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from __future__ import print_function
import json, time, os, sys, glob
import shutil
import numpy as np
import torch
from torch import optim
from torch.utils.data import DataLoader
from torch.utils.data.dataset import random_split, Subset
import copy
import torch.nn as nn
import torch.nn.functional as F
import random
import itertools
#A number of functions/classes are adopted from: https://github.com/jingraham/neurips19-graph-protein-design
def _scores(S, log_probs, mask):
""" Negative log probabilities """
criterion = torch.nn.NLLLoss(reduction='none')
loss = criterion(
log_probs.contiguous().view(-1,log_probs.size(-1)),
S.contiguous().view(-1)
).view(S.size())
scores = torch.sum(loss * mask, dim=-1) / torch.sum(mask, dim=-1)
return scores
def _S_to_seq(S, mask):
alphabet = 'ACDEFGHIKLMNPQRSTVWYX'
seq = ''.join([alphabet[c] for c, m in zip(S.tolist(), mask.tolist()) if m > 0])
return seq
def parse_PDB_biounits(x, atoms=['N','CA','C'], chain=None):
'''
input: x = PDB filename
atoms = atoms to extract (optional)
output: (length, atoms, coords=(x,y,z)), sequence
'''
alpha_1 = list("ARNDCQEGHILKMFPSTWYV-")
states = len(alpha_1)
alpha_3 = ['ALA','ARG','ASN','ASP','CYS','GLN','GLU','GLY','HIS','ILE',
'LEU','LYS','MET','PHE','PRO','SER','THR','TRP','TYR','VAL','GAP']
aa_1_N = {a:n for n,a in enumerate(alpha_1)}
aa_3_N = {a:n for n,a in enumerate(alpha_3)}
aa_N_1 = {n:a for n,a in enumerate(alpha_1)}
aa_1_3 = {a:b for a,b in zip(alpha_1,alpha_3)}
aa_3_1 = {b:a for a,b in zip(alpha_1,alpha_3)}
def AA_to_N(x):
# ["ARND"] -> [[0,1,2,3]]
x = np.array(x);
if x.ndim == 0: x = x[None]
return [[aa_1_N.get(a, states-1) for a in y] for y in x]
def N_to_AA(x):
# [[0,1,2,3]] -> ["ARND"]
x = np.array(x);
if x.ndim == 1: x = x[None]
return ["".join([aa_N_1.get(a,"-") for a in y]) for y in x]
xyz,seq,min_resn,max_resn = {},{},1e6,-1e6
for line in open(x,"rb"):
line = line.decode("utf-8","ignore").rstrip()
if line[:6] == "HETATM" and line[17:17+3] == "MSE":
line = line.replace("HETATM","ATOM ")
line = line.replace("MSE","MET")
if line[:4] == "ATOM":
ch = line[21:22]
if ch == chain or chain is None:
atom = line[12:12+4].strip()
resi = line[17:17+3]
resn = line[22:22+5].strip()
x,y,z = [float(line[i:(i+8)]) for i in [30,38,46]]
if resn[-1].isalpha():
resa,resn = resn[-1],int(resn[:-1])-1
else:
resa,resn = "",int(resn)-1
# resn = int(resn)
if resn < min_resn:
min_resn = resn
if resn > max_resn:
max_resn = resn
if resn not in xyz:
xyz[resn] = {}
if resa not in xyz[resn]:
xyz[resn][resa] = {}
if resn not in seq:
seq[resn] = {}
if resa not in seq[resn]:
seq[resn][resa] = resi
if atom not in xyz[resn][resa]:
xyz[resn][resa][atom] = np.array([x,y,z])
# convert to numpy arrays, fill in missing values
seq_,xyz_ = [],[]
try:
for resn in range(min_resn,max_resn+1):
if resn in seq:
for k in sorted(seq[resn]): seq_.append(aa_3_N.get(seq[resn][k],20))
else: seq_.append(20)
if resn in xyz:
for k in sorted(xyz[resn]):
for atom in atoms:
if atom in xyz[resn][k]: xyz_.append(xyz[resn][k][atom])
else: xyz_.append(np.full(3,np.nan))
else:
for atom in atoms: xyz_.append(np.full(3,np.nan))
return np.array(xyz_).reshape(-1,len(atoms),3), N_to_AA(np.array(seq_))
except TypeError:
return 'no_chain', 'no_chain'
def parse_PDB(path_to_pdb, input_chain_list=None):
c=0
pdb_dict_list = []
init_alphabet = ['A', 'B', 'C', 'D', 'E', 'F', 'G','H', 'I', 'J','K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T','U', 'V','W','X', 'Y', 'Z', 'a', 'b', 'c', 'd', 'e', 'f', 'g','h', 'i', 'j','k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't','u', 'v','w','x', 'y', 'z']
extra_alphabet = [str(item) for item in list(np.arange(300))]
chain_alphabet = init_alphabet + extra_alphabet
if input_chain_list:
chain_alphabet = input_chain_list
biounit_names = [path_to_pdb]
for biounit in biounit_names:
my_dict = {}
s = 0
concat_seq = ''
concat_N = []
concat_CA = []
concat_C = []
concat_O = []
concat_mask = []
coords_dict = {}
for letter in chain_alphabet:
xyz, seq = parse_PDB_biounits(biounit, atoms=['N','CA','C','O'], chain=letter)
if type(xyz) != str:
concat_seq += seq[0]
my_dict['seq_chain_'+letter]=seq[0]
coords_dict_chain = {}
coords_dict_chain['N_chain_'+letter]=xyz[:,0,:].tolist()
coords_dict_chain['CA_chain_'+letter]=xyz[:,1,:].tolist()
coords_dict_chain['C_chain_'+letter]=xyz[:,2,:].tolist()
coords_dict_chain['O_chain_'+letter]=xyz[:,3,:].tolist()
my_dict['coords_chain_'+letter]=coords_dict_chain
s += 1
fi = biounit.rfind("/")
my_dict['name']=biounit[(fi+1):-4]
my_dict['num_of_chains'] = s
my_dict['seq'] = concat_seq
if s <= len(chain_alphabet):
pdb_dict_list.append(my_dict)
c+=1
return pdb_dict_list
def tied_featurize(batch, device, chain_dict, fixed_position_dict=None, omit_AA_dict=None, tied_positions_dict=None, pssm_dict=None, bias_by_res_dict=None):
""" Pack and pad batch into torch tensors """
alphabet = 'ACDEFGHIKLMNPQRSTVWYX'
B = len(batch)
lengths = np.array([len(b['seq']) for b in batch], dtype=np.int32) #sum of chain seq lengths
L_max = max([len(b['seq']) for b in batch])
X = np.zeros([B, L_max, 4, 3])
residue_idx = -100*np.ones([B, L_max], dtype=np.int32)
chain_M = np.zeros([B, L_max], dtype=np.int32) #1.0 for the bits that need to be predicted
pssm_coef_all = np.zeros([B, L_max], dtype=np.float32) #1.0 for the bits that need to be predicted
pssm_bias_all = np.zeros([B, L_max, 21], dtype=np.float32) #1.0 for the bits that need to be predicted
pssm_log_odds_all = 10000.0*np.ones([B, L_max, 21], dtype=np.float32) #1.0 for the bits that need to be predicted
chain_M_pos = np.zeros([B, L_max], dtype=np.int32) #1.0 for the bits that need to be predicted
bias_by_res_all = np.zeros([B, L_max, 21], dtype=np.float32)
chain_encoding_all = np.zeros([B, L_max], dtype=np.int32) #1.0 for the bits that need to be predicted
S = np.zeros([B, L_max], dtype=np.int32)
omit_AA_mask = np.zeros([B, L_max, len(alphabet)], dtype=np.int32)
# Build the batch
letter_list_list = []
visible_list_list = []
masked_list_list = []
masked_chain_length_list_list = []
tied_pos_list_of_lists_list = []
#shuffle all chains before the main loop
for i, b in enumerate(batch):
if chain_dict != None:
masked_chains, visible_chains = chain_dict[b['name']] #masked_chains a list of chain letters to predict [A, D, F]
else:
masked_chains = [item[-1:] for item in list(b) if item[:10]=='seq_chain_']
visible_chains = []
num_chains = b['num_of_chains']
all_chains = masked_chains + visible_chains
#random.shuffle(all_chains)
for i, b in enumerate(batch):
mask_dict = {}
a = 0
x_chain_list = []
chain_mask_list = []
chain_seq_list = []
chain_encoding_list = []
c = 1
letter_list = []
global_idx_start_list = [0]
visible_list = []
masked_list = []
masked_chain_length_list = []
fixed_position_mask_list = []
omit_AA_mask_list = []
pssm_coef_list = []
pssm_bias_list = []
pssm_log_odds_list = []
bias_by_res_list = []
l0 = 0
l1 = 0
for step, letter in enumerate(all_chains):
if letter in visible_chains:
letter_list.append(letter)
visible_list.append(letter)
chain_seq = b[f'seq_chain_{letter}']
chain_seq = ''.join([a if a!='-' else 'X' for a in chain_seq])
chain_length = len(chain_seq)
global_idx_start_list.append(global_idx_start_list[-1]+chain_length)
chain_coords = b[f'coords_chain_{letter}'] #this is a dictionary
chain_mask = np.zeros(chain_length) #0.0 for visible chains
x_chain = np.stack([chain_coords[c] for c in [f'N_chain_{letter}', f'CA_chain_{letter}', f'C_chain_{letter}', f'O_chain_{letter}']], 1) #[chain_lenght,4,3]
x_chain_list.append(x_chain)
chain_mask_list.append(chain_mask)
chain_seq_list.append(chain_seq)
chain_encoding_list.append(c*np.ones(np.array(chain_mask).shape[0]))
l1 += chain_length
residue_idx[i, l0:l1] = 100*(c-1)+np.arange(l0, l1)
l0 += chain_length
c+=1
fixed_position_mask = np.ones(chain_length)
fixed_position_mask_list.append(fixed_position_mask)
omit_AA_mask_temp = np.zeros([chain_length, len(alphabet)], np.int32)
omit_AA_mask_list.append(omit_AA_mask_temp)
pssm_coef = np.zeros(chain_length)
pssm_bias = np.zeros([chain_length, 21])
pssm_log_odds = 10000.0*np.ones([chain_length, 21])
pssm_coef_list.append(pssm_coef)
pssm_bias_list.append(pssm_bias)
pssm_log_odds_list.append(pssm_log_odds)
bias_by_res_list.append(np.zeros([chain_length, 21]))
if letter in masked_chains:
masked_list.append(letter)
letter_list.append(letter)
chain_seq = b[f'seq_chain_{letter}']
chain_seq = ''.join([a if a!='-' else 'X' for a in chain_seq])
chain_length = len(chain_seq)
global_idx_start_list.append(global_idx_start_list[-1]+chain_length)
masked_chain_length_list.append(chain_length)
chain_coords = b[f'coords_chain_{letter}'] #this is a dictionary
chain_mask = np.ones(chain_length) #1.0 for masked
x_chain = np.stack([chain_coords[c] for c in [f'N_chain_{letter}', f'CA_chain_{letter}', f'C_chain_{letter}', f'O_chain_{letter}']], 1) #[chain_lenght,4,3]
x_chain_list.append(x_chain)
chain_mask_list.append(chain_mask)
chain_seq_list.append(chain_seq)
chain_encoding_list.append(c*np.ones(np.array(chain_mask).shape[0]))
l1 += chain_length
residue_idx[i, l0:l1] = 100*(c-1)+np.arange(l0, l1)
l0 += chain_length
c+=1
fixed_position_mask = np.ones(chain_length)
if fixed_position_dict!=None:
fixed_pos_list = fixed_position_dict[b['name']][letter]
if fixed_pos_list:
fixed_position_mask[np.array(fixed_pos_list)-1] = 0.0
fixed_position_mask_list.append(fixed_position_mask)
omit_AA_mask_temp = np.zeros([chain_length, len(alphabet)], np.int32)
if omit_AA_dict!=None:
for item in omit_AA_dict[b['name']][letter]:
idx_AA = np.array(item[0])-1
AA_idx = np.array([np.argwhere(np.array(list(alphabet))== AA)[0][0] for AA in item[1]]).repeat(idx_AA.shape[0])
idx_ = np.array([[a, b] for a in idx_AA for b in AA_idx])
omit_AA_mask_temp[idx_[:,0], idx_[:,1]] = 1
omit_AA_mask_list.append(omit_AA_mask_temp)
pssm_coef = np.zeros(chain_length)
pssm_bias = np.zeros([chain_length, 21])
pssm_log_odds = 10000.0*np.ones([chain_length, 21])
if pssm_dict:
if pssm_dict[b['name']][letter]:
pssm_coef = pssm_dict[b['name']][letter]['pssm_coef']
pssm_bias = pssm_dict[b['name']][letter]['pssm_bias']
pssm_log_odds = pssm_dict[b['name']][letter]['pssm_log_odds']
pssm_coef_list.append(pssm_coef)
pssm_bias_list.append(pssm_bias)
pssm_log_odds_list.append(pssm_log_odds)
if bias_by_res_dict:
bias_by_res_list.append(bias_by_res_dict[b['name']][letter])
else:
bias_by_res_list.append(np.zeros([chain_length, 21]))
letter_list_np = np.array(letter_list)
tied_pos_list_of_lists = []
tied_beta = np.ones(L_max)
if tied_positions_dict!=None:
tied_pos_list = tied_positions_dict[b['name']]
if tied_pos_list:
set_chains_tied = set(list(itertools.chain(*[list(item) for item in tied_pos_list])))
for tied_item in tied_pos_list:
one_list = []
for k, v in tied_item.items():
start_idx = global_idx_start_list[np.argwhere(letter_list_np == k)[0][0]]
if isinstance(v[0], list):
for v_count in range(len(v[0])):
one_list.append(start_idx+v[0][v_count]-1)#make 0 to be the first
tied_beta[start_idx+v[0][v_count]-1] = v[1][v_count]
else:
for v_ in v:
one_list.append(start_idx+v_-1)#make 0 to be the first
tied_pos_list_of_lists.append(one_list)
tied_pos_list_of_lists_list.append(tied_pos_list_of_lists)
x = np.concatenate(x_chain_list,0) #[L, 4, 3]
all_sequence = "".join(chain_seq_list)
m = np.concatenate(chain_mask_list,0) #[L,], 1.0 for places that need to be predicted
chain_encoding = np.concatenate(chain_encoding_list,0)
m_pos = np.concatenate(fixed_position_mask_list,0) #[L,], 1.0 for places that need to be predicted
pssm_coef_ = np.concatenate(pssm_coef_list,0) #[L,], 1.0 for places that need to be predicted
pssm_bias_ = np.concatenate(pssm_bias_list,0) #[L,], 1.0 for places that need to be predicted
pssm_log_odds_ = np.concatenate(pssm_log_odds_list,0) #[L,], 1.0 for places that need to be predicted
bias_by_res_ = np.concatenate(bias_by_res_list, 0) #[L,21], 0.0 for places where AA frequencies don't need to be tweaked
l = len(all_sequence)
x_pad = np.pad(x, [[0,L_max-l], [0,0], [0,0]], 'constant', constant_values=(np.nan, ))
X[i,:,:,:] = x_pad
m_pad = np.pad(m, [[0,L_max-l]], 'constant', constant_values=(0.0, ))
m_pos_pad = np.pad(m_pos, [[0,L_max-l]], 'constant', constant_values=(0.0, ))
omit_AA_mask_pad = np.pad(np.concatenate(omit_AA_mask_list,0), [[0,L_max-l]], 'constant', constant_values=(0.0, ))
chain_M[i,:] = m_pad
chain_M_pos[i,:] = m_pos_pad
omit_AA_mask[i,] = omit_AA_mask_pad
chain_encoding_pad = np.pad(chain_encoding, [[0,L_max-l]], 'constant', constant_values=(0.0, ))
chain_encoding_all[i,:] = chain_encoding_pad
pssm_coef_pad = np.pad(pssm_coef_, [[0,L_max-l]], 'constant', constant_values=(0.0, ))
pssm_bias_pad = np.pad(pssm_bias_, [[0,L_max-l], [0,0]], 'constant', constant_values=(0.0, ))
pssm_log_odds_pad = np.pad(pssm_log_odds_, [[0,L_max-l], [0,0]], 'constant', constant_values=(0.0, ))
pssm_coef_all[i,:] = pssm_coef_pad
pssm_bias_all[i,:] = pssm_bias_pad
pssm_log_odds_all[i,:] = pssm_log_odds_pad
bias_by_res_pad = np.pad(bias_by_res_, [[0,L_max-l], [0,0]], 'constant', constant_values=(0.0, ))
bias_by_res_all[i,:] = bias_by_res_pad
# Convert to labels
indices = np.asarray([alphabet.index(a) for a in all_sequence], dtype=np.int32)
S[i, :l] = indices
letter_list_list.append(letter_list)
visible_list_list.append(visible_list)
masked_list_list.append(masked_list)
masked_chain_length_list_list.append(masked_chain_length_list)
isnan = np.isnan(X)
mask = np.isfinite(np.sum(X,(2,3))).astype(np.float32)
X[isnan] = 0.
# Conversion
pssm_coef_all = torch.from_numpy(pssm_coef_all).to(dtype=torch.float32, device=device)
pssm_bias_all = torch.from_numpy(pssm_bias_all).to(dtype=torch.float32, device=device)
pssm_log_odds_all = torch.from_numpy(pssm_log_odds_all).to(dtype=torch.float32, device=device)
tied_beta = torch.from_numpy(tied_beta).to(dtype=torch.float32, device=device)
jumps = ((residue_idx[:,1:]-residue_idx[:,:-1])==1).astype(np.float32)
bias_by_res_all = torch.from_numpy(bias_by_res_all).to(dtype=torch.float32, device=device)
phi_mask = np.pad(jumps, [[0,0],[1,0]])
psi_mask = np.pad(jumps, [[0,0],[0,1]])
omega_mask = np.pad(jumps, [[0,0],[0,1]])
dihedral_mask = np.concatenate([phi_mask[:,:,None], psi_mask[:,:,None], omega_mask[:,:,None]], -1) #[B,L,3]
dihedral_mask = torch.from_numpy(dihedral_mask).to(dtype=torch.float32, device=device)
residue_idx = torch.from_numpy(residue_idx).to(dtype=torch.long,device=device)
S = torch.from_numpy(S).to(dtype=torch.long,device=device)
X = torch.from_numpy(X).to(dtype=torch.float32, device=device)
mask = torch.from_numpy(mask).to(dtype=torch.float32, device=device)
chain_M = torch.from_numpy(chain_M).to(dtype=torch.float32, device=device)
chain_M_pos = torch.from_numpy(chain_M_pos).to(dtype=torch.float32, device=device)
omit_AA_mask = torch.from_numpy(omit_AA_mask).to(dtype=torch.float32, device=device)
chain_encoding_all = torch.from_numpy(chain_encoding_all).to(dtype=torch.long, device=device)
return X, S, mask, lengths, chain_M, chain_encoding_all, letter_list_list, visible_list_list, masked_list_list, masked_chain_length_list_list, chain_M_pos, omit_AA_mask, residue_idx, dihedral_mask, tied_pos_list_of_lists_list, pssm_coef_all, pssm_bias_all, pssm_log_odds_all, bias_by_res_all, tied_beta
def loss_nll(S, log_probs, mask):
""" Negative log probabilities """
criterion = torch.nn.NLLLoss(reduction='none')
loss = criterion(
log_probs.contiguous().view(-1, log_probs.size(-1)), S.contiguous().view(-1)
).view(S.size())
loss_av = torch.sum(loss * mask) / torch.sum(mask)
return loss, loss_av
def loss_smoothed(S, log_probs, mask, weight=0.1):
""" Negative log probabilities """
S_onehot = torch.nn.functional.one_hot(S, 21).float()
# Label smoothing
S_onehot = S_onehot + weight / float(S_onehot.size(-1))
S_onehot = S_onehot / S_onehot.sum(-1, keepdim=True)
loss = -(S_onehot * log_probs).sum(-1)
loss_av = torch.sum(loss * mask) / torch.sum(mask)
return loss, loss_av
class StructureDataset():
def __init__(self, jsonl_file, verbose=True, truncate=None, max_length=100,
alphabet='ACDEFGHIKLMNPQRSTVWYX-'):
alphabet_set = set([a for a in alphabet])
discard_count = {
'bad_chars': 0,
'too_long': 0,
'bad_seq_length': 0
}
with open(jsonl_file) as f:
self.data = []
lines = f.readlines()
start = time.time()
for i, line in enumerate(lines):
entry = json.loads(line)
seq = entry['seq']
name = entry['name']
# Convert raw coords to np arrays
#for key, val in entry['coords'].items():
# entry['coords'][key] = np.asarray(val)
# Check if in alphabet
bad_chars = set([s for s in seq]).difference(alphabet_set)
if len(bad_chars) == 0:
if len(entry['seq']) <= max_length:
if True:
self.data.append(entry)
else:
discard_count['bad_seq_length'] += 1
else:
discard_count['too_long'] += 1
else:
print(name, bad_chars, entry['seq'])
discard_count['bad_chars'] += 1
# Truncate early
if truncate is not None and len(self.data) == truncate:
return
if verbose and (i + 1) % 1000 == 0:
elapsed = time.time() - start
print('{} entries ({} loaded) in {:.1f} s'.format(len(self.data), i+1, elapsed))
print('discarded', discard_count)
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
return self.data[idx]
class StructureDatasetPDB():
def __init__(self, pdb_dict_list, verbose=True, truncate=None, max_length=100,
alphabet='ACDEFGHIKLMNPQRSTVWYX-'):
alphabet_set = set([a for a in alphabet])
discard_count = {
'bad_chars': 0,
'too_long': 0,
'bad_seq_length': 0
}
self.data = []
start = time.time()
for i, entry in enumerate(pdb_dict_list):
seq = entry['seq']
name = entry['name']
bad_chars = set([s for s in seq]).difference(alphabet_set)
if len(bad_chars) == 0:
if len(entry['seq']) <= max_length:
self.data.append(entry)
else:
discard_count['too_long'] += 1
else:
discard_count['bad_chars'] += 1
# Truncate early
if truncate is not None and len(self.data) == truncate:
return
if verbose and (i + 1) % 1000 == 0:
elapsed = time.time() - start
#print('Discarded', discard_count)
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
return self.data[idx]
class StructureLoader():
def __init__(self, dataset, batch_size=100, shuffle=True,
collate_fn=lambda x:x, drop_last=False):
self.dataset = dataset
self.size = len(dataset)
self.lengths = [len(dataset[i]['seq']) for i in range(self.size)]
self.batch_size = batch_size
sorted_ix = np.argsort(self.lengths)
# Cluster into batches of similar sizes
clusters, batch = [], []
batch_max = 0
for ix in sorted_ix:
size = self.lengths[ix]
if size * (len(batch) + 1) <= self.batch_size:
batch.append(ix)
batch_max = size
else:
clusters.append(batch)
batch, batch_max = [], 0
if len(batch) > 0:
clusters.append(batch)
self.clusters = clusters
def __len__(self):
return len(self.clusters)
def __iter__(self):
np.random.shuffle(self.clusters)
for b_idx in self.clusters:
batch = [self.dataset[i] for i in b_idx]
yield batch
# The following gather functions
def gather_edges(edges, neighbor_idx):
# Features [B,N,N,C] at Neighbor indices [B,N,K] => Neighbor features [B,N,K,C]
neighbors = neighbor_idx.unsqueeze(-1).expand(-1, -1, -1, edges.size(-1))
edge_features = torch.gather(edges, 2, neighbors)
return edge_features
def gather_nodes(nodes, neighbor_idx):
# Features [B,N,C] at Neighbor indices [B,N,K] => [B,N,K,C]
# Flatten and expand indices per batch [B,N,K] => [B,NK] => [B,NK,C]
neighbors_flat = neighbor_idx.view((neighbor_idx.shape[0], -1))
neighbors_flat = neighbors_flat.unsqueeze(-1).expand(-1, -1, nodes.size(2))
# Gather and re-pack
neighbor_features = torch.gather(nodes, 1, neighbors_flat)
neighbor_features = neighbor_features.view(list(neighbor_idx.shape)[:3] + [-1])
return neighbor_features
def gather_nodes_t(nodes, neighbor_idx):
# Features [B,N,C] at Neighbor index [B,K] => Neighbor features[B,K,C]
idx_flat = neighbor_idx.unsqueeze(-1).expand(-1, -1, nodes.size(2))
neighbor_features = torch.gather(nodes, 1, idx_flat)
return neighbor_features
def cat_neighbors_nodes(h_nodes, h_neighbors, E_idx):
h_nodes = gather_nodes(h_nodes, E_idx)
h_nn = torch.cat([h_neighbors, h_nodes], -1)
return h_nn
class EncLayer(nn.Module):
def __init__(self, num_hidden, num_in, dropout=0.1, num_heads=None, scale=30):
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)
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)
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
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 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 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):
""" 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.edge_embedding = nn.Linear(edge_in, edge_features, bias=False)
self.norm_edges = nn.LayerNorm(edge_features)
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):
if self.augment_eps !='':
if self.augment_eps > 0:
X = X + self.augment_eps * torch.randn_like(X)
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)
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
class ProteinMPNN(nn.Module):
def __init__(self, num_letters, node_features, edge_features,
hidden_dim, num_encoder_layers=3, num_decoder_layers=3,
vocab=21, k_neighbors=64, augment_eps=0.05, dropout=0.1):
super(ProteinMPNN, self).__init__()
# Hyperparameters
self.node_features = node_features
self.edge_features = edge_features
self.hidden_dim = hidden_dim
# Featurization layers
self.features = ProteinFeatures(node_features, edge_features, top_k=k_neighbors, augment_eps=augment_eps)
self.W_e = nn.Linear(edge_features, hidden_dim, bias=True)
self.W_s = nn.Embedding(vocab, hidden_dim)
# Encoder layers
self.encoder_layers = nn.ModuleList([
EncLayer(hidden_dim, hidden_dim*2, dropout=dropout)
for _ in range(num_encoder_layers)
])
# Decoder layers
self.decoder_layers = nn.ModuleList([
DecLayer(hidden_dim, hidden_dim*3, dropout=dropout)
for _ in range(num_decoder_layers)
])
self.W_out = nn.Linear(hidden_dim, num_letters, bias=True)
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def forward(self, X, S, mask, chain_M, residue_idx, chain_encoding_all, randn, use_input_decoding_order=False, decoding_order=None):
""" Graph-conditioned sequence model """
device=X.device
# Prepare node and edge embeddings
E, E_idx = self.features(X, mask, residue_idx, chain_encoding_all)
h_V = torch.zeros((E.shape[0], E.shape[1], E.shape[-1]), device=E.device)
h_E = self.W_e(E)
# Encoder is unmasked self-attention
mask_attend = gather_nodes(mask.unsqueeze(-1), E_idx).squeeze(-1)
mask_attend = mask.unsqueeze(-1) * mask_attend
for layer in self.encoder_layers:
h_V, h_E = layer(h_V, h_E, E_idx, mask, mask_attend)
# Concatenate sequence embeddings for autoregressive decoder
h_S = self.W_s(S)
h_ES = cat_neighbors_nodes(h_S, h_E, E_idx)
# Build encoder embeddings
h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_S), h_E, E_idx)
h_EXV_encoder = cat_neighbors_nodes(h_V, h_EX_encoder, E_idx)
chain_M = chain_M*mask #update chain_M to include missing regions
if not use_input_decoding_order:
decoding_order = torch.argsort((chain_M+0.0001)*(torch.abs(randn))) #[numbers will be smaller for places where chain_M = 0.0 and higher for places where chain_M = 1.0]
mask_size = E_idx.shape[1]
permutation_matrix_reverse = torch.nn.functional.one_hot(decoding_order, num_classes=mask_size).float()
order_mask_backward = torch.einsum('ij, biq, bjp->bqp',(1-torch.triu(torch.ones(mask_size,mask_size, device=device))), permutation_matrix_reverse, permutation_matrix_reverse)
mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1)
mask_1D = mask.view([mask.size(0), mask.size(1), 1, 1])
mask_bw = mask_1D * mask_attend
mask_fw = mask_1D * (1. - mask_attend)
h_EXV_encoder_fw = mask_fw * h_EXV_encoder
for layer in self.decoder_layers:
# Masked positions attend to encoder information, unmasked see.
h_ESV = cat_neighbors_nodes(h_V, h_ES, E_idx)
h_ESV = mask_bw * h_ESV + h_EXV_encoder_fw
h_V = layer(h_V, h_ESV, mask)
logits = self.W_out(h_V)
log_probs = F.log_softmax(logits, dim=-1)
return log_probs
def sample(self, X, randn, S_true, chain_mask, chain_encoding_all, residue_idx, mask=None, temperature=1.0, omit_AAs_np=None, bias_AAs_np=None, chain_M_pos=None, omit_AA_mask=None, pssm_coef=None, pssm_bias=None, pssm_multi=None, pssm_log_odds_flag=None, pssm_log_odds_mask=None, pssm_bias_flag=None, bias_by_res=None):
device = X.device
# Prepare node and edge embeddings
E, E_idx = self.features(X, mask, residue_idx, chain_encoding_all)
h_V = torch.zeros((E.shape[0], E.shape[1], E.shape[-1]), device=device)
h_E = self.W_e(E)
# Encoder is unmasked self-attention
mask_attend = gather_nodes(mask.unsqueeze(-1), E_idx).squeeze(-1)
mask_attend = mask.unsqueeze(-1) * mask_attend
for layer in self.encoder_layers:
h_V, h_E = layer(h_V, h_E, E_idx, mask, mask_attend)
# Decoder uses masked self-attention
chain_mask = chain_mask*chain_M_pos*mask #update chain_M to include missing regions
decoding_order = torch.argsort((chain_mask+0.0001)*(torch.abs(randn))) #[numbers will be smaller for places where chain_M = 0.0 and higher for places where chain_M = 1.0]
mask_size = E_idx.shape[1]
permutation_matrix_reverse = torch.nn.functional.one_hot(decoding_order, num_classes=mask_size).float()
order_mask_backward = torch.einsum('ij, biq, bjp->bqp',(1-torch.triu(torch.ones(mask_size,mask_size, device=device))), permutation_matrix_reverse, permutation_matrix_reverse)
mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1)
mask_1D = mask.view([mask.size(0), mask.size(1), 1, 1])
mask_bw = mask_1D * mask_attend
mask_fw = mask_1D * (1. - mask_attend)
N_batch, N_nodes = X.size(0), X.size(1)
log_probs = torch.zeros((N_batch, N_nodes, 21), device=device)
all_probs = torch.zeros((N_batch, N_nodes, 21), device=device, dtype=torch.float32)
h_S = torch.zeros_like(h_V, device=device)
S = torch.zeros((N_batch, N_nodes), dtype=torch.int64, device=device)
h_V_stack = [h_V] + [torch.zeros_like(h_V, device=device) for _ in range(len(self.decoder_layers))]
constant = torch.tensor(omit_AAs_np, device=device)
constant_bias = torch.tensor(bias_AAs_np, device=device)
#chain_mask_combined = chain_mask*chain_M_pos
omit_AA_mask_flag = omit_AA_mask != None
h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_S), h_E, E_idx)
h_EXV_encoder = cat_neighbors_nodes(h_V, h_EX_encoder, E_idx)
h_EXV_encoder_fw = mask_fw * h_EXV_encoder
for t_ in range(N_nodes):
t = decoding_order[:,t_] #[B]
chain_mask_gathered = torch.gather(chain_mask, 1, t[:,None]) #[B]
bias_by_res_gathered = torch.gather(bias_by_res, 1, t[:,None,None].repeat(1,1,21))[:,0,:] #[B, 21]
if (chain_mask_gathered==0).all():
S_t = torch.gather(S_true, 1, t[:,None])
else:
# Hidden layers
E_idx_t = torch.gather(E_idx, 1, t[:,None,None].repeat(1,1,E_idx.shape[-1]))
h_E_t = torch.gather(h_E, 1, t[:,None,None,None].repeat(1,1,h_E.shape[-2], h_E.shape[-1]))
h_ES_t = cat_neighbors_nodes(h_S, h_E_t, E_idx_t)
h_EXV_encoder_t = torch.gather(h_EXV_encoder_fw, 1, t[:,None,None,None].repeat(1,1,h_EXV_encoder_fw.shape[-2], h_EXV_encoder_fw.shape[-1]))
mask_t = torch.gather(mask, 1, t[:,None])
for l, layer in enumerate(self.decoder_layers):
# Updated relational features for future states
h_ESV_decoder_t = cat_neighbors_nodes(h_V_stack[l], h_ES_t, E_idx_t)
h_V_t = torch.gather(h_V_stack[l], 1, t[:,None,None].repeat(1,1,h_V_stack[l].shape[-1]))
h_ESV_t = torch.gather(mask_bw, 1, t[:,None,None,None].repeat(1,1,mask_bw.shape[-2], mask_bw.shape[-1])) * h_ESV_decoder_t + h_EXV_encoder_t
h_V_stack[l+1].scatter_(1, t[:,None,None].repeat(1,1,h_V.shape[-1]), layer(h_V_t, h_ESV_t, mask_V=mask_t))
# Sampling step
h_V_t = torch.gather(h_V_stack[-1], 1, t[:,None,None].repeat(1,1,h_V_stack[-1].shape[-1]))[:,0]
logits = self.W_out(h_V_t) / temperature
probs = F.softmax(logits-constant[None,:]*1e8+constant_bias[None,:]/temperature+bias_by_res_gathered/temperature, dim=-1)
if pssm_bias_flag:
pssm_coef_gathered = torch.gather(pssm_coef, 1, t[:,None])[:,0]
pssm_bias_gathered = torch.gather(pssm_bias, 1, t[:,None,None].repeat(1,1,pssm_bias.shape[-1]))[:,0]
probs = (1-pssm_multi*pssm_coef_gathered[:,None])*probs + pssm_multi*pssm_coef_gathered[:,None]*pssm_bias_gathered
if pssm_log_odds_flag:
pssm_log_odds_mask_gathered = torch.gather(pssm_log_odds_mask, 1, t[:,None, None].repeat(1,1,pssm_log_odds_mask.shape[-1]))[:,0] #[B, 21]
probs_masked = probs*pssm_log_odds_mask_gathered
probs_masked += probs * 0.001
probs = probs_masked/torch.sum(probs_masked, dim=-1, keepdim=True) #[B, 21]
if omit_AA_mask_flag:
omit_AA_mask_gathered = torch.gather(omit_AA_mask, 1, t[:,None, None].repeat(1,1,omit_AA_mask.shape[-1]))[:,0] #[B, 21]
probs_masked = probs*(1.0-omit_AA_mask_gathered)
probs = probs_masked/torch.sum(probs_masked, dim=-1, keepdim=True) #[B, 21]
S_t = torch.multinomial(probs, 1)
all_probs.scatter_(1, t[:,None,None].repeat(1,1,21), (chain_mask_gathered[:,:,None,]*probs[:,None,:]).float())
S_true_gathered = torch.gather(S_true, 1, t[:,None])
S_t = (S_t*chain_mask_gathered+S_true_gathered*(1.0-chain_mask_gathered)).long()
temp1 = self.W_s(S_t)
h_S.scatter_(1, t[:,None,None].repeat(1,1,temp1.shape[-1]), temp1)
S.scatter_(1, t[:,None], S_t)
output_dict = {"S": S, "probs": all_probs, "decoding_order": decoding_order}
return output_dict
def tied_sample(self, X, randn, S_true, chain_mask, chain_encoding_all, residue_idx, mask=None, temperature=1.0, omit_AAs_np=None, bias_AAs_np=None, chain_M_pos=None, omit_AA_mask=None, pssm_coef=None, pssm_bias=None, pssm_multi=None, pssm_log_odds_flag=None, pssm_log_odds_mask=None, pssm_bias_flag=None, tied_pos=None, tied_beta=None, bias_by_res=None):
device = X.device
# Prepare node and edge embeddings
E, E_idx = self.features(X, mask, residue_idx, chain_encoding_all)
h_V = torch.zeros((E.shape[0], E.shape[1], E.shape[-1]), device=device)
h_E = self.W_e(E)
# Encoder is unmasked self-attention
mask_attend = gather_nodes(mask.unsqueeze(-1), E_idx).squeeze(-1)
mask_attend = mask.unsqueeze(-1) * mask_attend
for layer in self.encoder_layers:
h_V, h_E = layer(h_V, h_E, E_idx, mask, mask_attend)
# Decoder uses masked self-attention
chain_mask = chain_mask*chain_M_pos*mask #update chain_M to include missing regions
decoding_order = torch.argsort((chain_mask+0.0001)*(torch.abs(randn))) #[numbers will be smaller for places where chain_M = 0.0 and higher for places where chain_M = 1.0]
new_decoding_order = []
for t_dec in list(decoding_order[0,].cpu().data.numpy()):
if t_dec not in list(itertools.chain(*new_decoding_order)):
list_a = [item for item in tied_pos if t_dec in item]
if list_a:
new_decoding_order.append(list_a[0])
else:
new_decoding_order.append([t_dec])
decoding_order = torch.tensor(list(itertools.chain(*new_decoding_order)), device=device)[None,].repeat(X.shape[0],1)
mask_size = E_idx.shape[1]
permutation_matrix_reverse = torch.nn.functional.one_hot(decoding_order, num_classes=mask_size).float()
order_mask_backward = torch.einsum('ij, biq, bjp->bqp',(1-torch.triu(torch.ones(mask_size,mask_size, device=device))), permutation_matrix_reverse, permutation_matrix_reverse)
mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1)
mask_1D = mask.view([mask.size(0), mask.size(1), 1, 1])
mask_bw = mask_1D * mask_attend
mask_fw = mask_1D * (1. - mask_attend)
N_batch, N_nodes = X.size(0), X.size(1)
log_probs = torch.zeros((N_batch, N_nodes, 21), device=device)
all_probs = torch.zeros((N_batch, N_nodes, 21), device=device, dtype=torch.float32)
h_S = torch.zeros_like(h_V, device=device)
S = torch.zeros((N_batch, N_nodes), dtype=torch.int64, device=device)
h_V_stack = [h_V] + [torch.zeros_like(h_V, device=device) for _ in range(len(self.decoder_layers))]
constant = torch.tensor(omit_AAs_np, device=device)
constant_bias = torch.tensor(bias_AAs_np, device=device)
omit_AA_mask_flag = omit_AA_mask != None
h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_S), h_E, E_idx)
h_EXV_encoder = cat_neighbors_nodes(h_V, h_EX_encoder, E_idx)
h_EXV_encoder_fw = mask_fw * h_EXV_encoder
for t_list in new_decoding_order:
logits = 0.0
logit_list = []
done_flag = False
for t in t_list:
if (chain_mask[:,t]==0).all():
S_t = S_true[:,t]
for t in t_list:
h_S[:,t,:] = self.W_s(S_t)
S[:,t] = S_t
done_flag = True
break
else:
E_idx_t = E_idx[:,t:t+1,:]
h_E_t = h_E[:,t:t+1,:,:]
h_ES_t = cat_neighbors_nodes(h_S, h_E_t, E_idx_t)
h_EXV_encoder_t = h_EXV_encoder_fw[:,t:t+1,:,:]
mask_t = mask[:,t:t+1]
for l, layer in enumerate(self.decoder_layers):
h_ESV_decoder_t = cat_neighbors_nodes(h_V_stack[l], h_ES_t, E_idx_t)
h_V_t = h_V_stack[l][:,t:t+1,:]
h_ESV_t = mask_bw[:,t:t+1,:,:] * h_ESV_decoder_t + h_EXV_encoder_t
h_V_stack[l+1][:,t,:] = layer(h_V_t, h_ESV_t, mask_V=mask_t).squeeze(1)
h_V_t = h_V_stack[-1][:,t,:]
logit_list.append((self.W_out(h_V_t) / temperature)/len(t_list))
logits += tied_beta[t]*(self.W_out(h_V_t) / temperature)/len(t_list)
if done_flag:
pass
else:
bias_by_res_gathered = bias_by_res[:,t,:] #[B, 21]
probs = F.softmax(logits-constant[None,:]*1e8+constant_bias[None,:]/temperature+bias_by_res_gathered/temperature, dim=-1)
if pssm_bias_flag:
pssm_coef_gathered = pssm_coef[:,t]
pssm_bias_gathered = pssm_bias[:,t]
probs = (1-pssm_multi*pssm_coef_gathered[:,None])*probs + pssm_multi*pssm_coef_gathered[:,None]*pssm_bias_gathered
if pssm_log_odds_flag:
pssm_log_odds_mask_gathered = pssm_log_odds_mask[:,t]
probs_masked = probs*pssm_log_odds_mask_gathered
probs_masked += probs * 0.001
probs = probs_masked/torch.sum(probs_masked, dim=-1, keepdim=True) #[B, 21]
if omit_AA_mask_flag:
omit_AA_mask_gathered = omit_AA_mask[:,t]
probs_masked = probs*(1.0-omit_AA_mask_gathered)
probs = probs_masked/torch.sum(probs_masked, dim=-1, keepdim=True) #[B, 21]
S_t_repeat = torch.multinomial(probs, 1).squeeze(-1)
for t in t_list:
h_S[:,t,:] = self.W_s(S_t_repeat)
S[:,t] = S_t_repeat
all_probs[:,t,:] = probs.float()
output_dict = {"S": S, "probs": all_probs, "decoding_order": decoding_order}
return output_dict
def conditional_probs(self, X, S, mask, chain_M, residue_idx, chain_encoding_all, randn, backbone_only=False):
""" Graph-conditioned sequence model """
device=X.device
# Prepare node and edge embeddings
E, E_idx = self.features(X, mask, residue_idx, chain_encoding_all)
h_V_enc = torch.zeros((E.shape[0], E.shape[1], E.shape[-1]), device=E.device)
h_E = self.W_e(E)
# Encoder is unmasked self-attention
mask_attend = gather_nodes(mask.unsqueeze(-1), E_idx).squeeze(-1)
mask_attend = mask.unsqueeze(-1) * mask_attend
for layer in self.encoder_layers:
h_V_enc, h_E = layer(h_V_enc, h_E, E_idx, mask, mask_attend)
# Concatenate sequence embeddings for autoregressive decoder
h_S = self.W_s(S)
h_ES = cat_neighbors_nodes(h_S, h_E, E_idx)
# Build encoder embeddings
h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_S), h_E, E_idx)
h_EXV_encoder = cat_neighbors_nodes(h_V_enc, h_EX_encoder, E_idx)
chain_M = chain_M*mask #update chain_M to include missing regions
chain_M_np = chain_M.cpu().numpy()
idx_to_loop = np.argwhere(chain_M_np[0,:]==1)[:,0]
log_conditional_probs = torch.zeros([X.shape[0], chain_M.shape[1], 21], device=device).float()
for idx in idx_to_loop:
h_V = torch.clone(h_V_enc)
order_mask = torch.zeros(chain_M.shape[1], device=device).float()
if backbone_only:
order_mask = torch.ones(chain_M.shape[1], device=device).float()
order_mask[idx] = 0.
else:
order_mask = torch.zeros(chain_M.shape[1], device=device).float()
order_mask[idx] = 1.
decoding_order = torch.argsort((order_mask[None,]+0.0001)*(torch.abs(randn))) #[numbers will be smaller for places where chain_M = 0.0 and higher for places where chain_M = 1.0]
mask_size = E_idx.shape[1]
permutation_matrix_reverse = torch.nn.functional.one_hot(decoding_order, num_classes=mask_size).float()
order_mask_backward = torch.einsum('ij, biq, bjp->bqp',(1-torch.triu(torch.ones(mask_size,mask_size, device=device))), permutation_matrix_reverse, permutation_matrix_reverse)
mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1)
mask_1D = mask.view([mask.size(0), mask.size(1), 1, 1])
mask_bw = mask_1D * mask_attend
mask_fw = mask_1D * (1. - mask_attend)
h_EXV_encoder_fw = mask_fw * h_EXV_encoder
for layer in self.decoder_layers:
# Masked positions attend to encoder information, unmasked see.
h_ESV = cat_neighbors_nodes(h_V, h_ES, E_idx)
h_ESV = mask_bw * h_ESV + h_EXV_encoder_fw
h_V = layer(h_V, h_ESV, mask)
logits = self.W_out(h_V)
log_probs = F.log_softmax(logits, dim=-1)
log_conditional_probs[:,idx,:] = log_probs[:,idx,:]
return log_conditional_probs
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