalize an empty stack with shape (m x n x 3) 
    stack = AtomArrayStack(M, ligand.shape[0])
    
    # copy annotations from ligand
    for annot in ligand.get_annotation_categories():
        stack.set_annotation(annot, np.copy(ligand.get_annotation(annot)))
    
    # Random translations sampled along 0-50 angstroms per axis
    translation_magnitudes = np.linspace(
        0, M + 1,
        num=M + 1,
        endpoint=False
    )
    # generate one pose at a time
    for i in range(M):
        q = R.random()
        translation_vec = [
            random.choice(translation_magnitudes), # x
            random.choice(translation_magnitudes), # y
            random.choice(translation_magnitudes), # z
        ]
        # transform the ligand chain
        stack.coord[i, ...] = q.apply(ligand.coord) + translation_vec
        
    # Find clashes (1.2 A contact radius)
    stack_conts = get_stack_contacts(receptor, stack, threshold=1.2)
    
    # Keep the "best" pose based on pose w/fewest clashes
    pose_clashes = []
    for i in range(stack_conts.shape[0]):
        pose_conts = stack_conts[i]
        pose_clashes.append((i, np.argwhere(pose_conts != -1).shape[0]))
    
    best_pose_idx = sorted(pose_clashes, key=lambda x: x[1])[0][0]
    best_pose = receptor + stack[best_pose_idx]
    
    output_dir = Path(receptor_pdb).parent
    # System ID
    pdb_name = "--".join([
        Path(receptor_pdb).stem.rstrip("-R"), Path(ligand_pdb).stem.rstrip("-L")
    ]) + ".pdb"
    output_pdb = output_dir / pdb_name
    write_pdb(best_pose, output_pdb)
    end_time = time.time()
    run_time = end_time - start_time
    return str(output_pdb), run_time


with gr.Blocks() as app:

    gr.Markdown("# Template for inference")

    gr.Markdown("Title, description, and other information about the model")   
    with gr.Row():
        with gr.Column():
            input_protein_1 = gr.File(label="Input Protein 1 monomer (PDB)")
            input_fasta_1 = gr.File(label="Input Protein 1 monomer sequence (FASTA)")
        with gr.Column():
            input_protein_2 = gr.File(label="Input Protein 2 monomer (PDB)")
            input_fasta_2 = gr.File(label="Input Protein 2 monomer sequence (FASTA)")
        
        
    
    # define any options here

    # for automated inference the default options are used
    # slider_option = gr.Slider(0,10, label="Slider Option")
    # checkbox_option = gr.Checkbox(label="Checkbox Option")
    # dropdown_option = gr.Dropdown(["Option 1", "Option 2", "Option 3"], label="Radio Option")

    btn = gr.Button("Run Inference")

    gr.Examples(
        [
            [
                "8i5w_R.pdb",
                "8i5w_R.fasta",
                "8i5w_L.pdb",
                "8i5w_L.fasta",
            ],
        ],
        [input_protein_1, input_fasta_1, input_protein_2, input_fasta_2],
    )
    reps =    [
    {
      "model": 0,
      "style": "cartoon",
      "chain": "R",
      "color": "whiteCarbon",
    },
    {
      "model": 0,
      "style": "cartoon",
       "chain": "L",
      "color": "greenCarbon",
    },
    {
      "model": 0,
      "chain": "R",
      "style": "stick",
      "sidechain": True,
      "color": "whiteCarbon",
    },
    {
      "model": 0,
      "chain": "L",
      "style": "stick",
      "sidechain": True,
      "color": "greenCarbon"
    }      
  ]
    
    out = Molecule3D(reps=reps)
    run_time = gr.Textbox(label="Runtime")

    btn.click(predict, inputs=[input_protein_1, input_protein_2, input_fasta_1,  input_fasta_2], outputs=[out, run_time])

app.launch()