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import torch
import pytorch_lightning as pl
from torch.utils.data import DataLoader
from datasets import load_from_disk
from transformers import AutoTokenizer
from sklearn.metrics import roc_auc_score, f1_score, matthews_corrcoef, accuracy_score
from argparse import ArgumentParser
import os
import torch.distributed as dist
import pandas as pd
import pdb
from modules.bindevaluator_modules import * # Import your model and other necessary classes/functions here
def parse_motifs(motif: str) -> list:
parts = motif.split(',')
result = []
for part in parts:
part = part.strip()
if '-' in part:
start, end = map(int, part.split('-'))
result.extend(range(start, end + 1))
else:
result.append(int(part))
result = [pos-1 for pos in result]
print(f'Target Motifs: {result}')
return torch.tensor(result)
class PeptideModel(pl.LightningModule):
def __init__(self, n_layers, d_model, d_hidden, n_head,
d_k, d_v, d_inner, dropout=0.2,
learning_rate=0.00001, max_epochs=15, kl_weight=1):
super(PeptideModel, self).__init__()
self.esm_model = EsmModel.from_pretrained("facebook/esm2_t33_650M_UR50D")
# freeze all the esm_model parameters
for param in self.esm_model.parameters():
param.requires_grad = False
self.repeated_module = RepeatedModule3(n_layers, d_model, d_hidden,
n_head, d_k, d_v, d_inner, dropout=dropout)
self.final_attention_layer = MultiHeadAttentionSequence(n_head, d_model,
d_k, d_v, dropout=dropout)
self.final_ffn = FFN(d_model, d_inner, dropout=dropout)
self.output_projection_prot = nn.Linear(d_model, 1)
self.learning_rate = learning_rate
self.max_epochs = max_epochs
self.kl_weight = kl_weight
self.classification_threshold = nn.Parameter(torch.tensor(0.5)) # Initial threshold
self.historical_memory = 0.9
self.class_weights = torch.tensor([3.000471363174231, 0.5999811490272925]) # binding_site weights, non-bidning site weights
def forward(self, binder_tokens, target_tokens):
peptide_sequence = self.esm_model(**binder_tokens).last_hidden_state
protein_sequence = self.esm_model(**target_tokens).last_hidden_state
prot_enc, sequence_enc, sequence_attention_list, prot_attention_list, \
seq_prot_attention_list, seq_prot_attention_list = self.repeated_module(peptide_sequence,
protein_sequence)
prot_enc, final_prot_seq_attention = self.final_attention_layer(prot_enc, sequence_enc, sequence_enc)
prot_enc = self.final_ffn(prot_enc)
prot_enc = self.output_projection_prot(prot_enc)
return prot_enc
def calculate_score(target_sequence, binder_sequence, model, args):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t33_650M_UR50D")
anchor_tokens = tokenizer(target_sequence, return_tensors='pt', padding=True, truncation=True, max_length=40000)
positive_tokens = tokenizer(binder_sequence, return_tensors='pt', padding=True, truncation=True, max_length=40000)
anchor_tokens['attention_mask'][0][0] = 0
anchor_tokens['attention_mask'][0][-1] = 0
positive_tokens['attention_mask'][0][0] = 0
positive_tokens['attention_mask'][0][-1] = 0
target_tokens = {'input_ids': anchor_tokens["input_ids"].to(device),
'attention_mask': anchor_tokens["attention_mask"].to(device)}
binder_tokens = {'input_ids': positive_tokens['input_ids'].to(device),
'attention_mask': positive_tokens['attention_mask'].to(device)}
model.eval()
# pdb.set_trace()
prediction = model(binder_tokens, target_tokens).squeeze(-1)[0][1:-1]
prediction = torch.sigmoid(prediction)
return prediction, model.classification_threshold
def compute_metrics(true_residues, predicted_residues, length):
# Initialize the true and predicted lists with 0
true_list = [0] * length
predicted_list = [0] * length
# Set the values to 1 based on the provided lists
for index in true_residues:
true_list[index] = 1
for index in predicted_residues:
predicted_list[index] = 1
# Compute the metrics
accuracy = accuracy_score(true_list, predicted_list)
f1 = f1_score(true_list, predicted_list)
mcc = matthews_corrcoef(true_list, predicted_list)
return accuracy, f1, mcc
def main():
parser = ArgumentParser()
parser.add_argument("-sm", default='/home/tc415/muPPIt/muppit/train_base_1/model-epoch=14-val_loss=0.40.ckpt',
help="File containing initial params", type=str)
parser.add_argument("-batch_size", type=int, default=32, help="Batch size")
parser.add_argument("-lr", type=float, default=1e-3)
parser.add_argument("-n_layers", type=int, default=6, help="Number of layers")
parser.add_argument("-d_model", type=int, default=64, help="Dimension of model")
parser.add_argument("-d_hidden", type=int, default=128, help="Dimension of CNN block")
parser.add_argument("-n_head", type=int, default=6, help="Number of heads")
parser.add_argument("-d_inner", type=int, default=64)
parser.add_argument("-target", type=str)
parser.add_argument("-binder", type=str)
parser.add_argument("-gt", type=str, default=None)
parser.add_argument("-motifs", type=str, default=None)
args = parser.parse_args()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = PeptideModel.load_from_checkpoint(args.sm,
n_layers=args.n_layers,
d_model=args.d_model,
d_hidden=args.d_hidden,
n_head=args.n_head,
d_k=64,
d_v=128,
d_inner=64).to(device)
prediction, _ = calculate_score(args.target, args.binder, model, args)
# print(prediction)
# print(model.classification_threshold)
binding_site = []
for i in range(len(prediction)):
if prediction[i] >= 0.5:
binding_site.append(i)
print("Prediction: ", binding_site)
prediction = prediction.detach().cpu().tolist()
np.set_printoptions(precision=2, suppress=True)
print(prediction)
if args.motifs is not None:
motifs = parse_motifs(args.motifs).tolist()
print(f"Motif Score: {torch.sum(prediction[motifs]) / len(motifs)}")
if args.gt is not None:
L = len(args.target)
# print(L)
gt = parse_motifs(args.gt)
print("Ground Truth: ", gt)
acc, f1, mcc = compute_metrics(gt, binding_site, L)
print(f"Accuracy={acc}\tF1={f1}\tMCC={mcc}")
# print("Prediction Logits: ", prediction[binding_site])
if __name__ == "__main__":
main()
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