app.py

import gradio as gr
import py3Dmol
import io
import numpy as np
import os
import traceback
from esm.sdk import client
from esm.sdk.api import ESM3InferenceClient, ESMProtein, GenerationConfig, ESMProteinError
from esm.utils.structure.protein_chain import ProteinChain
from Bio.Data import PDBData
import biotite.structure as bs
from biotite.structure.io import pdb
from esm.utils import residue_constants as RC
import requests
from dotenv import load_dotenv
import torch
import json 
import time

load_dotenv()

API_URL = "https://forge.evolutionaryscale.ai/api/v1"
MODEL = "esm3-open-2024-03"
API_TOKEN = os.environ.get("ESM_API_TOKEN")
if not API_TOKEN:
    raise ValueError("ESM_API_TOKEN environment variable is not set")

model = client(
    model=MODEL,
    url=API_URL,
    token="2x0lifRJCpo8klurAJtRom"
)

amino3to1 = {
    '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'
}


def read_pdb_io(pdb_file):
    if isinstance(pdb_file, io.StringIO):
        pdb_content = pdb_file.getvalue()
    elif hasattr(pdb_file, 'name'):
        with open(pdb_file.name, 'r') as f:
            pdb_content = f.read()
    else:
        raise ValueError("Unsupported file type")
    
    if not pdb_content.strip():
        raise ValueError("The PDB file is empty.")
    
    pdb_io = io.StringIO(pdb_content)
    return pdb_io, pdb_content

def get_protein(pdb_file) -> ESMProtein:
    try:
        pdb_io, content = read_pdb_io(pdb_file)
        
        if not content.strip():
            raise ValueError("The PDB file is empty")
        
        # Parse the PDB file using biotite
        pdb_file = pdb.PDBFile.read(pdb_io)
        structure = pdb_file.get_structure()
        
        # Check if the structure contains any atoms
        if structure.array_length() == 0:
            raise ValueError("The PDB file does not contain any valid atoms")
        
        # Filter for amino acids and create a sequence
        valid_residues = []
        for res in bs.residue_iter(structure):
            res_name = res.res_name
            if isinstance(res_name, np.ndarray):
                res_name = res_name[0]  # Take the first element if it's an array
            if res_name in amino3to1:
                valid_residues.append(res)
        
        if not valid_residues:
            raise ValueError("No valid amino acid residues found in the PDB file")
        
        sequence = ''.join(amino3to1.get(res.res_name[0] if isinstance(res.res_name, np.ndarray) else res.res_name, 'X') for res in valid_residues)
        
        # Handle res_id as a potential sequence
        residue_indices = []
        for res in valid_residues:
            if isinstance(res.res_id, (list, tuple, np.ndarray)):
                residue_indices.append(res.res_id[0])  # Take the first element if it's a sequence
            else:
                residue_indices.append(res.res_id)
        
        # Create a ProteinChain object
        protein_chain = ProteinChain(
            id="test",
            sequence=sequence,
            chain_id="A",
            entity_id=None,
            residue_index=np.array(residue_indices, dtype=int),
            insertion_code=np.full(len(sequence), "", dtype="<U4"),
            atom37_positions=np.full((len(sequence), 37, 3), np.nan),
            atom37_mask=np.zeros((len(sequence), 37), dtype=bool),
            confidence=np.ones(len(sequence), dtype=np.float32)
        )
        
        # Fill in atom positions and mask
        for i, res in enumerate(valid_residues):
            for atom in res:
                atom_name = atom.atom_name
                if isinstance(atom_name, np.ndarray):
                    atom_name = atom_name[0]  # Take the first element if it's an array
                if atom_name in RC.atom_order:
                    idx = RC.atom_order[atom_name]
                    coord = atom.coord
                    if coord.ndim > 1:
                        coord = coord[0]  # Take the first coordinate set if multiple are present
                    protein_chain.atom37_positions[i, idx] = coord
                    protein_chain.atom37_mask[i, idx] = True
        
        protein = ESMProtein.from_protein_chain(protein_chain)
        return protein
    except Exception as e:
        print(f"Error processing PDB file: {str(e)}")
        raise ValueError(f"Unable to process the PDB file: {str(e)}")

def add_noise_to_coordinates(protein: ESMProtein, noise_level: float) -> ESMProtein:
    """Add Gaussian noise to the atom positions of the protein."""
    coordinates = protein.coordinates
    noise = np.random.randn(*coordinates.shape) * noise_level
    noisy_coordinates = coordinates + noise
    return ESMProtein(sequence=protein.sequence, coordinates=noisy_coordinates)

def run_structure_prediction(protein: ESMProtein) -> ESMProtein:
    structure_prediction_config = GenerationConfig(
        track="structure",
        num_steps=10,
        temperature=0.7,
    )
    try:
        response = model.generate(protein, structure_prediction_config)
        
        if isinstance(response, ESMProtein):
            return response
        elif isinstance(response, ESMProteinError):
            print(f"ESMProteinError during structure prediction: {response.error_msg}")
            return None
        else:
            raise ValueError(f"Unexpected response type: {type(response)}")
    except Exception as e:
        print(f"Error during structure prediction: {str(e)}")
        return None

def align_after_prediction(protein: ESMProtein, structure_prediction: ESMProtein) -> tuple[ESMProtein, float]:
    if structure_prediction is None:
        return None, float('inf')
    
    try:
        structure_prediction_chain = structure_prediction.to_protein_chain()
        protein_chain = protein.to_protein_chain()
        
        # Ensure both chains have the same length
        min_length = min(len(structure_prediction_chain.sequence), len(protein_chain.sequence))
        structure_indices = np.arange(0, min_length)
        
        # Perform alignment
        aligned_chain = structure_prediction_chain.align(
            protein_chain, 
            mobile_inds=structure_indices, 
            target_inds=structure_indices
        )
        
        # Calculate RMSD
        crmsd = structure_prediction_chain.rmsd(
            protein_chain, 
            mobile_inds=structure_indices, 
            target_inds=structure_indices
        )
        
        return ESMProtein.from_protein_chain(aligned_chain), crmsd
    except AttributeError as e:
        print(f"Error during alignment: {str(e)}")
        print(f"Structure prediction type: {type(structure_prediction)}")
        print(f"Structure prediction attributes: {dir(structure_prediction)}")
        return None, float('inf')
    except Exception as e:
        print(f"Unexpected error during alignment: {str(e)}")
        return None, float('inf')
    
def visualize_after_pred(protein: ESMProtein, aligned: ESMProtein):
    if aligned is None:
        return None
    
    viewer = py3Dmol.view(width=800, height=600)
    viewer.addModel(protein_to_pdb(protein), "pdb")
    viewer.setStyle({"cartoon": {"color": "lightgrey"}})
    viewer.addModel(protein_to_pdb(aligned), "pdb")
    viewer.setStyle({"model": -1}, {"cartoon": {"color": "lightgreen"}})
    viewer.zoomTo()
    
    return viewer.render()

def protein_to_pdb(protein: ESMProtein):
    pdb_str = ""
    for i, (aa, coords) in enumerate(zip(protein.sequence, protein.coordinates)):
        for j, atom in enumerate(RC.atom_types):
            if not torch.isnan(coords[j][0]):
                x, y, z = coords[j].tolist()
                pdb_str += f"ATOM  {i*37+j+1:5d}  {atom:3s} {aa:3s} A{i+1:4d}    {x:8.3f}{y:8.3f}{z:8.3f}\n"
    return pdb_str

def prediction_visualization(pdb_file, num_runs: int, noise_level: float, num_frames: int, progress=gr.Progress()):
    protein = get_protein(pdb_file)
    runs = []
    
    total_iterations = num_frames * num_runs
    progress(0, desc="Starting predictions")
    
    for frame in progress.tqdm(range(num_frames), desc="Processing frames"):
        noisy_protein = add_noise_to_coordinates(protein, noise_level)
        
        for i in range(num_runs):
            progress((frame * num_runs + i + 1) / total_iterations, desc=f"Frame {frame+1}, Run {i+1}")
            structure_prediction = run_structure_prediction(noisy_protein)
            if structure_prediction is not None:
                aligned, crmsd = align_after_prediction(protein, structure_prediction)
                if aligned is not None:
                    runs.append((crmsd, aligned))
            time.sleep(0.1)  # Small delay to allow for UI updates
    
    if not runs:
        return None, "No successful predictions"
    
    best_aligned = sorted(runs, key=lambda x: x[0])[0]
    view_data = visualize_after_pred(protein, best_aligned[1])
    return view_data, f"Best cRMSD: {best_aligned[0]:.4f}"

def run_prediction(pdb_file, num_runs, noise_level, num_frames, progress=gr.Progress()):
    try:
        if pdb_file is None:
            return "Please upload a PDB file.", "No file uploaded"
        
        progress(0, desc="Starting prediction")
        view_data, crmsd_text = prediction_visualization(pdb_file, num_runs, noise_level, num_frames, progress)
        if view_data is None:
            return "No successful predictions were made. Try adjusting the parameters or check the PDB file.", crmsd_text
        
        progress(0.9, desc="Rendering visualization")
        html_content = f"""
        <div style="height: 600px; width: 100%;">
            <script src="https://3dmol.csb.pitt.edu/build/3Dmol-min.js"></script>
            <div id="container-{id(view_data)}" style="height: 100%; width: 100%; position: relative;"></div>
            <script>
                var viewer = $3Dmol.createViewer(document.getElementById("container-{id(view_data)}"), {{defaultcolors: $3Dmol.rasmolElementColors}});
                viewer.addModel({json.dumps(view_data['pdb'])}, "pdb");
                viewer.setStyle({{}}, {json.dumps(view_data['style'])});
                viewer.zoomTo();
                viewer.render();
            </script>
        </div>
        """
        progress(1.0, desc="Completed")
        return html_content, crmsd_text
    except Exception as e:
        error_message = str(e)
        stack_trace = traceback.format_exc()
        return f"""
        <div style='color: red;'>
            <h3>Error:</h3>
            <p>{error_message}</p>
            <h4>Stack Trace:</h4>
            <pre>{stack_trace}</pre>
        </div>
        """, "Error occurred"

def create_demo():
    with gr.Blocks() as demo:
        gr.Markdown("# Protein Structure Prediction and Visualization with Noise and MD Frames")
        
        with gr.Row():
            with gr.Column(scale=1):
                pdb_file = gr.File(label="Upload PDB file")
                num_runs = gr.Slider(minimum=1, maximum=10, step=1, value=3, label="Number of runs per frame")
                noise_level = gr.Slider(minimum=0, maximum=1, step=0.1, value=0.1, label="Noise level")
                num_frames = gr.Slider(minimum=1, maximum=10, step=1, value=1, label="Number of MD frames")
                run_button = gr.Button("Run Prediction")
            
            with gr.Column(scale=2):
                visualization = gr.HTML(label="3D Visualization")
                alignment_result = gr.Textbox(label="Alignment Result")
        
        run_button.click(
            fn=run_prediction,
            inputs=[pdb_file, num_runs, noise_level, num_frames],
            outputs=[visualization, alignment_result]
        )

        gr.Markdown("""
        ## How to use
        1. Upload a PDB file using the file uploader.
        2. Adjust the number of prediction runs per frame using the slider.
        3. Set the noise level to add random perturbations to the structure.
        4. Choose the number of MD frames to simulate.
        5. Click the "Run Prediction" button to start the process.
        6. The 3D visualization will show the original structure (grey) and the best predicted structure (green).
        7. The alignment result will display the best cRMSD (lower is better).

        ## About
        This demo uses the ESM3 model to predict protein structures from PDB files. 
        It runs multiple predictions with added noise and simulated MD frames, displaying the best result based on the lowest cRMSD.
        """)

    return demo

if __name__ == "__main__":
    demo = create_demo()
    demo.queue()
    demo.launch()