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protein_mpnn_utils.py
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protein_mpnn_utils.py
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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 parse_fasta(filename,limit=-1, omit=[]):
header = []
sequence = []
lines = open(filename, "r")
for line in lines:
line = line.rstrip()
if line[0] == ">":
if len(header) == limit:
break
header.append(line[1:])
sequence.append([])
else:
if omit:
line = [item for item in line if item not in omit]
line = ''.join(line)
line = ''.join(line)
sequence[-1].append(line)
lines.close()
sequence = [''.join(seq) for seq in sequence]
return np.array(header), np.array(sequence)
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, ca_only=False):
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:
if ca_only:
sidechain_atoms = ['CA']
else:
sidechain_atoms = ['N', 'CA', 'C', 'O']
xyz, seq = parse_PDB_biounits(biounit, atoms=sidechain_atoms, chain=letter)
if type(xyz) != str:
concat_seq += seq[0]
my_dict['seq_chain_'+letter]=seq[0]
coords_dict_chain = {}
if ca_only:
coords_dict_chain['CA_chain_'+letter]=xyz.tolist()
else:
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, ca_only=False):
""" 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])
if ca_only:
X = np.zeros([B, L_max, 1, 3])
else:
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 = []
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 = []
masked_chains.sort() #sort masked_chains
visible_chains.sort() #sort visible_chains
all_chains = masked_chains + visible_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
if ca_only:
x_chain = np.array(chain_coords[f'CA_chain_{letter}']) #[chain_lenght,1,3] #CA_diff
if len(x_chain.shape) == 2:
x_chain = x_chain[:,None,:]
else:
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
if ca_only:
x_chain = np.array(chain_coords[f'CA_chain_{letter}']) #[chain_lenght,1,3] #CA_diff
if len(x_chain.shape) == 2:
x_chain = x_chain[:,None,:]
else:
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)
if ca_only:
X_out = X[:,:,0]
else:
X_out = X
return X_out, 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:
if verbose:
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))
if verbose:
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 CA_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(CA_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
# Positional encoding
self.embeddings = PositionalEncodings(num_positional_embeddings)
# Normalization and embedding
node_in, edge_in = 3, num_positional_embeddings + num_rbf*9 + 7
self.node_embedding = nn.Linear(node_in, node_features, bias=False) #NOT USED
self.edge_embedding = nn.Linear(edge_in, edge_features, bias=False)
self.norm_nodes = nn.LayerNorm(node_features)
self.norm_edges = nn.LayerNorm(edge_features)
def _quaternions(self, R):
""" Convert a batch of 3D rotations [R] to quaternions [Q]
R [...,3,3]
Q [...,4]
"""
# Simple Wikipedia version
# en.wikipedia.org/wiki/Rotation_matrix#Quaternion
# For other options see math.stackexchange.com/questions/2074316/calculating-rotation-axis-from-rotation-matrix
diag = torch.diagonal(R, dim1=-2, dim2=-1)
Rxx, Ryy, Rzz = diag.unbind(-1)
magnitudes = 0.5 * torch.sqrt(torch.abs(1 + torch.stack([
Rxx - Ryy - Rzz,
- Rxx + Ryy - Rzz,
- Rxx - Ryy + Rzz
], -1)))
_R = lambda i,j: R[:,:,:,i,j]
signs = torch.sign(torch.stack([
_R(2,1) - _R(1,2),
_R(0,2) - _R(2,0),
_R(1,0) - _R(0,1)
], -1))
xyz = signs * magnitudes
# The relu enforces a non-negative trace
w = torch.sqrt(F.relu(1 + diag.sum(-1, keepdim=True))) / 2.
Q = torch.cat((xyz, w), -1)
Q = F.normalize(Q, dim=-1)
return Q
def _orientations_coarse(self, X, E_idx, eps=1e-6):
dX = X[:,1:,:] - X[:,:-1,:]
dX_norm = torch.norm(dX,dim=-1)
dX_mask = (3.6<dX_norm) & (dX_norm<4.0) #exclude CA-CA jumps
dX = dX*dX_mask[:,:,None]
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)
n_1 = F.normalize(torch.cross(u_1, u_0), dim=-1)
# Bond angle calculation
cosA = -(u_1 * u_0).sum(-1)
cosA = torch.clamp(cosA, -1+eps, 1-eps)
A = torch.acos(cosA)
# 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)
# Backbone features
AD_features = torch.stack((torch.cos(A), torch.sin(A) * torch.cos(D), torch.sin(A) * torch.sin(D)), 2)
AD_features = F.pad(AD_features, (0,0,1,2), 'constant', 0)
# Build relative orientations
o_1 = F.normalize(u_2 - u_1, dim=-1)
O = torch.stack((o_1, n_2, torch.cross(o_1, n_2)), 2)
O = O.view(list(O.shape[:2]) + [9])
O = F.pad(O, (0,0,1,2), 'constant', 0)
O_neighbors = gather_nodes(O, E_idx)
X_neighbors = gather_nodes(X, E_idx)
# Re-view as rotation matrices
O = O.view(list(O.shape[:2]) + [3,3])
O_neighbors = O_neighbors.view(list(O_neighbors.shape[:3]) + [3,3])
# Rotate into local reference frames
dX = X_neighbors - X.unsqueeze(-2)
dU = torch.matmul(O.unsqueeze(2), dX.unsqueeze(-1)).squeeze(-1)
dU = F.normalize(dU, dim=-1)
R = torch.matmul(O.unsqueeze(2).transpose(-1,-2), O_neighbors)
Q = self._quaternions(R)
# Orientation features
O_features = torch.cat((dU,Q), dim=-1)
return AD_features, O_features
def _dist(self, X, mask, eps=1E-6):
""" Pairwise euclidean distances """
# Convolutional network on NCHW
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)
# Identify k nearest neighbors (including self)
D_max, _ = torch.max(D, -1, keepdim=True)
D_adjust = D + (1. - mask_2D) * D_max
D_neighbors, E_idx = torch.topk(D_adjust, np.minimum(self.top_k, X.shape[1]), dim=-1, largest=False)
mask_neighbors = gather_edges(mask_2D.unsqueeze(-1), E_idx)
return D_neighbors, E_idx, mask_neighbors
def _rbf(self, D):
# Distance radial basis function
device = D.device
D_min, D_max, D_count = 2., 22., self.num_rbf
D_mu = torch.linspace(D_min, D_max, D_count).to(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, Ca, mask, residue_idx, chain_labels):
""" Featurize coordinates as an attributed graph """
if self.augment_eps > 0:
Ca = Ca + self.augment_eps * torch.randn_like(Ca)
D_neighbors, E_idx, mask_neighbors = self._dist(Ca, mask)
Ca_0 = torch.zeros(Ca.shape, device=Ca.device)
Ca_2 = torch.zeros(Ca.shape, device=Ca.device)
Ca_0[:,1:,:] = Ca[:,:-1,:]
Ca_1 = Ca
Ca_2[:,:-1,:] = Ca[:,1:,:]
V, O_features = self._orientations_coarse(Ca, E_idx)
RBF_all = []
RBF_all.append(self._rbf(D_neighbors)) #Ca_1-Ca_1
RBF_all.append(self._get_rbf(Ca_0, Ca_0, E_idx))
RBF_all.append(self._get_rbf(Ca_2, Ca_2, E_idx))
RBF_all.append(self._get_rbf(Ca_0, Ca_1, E_idx))
RBF_all.append(self._get_rbf(Ca_0, Ca_2, E_idx))
RBF_all.append(self._get_rbf(Ca_1, Ca_0, E_idx))
RBF_all.append(self._get_rbf(Ca_1, Ca_2, E_idx))
RBF_all.append(self._get_rbf(Ca_2, Ca_0, E_idx))
RBF_all.append(self._get_rbf(Ca_2, Ca_1, E_idx))
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()
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, O_features), -1)
E = self.edge_embedding(E)
E = self.norm_edges(E)
return E, E_idx
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 > 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