# MIT License # Copyright (c) 2022 Justas Dauparas # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. ''' Adapted from original code by alexechu. ''' import json, time, os, sys, glob import shutil import warnings import copy import random import os.path import subprocess import itertools from einops.layers.torch import Rearrange 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 torch.nn as nn import torch.nn.functional as F def get_mpnn_model(model_name='v_48_020', path_to_model_weights='', ca_only=False, backbone_noise=0.0, verbose=False, device=None): hidden_dim = 128 num_layers = 3 if device is None: device = torch.device("cuda:0" if (torch.cuda.is_available()) else "cpu") if path_to_model_weights: model_folder_path = path_to_model_weights if model_folder_path[-1] != '/': model_folder_path = model_folder_path + '/' else: file_path = os.path.realpath(__file__) k = file_path.rfind("/") k = file_path[:k].rfind("/") if ca_only: model_folder_path = file_path[:k] + '/ProteinMPNN/ca_model_weights/' else: model_folder_path = file_path[:k] + '/ProteinMPNN/vanilla_model_weights/' checkpoint_path = model_folder_path + f'{model_name}.pt' checkpoint = torch.load(checkpoint_path, map_location=device) noise_level_print = checkpoint['noise_level'] model = ProteinMPNN(ca_only=ca_only, num_letters=21, node_features=hidden_dim, edge_features=hidden_dim, hidden_dim=hidden_dim, num_encoder_layers=num_layers, num_decoder_layers=num_layers, augment_eps=backbone_noise, k_neighbors=checkpoint['num_edges']) model.to(device) model.load_state_dict(checkpoint['model_state_dict']) model.eval() if verbose: print(40*'-') print('Model loaded...') print('Number of edges:', checkpoint['num_edges']) print(f'Training noise level: {noise_level_print}A') return model def run_proteinmpnn(model=None, pdb_path='', pdb_path_chains='', path_to_model_weights='', model_name='v_48_020', seed=0, ca_only=False, out_folder='', num_seq_per_target=1, batch_size=1, sampling_temps=[0.1], backbone_noise=0.0, max_length=200000, omit_AAs=[], print_all=False, chain_id_jsonl='', fixed_positions_jsonl='', pssm_jsonl='', omit_AA_jsonl='', bias_AA_jsonl='', tied_positions_jsonl='', bias_by_res_jsonl='', jsonl_path='', pssm_threshold=0.0, pssm_multi=0.0, pssm_log_odds_flag=False, pssm_bias_flag=False, write_output_files=False): if model is None: model = get_mpnn_model(model_name=model_name, path_to_model_weights=path_to_model_weights, ca_only=ca_only, backbone_noise=backbone_noise, verbose=print_all) if seed: seed=seed else: seed=int(np.random.randint(0, high=999, size=1, dtype=int)[0]) torch.manual_seed(seed) random.seed(seed) np.random.seed(seed) NUM_BATCHES = num_seq_per_target//batch_size BATCH_COPIES = batch_size temperatures = sampling_temps omit_AAs_list = omit_AAs alphabet = 'ACDEFGHIKLMNPQRSTVWYX' alphabet_dict = dict(zip(alphabet, range(21))) omit_AAs_np = np.array([AA in omit_AAs_list for AA in alphabet]).astype(np.float32) device = torch.device("cuda:0" if (torch.cuda.is_available()) else "cpu") if os.path.isfile(chain_id_jsonl): with open(chain_id_jsonl, 'r') as json_file: json_list = list(json_file) for json_str in json_list: chain_id_dict = json.loads(json_str) else: chain_id_dict = None if print_all: print(40*'-') print('chain_id_jsonl is NOT loaded') if os.path.isfile(fixed_positions_jsonl): with open(fixed_positions_jsonl, 'r') as json_file: json_list = list(json_file) for json_str in json_list: fixed_positions_dict = json.loads(json_str) else: if print_all: print(40*'-') print('fixed_positions_jsonl is NOT loaded') fixed_positions_dict = None if os.path.isfile(pssm_jsonl): with open(pssm_jsonl, 'r') as json_file: json_list = list(json_file) pssm_dict = {} for json_str in json_list: pssm_dict.update(json.loads(json_str)) else: if print_all: print(40*'-') print('pssm_jsonl is NOT loaded') pssm_dict = None if os.path.isfile(omit_AA_jsonl): with open(omit_AA_jsonl, 'r') as json_file: json_list = list(json_file) for json_str in json_list: omit_AA_dict = json.loads(json_str) else: if print_all: print(40*'-') print('omit_AA_jsonl is NOT loaded') omit_AA_dict = None if os.path.isfile(bias_AA_jsonl): with open(bias_AA_jsonl, 'r') as json_file: json_list = list(json_file) for json_str in json_list: bias_AA_dict = json.loads(json_str) else: if print_all: print(40*'-') print('bias_AA_jsonl is NOT loaded') bias_AA_dict = None if os.path.isfile(tied_positions_jsonl): with open(tied_positions_jsonl, 'r') as json_file: json_list = list(json_file) for json_str in json_list: tied_positions_dict = json.loads(json_str) else: if print_all: print(40*'-') print('tied_positions_jsonl is NOT loaded') tied_positions_dict = None if os.path.isfile(bias_by_res_jsonl): with open(bias_by_res_jsonl, 'r') as json_file: json_list = list(json_file) for json_str in json_list: bias_by_res_dict = json.loads(json_str) if print_all: print('bias by residue dictionary is loaded') else: if print_all: print(40*'-') print('bias by residue dictionary is not loaded, or not provided') bias_by_res_dict = None if print_all: print(40*'-') bias_AAs_np = np.zeros(len(alphabet)) if bias_AA_dict: for n, AA in enumerate(alphabet): if AA in list(bias_AA_dict.keys()): bias_AAs_np[n] = bias_AA_dict[AA] if pdb_path: pdb_dict_list = parse_PDB(pdb_path, ca_only=ca_only) dataset_valid = StructureDatasetPDB(pdb_dict_list, truncate=None, max_length=max_length) all_chain_list = [item[-1:] for item in list(pdb_dict_list[0]) if item[:9]=='seq_chain'] #['A','B', 'C',...] if pdb_path_chains: designed_chain_list = [str(item) for item in pdb_path_chains.split()] else: designed_chain_list = all_chain_list fixed_chain_list = [letter for letter in all_chain_list if letter not in designed_chain_list] chain_id_dict = {} chain_id_dict[pdb_dict_list[0]['name']]= (designed_chain_list, fixed_chain_list) else: dataset_valid = StructureDataset(jsonl_path, truncate=None, max_length=max_length, verbose=print_all) # Build paths for experiment if write_output_files: folder_for_outputs = out_folder base_folder = folder_for_outputs if base_folder[-1] != '/': base_folder = base_folder + '/' if not os.path.exists(base_folder): os.makedirs(base_folder) if not os.path.exists(base_folder + 'seqs'): os.makedirs(base_folder + 'seqs') # if args.save_score: # if not os.path.exists(base_folder + 'scores'): # os.makedirs(base_folder + 'scores') # if args.score_only: # if not os.path.exists(base_folder + 'score_only'): # os.makedirs(base_folder + 'score_only') # if args.conditional_probs_only: # if not os.path.exists(base_folder + 'conditional_probs_only'): # os.makedirs(base_folder + 'conditional_probs_only') # if args.unconditional_probs_only: # if not os.path.exists(base_folder + 'unconditional_probs_only'): # os.makedirs(base_folder + 'unconditional_probs_only') # if args.save_probs: # if not os.path.exists(base_folder + 'probs'): # os.makedirs(base_folder + 'probs') # Timing start_time = time.time() total_residues = 0 protein_list = [] total_step = 0 # Validation epoch new_mpnn_seqs = [] with torch.no_grad(): test_sum, test_weights = 0., 0. for ix, protein in enumerate(dataset_valid): score_list = [] global_score_list = [] all_probs_list = [] all_log_probs_list = [] S_sample_list = [] batch_clones = [copy.deepcopy(protein) for i in range(BATCH_COPIES)] X, S, mask, lengths, chain_M, chain_encoding_all, chain_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, pssm_bias, pssm_log_odds_all, bias_by_res_all, tied_beta = tied_featurize(batch_clones, device, chain_id_dict, fixed_positions_dict, omit_AA_dict, tied_positions_dict, pssm_dict, bias_by_res_dict, ca_only=ca_only) pssm_log_odds_mask = (pssm_log_odds_all > pssm_threshold).float() #1.0 for true, 0.0 for false name_ = batch_clones[0]['name'] if False: pass # if args.score_only: # loop_c = 0 # if args.path_to_fasta: # fasta_names, fasta_seqs = parse_fasta(args.path_to_fasta, omit=["/"]) # loop_c = len(fasta_seqs) # for fc in range(1+loop_c): # if fc == 0: # structure_sequence_score_file = base_folder + '/score_only/' + batch_clones[0]['name'] + f'_pdb' # else: # structure_sequence_score_file = base_folder + '/score_only/' + batch_clones[0]['name'] + f'_fasta_{fc}' # native_score_list = [] # global_native_score_list = [] # if fc > 0: # input_seq_length = len(fasta_seqs[fc-1]) # S_input = torch.tensor([alphabet_dict[AA] for AA in fasta_seqs[fc-1]], device=device)[None,:].repeat(X.shape[0], 1) # S[:,:input_seq_length] = S_input #assumes that S and S_input are alphabetically sorted for masked_chains # for j in range(NUM_BATCHES): # randn_1 = torch.randn(chain_M.shape, device=X.device) # log_probs = model(X, S, mask, chain_M*chain_M_pos, residue_idx, chain_encoding_all, randn_1) # mask_for_loss = mask*chain_M*chain_M_pos # scores = _scores(S, log_probs, mask_for_loss) # native_score = scores.cpu().data.numpy() # native_score_list.append(native_score) # global_scores = _scores(S, log_probs, mask) # global_native_score = global_scores.cpu().data.numpy() # global_native_score_list.append(global_native_score) # native_score = np.concatenate(native_score_list, 0) # global_native_score = np.concatenate(global_native_score_list, 0) # ns_mean = native_score.mean() # ns_mean_print = np.format_float_positional(np.float32(ns_mean), unique=False, precision=4) # ns_std = native_score.std() # ns_std_print = np.format_float_positional(np.float32(ns_std), unique=False, precision=4) # global_ns_mean = global_native_score.mean() # global_ns_mean_print = np.format_float_positional(np.float32(global_ns_mean), unique=False, precision=4) # global_ns_std = global_native_score.std() # global_ns_std_print = np.format_float_positional(np.float32(global_ns_std), unique=False, precision=4) # ns_sample_size = native_score.shape[0] # seq_str = _S_to_seq(S[0,], chain_M[0,]) # np.savez(structure_sequence_score_file, score=native_score, global_score=global_native_score, S=S[0,].cpu().numpy(), seq_str=seq_str) # if print_all: # if fc == 0: # print(f'Score for {name_} from PDB, mean: {ns_mean_print}, std: {ns_std_print}, sample size: {ns_sample_size}, global score, mean: {global_ns_mean_print}, std: {global_ns_std_print}, sample size: {ns_sample_size}') # else: # print(f'Score for {name_}_{fc} from FASTA, mean: {ns_mean_print}, std: {ns_std_print}, sample size: {ns_sample_size}, global score, mean: {global_ns_mean_print}, std: {global_ns_std_print}, sample size: {ns_sample_size}') # elif args.conditional_probs_only: # if print_all: # print(f'Calculating conditional probabilities for {name_}') # conditional_probs_only_file = base_folder + '/conditional_probs_only/' + batch_clones[0]['name'] # log_conditional_probs_list = [] # for j in range(NUM_BATCHES): # randn_1 = torch.randn(chain_M.shape, device=X.device) # log_conditional_probs = model.conditional_probs(X, S, mask, chain_M*chain_M_pos, residue_idx, chain_encoding_all, randn_1, args.conditional_probs_only_backbone) # log_conditional_probs_list.append(log_conditional_probs.cpu().numpy()) # concat_log_p = np.concatenate(log_conditional_probs_list, 0) #[B, L, 21] # mask_out = (chain_M*chain_M_pos*mask)[0,].cpu().numpy() # np.savez(conditional_probs_only_file, log_p=concat_log_p, S=S[0,].cpu().numpy(), mask=mask[0,].cpu().numpy(), design_mask=mask_out) # elif args.unconditional_probs_only: # if print_all: # print(f'Calculating sequence unconditional probabilities for {name_}') # unconditional_probs_only_file = base_folder + '/unconditional_probs_only/' + batch_clones[0]['name'] # log_unconditional_probs_list = [] # for j in range(NUM_BATCHES): # log_unconditional_probs = model.unconditional_probs(X, mask, residue_idx, chain_encoding_all) # log_unconditional_probs_list.append(log_unconditional_probs.cpu().numpy()) # concat_log_p = np.concatenate(log_unconditional_probs_list, 0) #[B, L, 21] # mask_out = (chain_M*chain_M_pos*mask)[0,].cpu().numpy() # np.savez(unconditional_probs_only_file, log_p=concat_log_p, S=S[0,].cpu().numpy(), mask=mask[0,].cpu().numpy(), design_mask=mask_out) else: randn_1 = torch.randn(chain_M.shape, device=X.device) log_probs = model(X, S, mask, chain_M*chain_M_pos, residue_idx, chain_encoding_all, randn_1) mask_for_loss = mask*chain_M*chain_M_pos scores = _scores(S, log_probs, mask_for_loss) #score only the redesigned part native_score = scores.cpu().data.numpy() global_scores = _scores(S, log_probs, mask) #score the whole structure-sequence global_native_score = global_scores.cpu().data.numpy() # Generate some sequences if write_output_files: ali_file = base_folder + '/seqs/' + batch_clones[0]['name'] + '.fa' score_file = base_folder + '/scores/' + batch_clones[0]['name'] + '.npz' probs_file = base_folder + '/probs/' + batch_clones[0]['name'] + '.npz' f = open(ali_file, 'w') if print_all: print(f'Generating sequences for: {name_}') t0 = time.time() for temp in temperatures: for j in range(NUM_BATCHES): randn_2 = torch.randn(chain_M.shape, device=X.device) if tied_positions_dict == None: sample_dict = model.sample(X, randn_2, S, chain_M, chain_encoding_all, residue_idx, mask=mask, temperature=temp, omit_AAs_np=omit_AAs_np, bias_AAs_np=bias_AAs_np, chain_M_pos=chain_M_pos, omit_AA_mask=omit_AA_mask, pssm_coef=pssm_coef, pssm_bias=pssm_bias, pssm_multi=pssm_multi, pssm_log_odds_flag=bool(pssm_log_odds_flag), pssm_log_odds_mask=pssm_log_odds_mask, pssm_bias_flag=bool(pssm_bias_flag), bias_by_res=bias_by_res_all) S_sample = sample_dict["S"] else: sample_dict = model.tied_sample(X, randn_2, S, chain_M, chain_encoding_all, residue_idx, mask=mask, temperature=temp, omit_AAs_np=omit_AAs_np, bias_AAs_np=bias_AAs_np, chain_M_pos=chain_M_pos, omit_AA_mask=omit_AA_mask, pssm_coef=pssm_coef, pssm_bias=pssm_bias, pssm_multi=pssm_multi, pssm_log_odds_flag=bool(pssm_log_odds_flag), pssm_log_odds_mask=pssm_log_odds_mask, pssm_bias_flag=bool(pssm_bias_flag), tied_pos=tied_pos_list_of_lists_list[0], tied_beta=tied_beta, bias_by_res=bias_by_res_all) # Compute scores S_sample = sample_dict["S"] log_probs = model(X, S_sample, mask, chain_M*chain_M_pos, residue_idx, chain_encoding_all, randn_2, use_input_decoding_order=True, decoding_order=sample_dict["decoding_order"]) mask_for_loss = mask*chain_M*chain_M_pos scores = _scores(S_sample, log_probs, mask_for_loss) scores = scores.cpu().data.numpy() global_scores = _scores(S_sample, log_probs, mask) #score the whole structure-sequence global_scores = global_scores.cpu().data.numpy() all_probs_list.append(sample_dict["probs"].cpu().data.numpy()) all_log_probs_list.append(log_probs.cpu().data.numpy()) S_sample_list.append(S_sample.cpu().data.numpy()) for b_ix in range(BATCH_COPIES): masked_chain_length_list = masked_chain_length_list_list[b_ix] masked_list = masked_list_list[b_ix] seq_recovery_rate = torch.sum(torch.sum(torch.nn.functional.one_hot(S[b_ix], 21)*torch.nn.functional.one_hot(S_sample[b_ix], 21),axis=-1)*mask_for_loss[b_ix])/torch.sum(mask_for_loss[b_ix]) seq = _S_to_seq(S_sample[b_ix], chain_M[b_ix]) new_mpnn_seqs.append(seq) score = scores[b_ix] score_list.append(score) global_score = global_scores[b_ix] global_score_list.append(global_score) native_seq = _S_to_seq(S[b_ix], chain_M[b_ix]) if b_ix == 0 and j==0 and temp==temperatures[0]: start = 0 end = 0 list_of_AAs = [] for mask_l in masked_chain_length_list: end += mask_l list_of_AAs.append(native_seq[start:end]) start = end native_seq = "".join(list(np.array(list_of_AAs)[np.argsort(masked_list)])) l0 = 0 for mc_length in list(np.array(masked_chain_length_list)[np.argsort(masked_list)])[:-1]: l0 += mc_length native_seq = native_seq[:l0] + '/' + native_seq[l0:] l0 += 1 sorted_masked_chain_letters = np.argsort(masked_list_list[0]) print_masked_chains = [masked_list_list[0][i] for i in sorted_masked_chain_letters] sorted_visible_chain_letters = np.argsort(visible_list_list[0]) print_visible_chains = [visible_list_list[0][i] for i in sorted_visible_chain_letters] native_score_print = np.format_float_positional(np.float32(native_score.mean()), unique=False, precision=4) global_native_score_print = np.format_float_positional(np.float32(global_native_score.mean()), unique=False, precision=4) script_dir = os.path.dirname(os.path.realpath(__file__)) try: commit_str = subprocess.check_output(f'git --git-dir {script_dir}/.git rev-parse HEAD', shell=True, stderr=subprocess.DEVNULL).decode().strip() except subprocess.CalledProcessError: commit_str = 'unknown' if ca_only: print_model_name = 'CA_model_name' else: print_model_name = 'model_name' if write_output_files: f.write('>{}, score={}, global_score={}, fixed_chains={}, designed_chains={}, {}={}, git_hash={}, seed={}\n{}\n'.format(name_, native_score_print, global_native_score_print, print_visible_chains, print_masked_chains, print_model_name, model_name, commit_str, seed, native_seq)) #write the native sequence start = 0 end = 0 list_of_AAs = [] for mask_l in masked_chain_length_list: end += mask_l list_of_AAs.append(seq[start:end]) start = end seq = "".join(list(np.array(list_of_AAs)[np.argsort(masked_list)])) l0 = 0 for mc_length in list(np.array(masked_chain_length_list)[np.argsort(masked_list)])[:-1]: l0 += mc_length seq = seq[:l0] + '/' + seq[l0:] l0 += 1 score_print = np.format_float_positional(np.float32(score), unique=False, precision=4) global_score_print = np.format_float_positional(np.float32(global_score), unique=False, precision=4) seq_rec_print = np.format_float_positional(np.float32(seq_recovery_rate.detach().cpu().numpy()), unique=False, precision=4) sample_number = j*BATCH_COPIES+b_ix+1 if write_output_files: f.write('>T={}, sample={}, score={}, global_score={}, seq_recovery={}\n{}\n'.format(temp,sample_number,score_print,global_score_print,seq_rec_print,seq)) #write generated sequence # if args.save_score: # np.savez(score_file, score=np.array(score_list, np.float32), global_score=np.array(global_score_list, np.float32)) # if args.save_probs: # all_probs_concat = np.concatenate(all_probs_list) # all_log_probs_concat = np.concatenate(all_log_probs_list) # S_sample_concat = np.concatenate(S_sample_list) # np.savez(probs_file, probs=np.array(all_probs_concat, np.float32), log_probs=np.array(all_log_probs_concat, np.float32), S=np.array(S_sample_concat, np.int32), mask=mask_for_loss.cpu().data.numpy(), chain_order=chain_list_list) t1 = time.time() dt = round(float(t1-t0), 4) num_seqs = len(temperatures)*NUM_BATCHES*BATCH_COPIES total_length = X.shape[1] if print_all: print(f'{num_seqs} sequences of length {total_length} generated in {dt} seconds') if write_output_files: f.close() return new_mpnn_seqs 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, time_cond_dim=None): 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) if time_cond_dim is not None: self.time_block1 = nn.Sequential( Rearrange('b 1 d -> b 1 1 d'), nn.SiLU(), nn.Linear(time_cond_dim, num_hidden * 2)) self.time_block2 = nn.Sequential( Rearrange('b 1 d -> b 1 1 d'), nn.SiLU(), nn.Linear(time_cond_dim, num_hidden * 2)) 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, time_cond=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.act(self.W2(self.act(self.W1(h_EV)))) if time_cond is not None: scale, shift = self.time_block1(time_cond).chunk(2, dim=-1) h_message = h_message * (scale + 1) + shift h_message = self.W3(h_message) 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.act(self.W12(self.act(self.W11(h_EV)))) if time_cond is not None: scale, shift = self.time_block2(time_cond).chunk(2, dim=-1) h_message = h_message * (scale + 1) + shift h_message = self.W13(h_message) 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, time_cond_dim=None): 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) if time_cond_dim is not None: self.time_block = nn.Sequential( Rearrange('b 1 d -> b 1 1 d'), nn.SiLU(), nn.Linear(time_cond_dim, num_hidden * 2)) 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, time_cond=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.act(self.W2(self.act(self.W1(h_EV)))) if time_cond is not None: scale, shift = self.time_block(time_cond).chunk(2, dim=-1) h_message = h_message * (scale + 1) + shift h_message = self.W3(h_message) 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 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 def get_closest_neighbors(X, mask, top_k, eps=1e-6): # X is ca coords (b, n, 3), mask is seq mask 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 = top_k D_neighbors, E_idx = torch.topk(D_adjust, np.minimum(top_k, X.shape[1]), dim=-1, largest=False) return D_neighbors, 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 return get_closest_neighbors(X, mask, self.top_k, eps=eps) 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 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, time_cond_dim=None, input_S_is_embeddings=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.input_S_is_embeddings = input_S_is_embeddings if not self.input_S_is_embeddings: self.W_s = nn.Embedding(vocab, hidden_dim) if time_cond_dim is not None: self.time_block = nn.Sequential( nn.SiLU(), nn.Linear(time_cond_dim, hidden_dim) ) # Encoder layers self.encoder_layers = nn.ModuleList([ EncLayer(hidden_dim, hidden_dim*2, dropout=dropout, time_cond_dim=time_cond_dim) for _ in range(num_encoder_layers) ]) # Decoder layers self.decoder_layers = nn.ModuleList([ DecLayer(hidden_dim, hidden_dim*3, dropout=dropout, time_cond_dim=time_cond_dim) 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, causal_mask=True, time_cond=None, return_node_embs=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 = torch.zeros((E.shape[0], E.shape[1], E.shape[-1]), device=E.device) if time_cond is not None: time_cond_nodes = self.time_block(time_cond) h_V += time_cond_nodes # time_cond is b, 1, c 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, time_cond=time_cond) encoder_embs = h_V # Concatenate sequence embeddings for autoregressive decoder if self.input_S_is_embeddings: h_S = S else: 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 mask_size = E_idx.shape[1] if causal_mask: 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] 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) else: order_mask_backward = torch.ones(X.shape[0], mask_size, mask_size, 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. - 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, time_cond=time_cond) if return_node_embs: return h_V, encoder_embs else: 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] 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. - 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. 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 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. - 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