from __future__ import print_function import copy import glob import itertools import json import os import random import shutil import sys import time import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from torch import optim from torch.utils.data import DataLoader from torch.utils.data.dataset import Subset, random_split # 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, 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=int ) # 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=int) chain_M = np.zeros( [B, L_max], dtype=int ) # 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=int ) # 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=int ) # 1.0 for the bits that need to be predicted S = np.zeros([B, L_max], dtype=int) omit_AA_mask = np.zeros([B, L_max, len(alphabet)], dtype=int) # 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 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)], int) 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)], int) 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=int) 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] = 0.0 # 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: 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 CA_ProteinFeatures(nn.Module): def __init__( self, edge_features, node_features, num_positional_embeddings=16, num_rbf=16, top_k=30, augment_eps=0.0, 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))) / 2.0 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 + (1.0 - 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 = 2.0, 22.0, 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=0.0, 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 + (1.0 - 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 = 2.0, 22.0, 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 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, ca_only=False, ): super(ProteinMPNN, self).__init__() # Hyperparameters self.node_features = node_features self.edge_features = edge_features self.hidden_dim = hidden_dim # Featurization layers if ca_only: self.features = CA_ProteinFeatures( node_features, edge_features, top_k=k_neighbors, augment_eps=augment_eps ) self.W_v = nn.Linear(node_features, hidden_dim, bias=True) else: 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 * (1.0 - 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 # TODO: Impose different decoding order. 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 * (1.0 - 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] mask_gathered = torch.gather(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 (mask_gathered == 0).all(): # for padded or missing regions only 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 * (1.0 - 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 (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) S_t_repeat = ( chain_mask[:, t] * S_t_repeat + (1 - chain_mask[:, t]) * S_true[:, t] ).long() # hard pick fixed positions 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] = 0.0 else: order_mask = torch.zeros(chain_M.shape[1], device=device).float() order_mask[idx] = 1.0 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 * (1.0 - 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 def unconditional_probs(self, X, mask, residue_idx, chain_encoding_all): """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) # Build encoder embeddings h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_V), h_E, E_idx) h_EXV_encoder = cat_neighbors_nodes(h_V, h_EX_encoder, E_idx) order_mask_backward = torch.zeros( [X.shape[0], X.shape[1], X.shape[1]], device=device ) 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 * (1.0 - mask_attend) h_EXV_encoder_fw = mask_fw * h_EXV_encoder for layer in self.decoder_layers: h_V = layer(h_V, h_EXV_encoder_fw, mask) logits = self.W_out(h_V) log_probs = F.log_softmax(logits, dim=-1) return log_probs