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Zero
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import numpy as np
import torch
import torch.nn as nn
from .graphtrans_module import gather_edges, cat_neighbors_nodes
class PositionWiseFeedForward(nn.Module):
def __init__(self, num_hidden, num_ff):
super(PositionWiseFeedForward, self).__init__()
self.W_in = nn.Linear(num_hidden, num_ff, bias=True)
self.W_out = nn.Linear(num_ff, num_hidden, bias=True)
self.act = torch.nn.GELU()
def forward(self, h_V):
h = self.act(self.W_in(h_V))
h = self.W_out(h)
return h
class PositionalEncodings(nn.Module):
def __init__(self, num_embeddings, max_relative_feature=32):
super(PositionalEncodings, self).__init__()
self.num_embeddings = num_embeddings
self.max_relative_feature = max_relative_feature
self.linear = nn.Linear(2*max_relative_feature+1+1, num_embeddings)
def forward(self, offset, mask):
d = torch.clip(offset + self.max_relative_feature, 0, 2*self.max_relative_feature)*mask + (1-mask)*(2*self.max_relative_feature+1)
d_onehot = torch.nn.functional.one_hot(d, 2*self.max_relative_feature+1+1)
E = self.linear(d_onehot.float())
return E
class EncLayer(nn.Module):
def __init__(self, num_hidden, num_in, dropout=0.1, num_heads=None, scale=30, proteinmpnn_type=0):
super(EncLayer, self).__init__()
self.num_hidden = num_hidden
self.num_in = num_in
self.scale = scale
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
self.dropout3 = nn.Dropout(dropout)
self.norm1 = nn.LayerNorm(num_hidden)
self.norm2 = nn.LayerNorm(num_hidden)
self.norm3 = nn.LayerNorm(num_hidden)
self.W1 = nn.Linear(num_hidden + num_in, num_hidden, bias=True)
self.W2 = nn.Linear(num_hidden, num_hidden, bias=True)
self.W3 = nn.Linear(num_hidden, num_hidden, bias=True)
self.W11 = nn.Linear(num_hidden + num_in, num_hidden, bias=True)
self.W12 = nn.Linear(num_hidden, num_hidden, bias=True)
self.W13 = nn.Linear(num_hidden, num_hidden, bias=True)
self.act = torch.nn.GELU()
self.dense = PositionWiseFeedForward(num_hidden, num_hidden * 4)
self.proteinmpnn_type = proteinmpnn_type
def forward(self, h_V, h_E, E_idx, mask_V=None, mask_attend=None):
""" Parallel computation of full transformer layer """
h_EV = cat_neighbors_nodes(h_V, h_E, E_idx)
h_V_expand = h_V.unsqueeze(-2).expand(-1,-1,h_EV.size(-2),-1)
# import pdb; pdb.set_trace()
h_EV = torch.cat([h_V_expand, h_EV], -1)
h_message = self.W3(self.act(self.W2(self.act(self.W1(h_EV)))))
if mask_attend is not None:
h_message = mask_attend.unsqueeze(-1) * h_message
dh = torch.sum(h_message, -2) / self.scale
h_V = self.norm1(h_V + self.dropout1(dh))
dh = self.dense(h_V)
h_V = self.norm2(h_V + self.dropout2(dh))
if mask_V is not None:
mask_V = mask_V.unsqueeze(-1)
h_V = mask_V * h_V
if self.proteinmpnn_type != 3:
h_EV = cat_neighbors_nodes(h_V, h_E, E_idx)
h_V_expand = h_V.unsqueeze(-2).expand(-1,-1,h_EV.size(-2),-1)
h_EV = torch.cat([h_V_expand, h_EV], -1)
h_message = self.W13(self.act(self.W12(self.act(self.W11(h_EV)))))
h_E = self.norm3(h_E + self.dropout3(h_message))
return h_V, h_E
class DecLayer(nn.Module):
def __init__(self, num_hidden, num_in, dropout=0.1, num_heads=None, scale=30):
super(DecLayer, self).__init__()
self.num_hidden = num_hidden
self.num_in = num_in
self.scale = scale
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
self.norm1 = nn.LayerNorm(num_hidden)
self.norm2 = nn.LayerNorm(num_hidden)
self.W1 = nn.Linear(num_hidden + num_in, num_hidden, bias=True)
self.W2 = nn.Linear(num_hidden, num_hidden, bias=True)
self.W3 = nn.Linear(num_hidden, num_hidden, bias=True)
self.act = torch.nn.GELU()
self.dense = PositionWiseFeedForward(num_hidden, num_hidden * 4)
def forward(self, h_V, h_E, mask_V=None, mask_attend=None):
""" Parallel computation of full transformer layer """
# Concatenate h_V_i to h_E_ij
h_V_expand = h_V.unsqueeze(-2).expand(-1,-1,h_E.size(-2),-1)
h_EV = torch.cat([h_V_expand, h_E], -1)
h_message = self.W3(self.act(self.W2(self.act(self.W1(h_EV)))))
if mask_attend is not None:
h_message = mask_attend.unsqueeze(-1) * h_message
dh = torch.sum(h_message, -2) / self.scale
h_V = self.norm1(h_V + self.dropout1(dh))
# Position-wise feedforward
dh = self.dense(h_V)
h_V = self.norm2(h_V + self.dropout2(dh))
if mask_V is not None:
mask_V = mask_V.unsqueeze(-1)
h_V = mask_V * h_V
return h_V
class ProteinFeatures(nn.Module):
def __init__(self, edge_features, node_features, num_positional_embeddings=16,
num_rbf=16, top_k=30, augment_eps=0., num_chain_embeddings=16, proteinmpnn_type=0):
""" Extract protein features """
super(ProteinFeatures, self).__init__()
self.edge_features = edge_features
self.node_features = node_features
self.top_k = top_k
self.augment_eps = augment_eps
self.num_rbf = num_rbf
self.num_positional_embeddings = num_positional_embeddings
self.embeddings = PositionalEncodings(num_positional_embeddings)
node_in, edge_in = 6, num_positional_embeddings + num_rbf*25
self.proteinmpnn_type = proteinmpnn_type
if self.proteinmpnn_type == 2:
edge_in = 32
self.edge_embedding = nn.Linear(edge_in, edge_features, bias=False)
self.norm_edges = nn.LayerNorm(edge_features)
self.proteinmpnn_type = proteinmpnn_type
def _dist(self, X, mask, eps=1E-6):
mask_2D = torch.unsqueeze(mask,1) * torch.unsqueeze(mask,2)
dX = torch.unsqueeze(X,1) - torch.unsqueeze(X,2)
D = mask_2D * torch.sqrt(torch.sum(dX**2, 3) + eps)
D_max, _ = torch.max(D, -1, keepdim=True)
D_adjust = D + (1. - mask_2D) * D_max
sampled_top_k = self.top_k
D_neighbors, E_idx = torch.topk(D_adjust, np.minimum(self.top_k, X.shape[1]), dim=-1, largest=False)
return D_neighbors, E_idx
def _rbf(self, D):
device = D.device
D_min, D_max, D_count = 2., 22., self.num_rbf
D_mu = torch.linspace(D_min, D_max, D_count, device=device)
D_mu = D_mu.view([1,1,1,-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
def _get_rbf(self, A, B, E_idx):
D_A_B = torch.sqrt(torch.sum((A[:,:,None,:] - B[:,None,:,:])**2,-1) + 1e-6) #[B, L, L]
D_A_B_neighbors = gather_edges(D_A_B[:,:,:,None], E_idx)[:,:,:,0] #[B,L,K]
RBF_A_B = self._rbf(D_A_B_neighbors)
return RBF_A_B
def forward(self, X, mask, residue_idx, chain_labels):
b = X[:,:,1,:] - X[:,:,0,:]
c = X[:,:,2,:] - X[:,:,1,:]
a = torch.cross(b, c, dim=-1)
Cb = -0.58273431*a + 0.56802827*b - 0.54067466*c + X[:,:,1,:]
Ca = X[:,:,1,:]
N = X[:,:,0,:]
C = X[:,:,2,:]
O = X[:,:,3,:]
D_neighbors, E_idx = self._dist(Ca, mask)
if self.proteinmpnn_type == 2:
RBF_all = self._rbf(D_neighbors)
else:
RBF_all = []
RBF_all.append(self._rbf(D_neighbors)) #Ca-Ca
RBF_all.append(self._get_rbf(N, N, E_idx)) #N-N
RBF_all.append(self._get_rbf(C, C, E_idx)) #C-C
RBF_all.append(self._get_rbf(O, O, E_idx)) #O-O
RBF_all.append(self._get_rbf(Cb, Cb, E_idx)) #Cb-Cb
RBF_all.append(self._get_rbf(Ca, N, E_idx)) #Ca-N
RBF_all.append(self._get_rbf(Ca, C, E_idx)) #Ca-C
RBF_all.append(self._get_rbf(Ca, O, E_idx)) #Ca-O
RBF_all.append(self._get_rbf(Ca, Cb, E_idx)) #Ca-Cb
RBF_all.append(self._get_rbf(N, C, E_idx)) #N-C
RBF_all.append(self._get_rbf(N, O, E_idx)) #N-O
RBF_all.append(self._get_rbf(N, Cb, E_idx)) #N-Cb
RBF_all.append(self._get_rbf(Cb, C, E_idx)) #Cb-C
RBF_all.append(self._get_rbf(Cb, O, E_idx)) #Cb-O
RBF_all.append(self._get_rbf(O, C, E_idx)) #O-C
RBF_all.append(self._get_rbf(N, Ca, E_idx)) #N-Ca
RBF_all.append(self._get_rbf(C, Ca, E_idx)) #C-Ca
RBF_all.append(self._get_rbf(O, Ca, E_idx)) #O-Ca
RBF_all.append(self._get_rbf(Cb, Ca, E_idx)) #Cb-Ca
RBF_all.append(self._get_rbf(C, N, E_idx)) #C-N
RBF_all.append(self._get_rbf(O, N, E_idx)) #O-N
RBF_all.append(self._get_rbf(Cb, N, E_idx)) #Cb-N
RBF_all.append(self._get_rbf(C, Cb, E_idx)) #C-Cb
RBF_all.append(self._get_rbf(O, Cb, E_idx)) #O-Cb
RBF_all.append(self._get_rbf(C, O, E_idx)) #C-O
RBF_all = torch.cat(tuple(RBF_all), dim=-1)
offset = residue_idx[:,:,None]-residue_idx[:,None,:]
offset = gather_edges(offset[:,:,:,None], E_idx)[:,:,:,0] #[B, L, K]
d_chains = ((chain_labels[:, :, None] - chain_labels[:,None,:])==0).long() #find self vs non-self interaction
E_chains = gather_edges(d_chains[:,:,:,None], E_idx)[:,:,:,0]
E_positional = self.embeddings(offset.long(), E_chains)
E = torch.cat((E_positional, RBF_all), -1)
E = self.edge_embedding(E)
E = self.norm_edges(E)
return E, E_idx |