.ipynb_checkpoints/app-checkpoint.py

import gradio as gr
from model_loader import load_model

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader

import re
import numpy as np
import os
import pandas as pd
import copy

import transformers, datasets
from transformers import AutoTokenizer
from transformers import DataCollatorForTokenClassification

from datasets import Dataset

from scipy.special import expit

import requests

# Biopython imports
from Bio.PDB import PDBParser, Select
from Bio.PDB.DSSP import DSSP

from gradio_molecule3d import Molecule3D

# Configuration
checkpoint = 'ThorbenF/prot_t5_xl_uniref50'
max_length = 1500

# Load model and move to device
model, tokenizer = load_model(checkpoint, max_length)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
model.eval()

def create_dataset(tokenizer, seqs, labels, checkpoint):
    tokenized = tokenizer(seqs, max_length=max_length, padding=False, truncation=True)
    dataset = Dataset.from_dict(tokenized)
    
    # Adjust labels based on checkpoint
    if ("esm" in checkpoint) or ("ProstT5" in checkpoint):
        labels = [l[:max_length-2] for l in labels] 
    else:
        labels = [l[:max_length-1] for l in labels] 
        
    dataset = dataset.add_column("labels", labels)
     
    return dataset

def convert_predictions(input_logits):
    all_probs = []
    for logits in input_logits:
        logits = logits.reshape(-1, 2)
        probabilities_class1 = expit(logits[:, 1] - logits[:, 0])
        all_probs.append(probabilities_class1)
        
    return np.concatenate(all_probs)

def normalize_scores(scores):
    min_score = np.min(scores)
    max_score = np.max(scores)
    return (scores - min_score) / (max_score - min_score) if max_score > min_score else scores

def predict_protein_sequence(test_one_letter_sequence):    
    # Sanitize input sequence
    test_one_letter_sequence = test_one_letter_sequence.replace("O", "X") \
        .replace("B", "X").replace("U", "X") \
        .replace("Z", "X").replace("J", "X")
    
    # Prepare sequence for different model types
    if ("prot_t5" in checkpoint) or ("ProstT5" in checkpoint):
        test_one_letter_sequence = " ".join(test_one_letter_sequence)
    
    if "ProstT5" in checkpoint:
        test_one_letter_sequence = "<AA2fold> " + test_one_letter_sequence
    
    # Create dummy labels 
    dummy_labels = [np.zeros(len(test_one_letter_sequence))]
    
    # Create dataset
    test_dataset = create_dataset(tokenizer, 
                                  [test_one_letter_sequence], 
                                  dummy_labels, 
                                  checkpoint)
    
    # Select appropriate data collator
    data_collator = (DataCollatorForTokenClassification(tokenizer) 
                     if "esm" not in checkpoint and "ProstT5" not in checkpoint 
                     else DataCollatorForTokenClassification(tokenizer))
    
    # Create data loader
    test_loader = DataLoader(test_dataset, batch_size=1, collate_fn=data_collator)
    
    # Predict
    for batch in test_loader:
        input_ids = batch['input_ids'].to(device)
        attention_mask = batch['attention_mask'].to(device)
        
        with torch.no_grad():
            outputs = model(input_ids, attention_mask=attention_mask)
            logits = outputs.logits.detach().cpu().numpy()

    # Process logits
    logits = logits[:, :-1]  # Remove last element for prot_t5
    logits = convert_predictions(logits)
      
    # Normalize and format results
    normalized_scores = normalize_scores(logits)
    test_one_letter_sequence = test_one_letter_sequence.replace(" ", "")
    
    return test_one_letter_sequence, normalized_scores

def fetch_pdb(pdb_id):
    try:
        # Create a directory to store PDB files if it doesn't exist
        os.makedirs('pdb_files', exist_ok=True)
        
        # Fetch the PDB structure from RCSB
        pdb_url = f'https://files.rcsb.org/download/{pdb_id}.pdb'
        pdb_path = f'pdb_files/{pdb_id}.pdb'
        
        # Download the file
        response = requests.get(pdb_url)
        
        if response.status_code == 200:
            with open(pdb_path, 'wb') as f:
                f.write(response.content)
            return pdb_path
        else:
            return None
    
    except Exception as e:
        print(f"Error fetching PDB: {e}")
        return None

def extract_protein_sequence(pdb_path):
    """
    Extract the longest protein sequence from a PDB file
    """
    parser = PDBParser(QUIET=1)
    structure = parser.get_structure('protein', pdb_path)
    
    class ProteinSelect(Select):
        def accept_residue(self, residue):
            # Only accept standard amino acids
            standard_aa = set('ACDEFGHIKLMNPQRSTVWY')
            return residue.get_resname() in standard_aa
    
    # Find the longest protein chain
    longest_sequence = ""
    longest_chain = None
    for model in structure:
        for chain in model:
            sequence = ""
            for residue in chain:
                if Select().accept_residue(residue):
                    sequence += residue.get_resname()
            
            # Convert 3-letter amino acid codes to 1-letter
            aa_dict = {
                'ALA':'A', 'CYS':'C', 'ASP':'D', 'GLU':'E', 'PHE':'F',
                'GLY':'G', 'HIS':'H', 'ILE':'I', 'LYS':'K', 'LEU':'L',
                'MET':'M', 'ASN':'N', 'PRO':'P', 'GLN':'Q', 'ARG':'R',
                'SER':'S', 'THR':'T', 'VAL':'V', 'TRP':'W', 'TYR':'Y'
            }
            
            one_letter_sequence = ''.join([aa_dict.get(res, 'X') for res in sequence])
            
            # Track the longest sequence
            if len(one_letter_sequence) > len(longest_sequence) and \
               10 < len(one_letter_sequence) < 1500:
                longest_sequence = one_letter_sequence
                longest_chain = chain
    
    return longest_sequence, longest_chain

def process_pdb(pdb_id):
    # Fetch PDB file
    pdb_path = fetch_pdb(pdb_id)
    
    if not pdb_path:
        return "Failed to fetch PDB file", None, None
    
    # Extract protein sequence and chain
    protein_sequence, chain = extract_protein_sequence(pdb_path)
    
    if not protein_sequence:
        return "No suitable protein sequence found", None, None
    
    # Predict binding sites
    sequence, normalized_scores = predict_protein_sequence(protein_sequence)
    
    # Prepare representations for coloring residues
    reps = []
    for i, (res, score) in enumerate(zip(sequence, normalized_scores), start=1):
        # Map score to a color gradient from blue (low) to red (high)
        color_intensity = int(score * 255)
        color = f'rgb({color_intensity}, 0, {255-color_intensity})'
        
        rep = {
            "model": 0,
            "chain": chain.id,
            "resname": res,
            "resnum": i,
            "style": "cartoon",
            "color": color,
            "residue_range": f"{i}-{i}",
            "around": 0,
            "byres": True,
            "visible": True
        }
        reps.append(rep)
    
    # Prepare result string
    result_str = "\n".join([f"{aa}: {score:.2f}" for aa, score in zip(sequence, normalized_scores)])
    
    return result_str, reps, pdb_path

# Create Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Protein Binding Site Prediction")
    
    with gr.Row():
        with gr.Column():
            # PDB ID input with default suggestion
            pdb_input = gr.Textbox(
                value="2IWI", 
                label="PDB ID",
                placeholder="Enter PDB ID here..."
            )
            
            # Predict button
            predict_btn = gr.Button("Predict Binding Sites")
        
        with gr.Column():
            # Binding site predictions output
            predictions_output = gr.Textbox(
                label="Binding Site Predictions"
            )
            
            # 3D Molecule visualization
            molecule_output = Molecule3D(
                label="Protein Structure", 
                reps=[]  # Start with empty representations
            )
    
    # Prediction logic
    predict_btn.click(
        process_pdb, 
        inputs=[pdb_input], 
        outputs=[predictions_output, molecule_output, molecule_output]
    )

    # Add some example inputs
    gr.Markdown("## Examples")
    gr.Examples(
        examples=[
            ["2IWI"],
            ["1ABC"],
            ["4HHB"]
        ],
        inputs=[pdb_input],
        outputs=[predictions_output, molecule_output, molecule_output]
    )

demo.launch()