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import torch |
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import torch.nn as nn |
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import esm |
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import numpy as np |
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import pdb |
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vhse8_values = { |
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'A': [0.15, -1.11, -1.35, -0.92, 0.02, -0.91, 0.36, -0.48], |
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'R': [-1.47, 1.45, 1.24, 1.27, 1.55, 1.47, 1.30, 0.83], |
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'N': [-0.99, 0.00, 0.69, -0.37, -0.55, 0.85, 0.73, -0.80], |
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'D': [-1.15, 0.67, -0.41, -0.01, -2.68, 1.31, 0.03, 0.56], |
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'C': [0.18, -1.67, -0.21, 0.00, 1.20, -1.61, -0.19, -0.41], |
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'Q': [-0.96, 0.12, 0.18, 0.16, 0.09, 0.42, -0.20, -0.41], |
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'E': [-1.18, 0.40, 0.10, 0.36, -2.16, -0.17, 0.91, 0.36], |
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'G': [-0.20, -1.53, -2.63, 2.28, -0.53, -1.18, -1.34, 1.10], |
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'H': [-0.43, -0.25, 0.37, 0.19, 0.51, 1.28, 0.93, 0.65], |
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'I': [1.27, 0.14, 0.30, -1.80, 0.30, -1.61, -0.16, -0.13], |
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'L': [1.36, 0.07, 0.26, -0.80, 0.22, -1.37, 0.08, -0.62], |
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'K': [-1.17, 0.70, 0.80, 1.64, 0.67, 1.63, 0.13, -0.01], |
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'M': [1.01, -0.53, 0.43, 0.00, 0.23, 0.10, -0.86, -0.68], |
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'F': [1.52, 0.61, 0.95, -0.16, 0.25, 0.28, -1.33, -0.65], |
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'P': [0.22, -0.17, -0.50, -0.05, 0.01, -1.34, 0.19, 3.56], |
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'S': [-0.67, -0.86, -1.07, -0.41, -0.32, 0.27, -0.64, 0.11], |
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'T': [-0.34, -0.51, -0.55, -1.06, 0.01, -0.01, -0.79, 0.39], |
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'W': [1.50, 2.06, 1.79, 0.75, 0.75, 0.13, -1.06, -0.85], |
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'Y': [0.61, 1.60, 1.17, 0.73, 0.53, 0.25, -0.96, -0.52], |
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'V': [0.76, -0.92, 0.17, -1.91, 0.22, -1.40, -0.24, -0.03], |
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} |
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aa_to_idx = {'A': 5, 'R': 10, 'N': 17, 'D': 13, 'C': 23, 'Q': 16, 'E': 9, 'G': 6, 'H': 21, 'I': 12, 'L': 4, 'K': 15, 'M': 20, 'F': 18, 'P': 14, 'S': 8, 'T': 11, 'W': 22, 'Y': 19, 'V': 7} |
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vhse8_tensor = torch.zeros(33, 8) |
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for aa, values in vhse8_values.items(): |
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aa_index = aa_to_idx[aa] |
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vhse8_tensor[aa_index] = torch.tensor(values) |
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class muPPIt(torch.nn.Module): |
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def __init__(self, d_node, num_heads, margin, lr, device): |
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super(muPPIt, self).__init__() |
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self.esm, self.alphabet = esm.pretrained.esm2_t33_650M_UR50D() |
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for param in self.esm.parameters(): |
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param.requires_grad = False |
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self.attention = torch.nn.MultiheadAttention(embed_dim=d_node, num_heads=num_heads) |
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self.layer_norm = torch.nn.LayerNorm(d_node) |
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self.map = torch.nn.Sequential( |
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torch.nn.Linear(d_node, d_node // 2), |
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torch.nn.SiLU(), |
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torch.nn.Linear(d_node // 2, 1) |
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) |
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for layer in self.map: |
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if isinstance(layer, nn.Linear): |
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nn.init.kaiming_uniform_(layer.weight, a=0, mode='fan_in', nonlinearity='leaky_relu') |
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if layer.bias is not None: |
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nn.init.zeros_(layer.bias) |
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self.margin = margin |
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self.learning_rate = lr |
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self.loss_threshold = 20 |
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self.device = device |
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self.easy_example_indices = np.load('/home/tc415/muPPIt_embedding/dataset/ppiref_index.npy').tolist() |
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self.hard_example_indices = np.load('/home/tc415/muPPIt_embedding/dataset/skempi_index.npy').tolist() |
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def forward(self, binder_tokens, wt_embed, mut_embed): |
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device = wt_embed.device |
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global vhse8_tensor |
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vhse8_tensor = vhse8_tensor.to(device) |
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vhse8_tensor.requires_grad = False |
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with torch.no_grad(): |
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binder_embed = self.esm(binder_tokens, repr_layers=[33], return_contacts=False)["representations"][33] |
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binder_vhse8 = vhse8_tensor[binder_tokens] |
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binder_embed = torch.concat([binder_embed, binder_vhse8], dim=-1) |
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binder_wt = torch.concat([binder_embed, wt_embed], dim=1) |
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binder_mut = torch.concat([binder_embed, mut_embed], dim=1) |
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binder_wt = binder_wt.transpose(0,1) |
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binder_mut = binder_mut.transpose(0,1) |
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binder_wt_attn, _ = self.attention(binder_wt, binder_wt, binder_wt) |
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binder_mut_attn, _ = self.attention(binder_mut, binder_mut, binder_mut) |
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binder_wt_attn = binder_wt + binder_wt_attn |
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binder_mut_attn = binder_mut + binder_mut_attn |
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binder_wt_attn = binder_wt_attn.transpose(0, 1) |
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binder_mut_attn = binder_mut_attn.transpose(0, 1) |
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binder_wt_attn = self.layer_norm(binder_wt_attn) |
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binder_mut_attn = self.layer_norm(binder_mut_attn) |
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mapped_binder_wt = self.map(binder_wt_attn).squeeze(-1) |
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mapped_binder_mut = self.map(binder_mut_attn).squeeze(-1) |
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distance = torch.sqrt(torch.sum((mapped_binder_wt - mapped_binder_mut) ** 2, dim=-1)) |
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return distance |
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def get_log_probs(self, binder_tokens, wt_embed, mut_embed): |
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distance = self.forward(binder_tokens, wt_embed, mut_embed) |
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log_prob = torch.log(1e-8 + torch.sigmoid(distance - self.margin)) |
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return log_prob |
