Spaces:
Sleeping
Sleeping
ThorbenFroehlking
commited on
Commit
·
c85a5b0
1
Parent(s):
8bef2d8
Update
Browse files- .ipynb_checkpoints/app-checkpoint.py +173 -62
- .ipynb_checkpoints/requirements-checkpoint.txt +1 -2
- .ipynb_checkpoints/test-checkpoint.ipynb +452 -0
- .ipynb_checkpoints/test2-checkpoint.ipynb +1193 -0
- app.py +173 -62
- model_loader.ipynb +0 -871
- requirements.txt +1 -2
- test.ipynb +846 -0
- test2.ipynb +1193 -0
.ipynb_checkpoints/app-checkpoint.py
CHANGED
@@ -1,4 +1,11 @@
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import gradio as gr
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from model_loader import load_model
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import torch
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from torch.utils.data import DataLoader
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import re
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import numpy as np
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import os
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import pandas as pd
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import copy
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@@ -20,18 +25,6 @@ from datasets import Dataset
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from scipy.special import expit
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import requests
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from gradio_molecule3d import Molecule3D
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# Biopython imports
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from Bio.PDB import PDBParser, Select, PDBIO
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from Bio.PDB.DSSP import DSSP
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from Bio.PDB import PDBList
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from matplotlib import cm # For color mapping
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from matplotlib.colors import Normalize
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# Load model and move to device
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checkpoint = 'ThorbenF/prot_t5_xl_uniref50'
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max_length = 1500
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model.to(device)
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model.eval()
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def fetch_pdb(pdb_id):
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pdb_url = f'https://files.rcsb.org/download/{pdb_id}.pdb'
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pdb_path = f'
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os.makedirs('pdb_files', exist_ok=True)
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response = requests.get(pdb_url)
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if response.status_code == 200:
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with open(pdb_path, 'wb') as f:
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f.write(response.content)
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return pdb_path
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def normalize_scores(scores):
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min_score = np.min(scores)
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max_score = np.max(scores)
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return (scores - min_score) / (max_score - min_score) if max_score > min_score else scores
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def process_pdb(pdb_id, segment):
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pdb_path = fetch_pdb(pdb_id)
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@@ -65,7 +61,11 @@ def process_pdb(pdb_id, segment):
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parser = PDBParser(QUIET=1)
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structure = parser.get_structure('protein', pdb_path)
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-
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# Comprehensive amino acid mapping
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aa_dict = {
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}
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# Exclude non-amino acid residues
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sequence =
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-
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for residue in chain
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if residue.get_resname().strip() in aa_dict
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-
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# Prepare input for model prediction
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input_ids = tokenizer(" ".join(sequence), return_tensors="pt").input_ids.to(device)
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# Calculate scores and normalize them
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scores = expit(outputs[:, 1] - outputs[:, 0])
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normalized_scores = normalize_scores(scores)
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#
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for i, res in enumerate(chain) if res.get_resname().strip() in aa_dict
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])
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# Save predictions to file
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with open(f"{pdb_id}_predictions.txt", "w") as f:
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f.write(result_str)
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return result_str, pdb_path,
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-
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# Gradio UI
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with gr.Blocks() as demo:
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gr.Markdown("# Protein Binding Site Prediction")
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with gr.Row():
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pdb_input = gr.Textbox(value="2IWI",
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placeholder="Enter Chain ID here...")
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visualize_btn = gr.Button("Visualize Sructure")
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prediction_btn = gr.Button("Predict Ligand Binding Site")
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molecule_output = Molecule3D(label="Protein Structure", reps=reps)
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predictions_output = gr.Textbox(label="Binding Site Predictions")
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download_output = gr.File(label="Download Predictions")
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visualize_btn.click(fetch_pdb, inputs=[pdb_input], outputs=
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outputs=[predictions_output, molecule_output, download_output]
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)
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gr.Markdown("## Examples")
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gr.Examples(
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examples=[
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["2IWI"],
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["7RPZ"],
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["3TJN"]
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],
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inputs=[pdb_input, segment_input],
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outputs=[predictions_output, molecule_output, download_output]
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)
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import gradio as gr
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import requests
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from Bio.PDB import PDBParser
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import numpy as np
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import os
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from gradio_molecule3d import Molecule3D
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from model_loader import load_model
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import torch
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from torch.utils.data import DataLoader
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import re
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import pandas as pd
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import copy
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from scipy.special import expit
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# Load model and move to device
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checkpoint = 'ThorbenF/prot_t5_xl_uniref50'
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max_length = 1500
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model.to(device)
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model.eval()
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def normalize_scores(scores):
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min_score = np.min(scores)
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max_score = np.max(scores)
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return (scores - min_score) / (max_score - min_score) if max_score > min_score else scores
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def read_mol(pdb_path):
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"""Read PDB file and return its content as a string"""
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with open(pdb_path, 'r') as f:
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return f.read()
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def fetch_pdb(pdb_id):
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pdb_url = f'https://files.rcsb.org/download/{pdb_id}.pdb'
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pdb_path = f'{pdb_id}.pdb'
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response = requests.get(pdb_url)
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if response.status_code == 200:
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with open(pdb_path, 'wb') as f:
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f.write(response.content)
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return pdb_path
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else:
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return None
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def process_pdb(pdb_id, segment):
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pdb_path = fetch_pdb(pdb_id)
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parser = PDBParser(QUIET=1)
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structure = parser.get_structure('protein', pdb_path)
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try:
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chain = structure[0][segment]
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except KeyError:
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return "Invalid Chain ID", None, None
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# Comprehensive amino acid mapping
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aa_dict = {
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}
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# Exclude non-amino acid residues
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sequence = [
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residue for residue in chain
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if residue.get_resname().strip() in aa_dict
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]
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# Prepare input for model prediction
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input_ids = tokenizer(" ".join(sequence), return_tensors="pt").input_ids.to(device)
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# Calculate scores and normalize them
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scores = expit(outputs[:, 1] - outputs[:, 0])
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normalized_scores = normalize_scores(scores)
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result_str = "\n".join(
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f"{aa_dict[res.get_resname()]} {res.id[1]} {score:.2f}"
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for res, score in zip(sequence, normalized_scores)
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)
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# Save the predictions to a file
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prediction_file = f"{pdb_id}_predictions.txt"
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with open(prediction_file, "w") as f:
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f.write(result_str)
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return result_str, molecule(pdb_path, random_scores, segment), prediction_file
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def molecule(input_pdb, scores=None, segment='A'):
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mol = read_mol(input_pdb) # Read PDB file content
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# Prepare high-scoring residues script if scores are provided
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high_score_script = ""
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if scores is not None:
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high_score_script = """
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// Reset all styles first
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viewer.getModel(0).setStyle({}, {});
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// Show only the selected chain
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viewer.getModel(0).setStyle(
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{"chain": "%s"},
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{ cartoon: {colorscheme:"whiteCarbon"} }
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);
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// Highlight high-scoring residues only for the selected chain
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let highScoreResidues = [%s];
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viewer.getModel(0).setStyle(
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{"chain": "%s", "resi": highScoreResidues},
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{"stick": {"color": "red"}}
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);
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// Highlight high-scoring residues only for the selected chain
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let highScoreResidues2 = [%s];
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viewer.getModel(0).setStyle(
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{"chain": "%s", "resi": highScoreResidues2},
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{"stick": {"color": "orange"}}
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);
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""" % (segment,
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", ".join(str(i+1) for i, score in enumerate(scores) if score > 0.8),
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segment,
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", ".join(str(i+1) for i, score in enumerate(scores) if (score > 0.5) and (score < 0.8)),
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segment)
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html_content = f"""
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<!DOCTYPE html>
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<html>
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<head>
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<meta http-equiv="content-type" content="text/html; charset=UTF-8" />
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<style>
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.mol-container {{
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width: 100%;
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height: 700px;
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position: relative;
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}}
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</style>
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<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.6.3/jquery.min.js"></script>
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<script src="https://3Dmol.csb.pitt.edu/build/3Dmol-min.js"></script>
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</head>
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<body>
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<div id="container" class="mol-container"></div>
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<script>
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let pdb = `{mol}`; // Use template literal to properly escape PDB content
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$(document).ready(function () {{
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let element = $("#container");
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let config = {{ backgroundColor: "white" }};
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let viewer = $3Dmol.createViewer(element, config);
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viewer.addModel(pdb, "pdb");
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// Reset all styles and show only selected chain
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viewer.getModel(0).setStyle(
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{{"chain": "{segment}"}},
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{{ cartoon: {{ colorscheme:"whiteCarbon" }} }}
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);
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{high_score_script}
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// Add hover functionality
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viewer.setHoverable(
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{{}},
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true,
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function(atom, viewer, event, container) {{
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if (!atom.label) {{
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atom.label = viewer.addLabel(
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atom.resn + ":" + atom.atom,
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{{
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position: atom,
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backgroundColor: 'mintcream',
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fontColor: 'black',
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fontSize: 12,
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padding: 2
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}}
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);
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}}
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}},
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function(atom, viewer) {{
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if (atom.label) {{
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viewer.removeLabel(atom.label);
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delete atom.label;
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}}
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}}
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);
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viewer.zoomTo();
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viewer.render();
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viewer.zoom(0.8, 2000);
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}});
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</script>
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</body>
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</html>
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"""
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# Return the HTML content within an iframe safely encoded for special characters
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return f'<iframe width="100%" height="700" srcdoc="{html_content.replace(chr(34), """).replace(chr(39), "'")}"></iframe>'
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reps = [
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{
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"model": 0,
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"style": "cartoon",
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"color": "whiteCarbon",
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"residue_range": "",
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"around": 0,
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"byres": False,
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}
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]
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# Gradio UI
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with gr.Blocks() as demo:
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gr.Markdown("# Protein Binding Site Prediction (Random Scores)")
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with gr.Row():
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pdb_input = gr.Textbox(value="2IWI", label="PDB ID", placeholder="Enter PDB ID here...")
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visualize_btn = gr.Button("Visualize Structure")
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molecule_output2 = Molecule3D(label="Protein Structure", reps=reps)
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with gr.Row():
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pdb_input = gr.Textbox(value="2IWI", label="PDB ID", placeholder="Enter PDB ID here...")
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segment_input = gr.Textbox(value="A", label="Chain ID", placeholder="Enter Chain ID here...")
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prediction_btn = gr.Button("Predict Random Binding Site Scores")
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molecule_output = gr.HTML(label="Protein Structure")
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predictions_output = gr.Textbox(label="Binding Site Predictions")
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download_output = gr.File(label="Download Predictions")
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visualize_btn.click(fetch_pdb, inputs=[pdb_input], outputs=molecule_output2)
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prediction_btn.click(process_pdb, inputs=[pdb_input, segment_input], outputs=[predictions_output, molecule_output, download_output])
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gr.Markdown("## Examples")
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gr.Examples(
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examples=[
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["2IWI", "A"],
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["7RPZ", "B"],
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["3TJN", "C"]
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],
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inputs=[pdb_input, segment_input],
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outputs=[predictions_output, molecule_output, download_output]
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)
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.ipynb_checkpoints/requirements-checkpoint.txt
CHANGED
@@ -10,5 +10,4 @@ sentencepiece
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10 |
huggingface_hub>=0.15.0
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11 |
requests
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12 |
gradio_molecule3d
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13 |
-
biopython>=1.81
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matplotlib
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10 |
huggingface_hub>=0.15.0
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requests
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gradio_molecule3d
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biopython>=1.81
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.ipynb_checkpoints/test-checkpoint.ipynb
ADDED
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1 |
+
{
|
2 |
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"cells": [
|
3 |
+
{
|
4 |
+
"cell_type": "code",
|
5 |
+
"execution_count": 5,
|
6 |
+
"id": "d2208d17-47b6-4ff1-b6b6-ba09a9d490c7",
|
7 |
+
"metadata": {},
|
8 |
+
"outputs": [
|
9 |
+
{
|
10 |
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"name": "stdout",
|
11 |
+
"output_type": "stream",
|
12 |
+
"text": [
|
13 |
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"* Running on local URL: http://127.0.0.1:7864\n",
|
14 |
+
"\n",
|
15 |
+
"To create a public link, set `share=True` in `launch()`.\n"
|
16 |
+
]
|
17 |
+
},
|
18 |
+
{
|
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"data": {
|
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"text/html": [
|
21 |
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"<div><iframe src=\"http://127.0.0.1:7864/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
|
22 |
+
],
|
23 |
+
"text/plain": [
|
24 |
+
"<IPython.core.display.HTML object>"
|
25 |
+
]
|
26 |
+
},
|
27 |
+
"metadata": {},
|
28 |
+
"output_type": "display_data"
|
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+
},
|
30 |
+
{
|
31 |
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"data": {
|
32 |
+
"text/plain": []
|
33 |
+
},
|
34 |
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"execution_count": 5,
|
35 |
+
"metadata": {},
|
36 |
+
"output_type": "execute_result"
|
37 |
+
}
|
38 |
+
],
|
39 |
+
"source": [
|
40 |
+
"import gradio as gr\n",
|
41 |
+
"import requests\n",
|
42 |
+
"from Bio.PDB import PDBParser\n",
|
43 |
+
"from gradio_molecule3d import Molecule3D\n",
|
44 |
+
"import numpy as np\n",
|
45 |
+
"\n",
|
46 |
+
"# Function to fetch a PDB file from RCSB PDB\n",
|
47 |
+
"def fetch_pdb(pdb_id):\n",
|
48 |
+
" pdb_url = f'https://files.rcsb.org/download/{pdb_id}.pdb'\n",
|
49 |
+
" pdb_path = f'{pdb_id}.pdb'\n",
|
50 |
+
" response = requests.get(pdb_url)\n",
|
51 |
+
" if response.status_code == 200:\n",
|
52 |
+
" with open(pdb_path, 'wb') as f:\n",
|
53 |
+
" f.write(response.content)\n",
|
54 |
+
" return pdb_path\n",
|
55 |
+
" else:\n",
|
56 |
+
" return None\n",
|
57 |
+
"\n",
|
58 |
+
"# Function to process the PDB file and return random predictions\n",
|
59 |
+
"def process_pdb(pdb_id, segment):\n",
|
60 |
+
" pdb_path = fetch_pdb(pdb_id)\n",
|
61 |
+
" if not pdb_path:\n",
|
62 |
+
" return \"Failed to fetch PDB file\", None, None\n",
|
63 |
+
"\n",
|
64 |
+
" parser = PDBParser(QUIET=True)\n",
|
65 |
+
" structure = parser.get_structure('protein', pdb_path)\n",
|
66 |
+
" \n",
|
67 |
+
" try:\n",
|
68 |
+
" chain = structure[0][segment]\n",
|
69 |
+
" except KeyError:\n",
|
70 |
+
" return \"Invalid Chain ID\", None, None\n",
|
71 |
+
"\n",
|
72 |
+
" sequence = [residue.get_resname() for residue in chain if residue.id[0] == ' ']\n",
|
73 |
+
" random_scores = np.random.rand(len(sequence))\n",
|
74 |
+
"\n",
|
75 |
+
" result_str = \"\\n\".join(\n",
|
76 |
+
" f\"{seq} {res.id[1]} {score:.2f}\" \n",
|
77 |
+
" for seq, res, score in zip(sequence, chain, random_scores)\n",
|
78 |
+
" )\n",
|
79 |
+
"\n",
|
80 |
+
" # Save the predictions to a file\n",
|
81 |
+
" prediction_file = f\"{pdb_id}_predictions.txt\"\n",
|
82 |
+
" with open(prediction_file, \"w\") as f:\n",
|
83 |
+
" f.write(result_str)\n",
|
84 |
+
" \n",
|
85 |
+
" return result_str, pdb_path, prediction_file\n",
|
86 |
+
"\n",
|
87 |
+
"#reps = [{\"model\": 0, \"style\": \"cartoon\", \"color\": \"spectrum\"}]\n",
|
88 |
+
"\n",
|
89 |
+
"reps = [\n",
|
90 |
+
" {\n",
|
91 |
+
" \"model\": 0,\n",
|
92 |
+
" \"style\": \"cartoon\",\n",
|
93 |
+
" \"color\": \"whiteCarbon\",\n",
|
94 |
+
" \"residue_range\": \"\",\n",
|
95 |
+
" \"around\": 0,\n",
|
96 |
+
" \"byres\": False,\n",
|
97 |
+
" },\n",
|
98 |
+
" {\n",
|
99 |
+
" \"model\": 0,\n",
|
100 |
+
" \"chain\": \"A\",\n",
|
101 |
+
" \"resname\": \"HIS\",\n",
|
102 |
+
" \"style\": \"stick\",\n",
|
103 |
+
" \"color\": \"red\"\n",
|
104 |
+
" }\n",
|
105 |
+
" ]\n",
|
106 |
+
"\n",
|
107 |
+
"\n",
|
108 |
+
"# Gradio UI\n",
|
109 |
+
"with gr.Blocks() as demo:\n",
|
110 |
+
" gr.Markdown(\"# Protein Binding Site Prediction (Random Scores)\")\n",
|
111 |
+
"\n",
|
112 |
+
" with gr.Row():\n",
|
113 |
+
" pdb_input = gr.Textbox(value=\"2IWI\", label=\"PDB ID\", placeholder=\"Enter PDB ID here...\")\n",
|
114 |
+
" segment_input = gr.Textbox(value=\"A\", label=\"Chain ID\", placeholder=\"Enter Chain ID here...\")\n",
|
115 |
+
" visualize_btn = gr.Button(\"Visualize Structure\")\n",
|
116 |
+
" prediction_btn = gr.Button(\"Predict Random Binding Site Scores\")\n",
|
117 |
+
"\n",
|
118 |
+
" molecule_output = Molecule3D(label=\"Protein Structure\", reps=reps)\n",
|
119 |
+
" predictions_output = gr.Textbox(label=\"Binding Site Predictions\")\n",
|
120 |
+
" download_output = gr.File(label=\"Download Predictions\")\n",
|
121 |
+
"\n",
|
122 |
+
" visualize_btn.click(fetch_pdb, inputs=[pdb_input], outputs=molecule_output)\n",
|
123 |
+
" prediction_btn.click(process_pdb, inputs=[pdb_input, segment_input], outputs=[predictions_output, molecule_output, download_output])\n",
|
124 |
+
"\n",
|
125 |
+
" gr.Markdown(\"## Examples\")\n",
|
126 |
+
" gr.Examples(\n",
|
127 |
+
" examples=[\n",
|
128 |
+
" [\"2IWI\", \"A\"],\n",
|
129 |
+
" [\"7RPZ\", \"B\"],\n",
|
130 |
+
" [\"3TJN\", \"C\"]\n",
|
131 |
+
" ],\n",
|
132 |
+
" inputs=[pdb_input, segment_input],\n",
|
133 |
+
" outputs=[predictions_output, molecule_output, download_output]\n",
|
134 |
+
" )\n",
|
135 |
+
"\n",
|
136 |
+
"demo.launch()"
|
137 |
+
]
|
138 |
+
},
|
139 |
+
{
|
140 |
+
"cell_type": "code",
|
141 |
+
"execution_count": null,
|
142 |
+
"id": "bd50ff2e-ed03-498e-8af2-73c0fb8ea07e",
|
143 |
+
"metadata": {},
|
144 |
+
"outputs": [],
|
145 |
+
"source": []
|
146 |
+
},
|
147 |
+
{
|
148 |
+
"cell_type": "code",
|
149 |
+
"execution_count": 4,
|
150 |
+
"id": "a1088e14-f09c-48ff-8632-cc4685306d7c",
|
151 |
+
"metadata": {},
|
152 |
+
"outputs": [
|
153 |
+
{
|
154 |
+
"name": "stdout",
|
155 |
+
"output_type": "stream",
|
156 |
+
"text": [
|
157 |
+
"* Running on local URL: http://127.0.0.1:7863\n",
|
158 |
+
"\n",
|
159 |
+
"To create a public link, set `share=True` in `launch()`.\n"
|
160 |
+
]
|
161 |
+
},
|
162 |
+
{
|
163 |
+
"data": {
|
164 |
+
"text/html": [
|
165 |
+
"<div><iframe src=\"http://127.0.0.1:7863/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
|
166 |
+
],
|
167 |
+
"text/plain": [
|
168 |
+
"<IPython.core.display.HTML object>"
|
169 |
+
]
|
170 |
+
},
|
171 |
+
"metadata": {},
|
172 |
+
"output_type": "display_data"
|
173 |
+
}
|
174 |
+
],
|
175 |
+
"source": [
|
176 |
+
"import gradio as gr\n",
|
177 |
+
"from gradio_molecule3d import Molecule3D\n",
|
178 |
+
"\n",
|
179 |
+
"\n",
|
180 |
+
"example = Molecule3D().example_value()\n",
|
181 |
+
"\n",
|
182 |
+
"\n",
|
183 |
+
"reps = [\n",
|
184 |
+
" {\n",
|
185 |
+
" \"model\": 0,\n",
|
186 |
+
" \"style\": \"cartoon\",\n",
|
187 |
+
" \"color\": \"whiteCarbon\",\n",
|
188 |
+
" \"residue_range\": \"\",\n",
|
189 |
+
" \"around\": 0,\n",
|
190 |
+
" \"byres\": False,\n",
|
191 |
+
" },\n",
|
192 |
+
" {\n",
|
193 |
+
" \"model\": 0,\n",
|
194 |
+
" \"chain\": \"A\",\n",
|
195 |
+
" \"resname\": \"HIS\",\n",
|
196 |
+
" \"style\": \"stick\",\n",
|
197 |
+
" \"color\": \"red\"\n",
|
198 |
+
" }\n",
|
199 |
+
" ]\n",
|
200 |
+
"\n",
|
201 |
+
"\n",
|
202 |
+
"\n",
|
203 |
+
"def predict(x):\n",
|
204 |
+
" print(\"predict function\", x)\n",
|
205 |
+
" print(x.name)\n",
|
206 |
+
" return x\n",
|
207 |
+
"\n",
|
208 |
+
"with gr.Blocks() as demo:\n",
|
209 |
+
" gr.Markdown(\"# Molecule3D\")\n",
|
210 |
+
" inp = Molecule3D(label=\"Molecule3D\", reps=reps)\n",
|
211 |
+
" out = Molecule3D(label=\"Output\", reps=reps)\n",
|
212 |
+
"\n",
|
213 |
+
" btn = gr.Button(\"Predict\")\n",
|
214 |
+
" gr.Markdown(\"\"\" \n",
|
215 |
+
" You can configure the default rendering of the molecule by adding a list of representations\n",
|
216 |
+
" <pre>\n",
|
217 |
+
" reps = [\n",
|
218 |
+
" {\n",
|
219 |
+
" \"model\": 0,\n",
|
220 |
+
" \"style\": \"cartoon\",\n",
|
221 |
+
" \"color\": \"whiteCarbon\",\n",
|
222 |
+
" \"residue_range\": \"\",\n",
|
223 |
+
" \"around\": 0,\n",
|
224 |
+
" \"byres\": False,\n",
|
225 |
+
" },\n",
|
226 |
+
" {\n",
|
227 |
+
" \"model\": 0,\n",
|
228 |
+
" \"chain\": \"A\",\n",
|
229 |
+
" \"resname\": \"HIS\",\n",
|
230 |
+
" \"style\": \"stick\",\n",
|
231 |
+
" \"color\": \"red\"\n",
|
232 |
+
" }\n",
|
233 |
+
" ]\n",
|
234 |
+
" </pre>\n",
|
235 |
+
" \"\"\")\n",
|
236 |
+
" btn.click(predict, inputs=inp, outputs=out)\n",
|
237 |
+
"\n",
|
238 |
+
"\n",
|
239 |
+
"if __name__ == \"__main__\":\n",
|
240 |
+
" demo.launch()"
|
241 |
+
]
|
242 |
+
},
|
243 |
+
{
|
244 |
+
"cell_type": "code",
|
245 |
+
"execution_count": null,
|
246 |
+
"id": "d27cc368-26a0-42c2-a68a-8833de7bb4a0",
|
247 |
+
"metadata": {},
|
248 |
+
"outputs": [],
|
249 |
+
"source": []
|
250 |
+
},
|
251 |
+
{
|
252 |
+
"cell_type": "code",
|
253 |
+
"execution_count": 8,
|
254 |
+
"id": "cdf7fd26-0464-40d9-9107-71c29dbcaef8",
|
255 |
+
"metadata": {},
|
256 |
+
"outputs": [
|
257 |
+
{
|
258 |
+
"name": "stdout",
|
259 |
+
"output_type": "stream",
|
260 |
+
"text": [
|
261 |
+
"* Running on local URL: http://127.0.0.1:7867\n",
|
262 |
+
"\n",
|
263 |
+
"To create a public link, set `share=True` in `launch()`.\n"
|
264 |
+
]
|
265 |
+
},
|
266 |
+
{
|
267 |
+
"data": {
|
268 |
+
"text/html": [
|
269 |
+
"<div><iframe src=\"http://127.0.0.1:7867/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
|
270 |
+
],
|
271 |
+
"text/plain": [
|
272 |
+
"<IPython.core.display.HTML object>"
|
273 |
+
]
|
274 |
+
},
|
275 |
+
"metadata": {},
|
276 |
+
"output_type": "display_data"
|
277 |
+
},
|
278 |
+
{
|
279 |
+
"data": {
|
280 |
+
"text/plain": []
|
281 |
+
},
|
282 |
+
"execution_count": 8,
|
283 |
+
"metadata": {},
|
284 |
+
"output_type": "execute_result"
|
285 |
+
},
|
286 |
+
{
|
287 |
+
"name": "stderr",
|
288 |
+
"output_type": "stream",
|
289 |
+
"text": [
|
290 |
+
"/var/folders/tm/ym2tckv54b96ws82y3b7cqhh0000gn/T/ipykernel_11794/4072855226.py:39: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.\n",
|
291 |
+
" colors = [cm.get_cmap('coolwarm')(score)[:3] for score in normalized_scores]\n",
|
292 |
+
"Traceback (most recent call last):\n",
|
293 |
+
" File \"/Users/thorben_froehlking/anaconda3/envs/LLM/lib/python3.12/site-packages/gradio/queueing.py\", line 622, in process_events\n",
|
294 |
+
" response = await route_utils.call_process_api(\n",
|
295 |
+
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
|
296 |
+
" File \"/Users/thorben_froehlking/anaconda3/envs/LLM/lib/python3.12/site-packages/gradio/route_utils.py\", line 323, in call_process_api\n",
|
297 |
+
" output = await app.get_blocks().process_api(\n",
|
298 |
+
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
|
299 |
+
" File \"/Users/thorben_froehlking/anaconda3/envs/LLM/lib/python3.12/site-packages/gradio/blocks.py\", line 2024, in process_api\n",
|
300 |
+
" data = await self.postprocess_data(block_fn, result[\"prediction\"], state)\n",
|
301 |
+
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
|
302 |
+
" File \"/Users/thorben_froehlking/anaconda3/envs/LLM/lib/python3.12/site-packages/gradio/blocks.py\", line 1830, in postprocess_data\n",
|
303 |
+
" prediction_value = block.postprocess(prediction_value)\n",
|
304 |
+
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
|
305 |
+
" File \"/Users/thorben_froehlking/anaconda3/envs/LLM/lib/python3.12/site-packages/gradio_molecule3d/molecule3d.py\", line 210, in postprocess\n",
|
306 |
+
" orig_name=Path(file).name,\n",
|
307 |
+
" ^^^^^^^^^^\n",
|
308 |
+
" File \"/Users/thorben_froehlking/anaconda3/envs/LLM/lib/python3.12/pathlib.py\", line 1162, in __init__\n",
|
309 |
+
" super().__init__(*args)\n",
|
310 |
+
" File \"/Users/thorben_froehlking/anaconda3/envs/LLM/lib/python3.12/pathlib.py\", line 373, in __init__\n",
|
311 |
+
" raise TypeError(\n",
|
312 |
+
"TypeError: argument should be a str or an os.PathLike object where __fspath__ returns a str, not 'dict'\n"
|
313 |
+
]
|
314 |
+
}
|
315 |
+
],
|
316 |
+
"source": [
|
317 |
+
"import gradio as gr\n",
|
318 |
+
"import requests\n",
|
319 |
+
"from Bio.PDB import PDBParser\n",
|
320 |
+
"from gradio_molecule3d import Molecule3D\n",
|
321 |
+
"import numpy as np\n",
|
322 |
+
"from matplotlib import cm\n",
|
323 |
+
"\n",
|
324 |
+
"# Function to fetch a PDB file from RCSB PDB\n",
|
325 |
+
"def fetch_pdb(pdb_id):\n",
|
326 |
+
" pdb_url = f'https://files.rcsb.org/download/{pdb_id}.pdb'\n",
|
327 |
+
" pdb_path = f'{pdb_id}.pdb'\n",
|
328 |
+
" response = requests.get(pdb_url)\n",
|
329 |
+
" if response.status_code == 200:\n",
|
330 |
+
" with open(pdb_path, 'wb') as f:\n",
|
331 |
+
" f.write(response.content)\n",
|
332 |
+
" return pdb_path\n",
|
333 |
+
" else:\n",
|
334 |
+
" return None\n",
|
335 |
+
"\n",
|
336 |
+
"# Function to process the PDB file and return random predictions\n",
|
337 |
+
"def process_pdb(pdb_id, segment):\n",
|
338 |
+
" pdb_path = fetch_pdb(pdb_id)\n",
|
339 |
+
" if not pdb_path:\n",
|
340 |
+
" return \"Failed to fetch PDB file\", None, None, None\n",
|
341 |
+
"\n",
|
342 |
+
" parser = PDBParser(QUIET=True)\n",
|
343 |
+
" structure = parser.get_structure('protein', pdb_path)\n",
|
344 |
+
"\n",
|
345 |
+
" try:\n",
|
346 |
+
" chain = structure[0][segment]\n",
|
347 |
+
" except KeyError:\n",
|
348 |
+
" return \"Invalid Chain ID\", None, None, None\n",
|
349 |
+
"\n",
|
350 |
+
" sequence = [residue.get_resname() for residue in chain if residue.id[0] == ' ']\n",
|
351 |
+
" random_scores = np.random.rand(len(sequence))\n",
|
352 |
+
"\n",
|
353 |
+
" # Normalize scores for coloring (0 = blue, 1 = red)\n",
|
354 |
+
" normalized_scores = (random_scores - np.min(random_scores)) / (np.max(random_scores) - np.min(random_scores))\n",
|
355 |
+
" colors = [cm.get_cmap('coolwarm')(score)[:3] for score in normalized_scores]\n",
|
356 |
+
" hex_colors = [f'#{int(r*255):02x}{int(g*255):02x}{int(b*255):02x}' for r, g, b in colors]\n",
|
357 |
+
"\n",
|
358 |
+
" # Result string and representation\n",
|
359 |
+
" result_str = \"\\n\".join(\n",
|
360 |
+
" f\"{seq} {res.id[1]} {score:.2f}\" \n",
|
361 |
+
" for seq, res, score in zip(sequence, chain, random_scores)\n",
|
362 |
+
" )\n",
|
363 |
+
"\n",
|
364 |
+
" # Representation for the protein structure\n",
|
365 |
+
" reps = [\n",
|
366 |
+
" {\n",
|
367 |
+
" \"model\": 0,\n",
|
368 |
+
" \"style\": \"cartoon\",\n",
|
369 |
+
" \"color\": \"whiteCarbon\"\n",
|
370 |
+
" }\n",
|
371 |
+
" ] + [\n",
|
372 |
+
" {\n",
|
373 |
+
" \"model\": 0,\n",
|
374 |
+
" \"style\": \"cartoon\",\n",
|
375 |
+
" \"residue_index\": i,\n",
|
376 |
+
" \"color\": color\n",
|
377 |
+
" }\n",
|
378 |
+
" for i, color in enumerate(hex_colors)\n",
|
379 |
+
" ]\n",
|
380 |
+
"\n",
|
381 |
+
" # Save the predictions to a file\n",
|
382 |
+
" prediction_file = f\"{pdb_id}_predictions.txt\"\n",
|
383 |
+
" with open(prediction_file, \"w\") as f:\n",
|
384 |
+
" f.write(result_str)\n",
|
385 |
+
" \n",
|
386 |
+
" return result_str, reps, prediction_file\n",
|
387 |
+
"\n",
|
388 |
+
"# Gradio UI\n",
|
389 |
+
"with gr.Blocks() as demo:\n",
|
390 |
+
" gr.Markdown(\"# Protein Binding Site Prediction (Random Scores)\")\n",
|
391 |
+
"\n",
|
392 |
+
" with gr.Row():\n",
|
393 |
+
" pdb_input = gr.Textbox(value=\"2IWI\", label=\"PDB ID\", placeholder=\"Enter PDB ID here...\")\n",
|
394 |
+
" segment_input = gr.Textbox(value=\"A\", label=\"Chain ID\", placeholder=\"Enter Chain ID here...\")\n",
|
395 |
+
" visualize_btn = gr.Button(\"Visualize Structure\")\n",
|
396 |
+
" prediction_btn = gr.Button(\"Predict Random Binding Site Scores\")\n",
|
397 |
+
"\n",
|
398 |
+
" molecule_output = Molecule3D(label=\"Protein Structure\", reps=reps)\n",
|
399 |
+
" predictions_output = gr.Textbox(label=\"Binding Site Predictions\")\n",
|
400 |
+
" download_output = gr.File(label=\"Download Predictions\")\n",
|
401 |
+
"\n",
|
402 |
+
" prediction_btn.click(\n",
|
403 |
+
" fn=process_pdb,\n",
|
404 |
+
" inputs=[pdb_input, segment_input],\n",
|
405 |
+
" outputs=[predictions_output, molecule_output, download_output]\n",
|
406 |
+
" )\n",
|
407 |
+
"\n",
|
408 |
+
" gr.Markdown(\"## Examples\")\n",
|
409 |
+
" gr.Examples(\n",
|
410 |
+
" examples=[\n",
|
411 |
+
" [\"2IWI\", \"A\"],\n",
|
412 |
+
" [\"7RPZ\", \"B\"],\n",
|
413 |
+
" [\"3TJN\", \"C\"]\n",
|
414 |
+
" ],\n",
|
415 |
+
" inputs=[pdb_input, segment_input],\n",
|
416 |
+
" outputs=[predictions_output, molecule_output, download_output]\n",
|
417 |
+
" )\n",
|
418 |
+
"\n",
|
419 |
+
"demo.launch()"
|
420 |
+
]
|
421 |
+
},
|
422 |
+
{
|
423 |
+
"cell_type": "code",
|
424 |
+
"execution_count": null,
|
425 |
+
"id": "ee215c16-a1fb-450f-bb93-37aaee6fb3f1",
|
426 |
+
"metadata": {},
|
427 |
+
"outputs": [],
|
428 |
+
"source": []
|
429 |
+
}
|
430 |
+
],
|
431 |
+
"metadata": {
|
432 |
+
"kernelspec": {
|
433 |
+
"display_name": "Python (LLM)",
|
434 |
+
"language": "python",
|
435 |
+
"name": "llm"
|
436 |
+
},
|
437 |
+
"language_info": {
|
438 |
+
"codemirror_mode": {
|
439 |
+
"name": "ipython",
|
440 |
+
"version": 3
|
441 |
+
},
|
442 |
+
"file_extension": ".py",
|
443 |
+
"mimetype": "text/x-python",
|
444 |
+
"name": "python",
|
445 |
+
"nbconvert_exporter": "python",
|
446 |
+
"pygments_lexer": "ipython3",
|
447 |
+
"version": "3.12.7"
|
448 |
+
}
|
449 |
+
},
|
450 |
+
"nbformat": 4,
|
451 |
+
"nbformat_minor": 5
|
452 |
+
}
|
.ipynb_checkpoints/test2-checkpoint.ipynb
ADDED
@@ -0,0 +1,1193 @@
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|
1 |
+
{
|
2 |
+
"cells": [
|
3 |
+
{
|
4 |
+
"cell_type": "code",
|
5 |
+
"execution_count": 2,
|
6 |
+
"id": "f3b7f6b0-6685-4a5c-9529-45e0ca905a3b",
|
7 |
+
"metadata": {},
|
8 |
+
"outputs": [
|
9 |
+
{
|
10 |
+
"name": "stdout",
|
11 |
+
"output_type": "stream",
|
12 |
+
"text": [
|
13 |
+
"* Running on local URL: http://127.0.0.1:7860\n",
|
14 |
+
"\n",
|
15 |
+
"To create a public link, set `share=True` in `launch()`.\n"
|
16 |
+
]
|
17 |
+
},
|
18 |
+
{
|
19 |
+
"data": {
|
20 |
+
"text/html": [
|
21 |
+
"<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
|
22 |
+
],
|
23 |
+
"text/plain": [
|
24 |
+
"<IPython.core.display.HTML object>"
|
25 |
+
]
|
26 |
+
},
|
27 |
+
"metadata": {},
|
28 |
+
"output_type": "display_data"
|
29 |
+
},
|
30 |
+
{
|
31 |
+
"data": {
|
32 |
+
"text/plain": []
|
33 |
+
},
|
34 |
+
"execution_count": 2,
|
35 |
+
"metadata": {},
|
36 |
+
"output_type": "execute_result"
|
37 |
+
}
|
38 |
+
],
|
39 |
+
"source": [
|
40 |
+
"import gradio as gr\n",
|
41 |
+
"import requests\n",
|
42 |
+
"from Bio.PDB import PDBParser\n",
|
43 |
+
"import numpy as np\n",
|
44 |
+
"import os\n",
|
45 |
+
"from gradio_molecule3d import Molecule3D\n",
|
46 |
+
"\n",
|
47 |
+
"def read_mol(pdb_path):\n",
|
48 |
+
" \"\"\"Read PDB file and return its content as a string\"\"\"\n",
|
49 |
+
" with open(pdb_path, 'r') as f:\n",
|
50 |
+
" return f.read()\n",
|
51 |
+
"\n",
|
52 |
+
"def fetch_pdb(pdb_id):\n",
|
53 |
+
" pdb_url = f'https://files.rcsb.org/download/{pdb_id}.pdb'\n",
|
54 |
+
" pdb_path = f'{pdb_id}.pdb'\n",
|
55 |
+
" response = requests.get(pdb_url)\n",
|
56 |
+
" if response.status_code == 200:\n",
|
57 |
+
" with open(pdb_path, 'wb') as f:\n",
|
58 |
+
" f.write(response.content)\n",
|
59 |
+
" return pdb_path\n",
|
60 |
+
" else:\n",
|
61 |
+
" return None\n",
|
62 |
+
"\n",
|
63 |
+
"def process_pdb(pdb_id, segment):\n",
|
64 |
+
" pdb_path = fetch_pdb(pdb_id)\n",
|
65 |
+
" if not pdb_path:\n",
|
66 |
+
" return \"Failed to fetch PDB file\", None, None\n",
|
67 |
+
" \n",
|
68 |
+
" parser = PDBParser(QUIET=1)\n",
|
69 |
+
" structure = parser.get_structure('protein', pdb_path)\n",
|
70 |
+
" \n",
|
71 |
+
" try:\n",
|
72 |
+
" chain = structure[0][segment]\n",
|
73 |
+
" except KeyError:\n",
|
74 |
+
" return \"Invalid Chain ID\", None, None\n",
|
75 |
+
" \n",
|
76 |
+
" # Comprehensive amino acid mapping\n",
|
77 |
+
" aa_dict = {\n",
|
78 |
+
" 'ALA': 'A', 'CYS': 'C', 'ASP': 'D', 'GLU': 'E', 'PHE': 'F',\n",
|
79 |
+
" 'GLY': 'G', 'HIS': 'H', 'ILE': 'I', 'LYS': 'K', 'LEU': 'L',\n",
|
80 |
+
" 'MET': 'M', 'ASN': 'N', 'PRO': 'P', 'GLN': 'Q', 'ARG': 'R',\n",
|
81 |
+
" 'SER': 'S', 'THR': 'T', 'VAL': 'V', 'TRP': 'W', 'TYR': 'Y',\n",
|
82 |
+
" 'MSE': 'M', 'SEP': 'S', 'TPO': 'T', 'CSO': 'C', 'PTR': 'Y', 'HYP': 'P'\n",
|
83 |
+
" }\n",
|
84 |
+
" \n",
|
85 |
+
" # Exclude non-amino acid residues\n",
|
86 |
+
" sequence = [\n",
|
87 |
+
" residue for residue in chain \n",
|
88 |
+
" if residue.get_resname().strip() in aa_dict\n",
|
89 |
+
" ]\n",
|
90 |
+
" \n",
|
91 |
+
" random_scores = np.random.rand(len(sequence))\n",
|
92 |
+
" result_str = \"\\n\".join(\n",
|
93 |
+
" f\"{aa_dict[res.get_resname()]} {res.id[1]} {score:.2f}\" \n",
|
94 |
+
" for res, score in zip(sequence, random_scores)\n",
|
95 |
+
" )\n",
|
96 |
+
" \n",
|
97 |
+
" # Save the predictions to a file\n",
|
98 |
+
" prediction_file = f\"{pdb_id}_predictions.txt\"\n",
|
99 |
+
" with open(prediction_file, \"w\") as f:\n",
|
100 |
+
" f.write(result_str)\n",
|
101 |
+
" \n",
|
102 |
+
" return result_str, molecule(pdb_path, random_scores, segment), prediction_file\n",
|
103 |
+
"\n",
|
104 |
+
"def molecule(input_pdb, scores=None, segment='A'):\n",
|
105 |
+
" mol = read_mol(input_pdb) # Read PDB file content\n",
|
106 |
+
" \n",
|
107 |
+
" # Prepare high-scoring residues script if scores are provided\n",
|
108 |
+
" high_score_script = \"\"\n",
|
109 |
+
" if scores is not None:\n",
|
110 |
+
" high_score_script = \"\"\"\n",
|
111 |
+
" // Reset all styles first\n",
|
112 |
+
" viewer.getModel(0).setStyle({}, {});\n",
|
113 |
+
" \n",
|
114 |
+
" // Show only the selected chain\n",
|
115 |
+
" viewer.getModel(0).setStyle(\n",
|
116 |
+
" {\"chain\": \"%s\"}, \n",
|
117 |
+
" { cartoon: {colorscheme:\"whiteCarbon\"} }\n",
|
118 |
+
" );\n",
|
119 |
+
" \n",
|
120 |
+
" // Highlight high-scoring residues only for the selected chain\n",
|
121 |
+
" let highScoreResidues = [%s];\n",
|
122 |
+
" viewer.getModel(0).setStyle(\n",
|
123 |
+
" {\"chain\": \"%s\", \"resi\": highScoreResidues}, \n",
|
124 |
+
" {\"stick\": {\"color\": \"red\"}}\n",
|
125 |
+
" );\n",
|
126 |
+
" \"\"\" % (segment, \n",
|
127 |
+
" \", \".join(str(i+1) for i, score in enumerate(scores) if score > 0.8),\n",
|
128 |
+
" segment)\n",
|
129 |
+
" \n",
|
130 |
+
" html_content = f\"\"\"\n",
|
131 |
+
" <!DOCTYPE html>\n",
|
132 |
+
" <html>\n",
|
133 |
+
" <head> \n",
|
134 |
+
" <meta http-equiv=\"content-type\" content=\"text/html; charset=UTF-8\" />\n",
|
135 |
+
" <style>\n",
|
136 |
+
" .mol-container {{\n",
|
137 |
+
" width: 100%;\n",
|
138 |
+
" height: 700px;\n",
|
139 |
+
" position: relative;\n",
|
140 |
+
" }}\n",
|
141 |
+
" </style>\n",
|
142 |
+
" <script src=\"https://cdnjs.cloudflare.com/ajax/libs/jquery/3.6.3/jquery.min.js\"></script>\n",
|
143 |
+
" <script src=\"https://3Dmol.csb.pitt.edu/build/3Dmol-min.js\"></script>\n",
|
144 |
+
" </head>\n",
|
145 |
+
" <body>\n",
|
146 |
+
" <div id=\"container\" class=\"mol-container\"></div>\n",
|
147 |
+
" <script>\n",
|
148 |
+
" let pdb = `{mol}`; // Use template literal to properly escape PDB content\n",
|
149 |
+
" $(document).ready(function () {{\n",
|
150 |
+
" let element = $(\"#container\");\n",
|
151 |
+
" let config = {{ backgroundColor: \"white\" }};\n",
|
152 |
+
" let viewer = $3Dmol.createViewer(element, config);\n",
|
153 |
+
" viewer.addModel(pdb, \"pdb\");\n",
|
154 |
+
" \n",
|
155 |
+
" // Reset all styles and show only selected chain\n",
|
156 |
+
" viewer.getModel(0).setStyle(\n",
|
157 |
+
" {{\"chain\": \"{segment}\"}}, \n",
|
158 |
+
" {{ cartoon: {{ colorscheme:\"whiteCarbon\" }} }}\n",
|
159 |
+
" );\n",
|
160 |
+
" \n",
|
161 |
+
" {high_score_script}\n",
|
162 |
+
" \n",
|
163 |
+
" viewer.zoomTo();\n",
|
164 |
+
" viewer.render();\n",
|
165 |
+
" viewer.zoom(0.8, 2000);\n",
|
166 |
+
" }});\n",
|
167 |
+
" </script>\n",
|
168 |
+
" </body>\n",
|
169 |
+
" </html>\n",
|
170 |
+
" \"\"\"\n",
|
171 |
+
" \n",
|
172 |
+
" # Return the HTML content within an iframe safely encoded for special characters\n",
|
173 |
+
" return f'<iframe width=\"100%\" height=\"700\" srcdoc=\"{html_content.replace(chr(34), \""\").replace(chr(39), \"'\")}\"></iframe>'\n",
|
174 |
+
"\n",
|
175 |
+
"reps = [\n",
|
176 |
+
" {\n",
|
177 |
+
" \"model\": 0,\n",
|
178 |
+
" \"style\": \"cartoon\",\n",
|
179 |
+
" \"color\": \"whiteCarbon\",\n",
|
180 |
+
" \"residue_range\": \"\",\n",
|
181 |
+
" \"around\": 0,\n",
|
182 |
+
" \"byres\": False,\n",
|
183 |
+
" }\n",
|
184 |
+
" ]\n",
|
185 |
+
"# Gradio UI\n",
|
186 |
+
"with gr.Blocks() as demo:\n",
|
187 |
+
" gr.Markdown(\"# Protein Binding Site Prediction (Random Scores)\")\n",
|
188 |
+
" with gr.Row():\n",
|
189 |
+
" pdb_input = gr.Textbox(value=\"2IWI\", label=\"PDB ID\", placeholder=\"Enter PDB ID here...\")\n",
|
190 |
+
" visualize_btn = gr.Button(\"Visualize Structure\")\n",
|
191 |
+
"\n",
|
192 |
+
" molecule_output2 = Molecule3D(label=\"Protein Structure\", reps=reps)\n",
|
193 |
+
"\n",
|
194 |
+
" with gr.Row():\n",
|
195 |
+
" pdb_input = gr.Textbox(value=\"2IWI\", label=\"PDB ID\", placeholder=\"Enter PDB ID here...\")\n",
|
196 |
+
" segment_input = gr.Textbox(value=\"A\", label=\"Chain ID\", placeholder=\"Enter Chain ID here...\")\n",
|
197 |
+
" prediction_btn = gr.Button(\"Predict Random Binding Site Scores\")\n",
|
198 |
+
"\n",
|
199 |
+
" molecule_output = gr.HTML(label=\"Protein Structure\")\n",
|
200 |
+
" predictions_output = gr.Textbox(label=\"Binding Site Predictions\")\n",
|
201 |
+
" download_output = gr.File(label=\"Download Predictions\")\n",
|
202 |
+
" \n",
|
203 |
+
" visualize_btn.click(fetch_pdb, inputs=[pdb_input], outputs=molecule_output2)\n",
|
204 |
+
" \n",
|
205 |
+
" prediction_btn.click(process_pdb, inputs=[pdb_input, segment_input], outputs=[predictions_output, molecule_output, download_output])\n",
|
206 |
+
" \n",
|
207 |
+
" gr.Markdown(\"## Examples\")\n",
|
208 |
+
" gr.Examples(\n",
|
209 |
+
" examples=[\n",
|
210 |
+
" [\"2IWI\", \"A\"],\n",
|
211 |
+
" [\"7RPZ\", \"B\"],\n",
|
212 |
+
" [\"3TJN\", \"C\"]\n",
|
213 |
+
" ],\n",
|
214 |
+
" inputs=[pdb_input, segment_input],\n",
|
215 |
+
" outputs=[predictions_output, molecule_output, download_output]\n",
|
216 |
+
" )\n",
|
217 |
+
"\n",
|
218 |
+
"demo.launch()"
|
219 |
+
]
|
220 |
+
},
|
221 |
+
{
|
222 |
+
"cell_type": "code",
|
223 |
+
"execution_count": 6,
|
224 |
+
"id": "28f8f28c-48d3-4e35-9766-3de9882179b5",
|
225 |
+
"metadata": {},
|
226 |
+
"outputs": [
|
227 |
+
{
|
228 |
+
"name": "stdout",
|
229 |
+
"output_type": "stream",
|
230 |
+
"text": [
|
231 |
+
"* Running on local URL: http://127.0.0.1:7864\n",
|
232 |
+
"\n",
|
233 |
+
"To create a public link, set `share=True` in `launch()`.\n"
|
234 |
+
]
|
235 |
+
},
|
236 |
+
{
|
237 |
+
"data": {
|
238 |
+
"text/html": [
|
239 |
+
"<div><iframe src=\"http://127.0.0.1:7864/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
|
240 |
+
],
|
241 |
+
"text/plain": [
|
242 |
+
"<IPython.core.display.HTML object>"
|
243 |
+
]
|
244 |
+
},
|
245 |
+
"metadata": {},
|
246 |
+
"output_type": "display_data"
|
247 |
+
},
|
248 |
+
{
|
249 |
+
"data": {
|
250 |
+
"text/plain": []
|
251 |
+
},
|
252 |
+
"execution_count": 6,
|
253 |
+
"metadata": {},
|
254 |
+
"output_type": "execute_result"
|
255 |
+
}
|
256 |
+
],
|
257 |
+
"source": [
|
258 |
+
"import gradio as gr\n",
|
259 |
+
"import requests\n",
|
260 |
+
"from Bio.PDB import PDBParser\n",
|
261 |
+
"import numpy as np\n",
|
262 |
+
"import os\n",
|
263 |
+
"from gradio_molecule3d import Molecule3D\n",
|
264 |
+
"\n",
|
265 |
+
"def read_mol(pdb_path):\n",
|
266 |
+
" \"\"\"Read PDB file and return its content as a string\"\"\"\n",
|
267 |
+
" with open(pdb_path, 'r') as f:\n",
|
268 |
+
" return f.read()\n",
|
269 |
+
"\n",
|
270 |
+
"def fetch_pdb(pdb_id):\n",
|
271 |
+
" pdb_url = f'https://files.rcsb.org/download/{pdb_id}.pdb'\n",
|
272 |
+
" pdb_path = f'{pdb_id}.pdb'\n",
|
273 |
+
" response = requests.get(pdb_url)\n",
|
274 |
+
" if response.status_code == 200:\n",
|
275 |
+
" with open(pdb_path, 'wb') as f:\n",
|
276 |
+
" f.write(response.content)\n",
|
277 |
+
" return pdb_path\n",
|
278 |
+
" else:\n",
|
279 |
+
" return None\n",
|
280 |
+
"\n",
|
281 |
+
"def process_pdb(pdb_id, segment):\n",
|
282 |
+
" pdb_path = fetch_pdb(pdb_id)\n",
|
283 |
+
" if not pdb_path:\n",
|
284 |
+
" return \"Failed to fetch PDB file\", None, None\n",
|
285 |
+
" \n",
|
286 |
+
" parser = PDBParser(QUIET=1)\n",
|
287 |
+
" structure = parser.get_structure('protein', pdb_path)\n",
|
288 |
+
" \n",
|
289 |
+
" try:\n",
|
290 |
+
" chain = structure[0][segment]\n",
|
291 |
+
" except KeyError:\n",
|
292 |
+
" return \"Invalid Chain ID\", None, None\n",
|
293 |
+
" \n",
|
294 |
+
" # Comprehensive amino acid mapping\n",
|
295 |
+
" aa_dict = {\n",
|
296 |
+
" 'ALA': 'A', 'CYS': 'C', 'ASP': 'D', 'GLU': 'E', 'PHE': 'F',\n",
|
297 |
+
" 'GLY': 'G', 'HIS': 'H', 'ILE': 'I', 'LYS': 'K', 'LEU': 'L',\n",
|
298 |
+
" 'MET': 'M', 'ASN': 'N', 'PRO': 'P', 'GLN': 'Q', 'ARG': 'R',\n",
|
299 |
+
" 'SER': 'S', 'THR': 'T', 'VAL': 'V', 'TRP': 'W', 'TYR': 'Y',\n",
|
300 |
+
" 'MSE': 'M', 'SEP': 'S', 'TPO': 'T', 'CSO': 'C', 'PTR': 'Y', 'HYP': 'P'\n",
|
301 |
+
" }\n",
|
302 |
+
" \n",
|
303 |
+
" # Exclude non-amino acid residues\n",
|
304 |
+
" sequence = [\n",
|
305 |
+
" residue for residue in chain \n",
|
306 |
+
" if residue.get_resname().strip() in aa_dict\n",
|
307 |
+
" ]\n",
|
308 |
+
" \n",
|
309 |
+
" random_scores = np.random.rand(len(sequence))\n",
|
310 |
+
" result_str = \"\\n\".join(\n",
|
311 |
+
" f\"{aa_dict[res.get_resname()]} {res.id[1]} {score:.2f}\" \n",
|
312 |
+
" for res, score in zip(sequence, random_scores)\n",
|
313 |
+
" )\n",
|
314 |
+
" \n",
|
315 |
+
" # Save the predictions to a file\n",
|
316 |
+
" prediction_file = f\"{pdb_id}_predictions.txt\"\n",
|
317 |
+
" with open(prediction_file, \"w\") as f:\n",
|
318 |
+
" f.write(result_str)\n",
|
319 |
+
" \n",
|
320 |
+
" return result_str, molecule(pdb_path, random_scores, segment), prediction_file\n",
|
321 |
+
"\n",
|
322 |
+
"def molecule(input_pdb, scores=None, segment='A'):\n",
|
323 |
+
" mol = read_mol(input_pdb) # Read PDB file content\n",
|
324 |
+
" \n",
|
325 |
+
" # Prepare high-scoring residues script if scores are provided\n",
|
326 |
+
" high_score_script = \"\"\n",
|
327 |
+
" if scores is not None:\n",
|
328 |
+
" high_score_script = \"\"\"\n",
|
329 |
+
" // Reset all styles first\n",
|
330 |
+
" viewer.getModel(0).setStyle({}, {});\n",
|
331 |
+
" \n",
|
332 |
+
" // Show only the selected chain\n",
|
333 |
+
" viewer.getModel(0).setStyle(\n",
|
334 |
+
" {\"chain\": \"%s\"}, \n",
|
335 |
+
" { cartoon: {colorscheme:\"whiteCarbon\"} }\n",
|
336 |
+
" );\n",
|
337 |
+
" \n",
|
338 |
+
" // Highlight high-scoring residues only for the selected chain\n",
|
339 |
+
" let highScoreResidues = [%s];\n",
|
340 |
+
" viewer.getModel(0).setStyle(\n",
|
341 |
+
" {\"chain\": \"%s\", \"resi\": highScoreResidues}, \n",
|
342 |
+
" {\"stick\": {\"color\": \"red\"}}\n",
|
343 |
+
" );\n",
|
344 |
+
" \"\"\" % (segment, \n",
|
345 |
+
" \", \".join(str(i+1) for i, score in enumerate(scores) if score > 0.8),\n",
|
346 |
+
" segment)\n",
|
347 |
+
" \n",
|
348 |
+
" html_content = f\"\"\"\n",
|
349 |
+
" <!DOCTYPE html>\n",
|
350 |
+
" <html>\n",
|
351 |
+
" <head> \n",
|
352 |
+
" <meta http-equiv=\"content-type\" content=\"text/html; charset=UTF-8\" />\n",
|
353 |
+
" <style>\n",
|
354 |
+
" .mol-container {{\n",
|
355 |
+
" width: 100%;\n",
|
356 |
+
" height: 700px;\n",
|
357 |
+
" position: relative;\n",
|
358 |
+
" }}\n",
|
359 |
+
" </style>\n",
|
360 |
+
" <script src=\"https://cdnjs.cloudflare.com/ajax/libs/jquery/3.6.3/jquery.min.js\"></script>\n",
|
361 |
+
" <script src=\"https://3Dmol.csb.pitt.edu/build/3Dmol-min.js\"></script>\n",
|
362 |
+
" </head>\n",
|
363 |
+
" <body>\n",
|
364 |
+
" <div id=\"container\" class=\"mol-container\"></div>\n",
|
365 |
+
" <script>\n",
|
366 |
+
" let pdb = `{mol}`; // Use template literal to properly escape PDB content\n",
|
367 |
+
" $(document).ready(function () {{\n",
|
368 |
+
" let element = $(\"#container\");\n",
|
369 |
+
" let config = {{ backgroundColor: \"white\" }};\n",
|
370 |
+
" let viewer = $3Dmol.createViewer(element, config);\n",
|
371 |
+
" viewer.addModel(pdb, \"pdb\");\n",
|
372 |
+
" \n",
|
373 |
+
" // Reset all styles and show only selected chain\n",
|
374 |
+
" viewer.getModel(0).setStyle(\n",
|
375 |
+
" {{\"chain\": \"{segment}\"}}, \n",
|
376 |
+
" {{ cartoon: {{ colorscheme:\"whiteCarbon\" }} }}\n",
|
377 |
+
" );\n",
|
378 |
+
" \n",
|
379 |
+
" {high_score_script}\n",
|
380 |
+
" \n",
|
381 |
+
" // Add hover functionality\n",
|
382 |
+
" viewer.setHoverable(\n",
|
383 |
+
" {{}}, \n",
|
384 |
+
" true, \n",
|
385 |
+
" function(atom, viewer, event, container) {{\n",
|
386 |
+
" if (!atom.label) {{\n",
|
387 |
+
" atom.label = viewer.addLabel(\n",
|
388 |
+
" atom.resn + \":\" + atom.atom, \n",
|
389 |
+
" {{\n",
|
390 |
+
" position: atom, \n",
|
391 |
+
" backgroundColor: 'mintcream', \n",
|
392 |
+
" fontColor: 'black',\n",
|
393 |
+
" fontSize: 12,\n",
|
394 |
+
" padding: 2\n",
|
395 |
+
" }}\n",
|
396 |
+
" );\n",
|
397 |
+
" }}\n",
|
398 |
+
" }},\n",
|
399 |
+
" function(atom, viewer) {{\n",
|
400 |
+
" if (atom.label) {{\n",
|
401 |
+
" viewer.removeLabel(atom.label);\n",
|
402 |
+
" delete atom.label;\n",
|
403 |
+
" }}\n",
|
404 |
+
" }}\n",
|
405 |
+
" );\n",
|
406 |
+
" \n",
|
407 |
+
" viewer.zoomTo();\n",
|
408 |
+
" viewer.render();\n",
|
409 |
+
" viewer.zoom(0.8, 2000);\n",
|
410 |
+
" }});\n",
|
411 |
+
" </script>\n",
|
412 |
+
" </body>\n",
|
413 |
+
" </html>\n",
|
414 |
+
" \"\"\"\n",
|
415 |
+
" \n",
|
416 |
+
" # Return the HTML content within an iframe safely encoded for special characters\n",
|
417 |
+
" return f'<iframe width=\"100%\" height=\"700\" srcdoc=\"{html_content.replace(chr(34), \""\").replace(chr(39), \"'\")}\"></iframe>'\n",
|
418 |
+
"\n",
|
419 |
+
"reps = [\n",
|
420 |
+
" {\n",
|
421 |
+
" \"model\": 0,\n",
|
422 |
+
" \"style\": \"cartoon\",\n",
|
423 |
+
" \"color\": \"whiteCarbon\",\n",
|
424 |
+
" \"residue_range\": \"\",\n",
|
425 |
+
" \"around\": 0,\n",
|
426 |
+
" \"byres\": False,\n",
|
427 |
+
" }\n",
|
428 |
+
" ]\n",
|
429 |
+
"\n",
|
430 |
+
"# Gradio UI\n",
|
431 |
+
"with gr.Blocks() as demo:\n",
|
432 |
+
" gr.Markdown(\"# Protein Binding Site Prediction (Random Scores)\")\n",
|
433 |
+
" with gr.Row():\n",
|
434 |
+
" pdb_input = gr.Textbox(value=\"2IWI\", label=\"PDB ID\", placeholder=\"Enter PDB ID here...\")\n",
|
435 |
+
" visualize_btn = gr.Button(\"Visualize Structure\")\n",
|
436 |
+
"\n",
|
437 |
+
" molecule_output2 = Molecule3D(label=\"Protein Structure\", reps=reps)\n",
|
438 |
+
"\n",
|
439 |
+
" with gr.Row():\n",
|
440 |
+
" pdb_input = gr.Textbox(value=\"2IWI\", label=\"PDB ID\", placeholder=\"Enter PDB ID here...\")\n",
|
441 |
+
" segment_input = gr.Textbox(value=\"A\", label=\"Chain ID\", placeholder=\"Enter Chain ID here...\")\n",
|
442 |
+
" prediction_btn = gr.Button(\"Predict Random Binding Site Scores\")\n",
|
443 |
+
"\n",
|
444 |
+
" molecule_output = gr.HTML(label=\"Protein Structure\")\n",
|
445 |
+
" predictions_output = gr.Textbox(label=\"Binding Site Predictions\")\n",
|
446 |
+
" download_output = gr.File(label=\"Download Predictions\")\n",
|
447 |
+
" \n",
|
448 |
+
" visualize_btn.click(fetch_pdb, inputs=[pdb_input], outputs=molecule_output2)\n",
|
449 |
+
" \n",
|
450 |
+
" prediction_btn.click(process_pdb, inputs=[pdb_input, segment_input], outputs=[predictions_output, molecule_output, download_output])\n",
|
451 |
+
" \n",
|
452 |
+
" gr.Markdown(\"## Examples\")\n",
|
453 |
+
" gr.Examples(\n",
|
454 |
+
" examples=[\n",
|
455 |
+
" [\"2IWI\", \"A\"],\n",
|
456 |
+
" [\"7RPZ\", \"B\"],\n",
|
457 |
+
" [\"3TJN\", \"C\"]\n",
|
458 |
+
" ],\n",
|
459 |
+
" inputs=[pdb_input, segment_input],\n",
|
460 |
+
" outputs=[predictions_output, molecule_output, download_output]\n",
|
461 |
+
" )\n",
|
462 |
+
"\n",
|
463 |
+
"demo.launch()"
|
464 |
+
]
|
465 |
+
},
|
466 |
+
{
|
467 |
+
"cell_type": "code",
|
468 |
+
"execution_count": null,
|
469 |
+
"id": "517a2fe7-419f-4d0b-a9ed-62a22c1c1284",
|
470 |
+
"metadata": {},
|
471 |
+
"outputs": [],
|
472 |
+
"source": []
|
473 |
+
},
|
474 |
+
{
|
475 |
+
"cell_type": "code",
|
476 |
+
"execution_count": 11,
|
477 |
+
"id": "d62be1b5-762e-4b69-aed4-e4ba2a44482f",
|
478 |
+
"metadata": {},
|
479 |
+
"outputs": [
|
480 |
+
{
|
481 |
+
"name": "stdout",
|
482 |
+
"output_type": "stream",
|
483 |
+
"text": [
|
484 |
+
"* Running on local URL: http://127.0.0.1:7867\n",
|
485 |
+
"\n",
|
486 |
+
"To create a public link, set `share=True` in `launch()`.\n"
|
487 |
+
]
|
488 |
+
},
|
489 |
+
{
|
490 |
+
"data": {
|
491 |
+
"text/html": [
|
492 |
+
"<div><iframe src=\"http://127.0.0.1:7867/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
|
493 |
+
],
|
494 |
+
"text/plain": [
|
495 |
+
"<IPython.core.display.HTML object>"
|
496 |
+
]
|
497 |
+
},
|
498 |
+
"metadata": {},
|
499 |
+
"output_type": "display_data"
|
500 |
+
},
|
501 |
+
{
|
502 |
+
"data": {
|
503 |
+
"text/plain": []
|
504 |
+
},
|
505 |
+
"execution_count": 11,
|
506 |
+
"metadata": {},
|
507 |
+
"output_type": "execute_result"
|
508 |
+
}
|
509 |
+
],
|
510 |
+
"source": [
|
511 |
+
"import gradio as gr\n",
|
512 |
+
"import requests\n",
|
513 |
+
"from Bio.PDB import PDBParser\n",
|
514 |
+
"import numpy as np\n",
|
515 |
+
"import os\n",
|
516 |
+
"from gradio_molecule3d import Molecule3D\n",
|
517 |
+
"\n",
|
518 |
+
"def read_mol(pdb_path):\n",
|
519 |
+
" \"\"\"Read PDB file and return its content as a string\"\"\"\n",
|
520 |
+
" with open(pdb_path, 'r') as f:\n",
|
521 |
+
" return f.read()\n",
|
522 |
+
"\n",
|
523 |
+
"def fetch_pdb(pdb_id):\n",
|
524 |
+
" pdb_url = f'https://files.rcsb.org/download/{pdb_id}.pdb'\n",
|
525 |
+
" pdb_path = f'{pdb_id}.pdb'\n",
|
526 |
+
" response = requests.get(pdb_url)\n",
|
527 |
+
" if response.status_code == 200:\n",
|
528 |
+
" with open(pdb_path, 'wb') as f:\n",
|
529 |
+
" f.write(response.content)\n",
|
530 |
+
" return pdb_path\n",
|
531 |
+
" else:\n",
|
532 |
+
" return None\n",
|
533 |
+
"\n",
|
534 |
+
"def process_pdb(pdb_id, segment):\n",
|
535 |
+
" pdb_path = fetch_pdb(pdb_id)\n",
|
536 |
+
" if not pdb_path:\n",
|
537 |
+
" return \"Failed to fetch PDB file\", None, None\n",
|
538 |
+
" \n",
|
539 |
+
" parser = PDBParser(QUIET=1)\n",
|
540 |
+
" structure = parser.get_structure('protein', pdb_path)\n",
|
541 |
+
" \n",
|
542 |
+
" try:\n",
|
543 |
+
" chain = structure[0][segment]\n",
|
544 |
+
" except KeyError:\n",
|
545 |
+
" return \"Invalid Chain ID\", None, None\n",
|
546 |
+
" \n",
|
547 |
+
" # Comprehensive amino acid mapping\n",
|
548 |
+
" aa_dict = {\n",
|
549 |
+
" 'ALA': 'A', 'CYS': 'C', 'ASP': 'D', 'GLU': 'E', 'PHE': 'F',\n",
|
550 |
+
" 'GLY': 'G', 'HIS': 'H', 'ILE': 'I', 'LYS': 'K', 'LEU': 'L',\n",
|
551 |
+
" 'MET': 'M', 'ASN': 'N', 'PRO': 'P', 'GLN': 'Q', 'ARG': 'R',\n",
|
552 |
+
" 'SER': 'S', 'THR': 'T', 'VAL': 'V', 'TRP': 'W', 'TYR': 'Y',\n",
|
553 |
+
" 'MSE': 'M', 'SEP': 'S', 'TPO': 'T', 'CSO': 'C', 'PTR': 'Y', 'HYP': 'P'\n",
|
554 |
+
" }\n",
|
555 |
+
" \n",
|
556 |
+
" # Exclude non-amino acid residues\n",
|
557 |
+
" sequence = [\n",
|
558 |
+
" residue for residue in chain \n",
|
559 |
+
" if residue.get_resname().strip() in aa_dict\n",
|
560 |
+
" ]\n",
|
561 |
+
" \n",
|
562 |
+
" random_scores = np.random.rand(len(sequence))\n",
|
563 |
+
" result_str = \"\\n\".join(\n",
|
564 |
+
" f\"{aa_dict[res.get_resname()]} {res.id[1]} {score:.2f}\" \n",
|
565 |
+
" for res, score in zip(sequence, random_scores)\n",
|
566 |
+
" )\n",
|
567 |
+
" \n",
|
568 |
+
" # Save the predictions to a file\n",
|
569 |
+
" prediction_file = f\"{pdb_id}_predictions.txt\"\n",
|
570 |
+
" with open(prediction_file, \"w\") as f:\n",
|
571 |
+
" f.write(result_str)\n",
|
572 |
+
" \n",
|
573 |
+
" return result_str, molecule(pdb_path, random_scores, segment), prediction_file\n",
|
574 |
+
"\n",
|
575 |
+
"def molecule(input_pdb, scores=None, segment='A'):\n",
|
576 |
+
" mol = read_mol(input_pdb) # Read PDB file content\n",
|
577 |
+
" \n",
|
578 |
+
" # Prepare high-scoring residues script if scores are provided\n",
|
579 |
+
" high_score_script = \"\"\n",
|
580 |
+
" if scores is not None:\n",
|
581 |
+
" high_score_script = \"\"\"\n",
|
582 |
+
" // Reset all styles first\n",
|
583 |
+
" viewer.getModel(0).setStyle({}, {});\n",
|
584 |
+
" \n",
|
585 |
+
" // Show only the selected chain\n",
|
586 |
+
" viewer.getModel(0).setStyle(\n",
|
587 |
+
" {\"chain\": \"%s\"}, \n",
|
588 |
+
" { cartoon: {colorscheme:\"whiteCarbon\"} }\n",
|
589 |
+
" );\n",
|
590 |
+
" \n",
|
591 |
+
" // Highlight high-scoring residues only for the selected chain\n",
|
592 |
+
" let highScoreResidues = [%s];\n",
|
593 |
+
" viewer.getModel(0).setStyle(\n",
|
594 |
+
" {\"chain\": \"%s\", \"resi\": highScoreResidues}, \n",
|
595 |
+
" {\"stick\": {\"color\": \"red\"}}\n",
|
596 |
+
" );\n",
|
597 |
+
"\n",
|
598 |
+
" // Highlight high-scoring residues only for the selected chain\n",
|
599 |
+
" let highScoreResidues2 = [%s];\n",
|
600 |
+
" viewer.getModel(0).setStyle(\n",
|
601 |
+
" {\"chain\": \"%s\", \"resi\": highScoreResidues2}, \n",
|
602 |
+
" {\"stick\": {\"color\": \"orange\"}}\n",
|
603 |
+
" );\n",
|
604 |
+
" \"\"\" % (segment, \n",
|
605 |
+
" \", \".join(str(i+1) for i, score in enumerate(scores) if score > 0.8),\n",
|
606 |
+
" segment,\n",
|
607 |
+
" \", \".join(str(i+1) for i, score in enumerate(scores) if (score > 0.5) and (score < 0.8)),\n",
|
608 |
+
" segment)\n",
|
609 |
+
" \n",
|
610 |
+
" html_content = f\"\"\"\n",
|
611 |
+
" <!DOCTYPE html>\n",
|
612 |
+
" <html>\n",
|
613 |
+
" <head> \n",
|
614 |
+
" <meta http-equiv=\"content-type\" content=\"text/html; charset=UTF-8\" />\n",
|
615 |
+
" <style>\n",
|
616 |
+
" .mol-container {{\n",
|
617 |
+
" width: 100%;\n",
|
618 |
+
" height: 700px;\n",
|
619 |
+
" position: relative;\n",
|
620 |
+
" }}\n",
|
621 |
+
" </style>\n",
|
622 |
+
" <script src=\"https://cdnjs.cloudflare.com/ajax/libs/jquery/3.6.3/jquery.min.js\"></script>\n",
|
623 |
+
" <script src=\"https://3Dmol.csb.pitt.edu/build/3Dmol-min.js\"></script>\n",
|
624 |
+
" </head>\n",
|
625 |
+
" <body>\n",
|
626 |
+
" <div id=\"container\" class=\"mol-container\"></div>\n",
|
627 |
+
" <script>\n",
|
628 |
+
" let pdb = `{mol}`; // Use template literal to properly escape PDB content\n",
|
629 |
+
" $(document).ready(function () {{\n",
|
630 |
+
" let element = $(\"#container\");\n",
|
631 |
+
" let config = {{ backgroundColor: \"white\" }};\n",
|
632 |
+
" let viewer = $3Dmol.createViewer(element, config);\n",
|
633 |
+
" viewer.addModel(pdb, \"pdb\");\n",
|
634 |
+
" \n",
|
635 |
+
" // Reset all styles and show only selected chain\n",
|
636 |
+
" viewer.getModel(0).setStyle(\n",
|
637 |
+
" {{\"chain\": \"{segment}\"}}, \n",
|
638 |
+
" {{ cartoon: {{ colorscheme:\"whiteCarbon\" }} }}\n",
|
639 |
+
" );\n",
|
640 |
+
" \n",
|
641 |
+
" {high_score_script}\n",
|
642 |
+
" \n",
|
643 |
+
" // Add hover functionality\n",
|
644 |
+
" viewer.setHoverable(\n",
|
645 |
+
" {{}}, \n",
|
646 |
+
" true, \n",
|
647 |
+
" function(atom, viewer, event, container) {{\n",
|
648 |
+
" if (!atom.label) {{\n",
|
649 |
+
" atom.label = viewer.addLabel(\n",
|
650 |
+
" atom.resn + \":\" + atom.atom, \n",
|
651 |
+
" {{\n",
|
652 |
+
" position: atom, \n",
|
653 |
+
" backgroundColor: 'mintcream', \n",
|
654 |
+
" fontColor: 'black',\n",
|
655 |
+
" fontSize: 12,\n",
|
656 |
+
" padding: 2\n",
|
657 |
+
" }}\n",
|
658 |
+
" );\n",
|
659 |
+
" }}\n",
|
660 |
+
" }},\n",
|
661 |
+
" function(atom, viewer) {{\n",
|
662 |
+
" if (atom.label) {{\n",
|
663 |
+
" viewer.removeLabel(atom.label);\n",
|
664 |
+
" delete atom.label;\n",
|
665 |
+
" }}\n",
|
666 |
+
" }}\n",
|
667 |
+
" );\n",
|
668 |
+
" \n",
|
669 |
+
" viewer.zoomTo();\n",
|
670 |
+
" viewer.render();\n",
|
671 |
+
" viewer.zoom(0.8, 2000);\n",
|
672 |
+
" }});\n",
|
673 |
+
" </script>\n",
|
674 |
+
" </body>\n",
|
675 |
+
" </html>\n",
|
676 |
+
" \"\"\"\n",
|
677 |
+
" \n",
|
678 |
+
" # Return the HTML content within an iframe safely encoded for special characters\n",
|
679 |
+
" return f'<iframe width=\"100%\" height=\"700\" srcdoc=\"{html_content.replace(chr(34), \""\").replace(chr(39), \"'\")}\"></iframe>'\n",
|
680 |
+
"\n",
|
681 |
+
"reps = [\n",
|
682 |
+
" {\n",
|
683 |
+
" \"model\": 0,\n",
|
684 |
+
" \"style\": \"cartoon\",\n",
|
685 |
+
" \"color\": \"whiteCarbon\",\n",
|
686 |
+
" \"residue_range\": \"\",\n",
|
687 |
+
" \"around\": 0,\n",
|
688 |
+
" \"byres\": False,\n",
|
689 |
+
" }\n",
|
690 |
+
" ]\n",
|
691 |
+
"\n",
|
692 |
+
"# Gradio UI\n",
|
693 |
+
"with gr.Blocks() as demo:\n",
|
694 |
+
" gr.Markdown(\"# Protein Binding Site Prediction (Random Scores)\")\n",
|
695 |
+
" with gr.Row():\n",
|
696 |
+
" pdb_input = gr.Textbox(value=\"2IWI\", label=\"PDB ID\", placeholder=\"Enter PDB ID here...\")\n",
|
697 |
+
" visualize_btn = gr.Button(\"Visualize Structure\")\n",
|
698 |
+
"\n",
|
699 |
+
" molecule_output2 = Molecule3D(label=\"Protein Structure\", reps=reps)\n",
|
700 |
+
"\n",
|
701 |
+
" with gr.Row():\n",
|
702 |
+
" pdb_input = gr.Textbox(value=\"2IWI\", label=\"PDB ID\", placeholder=\"Enter PDB ID here...\")\n",
|
703 |
+
" segment_input = gr.Textbox(value=\"A\", label=\"Chain ID\", placeholder=\"Enter Chain ID here...\")\n",
|
704 |
+
" prediction_btn = gr.Button(\"Predict Random Binding Site Scores\")\n",
|
705 |
+
"\n",
|
706 |
+
" molecule_output = gr.HTML(label=\"Protein Structure\")\n",
|
707 |
+
" predictions_output = gr.Textbox(label=\"Binding Site Predictions\")\n",
|
708 |
+
" download_output = gr.File(label=\"Download Predictions\")\n",
|
709 |
+
" \n",
|
710 |
+
" visualize_btn.click(fetch_pdb, inputs=[pdb_input], outputs=molecule_output2)\n",
|
711 |
+
" \n",
|
712 |
+
" prediction_btn.click(process_pdb, inputs=[pdb_input, segment_input], outputs=[predictions_output, molecule_output, download_output])\n",
|
713 |
+
" \n",
|
714 |
+
" gr.Markdown(\"## Examples\")\n",
|
715 |
+
" gr.Examples(\n",
|
716 |
+
" examples=[\n",
|
717 |
+
" [\"2IWI\", \"A\"],\n",
|
718 |
+
" [\"7RPZ\", \"B\"],\n",
|
719 |
+
" [\"3TJN\", \"C\"]\n",
|
720 |
+
" ],\n",
|
721 |
+
" inputs=[pdb_input, segment_input],\n",
|
722 |
+
" outputs=[predictions_output, molecule_output, download_output]\n",
|
723 |
+
" )\n",
|
724 |
+
"\n",
|
725 |
+
"demo.launch()"
|
726 |
+
]
|
727 |
+
},
|
728 |
+
{
|
729 |
+
"cell_type": "code",
|
730 |
+
"execution_count": null,
|
731 |
+
"id": "30f35243-852f-4771-9a4b-5cdd198552b5",
|
732 |
+
"metadata": {},
|
733 |
+
"outputs": [],
|
734 |
+
"source": []
|
735 |
+
},
|
736 |
+
{
|
737 |
+
"cell_type": "code",
|
738 |
+
"execution_count": null,
|
739 |
+
"id": "5eca6754-4aa1-463f-881a-25d2a0d6bb5b",
|
740 |
+
"metadata": {},
|
741 |
+
"outputs": [],
|
742 |
+
"source": [
|
743 |
+
"import gradio as gr\n",
|
744 |
+
"import requests\n",
|
745 |
+
"from Bio.PDB import PDBParser\n",
|
746 |
+
"import numpy as np\n",
|
747 |
+
"import os\n",
|
748 |
+
"from gradio_molecule3d import Molecule3D\n",
|
749 |
+
"\n",
|
750 |
+
"\n",
|
751 |
+
"from model_loader import load_model\n",
|
752 |
+
"\n",
|
753 |
+
"import torch\n",
|
754 |
+
"import torch.nn as nn\n",
|
755 |
+
"import torch.nn.functional as F\n",
|
756 |
+
"from torch.utils.data import DataLoader\n",
|
757 |
+
"\n",
|
758 |
+
"import re\n",
|
759 |
+
"import pandas as pd\n",
|
760 |
+
"import copy\n",
|
761 |
+
"\n",
|
762 |
+
"import transformers, datasets\n",
|
763 |
+
"from transformers import AutoTokenizer\n",
|
764 |
+
"from transformers import DataCollatorForTokenClassification\n",
|
765 |
+
"\n",
|
766 |
+
"from datasets import Dataset\n",
|
767 |
+
"\n",
|
768 |
+
"from scipy.special import expit\n",
|
769 |
+
"\n",
|
770 |
+
"# Load model and move to device\n",
|
771 |
+
"checkpoint = 'ThorbenF/prot_t5_xl_uniref50'\n",
|
772 |
+
"max_length = 1500\n",
|
773 |
+
"model, tokenizer = load_model(checkpoint, max_length)\n",
|
774 |
+
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
|
775 |
+
"model.to(device)\n",
|
776 |
+
"model.eval()\n",
|
777 |
+
"\n",
|
778 |
+
"def normalize_scores(scores):\n",
|
779 |
+
" min_score = np.min(scores)\n",
|
780 |
+
" max_score = np.max(scores)\n",
|
781 |
+
" return (scores - min_score) / (max_score - min_score) if max_score > min_score else scores\n",
|
782 |
+
" \n",
|
783 |
+
"def read_mol(pdb_path):\n",
|
784 |
+
" \"\"\"Read PDB file and return its content as a string\"\"\"\n",
|
785 |
+
" with open(pdb_path, 'r') as f:\n",
|
786 |
+
" return f.read()\n",
|
787 |
+
"\n",
|
788 |
+
"def fetch_pdb(pdb_id):\n",
|
789 |
+
" pdb_url = f'https://files.rcsb.org/download/{pdb_id}.pdb'\n",
|
790 |
+
" pdb_path = f'{pdb_id}.pdb'\n",
|
791 |
+
" response = requests.get(pdb_url)\n",
|
792 |
+
" if response.status_code == 200:\n",
|
793 |
+
" with open(pdb_path, 'wb') as f:\n",
|
794 |
+
" f.write(response.content)\n",
|
795 |
+
" return pdb_path\n",
|
796 |
+
" else:\n",
|
797 |
+
" return None\n",
|
798 |
+
"\n",
|
799 |
+
"def process_pdb(pdb_id, segment):\n",
|
800 |
+
" pdb_path = fetch_pdb(pdb_id)\n",
|
801 |
+
" if not pdb_path:\n",
|
802 |
+
" return \"Failed to fetch PDB file\", None, None\n",
|
803 |
+
" \n",
|
804 |
+
" parser = PDBParser(QUIET=1)\n",
|
805 |
+
" structure = parser.get_structure('protein', pdb_path)\n",
|
806 |
+
" \n",
|
807 |
+
" try:\n",
|
808 |
+
" chain = structure[0][segment]\n",
|
809 |
+
" except KeyError:\n",
|
810 |
+
" return \"Invalid Chain ID\", None, None\n",
|
811 |
+
" \n",
|
812 |
+
" # Comprehensive amino acid mapping\n",
|
813 |
+
" aa_dict = {\n",
|
814 |
+
" 'ALA': 'A', 'CYS': 'C', 'ASP': 'D', 'GLU': 'E', 'PHE': 'F',\n",
|
815 |
+
" 'GLY': 'G', 'HIS': 'H', 'ILE': 'I', 'LYS': 'K', 'LEU': 'L',\n",
|
816 |
+
" 'MET': 'M', 'ASN': 'N', 'PRO': 'P', 'GLN': 'Q', 'ARG': 'R',\n",
|
817 |
+
" 'SER': 'S', 'THR': 'T', 'VAL': 'V', 'TRP': 'W', 'TYR': 'Y',\n",
|
818 |
+
" 'MSE': 'M', 'SEP': 'S', 'TPO': 'T', 'CSO': 'C', 'PTR': 'Y', 'HYP': 'P'\n",
|
819 |
+
" }\n",
|
820 |
+
" \n",
|
821 |
+
" # Exclude non-amino acid residues\n",
|
822 |
+
" sequence = [\n",
|
823 |
+
" residue for residue in chain \n",
|
824 |
+
" if residue.get_resname().strip() in aa_dict\n",
|
825 |
+
" ]\n",
|
826 |
+
" \n",
|
827 |
+
" # Prepare input for model prediction\n",
|
828 |
+
" input_ids = tokenizer(\" \".join(sequence), return_tensors=\"pt\").input_ids.to(device)\n",
|
829 |
+
" with torch.no_grad():\n",
|
830 |
+
" outputs = model(input_ids).logits.detach().cpu().numpy().squeeze()\n",
|
831 |
+
"\n",
|
832 |
+
" # Calculate scores and normalize them\n",
|
833 |
+
" scores = expit(outputs[:, 1] - outputs[:, 0])\n",
|
834 |
+
" normalized_scores = normalize_scores(scores)\n",
|
835 |
+
"\n",
|
836 |
+
" result_str = \"\\n\".join(\n",
|
837 |
+
" f\"{aa_dict[res.get_resname()]} {res.id[1]} {score:.2f}\" \n",
|
838 |
+
" for res, score in zip(sequence, normalized_scores)\n",
|
839 |
+
" )\n",
|
840 |
+
" \n",
|
841 |
+
" # Save the predictions to a file\n",
|
842 |
+
" prediction_file = f\"{pdb_id}_predictions.txt\"\n",
|
843 |
+
" with open(prediction_file, \"w\") as f:\n",
|
844 |
+
" f.write(result_str)\n",
|
845 |
+
" \n",
|
846 |
+
" return result_str, molecule(pdb_path, random_scores, segment), prediction_file\n",
|
847 |
+
"\n",
|
848 |
+
"def molecule(input_pdb, scores=None, segment='A'):\n",
|
849 |
+
" mol = read_mol(input_pdb) # Read PDB file content\n",
|
850 |
+
" \n",
|
851 |
+
" # Prepare high-scoring residues script if scores are provided\n",
|
852 |
+
" high_score_script = \"\"\n",
|
853 |
+
" if scores is not None:\n",
|
854 |
+
" high_score_script = \"\"\"\n",
|
855 |
+
" // Reset all styles first\n",
|
856 |
+
" viewer.getModel(0).setStyle({}, {});\n",
|
857 |
+
" \n",
|
858 |
+
" // Show only the selected chain\n",
|
859 |
+
" viewer.getModel(0).setStyle(\n",
|
860 |
+
" {\"chain\": \"%s\"}, \n",
|
861 |
+
" { cartoon: {colorscheme:\"whiteCarbon\"} }\n",
|
862 |
+
" );\n",
|
863 |
+
" \n",
|
864 |
+
" // Highlight high-scoring residues only for the selected chain\n",
|
865 |
+
" let highScoreResidues = [%s];\n",
|
866 |
+
" viewer.getModel(0).setStyle(\n",
|
867 |
+
" {\"chain\": \"%s\", \"resi\": highScoreResidues}, \n",
|
868 |
+
" {\"stick\": {\"color\": \"red\"}}\n",
|
869 |
+
" );\n",
|
870 |
+
"\n",
|
871 |
+
" // Highlight high-scoring residues only for the selected chain\n",
|
872 |
+
" let highScoreResidues2 = [%s];\n",
|
873 |
+
" viewer.getModel(0).setStyle(\n",
|
874 |
+
" {\"chain\": \"%s\", \"resi\": highScoreResidues2}, \n",
|
875 |
+
" {\"stick\": {\"color\": \"orange\"}}\n",
|
876 |
+
" );\n",
|
877 |
+
" \"\"\" % (segment, \n",
|
878 |
+
" \", \".join(str(i+1) for i, score in enumerate(scores) if score > 0.8),\n",
|
879 |
+
" segment,\n",
|
880 |
+
" \", \".join(str(i+1) for i, score in enumerate(scores) if (score > 0.5) and (score < 0.8)),\n",
|
881 |
+
" segment)\n",
|
882 |
+
" \n",
|
883 |
+
" html_content = f\"\"\"\n",
|
884 |
+
" <!DOCTYPE html>\n",
|
885 |
+
" <html>\n",
|
886 |
+
" <head> \n",
|
887 |
+
" <meta http-equiv=\"content-type\" content=\"text/html; charset=UTF-8\" />\n",
|
888 |
+
" <style>\n",
|
889 |
+
" .mol-container {{\n",
|
890 |
+
" width: 100%;\n",
|
891 |
+
" height: 700px;\n",
|
892 |
+
" position: relative;\n",
|
893 |
+
" }}\n",
|
894 |
+
" </style>\n",
|
895 |
+
" <script src=\"https://cdnjs.cloudflare.com/ajax/libs/jquery/3.6.3/jquery.min.js\"></script>\n",
|
896 |
+
" <script src=\"https://3Dmol.csb.pitt.edu/build/3Dmol-min.js\"></script>\n",
|
897 |
+
" </head>\n",
|
898 |
+
" <body>\n",
|
899 |
+
" <div id=\"container\" class=\"mol-container\"></div>\n",
|
900 |
+
" <script>\n",
|
901 |
+
" let pdb = `{mol}`; // Use template literal to properly escape PDB content\n",
|
902 |
+
" $(document).ready(function () {{\n",
|
903 |
+
" let element = $(\"#container\");\n",
|
904 |
+
" let config = {{ backgroundColor: \"white\" }};\n",
|
905 |
+
" let viewer = $3Dmol.createViewer(element, config);\n",
|
906 |
+
" viewer.addModel(pdb, \"pdb\");\n",
|
907 |
+
" \n",
|
908 |
+
" // Reset all styles and show only selected chain\n",
|
909 |
+
" viewer.getModel(0).setStyle(\n",
|
910 |
+
" {{\"chain\": \"{segment}\"}}, \n",
|
911 |
+
" {{ cartoon: {{ colorscheme:\"whiteCarbon\" }} }}\n",
|
912 |
+
" );\n",
|
913 |
+
" \n",
|
914 |
+
" {high_score_script}\n",
|
915 |
+
" \n",
|
916 |
+
" // Add hover functionality\n",
|
917 |
+
" viewer.setHoverable(\n",
|
918 |
+
" {{}}, \n",
|
919 |
+
" true, \n",
|
920 |
+
" function(atom, viewer, event, container) {{\n",
|
921 |
+
" if (!atom.label) {{\n",
|
922 |
+
" atom.label = viewer.addLabel(\n",
|
923 |
+
" atom.resn + \":\" + atom.atom, \n",
|
924 |
+
" {{\n",
|
925 |
+
" position: atom, \n",
|
926 |
+
" backgroundColor: 'mintcream', \n",
|
927 |
+
" fontColor: 'black',\n",
|
928 |
+
" fontSize: 12,\n",
|
929 |
+
" padding: 2\n",
|
930 |
+
" }}\n",
|
931 |
+
" );\n",
|
932 |
+
" }}\n",
|
933 |
+
" }},\n",
|
934 |
+
" function(atom, viewer) {{\n",
|
935 |
+
" if (atom.label) {{\n",
|
936 |
+
" viewer.removeLabel(atom.label);\n",
|
937 |
+
" delete atom.label;\n",
|
938 |
+
" }}\n",
|
939 |
+
" }}\n",
|
940 |
+
" );\n",
|
941 |
+
" \n",
|
942 |
+
" viewer.zoomTo();\n",
|
943 |
+
" viewer.render();\n",
|
944 |
+
" viewer.zoom(0.8, 2000);\n",
|
945 |
+
" }});\n",
|
946 |
+
" </script>\n",
|
947 |
+
" </body>\n",
|
948 |
+
" </html>\n",
|
949 |
+
" \"\"\"\n",
|
950 |
+
" \n",
|
951 |
+
" # Return the HTML content within an iframe safely encoded for special characters\n",
|
952 |
+
" return f'<iframe width=\"100%\" height=\"700\" srcdoc=\"{html_content.replace(chr(34), \""\").replace(chr(39), \"'\")}\"></iframe>'\n",
|
953 |
+
"\n",
|
954 |
+
"reps = [\n",
|
955 |
+
" {\n",
|
956 |
+
" \"model\": 0,\n",
|
957 |
+
" \"style\": \"cartoon\",\n",
|
958 |
+
" \"color\": \"whiteCarbon\",\n",
|
959 |
+
" \"residue_range\": \"\",\n",
|
960 |
+
" \"around\": 0,\n",
|
961 |
+
" \"byres\": False,\n",
|
962 |
+
" }\n",
|
963 |
+
" ]\n",
|
964 |
+
"\n",
|
965 |
+
"# Gradio UI\n",
|
966 |
+
"with gr.Blocks() as demo:\n",
|
967 |
+
" gr.Markdown(\"# Protein Binding Site Prediction (Random Scores)\")\n",
|
968 |
+
" with gr.Row():\n",
|
969 |
+
" pdb_input = gr.Textbox(value=\"2IWI\", label=\"PDB ID\", placeholder=\"Enter PDB ID here...\")\n",
|
970 |
+
" visualize_btn = gr.Button(\"Visualize Structure\")\n",
|
971 |
+
"\n",
|
972 |
+
" molecule_output2 = Molecule3D(label=\"Protein Structure\", reps=reps)\n",
|
973 |
+
"\n",
|
974 |
+
" with gr.Row():\n",
|
975 |
+
" pdb_input = gr.Textbox(value=\"2IWI\", label=\"PDB ID\", placeholder=\"Enter PDB ID here...\")\n",
|
976 |
+
" segment_input = gr.Textbox(value=\"A\", label=\"Chain ID\", placeholder=\"Enter Chain ID here...\")\n",
|
977 |
+
" prediction_btn = gr.Button(\"Predict Random Binding Site Scores\")\n",
|
978 |
+
"\n",
|
979 |
+
" molecule_output = gr.HTML(label=\"Protein Structure\")\n",
|
980 |
+
" predictions_output = gr.Textbox(label=\"Binding Site Predictions\")\n",
|
981 |
+
" download_output = gr.File(label=\"Download Predictions\")\n",
|
982 |
+
" \n",
|
983 |
+
" visualize_btn.click(fetch_pdb, inputs=[pdb_input], outputs=molecule_output2)\n",
|
984 |
+
" \n",
|
985 |
+
" prediction_btn.click(process_pdb, inputs=[pdb_input, segment_input], outputs=[predictions_output, molecule_output, download_output])\n",
|
986 |
+
" \n",
|
987 |
+
" gr.Markdown(\"## Examples\")\n",
|
988 |
+
" gr.Examples(\n",
|
989 |
+
" examples=[\n",
|
990 |
+
" [\"2IWI\", \"A\"],\n",
|
991 |
+
" [\"7RPZ\", \"B\"],\n",
|
992 |
+
" [\"3TJN\", \"C\"]\n",
|
993 |
+
" ],\n",
|
994 |
+
" inputs=[pdb_input, segment_input],\n",
|
995 |
+
" outputs=[predictions_output, molecule_output, download_output]\n",
|
996 |
+
" )\n",
|
997 |
+
"\n",
|
998 |
+
"demo.launch()"
|
999 |
+
]
|
1000 |
+
},
|
1001 |
+
{
|
1002 |
+
"cell_type": "code",
|
1003 |
+
"execution_count": null,
|
1004 |
+
"id": "95046d1c-ec7c-4e3e-8a98-1802cb09a25b",
|
1005 |
+
"metadata": {},
|
1006 |
+
"outputs": [],
|
1007 |
+
"source": []
|
1008 |
+
},
|
1009 |
+
{
|
1010 |
+
"cell_type": "code",
|
1011 |
+
"execution_count": null,
|
1012 |
+
"id": "a37cbe6f-d57f-41e5-8ae1-38258da39d47",
|
1013 |
+
"metadata": {},
|
1014 |
+
"outputs": [],
|
1015 |
+
"source": [
|
1016 |
+
"import gradio as gr\n",
|
1017 |
+
"from model_loader import load_model\n",
|
1018 |
+
"\n",
|
1019 |
+
"import torch\n",
|
1020 |
+
"import torch.nn as nn\n",
|
1021 |
+
"import torch.nn.functional as F\n",
|
1022 |
+
"from torch.utils.data import DataLoader\n",
|
1023 |
+
"\n",
|
1024 |
+
"import re\n",
|
1025 |
+
"import numpy as np\n",
|
1026 |
+
"import os\n",
|
1027 |
+
"import pandas as pd\n",
|
1028 |
+
"import copy\n",
|
1029 |
+
"\n",
|
1030 |
+
"import transformers, datasets\n",
|
1031 |
+
"from transformers import AutoTokenizer\n",
|
1032 |
+
"from transformers import DataCollatorForTokenClassification\n",
|
1033 |
+
"\n",
|
1034 |
+
"from datasets import Dataset\n",
|
1035 |
+
"\n",
|
1036 |
+
"from scipy.special import expit\n",
|
1037 |
+
"\n",
|
1038 |
+
"import requests\n",
|
1039 |
+
"\n",
|
1040 |
+
"from gradio_molecule3d import Molecule3D\n",
|
1041 |
+
"\n",
|
1042 |
+
"# Biopython imports\n",
|
1043 |
+
"from Bio.PDB import PDBParser, Select, PDBIO\n",
|
1044 |
+
"from Bio.PDB.DSSP import DSSP\n",
|
1045 |
+
"from Bio.PDB import PDBList\n",
|
1046 |
+
"\n",
|
1047 |
+
"from matplotlib import cm # For color mapping\n",
|
1048 |
+
"from matplotlib.colors import Normalize\n",
|
1049 |
+
"\n",
|
1050 |
+
"# Load model and move to device\n",
|
1051 |
+
"checkpoint = 'ThorbenF/prot_t5_xl_uniref50'\n",
|
1052 |
+
"max_length = 1500\n",
|
1053 |
+
"model, tokenizer = load_model(checkpoint, max_length)\n",
|
1054 |
+
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
|
1055 |
+
"model.to(device)\n",
|
1056 |
+
"model.eval()\n",
|
1057 |
+
"\n",
|
1058 |
+
"# Function to fetch a PDB file\n",
|
1059 |
+
"def fetch_pdb(pdb_id):\n",
|
1060 |
+
" pdb_url = f'https://files.rcsb.org/download/{pdb_id}.pdb'\n",
|
1061 |
+
" pdb_path = f'pdb_files/{pdb_id}.pdb'\n",
|
1062 |
+
" os.makedirs('pdb_files', exist_ok=True)\n",
|
1063 |
+
" response = requests.get(pdb_url)\n",
|
1064 |
+
" if response.status_code == 200:\n",
|
1065 |
+
" with open(pdb_path, 'wb') as f:\n",
|
1066 |
+
" f.write(response.content)\n",
|
1067 |
+
" return pdb_path\n",
|
1068 |
+
" return None\n",
|
1069 |
+
"\n",
|
1070 |
+
"\n",
|
1071 |
+
"def normalize_scores(scores):\n",
|
1072 |
+
" min_score = np.min(scores)\n",
|
1073 |
+
" max_score = np.max(scores)\n",
|
1074 |
+
" return (scores - min_score) / (max_score - min_score) if max_score > min_score else scores\n",
|
1075 |
+
"\n",
|
1076 |
+
"def process_pdb(pdb_id, segment):\n",
|
1077 |
+
" pdb_path = fetch_pdb(pdb_id)\n",
|
1078 |
+
" if not pdb_path:\n",
|
1079 |
+
" return \"Failed to fetch PDB file\", None, None\n",
|
1080 |
+
" \n",
|
1081 |
+
" parser = PDBParser(QUIET=1)\n",
|
1082 |
+
" structure = parser.get_structure('protein', pdb_path)\n",
|
1083 |
+
" chain = structure[0][segment]\n",
|
1084 |
+
" \n",
|
1085 |
+
" # Comprehensive amino acid mapping\n",
|
1086 |
+
" aa_dict = {\n",
|
1087 |
+
" 'ALA': 'A', 'CYS': 'C', 'ASP': 'D', 'GLU': 'E', 'PHE': 'F',\n",
|
1088 |
+
" 'GLY': 'G', 'HIS': 'H', 'ILE': 'I', 'LYS': 'K', 'LEU': 'L',\n",
|
1089 |
+
" 'MET': 'M', 'ASN': 'N', 'PRO': 'P', 'GLN': 'Q', 'ARG': 'R',\n",
|
1090 |
+
" 'SER': 'S', 'THR': 'T', 'VAL': 'V', 'TRP': 'W', 'TYR': 'Y',\n",
|
1091 |
+
" 'MSE': 'M', 'SEP': 'S', 'TPO': 'T', 'CSO': 'C', 'PTR': 'Y', 'HYP': 'P'\n",
|
1092 |
+
" }\n",
|
1093 |
+
" \n",
|
1094 |
+
" # Exclude non-amino acid residues\n",
|
1095 |
+
" sequence = \"\".join(\n",
|
1096 |
+
" aa_dict[residue.get_resname().strip()] \n",
|
1097 |
+
" for residue in chain \n",
|
1098 |
+
" if residue.get_resname().strip() in aa_dict\n",
|
1099 |
+
" )\n",
|
1100 |
+
" \n",
|
1101 |
+
" # Prepare input for model prediction\n",
|
1102 |
+
" input_ids = tokenizer(\" \".join(sequence), return_tensors=\"pt\").input_ids.to(device)\n",
|
1103 |
+
" with torch.no_grad():\n",
|
1104 |
+
" outputs = model(input_ids).logits.detach().cpu().numpy().squeeze()\n",
|
1105 |
+
"\n",
|
1106 |
+
" # Calculate scores and normalize them\n",
|
1107 |
+
" scores = expit(outputs[:, 1] - outputs[:, 0])\n",
|
1108 |
+
" normalized_scores = normalize_scores(scores)\n",
|
1109 |
+
" \n",
|
1110 |
+
" # Prepare the result string, including only amino acid residues\n",
|
1111 |
+
" result_str = \"\\n\".join([\n",
|
1112 |
+
" f\"{res.get_resname()} {res.id[1]} {sequence[i]} {normalized_scores[i]:.2f}\" \n",
|
1113 |
+
" for i, res in enumerate(chain) if res.get_resname().strip() in aa_dict\n",
|
1114 |
+
" ])\n",
|
1115 |
+
" \n",
|
1116 |
+
" # Save predictions to file\n",
|
1117 |
+
" with open(f\"{pdb_id}_predictions.txt\", \"w\") as f:\n",
|
1118 |
+
" f.write(result_str)\n",
|
1119 |
+
" \n",
|
1120 |
+
" return result_str, pdb_path, f\"{pdb_id}_predictions.txt\"\n",
|
1121 |
+
"\n",
|
1122 |
+
"reps = [{\"model\": 0, \"style\": \"cartoon\", \"color\": \"spectrum\"}]\n",
|
1123 |
+
"\n",
|
1124 |
+
"# Gradio UI\n",
|
1125 |
+
"with gr.Blocks() as demo:\n",
|
1126 |
+
" gr.Markdown(\"# Protein Binding Site Prediction\")\n",
|
1127 |
+
"\n",
|
1128 |
+
" with gr.Row():\n",
|
1129 |
+
" pdb_input = gr.Textbox(value=\"2IWI\",\n",
|
1130 |
+
" label=\"PDB ID\",\n",
|
1131 |
+
" placeholder=\"Enter PDB ID here...\")\n",
|
1132 |
+
" segment_input = gr.Textbox(value=\"A\",\n",
|
1133 |
+
" label=\"Chain ID (Segment)\",\n",
|
1134 |
+
" placeholder=\"Enter Chain ID here...\")\n",
|
1135 |
+
" visualize_btn = gr.Button(\"Visualize Sructure\")\n",
|
1136 |
+
" prediction_btn = gr.Button(\"Predict Ligand Binding Site\")\n",
|
1137 |
+
"\n",
|
1138 |
+
" molecule_output = Molecule3D(label=\"Protein Structure\", reps=reps)\n",
|
1139 |
+
" predictions_output = gr.Textbox(label=\"Binding Site Predictions\")\n",
|
1140 |
+
" download_output = gr.File(label=\"Download Predictions\")\n",
|
1141 |
+
"\n",
|
1142 |
+
" visualize_btn.click(fetch_pdb, inputs=[pdb_input], outputs=molecule_output)\n",
|
1143 |
+
" prediction_btn.click(\n",
|
1144 |
+
" process_pdb, \n",
|
1145 |
+
" inputs=[pdb_input, segment_input], \n",
|
1146 |
+
" outputs=[predictions_output, molecule_output, download_output]\n",
|
1147 |
+
" )\n",
|
1148 |
+
"\n",
|
1149 |
+
" gr.Markdown(\"## Examples\")\n",
|
1150 |
+
" gr.Examples(\n",
|
1151 |
+
" examples=[\n",
|
1152 |
+
" [\"2IWI\"],\n",
|
1153 |
+
" [\"7RPZ\"],\n",
|
1154 |
+
" [\"3TJN\"]\n",
|
1155 |
+
" ],\n",
|
1156 |
+
" inputs=[pdb_input, segment_input], \n",
|
1157 |
+
" outputs=[predictions_output, molecule_output, download_output]\n",
|
1158 |
+
" )\n",
|
1159 |
+
"\n",
|
1160 |
+
"demo.launch(share=True)"
|
1161 |
+
]
|
1162 |
+
},
|
1163 |
+
{
|
1164 |
+
"cell_type": "code",
|
1165 |
+
"execution_count": null,
|
1166 |
+
"id": "4c61bac4-4f2e-4f4a-aa1f-30dca209747c",
|
1167 |
+
"metadata": {},
|
1168 |
+
"outputs": [],
|
1169 |
+
"source": []
|
1170 |
+
}
|
1171 |
+
],
|
1172 |
+
"metadata": {
|
1173 |
+
"kernelspec": {
|
1174 |
+
"display_name": "Python (LLM)",
|
1175 |
+
"language": "python",
|
1176 |
+
"name": "llm"
|
1177 |
+
},
|
1178 |
+
"language_info": {
|
1179 |
+
"codemirror_mode": {
|
1180 |
+
"name": "ipython",
|
1181 |
+
"version": 3
|
1182 |
+
},
|
1183 |
+
"file_extension": ".py",
|
1184 |
+
"mimetype": "text/x-python",
|
1185 |
+
"name": "python",
|
1186 |
+
"nbconvert_exporter": "python",
|
1187 |
+
"pygments_lexer": "ipython3",
|
1188 |
+
"version": "3.12.7"
|
1189 |
+
}
|
1190 |
+
},
|
1191 |
+
"nbformat": 4,
|
1192 |
+
"nbformat_minor": 5
|
1193 |
+
}
|
app.py
CHANGED
@@ -1,4 +1,11 @@
|
|
1 |
import gradio as gr
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
2 |
from model_loader import load_model
|
3 |
|
4 |
import torch
|
@@ -7,8 +14,6 @@ import torch.nn.functional as F
|
|
7 |
from torch.utils.data import DataLoader
|
8 |
|
9 |
import re
|
10 |
-
import numpy as np
|
11 |
-
import os
|
12 |
import pandas as pd
|
13 |
import copy
|
14 |
|
@@ -20,18 +25,6 @@ from datasets import Dataset
|
|
20 |
|
21 |
from scipy.special import expit
|
22 |
|
23 |
-
import requests
|
24 |
-
|
25 |
-
from gradio_molecule3d import Molecule3D
|
26 |
-
|
27 |
-
# Biopython imports
|
28 |
-
from Bio.PDB import PDBParser, Select, PDBIO
|
29 |
-
from Bio.PDB.DSSP import DSSP
|
30 |
-
from Bio.PDB import PDBList
|
31 |
-
|
32 |
-
from matplotlib import cm # For color mapping
|
33 |
-
from matplotlib.colors import Normalize
|
34 |
-
|
35 |
# Load model and move to device
|
36 |
checkpoint = 'ThorbenF/prot_t5_xl_uniref50'
|
37 |
max_length = 1500
|
@@ -40,23 +33,26 @@ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
|
|
40 |
model.to(device)
|
41 |
model.eval()
|
42 |
|
43 |
-
|
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|
44 |
def fetch_pdb(pdb_id):
|
45 |
pdb_url = f'https://files.rcsb.org/download/{pdb_id}.pdb'
|
46 |
-
pdb_path = f'
|
47 |
-
os.makedirs('pdb_files', exist_ok=True)
|
48 |
response = requests.get(pdb_url)
|
49 |
if response.status_code == 200:
|
50 |
with open(pdb_path, 'wb') as f:
|
51 |
f.write(response.content)
|
52 |
return pdb_path
|
53 |
-
|
54 |
-
|
55 |
-
|
56 |
-
def normalize_scores(scores):
|
57 |
-
min_score = np.min(scores)
|
58 |
-
max_score = np.max(scores)
|
59 |
-
return (scores - min_score) / (max_score - min_score) if max_score > min_score else scores
|
60 |
|
61 |
def process_pdb(pdb_id, segment):
|
62 |
pdb_path = fetch_pdb(pdb_id)
|
@@ -65,7 +61,11 @@ def process_pdb(pdb_id, segment):
|
|
65 |
|
66 |
parser = PDBParser(QUIET=1)
|
67 |
structure = parser.get_structure('protein', pdb_path)
|
68 |
-
|
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|
69 |
|
70 |
# Comprehensive amino acid mapping
|
71 |
aa_dict = {
|
@@ -77,11 +77,10 @@ def process_pdb(pdb_id, segment):
|
|
77 |
}
|
78 |
|
79 |
# Exclude non-amino acid residues
|
80 |
-
sequence =
|
81 |
-
|
82 |
-
for residue in chain
|
83 |
if residue.get_resname().strip() in aa_dict
|
84 |
-
|
85 |
|
86 |
# Prepare input for model prediction
|
87 |
input_ids = tokenizer(" ".join(sequence), return_tensors="pt").input_ids.to(device)
|
@@ -91,54 +90,166 @@ def process_pdb(pdb_id, segment):
|
|
91 |
# Calculate scores and normalize them
|
92 |
scores = expit(outputs[:, 1] - outputs[:, 0])
|
93 |
normalized_scores = normalize_scores(scores)
|
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|
94 |
|
95 |
-
#
|
96 |
-
|
97 |
-
|
98 |
-
for i, res in enumerate(chain) if res.get_resname().strip() in aa_dict
|
99 |
-
])
|
100 |
-
|
101 |
-
# Save predictions to file
|
102 |
-
with open(f"{pdb_id}_predictions.txt", "w") as f:
|
103 |
f.write(result_str)
|
104 |
|
105 |
-
return result_str, pdb_path,
|
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|
106 |
|
107 |
-
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|
108 |
|
109 |
# Gradio UI
|
110 |
with gr.Blocks() as demo:
|
111 |
-
gr.Markdown("# Protein Binding Site Prediction")
|
|
|
|
|
|
|
|
|
|
|
112 |
|
113 |
with gr.Row():
|
114 |
-
pdb_input = gr.Textbox(value="2IWI",
|
115 |
-
|
116 |
-
|
117 |
-
|
118 |
-
|
119 |
-
placeholder="Enter Chain ID here...")
|
120 |
-
visualize_btn = gr.Button("Visualize Sructure")
|
121 |
-
prediction_btn = gr.Button("Predict Ligand Binding Site")
|
122 |
-
|
123 |
-
molecule_output = Molecule3D(label="Protein Structure", reps=reps)
|
124 |
predictions_output = gr.Textbox(label="Binding Site Predictions")
|
125 |
download_output = gr.File(label="Download Predictions")
|
126 |
-
|
127 |
-
visualize_btn.click(fetch_pdb, inputs=[pdb_input], outputs=
|
128 |
-
|
129 |
-
|
130 |
-
|
131 |
-
outputs=[predictions_output, molecule_output, download_output]
|
132 |
-
)
|
133 |
-
|
134 |
gr.Markdown("## Examples")
|
135 |
gr.Examples(
|
136 |
examples=[
|
137 |
-
["2IWI"],
|
138 |
-
["7RPZ"],
|
139 |
-
["3TJN"]
|
140 |
],
|
141 |
-
inputs=[pdb_input, segment_input],
|
142 |
outputs=[predictions_output, molecule_output, download_output]
|
143 |
)
|
144 |
|
|
|
1 |
import gradio as gr
|
2 |
+
import requests
|
3 |
+
from Bio.PDB import PDBParser
|
4 |
+
import numpy as np
|
5 |
+
import os
|
6 |
+
from gradio_molecule3d import Molecule3D
|
7 |
+
|
8 |
+
|
9 |
from model_loader import load_model
|
10 |
|
11 |
import torch
|
|
|
14 |
from torch.utils.data import DataLoader
|
15 |
|
16 |
import re
|
|
|
|
|
17 |
import pandas as pd
|
18 |
import copy
|
19 |
|
|
|
25 |
|
26 |
from scipy.special import expit
|
27 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
28 |
# Load model and move to device
|
29 |
checkpoint = 'ThorbenF/prot_t5_xl_uniref50'
|
30 |
max_length = 1500
|
|
|
33 |
model.to(device)
|
34 |
model.eval()
|
35 |
|
36 |
+
def normalize_scores(scores):
|
37 |
+
min_score = np.min(scores)
|
38 |
+
max_score = np.max(scores)
|
39 |
+
return (scores - min_score) / (max_score - min_score) if max_score > min_score else scores
|
40 |
+
|
41 |
+
def read_mol(pdb_path):
|
42 |
+
"""Read PDB file and return its content as a string"""
|
43 |
+
with open(pdb_path, 'r') as f:
|
44 |
+
return f.read()
|
45 |
+
|
46 |
def fetch_pdb(pdb_id):
|
47 |
pdb_url = f'https://files.rcsb.org/download/{pdb_id}.pdb'
|
48 |
+
pdb_path = f'{pdb_id}.pdb'
|
|
|
49 |
response = requests.get(pdb_url)
|
50 |
if response.status_code == 200:
|
51 |
with open(pdb_path, 'wb') as f:
|
52 |
f.write(response.content)
|
53 |
return pdb_path
|
54 |
+
else:
|
55 |
+
return None
|
|
|
|
|
|
|
|
|
|
|
56 |
|
57 |
def process_pdb(pdb_id, segment):
|
58 |
pdb_path = fetch_pdb(pdb_id)
|
|
|
61 |
|
62 |
parser = PDBParser(QUIET=1)
|
63 |
structure = parser.get_structure('protein', pdb_path)
|
64 |
+
|
65 |
+
try:
|
66 |
+
chain = structure[0][segment]
|
67 |
+
except KeyError:
|
68 |
+
return "Invalid Chain ID", None, None
|
69 |
|
70 |
# Comprehensive amino acid mapping
|
71 |
aa_dict = {
|
|
|
77 |
}
|
78 |
|
79 |
# Exclude non-amino acid residues
|
80 |
+
sequence = [
|
81 |
+
residue for residue in chain
|
|
|
82 |
if residue.get_resname().strip() in aa_dict
|
83 |
+
]
|
84 |
|
85 |
# Prepare input for model prediction
|
86 |
input_ids = tokenizer(" ".join(sequence), return_tensors="pt").input_ids.to(device)
|
|
|
90 |
# Calculate scores and normalize them
|
91 |
scores = expit(outputs[:, 1] - outputs[:, 0])
|
92 |
normalized_scores = normalize_scores(scores)
|
93 |
+
|
94 |
+
result_str = "\n".join(
|
95 |
+
f"{aa_dict[res.get_resname()]} {res.id[1]} {score:.2f}"
|
96 |
+
for res, score in zip(sequence, normalized_scores)
|
97 |
+
)
|
98 |
|
99 |
+
# Save the predictions to a file
|
100 |
+
prediction_file = f"{pdb_id}_predictions.txt"
|
101 |
+
with open(prediction_file, "w") as f:
|
|
|
|
|
|
|
|
|
|
|
102 |
f.write(result_str)
|
103 |
|
104 |
+
return result_str, molecule(pdb_path, random_scores, segment), prediction_file
|
105 |
+
|
106 |
+
def molecule(input_pdb, scores=None, segment='A'):
|
107 |
+
mol = read_mol(input_pdb) # Read PDB file content
|
108 |
+
|
109 |
+
# Prepare high-scoring residues script if scores are provided
|
110 |
+
high_score_script = ""
|
111 |
+
if scores is not None:
|
112 |
+
high_score_script = """
|
113 |
+
// Reset all styles first
|
114 |
+
viewer.getModel(0).setStyle({}, {});
|
115 |
+
|
116 |
+
// Show only the selected chain
|
117 |
+
viewer.getModel(0).setStyle(
|
118 |
+
{"chain": "%s"},
|
119 |
+
{ cartoon: {colorscheme:"whiteCarbon"} }
|
120 |
+
);
|
121 |
+
|
122 |
+
// Highlight high-scoring residues only for the selected chain
|
123 |
+
let highScoreResidues = [%s];
|
124 |
+
viewer.getModel(0).setStyle(
|
125 |
+
{"chain": "%s", "resi": highScoreResidues},
|
126 |
+
{"stick": {"color": "red"}}
|
127 |
+
);
|
128 |
|
129 |
+
// Highlight high-scoring residues only for the selected chain
|
130 |
+
let highScoreResidues2 = [%s];
|
131 |
+
viewer.getModel(0).setStyle(
|
132 |
+
{"chain": "%s", "resi": highScoreResidues2},
|
133 |
+
{"stick": {"color": "orange"}}
|
134 |
+
);
|
135 |
+
""" % (segment,
|
136 |
+
", ".join(str(i+1) for i, score in enumerate(scores) if score > 0.8),
|
137 |
+
segment,
|
138 |
+
", ".join(str(i+1) for i, score in enumerate(scores) if (score > 0.5) and (score < 0.8)),
|
139 |
+
segment)
|
140 |
+
|
141 |
+
html_content = f"""
|
142 |
+
<!DOCTYPE html>
|
143 |
+
<html>
|
144 |
+
<head>
|
145 |
+
<meta http-equiv="content-type" content="text/html; charset=UTF-8" />
|
146 |
+
<style>
|
147 |
+
.mol-container {{
|
148 |
+
width: 100%;
|
149 |
+
height: 700px;
|
150 |
+
position: relative;
|
151 |
+
}}
|
152 |
+
</style>
|
153 |
+
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.6.3/jquery.min.js"></script>
|
154 |
+
<script src="https://3Dmol.csb.pitt.edu/build/3Dmol-min.js"></script>
|
155 |
+
</head>
|
156 |
+
<body>
|
157 |
+
<div id="container" class="mol-container"></div>
|
158 |
+
<script>
|
159 |
+
let pdb = `{mol}`; // Use template literal to properly escape PDB content
|
160 |
+
$(document).ready(function () {{
|
161 |
+
let element = $("#container");
|
162 |
+
let config = {{ backgroundColor: "white" }};
|
163 |
+
let viewer = $3Dmol.createViewer(element, config);
|
164 |
+
viewer.addModel(pdb, "pdb");
|
165 |
+
|
166 |
+
// Reset all styles and show only selected chain
|
167 |
+
viewer.getModel(0).setStyle(
|
168 |
+
{{"chain": "{segment}"}},
|
169 |
+
{{ cartoon: {{ colorscheme:"whiteCarbon" }} }}
|
170 |
+
);
|
171 |
+
|
172 |
+
{high_score_script}
|
173 |
+
|
174 |
+
// Add hover functionality
|
175 |
+
viewer.setHoverable(
|
176 |
+
{{}},
|
177 |
+
true,
|
178 |
+
function(atom, viewer, event, container) {{
|
179 |
+
if (!atom.label) {{
|
180 |
+
atom.label = viewer.addLabel(
|
181 |
+
atom.resn + ":" + atom.atom,
|
182 |
+
{{
|
183 |
+
position: atom,
|
184 |
+
backgroundColor: 'mintcream',
|
185 |
+
fontColor: 'black',
|
186 |
+
fontSize: 12,
|
187 |
+
padding: 2
|
188 |
+
}}
|
189 |
+
);
|
190 |
+
}}
|
191 |
+
}},
|
192 |
+
function(atom, viewer) {{
|
193 |
+
if (atom.label) {{
|
194 |
+
viewer.removeLabel(atom.label);
|
195 |
+
delete atom.label;
|
196 |
+
}}
|
197 |
+
}}
|
198 |
+
);
|
199 |
+
|
200 |
+
viewer.zoomTo();
|
201 |
+
viewer.render();
|
202 |
+
viewer.zoom(0.8, 2000);
|
203 |
+
}});
|
204 |
+
</script>
|
205 |
+
</body>
|
206 |
+
</html>
|
207 |
+
"""
|
208 |
+
|
209 |
+
# Return the HTML content within an iframe safely encoded for special characters
|
210 |
+
return f'<iframe width="100%" height="700" srcdoc="{html_content.replace(chr(34), """).replace(chr(39), "'")}"></iframe>'
|
211 |
+
|
212 |
+
reps = [
|
213 |
+
{
|
214 |
+
"model": 0,
|
215 |
+
"style": "cartoon",
|
216 |
+
"color": "whiteCarbon",
|
217 |
+
"residue_range": "",
|
218 |
+
"around": 0,
|
219 |
+
"byres": False,
|
220 |
+
}
|
221 |
+
]
|
222 |
|
223 |
# Gradio UI
|
224 |
with gr.Blocks() as demo:
|
225 |
+
gr.Markdown("# Protein Binding Site Prediction (Random Scores)")
|
226 |
+
with gr.Row():
|
227 |
+
pdb_input = gr.Textbox(value="2IWI", label="PDB ID", placeholder="Enter PDB ID here...")
|
228 |
+
visualize_btn = gr.Button("Visualize Structure")
|
229 |
+
|
230 |
+
molecule_output2 = Molecule3D(label="Protein Structure", reps=reps)
|
231 |
|
232 |
with gr.Row():
|
233 |
+
pdb_input = gr.Textbox(value="2IWI", label="PDB ID", placeholder="Enter PDB ID here...")
|
234 |
+
segment_input = gr.Textbox(value="A", label="Chain ID", placeholder="Enter Chain ID here...")
|
235 |
+
prediction_btn = gr.Button("Predict Random Binding Site Scores")
|
236 |
+
|
237 |
+
molecule_output = gr.HTML(label="Protein Structure")
|
|
|
|
|
|
|
|
|
|
|
238 |
predictions_output = gr.Textbox(label="Binding Site Predictions")
|
239 |
download_output = gr.File(label="Download Predictions")
|
240 |
+
|
241 |
+
visualize_btn.click(fetch_pdb, inputs=[pdb_input], outputs=molecule_output2)
|
242 |
+
|
243 |
+
prediction_btn.click(process_pdb, inputs=[pdb_input, segment_input], outputs=[predictions_output, molecule_output, download_output])
|
244 |
+
|
|
|
|
|
|
|
245 |
gr.Markdown("## Examples")
|
246 |
gr.Examples(
|
247 |
examples=[
|
248 |
+
["2IWI", "A"],
|
249 |
+
["7RPZ", "B"],
|
250 |
+
["3TJN", "C"]
|
251 |
],
|
252 |
+
inputs=[pdb_input, segment_input],
|
253 |
outputs=[predictions_output, molecule_output, download_output]
|
254 |
)
|
255 |
|
model_loader.ipynb
DELETED
@@ -1,871 +0,0 @@
|
|
1 |
-
{
|
2 |
-
"cells": [
|
3 |
-
{
|
4 |
-
"cell_type": "code",
|
5 |
-
"execution_count": 38,
|
6 |
-
"id": "14ff5741-629c-445a-a8a9-b3d9db1f3ddb",
|
7 |
-
"metadata": {},
|
8 |
-
"outputs": [],
|
9 |
-
"source": [
|
10 |
-
"import torch\n",
|
11 |
-
"import torch.nn as nn\n",
|
12 |
-
"import torch.nn.functional as F\n",
|
13 |
-
"from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss\n",
|
14 |
-
"from torch.utils.data import DataLoader\n",
|
15 |
-
"\n",
|
16 |
-
"import re\n",
|
17 |
-
"import numpy as np\n",
|
18 |
-
"import os\n",
|
19 |
-
"import pandas as pd\n",
|
20 |
-
"import copy\n",
|
21 |
-
"\n",
|
22 |
-
"import transformers, datasets\n",
|
23 |
-
"from transformers.modeling_outputs import TokenClassifierOutput\n",
|
24 |
-
"from transformers.models.t5.modeling_t5 import T5Config, T5PreTrainedModel, T5Stack\n",
|
25 |
-
"from transformers.utils.model_parallel_utils import assert_device_map, get_device_map\n",
|
26 |
-
"from transformers import T5EncoderModel, T5Tokenizer\n",
|
27 |
-
"from transformers.models.esm.modeling_esm import EsmPreTrainedModel, EsmModel\n",
|
28 |
-
"from transformers import AutoTokenizer\n",
|
29 |
-
"from transformers import TrainingArguments, Trainer, set_seed\n",
|
30 |
-
"from transformers import DataCollatorForTokenClassification\n",
|
31 |
-
"\n",
|
32 |
-
"from dataclasses import dataclass\n",
|
33 |
-
"from typing import Dict, List, Optional, Tuple, Union\n",
|
34 |
-
"\n",
|
35 |
-
"# for custom DataCollator\n",
|
36 |
-
"from transformers.data.data_collator import DataCollatorMixin\n",
|
37 |
-
"from transformers.tokenization_utils_base import PreTrainedTokenizerBase\n",
|
38 |
-
"from transformers.utils import PaddingStrategy\n",
|
39 |
-
"\n",
|
40 |
-
"from datasets import Dataset\n",
|
41 |
-
"\n",
|
42 |
-
"from scipy.special import expit\n",
|
43 |
-
"\n",
|
44 |
-
"import peft\n",
|
45 |
-
"from peft import get_peft_config, PeftModel, PeftConfig, inject_adapter_in_model, LoraConfig"
|
46 |
-
]
|
47 |
-
},
|
48 |
-
{
|
49 |
-
"cell_type": "code",
|
50 |
-
"execution_count": 6,
|
51 |
-
"id": "5ec16a71-ed5d-46a6-98b2-55bc5d0fbe07",
|
52 |
-
"metadata": {},
|
53 |
-
"outputs": [],
|
54 |
-
"source": [
|
55 |
-
"cnn_head=True #False set True for Rostlab/prot_t5_xl_half_uniref50-enc\n",
|
56 |
-
"ffn_head=False #False\n",
|
57 |
-
"transformer_head=False\n",
|
58 |
-
"custom_lora=True #False #only true for Rostlab/prot_t5_xl_half_uniref50-enc"
|
59 |
-
]
|
60 |
-
},
|
61 |
-
{
|
62 |
-
"cell_type": "code",
|
63 |
-
"execution_count": 8,
|
64 |
-
"id": "cc7151ca-0daf-4e75-a865-ab52f9b28f2e",
|
65 |
-
"metadata": {},
|
66 |
-
"outputs": [],
|
67 |
-
"source": [
|
68 |
-
"class ClassConfig:\n",
|
69 |
-
" def __init__(self, dropout=0.2, num_labels=3):\n",
|
70 |
-
" self.dropout_rate = dropout\n",
|
71 |
-
" self.num_labels = num_labels\n",
|
72 |
-
"\n",
|
73 |
-
"class T5EncoderForTokenClassification(T5PreTrainedModel):\n",
|
74 |
-
"\n",
|
75 |
-
" def __init__(self, config: T5Config, class_config: ClassConfig):\n",
|
76 |
-
" super().__init__(config)\n",
|
77 |
-
" self.num_labels = class_config.num_labels\n",
|
78 |
-
" self.config = config\n",
|
79 |
-
"\n",
|
80 |
-
" self.shared = nn.Embedding(config.vocab_size, config.d_model)\n",
|
81 |
-
"\n",
|
82 |
-
" encoder_config = copy.deepcopy(config)\n",
|
83 |
-
" encoder_config.use_cache = False\n",
|
84 |
-
" encoder_config.is_encoder_decoder = False\n",
|
85 |
-
" self.encoder = T5Stack(encoder_config, self.shared)\n",
|
86 |
-
"\n",
|
87 |
-
" self.dropout = nn.Dropout(class_config.dropout_rate)\n",
|
88 |
-
"\n",
|
89 |
-
" # Initialize different heads based on class_config\n",
|
90 |
-
" if cnn_head:\n",
|
91 |
-
" self.cnn = nn.Conv1d(config.hidden_size, 512, kernel_size=3, padding=1)\n",
|
92 |
-
" self.classifier = nn.Linear(512, class_config.num_labels)\n",
|
93 |
-
" elif ffn_head:\n",
|
94 |
-
" # Multi-layer feed-forward network (FFN) head\n",
|
95 |
-
" self.ffn = nn.Sequential(\n",
|
96 |
-
" nn.Linear(config.hidden_size, 512),\n",
|
97 |
-
" nn.ReLU(),\n",
|
98 |
-
" nn.Linear(512, 256),\n",
|
99 |
-
" nn.ReLU(),\n",
|
100 |
-
" nn.Linear(256, class_config.num_labels)\n",
|
101 |
-
" )\n",
|
102 |
-
" elif transformer_head:\n",
|
103 |
-
" # Transformer layer head\n",
|
104 |
-
" encoder_layer = nn.TransformerEncoderLayer(d_model=config.hidden_size, nhead=8)\n",
|
105 |
-
" self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=1)\n",
|
106 |
-
" self.classifier = nn.Linear(config.hidden_size, class_config.num_labels)\n",
|
107 |
-
" else:\n",
|
108 |
-
" # Default classification head\n",
|
109 |
-
" self.classifier = nn.Linear(config.hidden_size, class_config.num_labels)\n",
|
110 |
-
" \n",
|
111 |
-
" self.post_init()\n",
|
112 |
-
"\n",
|
113 |
-
" # Model parallel\n",
|
114 |
-
" self.model_parallel = False\n",
|
115 |
-
" self.device_map = None\n",
|
116 |
-
"\n",
|
117 |
-
" def parallelize(self, device_map=None):\n",
|
118 |
-
" self.device_map = (\n",
|
119 |
-
" get_device_map(len(self.encoder.block), range(torch.cuda.device_count()))\n",
|
120 |
-
" if device_map is None\n",
|
121 |
-
" else device_map\n",
|
122 |
-
" )\n",
|
123 |
-
" assert_device_map(self.device_map, len(self.encoder.block))\n",
|
124 |
-
" self.encoder.parallelize(self.device_map)\n",
|
125 |
-
" self.classifier = self.classifier.to(self.encoder.first_device)\n",
|
126 |
-
" self.model_parallel = True\n",
|
127 |
-
"\n",
|
128 |
-
" def deparallelize(self):\n",
|
129 |
-
" self.encoder.deparallelize()\n",
|
130 |
-
" self.encoder = self.encoder.to(\"cpu\")\n",
|
131 |
-
" self.model_parallel = False\n",
|
132 |
-
" self.device_map = None\n",
|
133 |
-
" torch.cuda.empty_cache()\n",
|
134 |
-
"\n",
|
135 |
-
" def get_input_embeddings(self):\n",
|
136 |
-
" return self.shared\n",
|
137 |
-
"\n",
|
138 |
-
" def set_input_embeddings(self, new_embeddings):\n",
|
139 |
-
" self.shared = new_embeddings\n",
|
140 |
-
" self.encoder.set_input_embeddings(new_embeddings)\n",
|
141 |
-
"\n",
|
142 |
-
" def get_encoder(self):\n",
|
143 |
-
" return self.encoder\n",
|
144 |
-
"\n",
|
145 |
-
" def _prune_heads(self, heads_to_prune):\n",
|
146 |
-
" for layer, heads in heads_to_prune.items():\n",
|
147 |
-
" self.encoder.layer[layer].attention.prune_heads(heads)\n",
|
148 |
-
"\n",
|
149 |
-
" def forward(\n",
|
150 |
-
" self,\n",
|
151 |
-
" input_ids=None,\n",
|
152 |
-
" attention_mask=None,\n",
|
153 |
-
" head_mask=None,\n",
|
154 |
-
" inputs_embeds=None,\n",
|
155 |
-
" labels=None,\n",
|
156 |
-
" output_attentions=None,\n",
|
157 |
-
" output_hidden_states=None,\n",
|
158 |
-
" return_dict=None,\n",
|
159 |
-
" ):\n",
|
160 |
-
" return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n",
|
161 |
-
"\n",
|
162 |
-
" outputs = self.encoder(\n",
|
163 |
-
" input_ids=input_ids,\n",
|
164 |
-
" attention_mask=attention_mask,\n",
|
165 |
-
" inputs_embeds=inputs_embeds,\n",
|
166 |
-
" head_mask=head_mask,\n",
|
167 |
-
" output_attentions=output_attentions,\n",
|
168 |
-
" output_hidden_states=output_hidden_states,\n",
|
169 |
-
" return_dict=return_dict,\n",
|
170 |
-
" )\n",
|
171 |
-
"\n",
|
172 |
-
" sequence_output = outputs[0]\n",
|
173 |
-
" sequence_output = self.dropout(sequence_output)\n",
|
174 |
-
"\n",
|
175 |
-
" # Forward pass through the selected head\n",
|
176 |
-
" if cnn_head:\n",
|
177 |
-
" # CNN head\n",
|
178 |
-
" sequence_output = sequence_output.permute(0, 2, 1) # Prepare shape for CNN\n",
|
179 |
-
" cnn_output = self.cnn(sequence_output)\n",
|
180 |
-
" cnn_output = F.relu(cnn_output)\n",
|
181 |
-
" cnn_output = cnn_output.permute(0, 2, 1) # Shape back for classifier\n",
|
182 |
-
" logits = self.classifier(cnn_output)\n",
|
183 |
-
" elif ffn_head:\n",
|
184 |
-
" # FFN head\n",
|
185 |
-
" logits = self.ffn(sequence_output)\n",
|
186 |
-
" elif transformer_head:\n",
|
187 |
-
" # Transformer head\n",
|
188 |
-
" transformer_output = self.transformer_encoder(sequence_output)\n",
|
189 |
-
" logits = self.classifier(transformer_output)\n",
|
190 |
-
" else:\n",
|
191 |
-
" # Default classification head\n",
|
192 |
-
" logits = self.classifier(sequence_output)\n",
|
193 |
-
"\n",
|
194 |
-
" loss = None\n",
|
195 |
-
" if labels is not None:\n",
|
196 |
-
" loss_fct = CrossEntropyLoss()\n",
|
197 |
-
" active_loss = attention_mask.view(-1) == 1\n",
|
198 |
-
" active_logits = logits.view(-1, self.num_labels)\n",
|
199 |
-
" active_labels = torch.where(\n",
|
200 |
-
" active_loss, labels.view(-1), torch.tensor(-100).type_as(labels)\n",
|
201 |
-
" )\n",
|
202 |
-
" valid_logits = active_logits[active_labels != -100]\n",
|
203 |
-
" valid_labels = active_labels[active_labels != -100]\n",
|
204 |
-
" valid_labels = valid_labels.to(valid_logits.device)\n",
|
205 |
-
" valid_labels = valid_labels.long()\n",
|
206 |
-
" loss = loss_fct(valid_logits, valid_labels)\n",
|
207 |
-
"\n",
|
208 |
-
" if not return_dict:\n",
|
209 |
-
" output = (logits,) + outputs[2:]\n",
|
210 |
-
" return ((loss,) + output) if loss is not None else output\n",
|
211 |
-
"\n",
|
212 |
-
" return TokenClassifierOutput(\n",
|
213 |
-
" loss=loss,\n",
|
214 |
-
" logits=logits,\n",
|
215 |
-
" hidden_states=outputs.hidden_states,\n",
|
216 |
-
" attentions=outputs.attentions,\n",
|
217 |
-
" )"
|
218 |
-
]
|
219 |
-
},
|
220 |
-
{
|
221 |
-
"cell_type": "code",
|
222 |
-
"execution_count": 10,
|
223 |
-
"id": "e5e751ba-f4d3-4a28-bea0-82633f1dabb4",
|
224 |
-
"metadata": {},
|
225 |
-
"outputs": [],
|
226 |
-
"source": [
|
227 |
-
"# Modifies an existing transformer and introduce the LoRA layers\n",
|
228 |
-
"\n",
|
229 |
-
"class CustomLoRAConfig:\n",
|
230 |
-
" def __init__(self):\n",
|
231 |
-
" self.lora_rank = 4\n",
|
232 |
-
" self.lora_init_scale = 0.01\n",
|
233 |
-
" self.lora_modules = \".*SelfAttention|.*EncDecAttention\"\n",
|
234 |
-
" self.lora_layers = \"q|k|v|o\"\n",
|
235 |
-
" self.trainable_param_names = \".*layer_norm.*|.*lora_[ab].*\"\n",
|
236 |
-
" self.lora_scaling_rank = 1\n",
|
237 |
-
" # lora_modules and lora_layers are speicified with regular expressions\n",
|
238 |
-
" # see https://www.w3schools.com/python/python_regex.asp for reference\n",
|
239 |
-
" \n",
|
240 |
-
"class LoRALinear(nn.Module):\n",
|
241 |
-
" def __init__(self, linear_layer, rank, scaling_rank, init_scale):\n",
|
242 |
-
" super().__init__()\n",
|
243 |
-
" self.in_features = linear_layer.in_features\n",
|
244 |
-
" self.out_features = linear_layer.out_features\n",
|
245 |
-
" self.rank = rank\n",
|
246 |
-
" self.scaling_rank = scaling_rank\n",
|
247 |
-
" self.weight = linear_layer.weight\n",
|
248 |
-
" self.bias = linear_layer.bias\n",
|
249 |
-
" if self.rank > 0:\n",
|
250 |
-
" self.lora_a = nn.Parameter(torch.randn(rank, linear_layer.in_features) * init_scale)\n",
|
251 |
-
" if init_scale < 0:\n",
|
252 |
-
" self.lora_b = nn.Parameter(torch.randn(linear_layer.out_features, rank) * init_scale)\n",
|
253 |
-
" else:\n",
|
254 |
-
" self.lora_b = nn.Parameter(torch.zeros(linear_layer.out_features, rank))\n",
|
255 |
-
" if self.scaling_rank:\n",
|
256 |
-
" self.multi_lora_a = nn.Parameter(\n",
|
257 |
-
" torch.ones(self.scaling_rank, linear_layer.in_features)\n",
|
258 |
-
" + torch.randn(self.scaling_rank, linear_layer.in_features) * init_scale\n",
|
259 |
-
" )\n",
|
260 |
-
" if init_scale < 0:\n",
|
261 |
-
" self.multi_lora_b = nn.Parameter(\n",
|
262 |
-
" torch.ones(linear_layer.out_features, self.scaling_rank)\n",
|
263 |
-
" + torch.randn(linear_layer.out_features, self.scaling_rank) * init_scale\n",
|
264 |
-
" )\n",
|
265 |
-
" else:\n",
|
266 |
-
" self.multi_lora_b = nn.Parameter(torch.ones(linear_layer.out_features, self.scaling_rank))\n",
|
267 |
-
"\n",
|
268 |
-
" def forward(self, input):\n",
|
269 |
-
" if self.scaling_rank == 1 and self.rank == 0:\n",
|
270 |
-
" # parsimonious implementation for ia3 and lora scaling\n",
|
271 |
-
" if self.multi_lora_a.requires_grad:\n",
|
272 |
-
" hidden = F.linear((input * self.multi_lora_a.flatten()), self.weight, self.bias)\n",
|
273 |
-
" else:\n",
|
274 |
-
" hidden = F.linear(input, self.weight, self.bias)\n",
|
275 |
-
" if self.multi_lora_b.requires_grad:\n",
|
276 |
-
" hidden = hidden * self.multi_lora_b.flatten()\n",
|
277 |
-
" return hidden\n",
|
278 |
-
" else:\n",
|
279 |
-
" # general implementation for lora (adding and scaling)\n",
|
280 |
-
" weight = self.weight\n",
|
281 |
-
" if self.scaling_rank:\n",
|
282 |
-
" weight = weight * torch.matmul(self.multi_lora_b, self.multi_lora_a) / self.scaling_rank\n",
|
283 |
-
" if self.rank:\n",
|
284 |
-
" weight = weight + torch.matmul(self.lora_b, self.lora_a) / self.rank\n",
|
285 |
-
" return F.linear(input, weight, self.bias)\n",
|
286 |
-
"\n",
|
287 |
-
" def extra_repr(self):\n",
|
288 |
-
" return \"in_features={}, out_features={}, bias={}, rank={}, scaling_rank={}\".format(\n",
|
289 |
-
" self.in_features, self.out_features, self.bias is not None, self.rank, self.scaling_rank\n",
|
290 |
-
" )\n",
|
291 |
-
"\n",
|
292 |
-
"\n",
|
293 |
-
"def modify_with_lora(transformer, config):\n",
|
294 |
-
" for m_name, module in dict(transformer.named_modules()).items():\n",
|
295 |
-
" if re.fullmatch(config.lora_modules, m_name):\n",
|
296 |
-
" for c_name, layer in dict(module.named_children()).items():\n",
|
297 |
-
" if re.fullmatch(config.lora_layers, c_name):\n",
|
298 |
-
" assert isinstance(\n",
|
299 |
-
" layer, nn.Linear\n",
|
300 |
-
" ), f\"LoRA can only be applied to torch.nn.Linear, but {layer} is {type(layer)}.\"\n",
|
301 |
-
" setattr(\n",
|
302 |
-
" module,\n",
|
303 |
-
" c_name,\n",
|
304 |
-
" LoRALinear(layer, config.lora_rank, config.lora_scaling_rank, config.lora_init_scale),\n",
|
305 |
-
" )\n",
|
306 |
-
" return transformer\n",
|
307 |
-
"\n"
|
308 |
-
]
|
309 |
-
},
|
310 |
-
{
|
311 |
-
"cell_type": "code",
|
312 |
-
"execution_count": 12,
|
313 |
-
"id": "43a56311-3279-466a-bc95-590381f1b13c",
|
314 |
-
"metadata": {},
|
315 |
-
"outputs": [],
|
316 |
-
"source": [
|
317 |
-
"def load_T5_model_classification(checkpoint, num_labels, half_precision, full = False, deepspeed=True):\n",
|
318 |
-
" # Load model and tokenizer\n",
|
319 |
-
"\n",
|
320 |
-
" if \"ankh\" in checkpoint :\n",
|
321 |
-
" model = T5EncoderModel.from_pretrained(checkpoint)\n",
|
322 |
-
" tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
|
323 |
-
"\n",
|
324 |
-
" elif \"prot_t5\" in checkpoint:\n",
|
325 |
-
" # possible to load the half precision model (thanks to @pawel-rezo for pointing that out)\n",
|
326 |
-
" if half_precision and deepspeed:\n",
|
327 |
-
" #tokenizer = T5Tokenizer.from_pretrained('Rostlab/prot_t5_xl_half_uniref50-enc', do_lower_case=False)\n",
|
328 |
-
" #model = T5EncoderModel.from_pretrained(\"Rostlab/prot_t5_xl_half_uniref50-enc\", torch_dtype=torch.float16)#.to(torch.device('cuda')\n",
|
329 |
-
" tokenizer = T5Tokenizer.from_pretrained(checkpoint, do_lower_case=False)\n",
|
330 |
-
" model = T5EncoderModel.from_pretrained(checkpoint, torch_dtype=torch.float16).to(torch.device('cuda'))\n",
|
331 |
-
" else:\n",
|
332 |
-
" model = T5EncoderModel.from_pretrained(checkpoint)\n",
|
333 |
-
" tokenizer = T5Tokenizer.from_pretrained(checkpoint)\n",
|
334 |
-
" \n",
|
335 |
-
" elif \"ProstT5\" in checkpoint:\n",
|
336 |
-
" if half_precision and deepspeed: \n",
|
337 |
-
" tokenizer = T5Tokenizer.from_pretrained(checkpoint, do_lower_case=False)\n",
|
338 |
-
" model = T5EncoderModel.from_pretrained(checkpoint, torch_dtype=torch.float16).to(torch.device('cuda'))\n",
|
339 |
-
" else:\n",
|
340 |
-
" model = T5EncoderModel.from_pretrained(checkpoint)\n",
|
341 |
-
" tokenizer = T5Tokenizer.from_pretrained(checkpoint) \n",
|
342 |
-
" \n",
|
343 |
-
" # Create new Classifier model with PT5 dimensions\n",
|
344 |
-
" class_config=ClassConfig(num_labels=num_labels)\n",
|
345 |
-
" class_model=T5EncoderForTokenClassification(model.config,class_config)\n",
|
346 |
-
" \n",
|
347 |
-
" # Set encoder and embedding weights to checkpoint weights\n",
|
348 |
-
" class_model.shared=model.shared\n",
|
349 |
-
" class_model.encoder=model.encoder \n",
|
350 |
-
" \n",
|
351 |
-
" # Delete the checkpoint model\n",
|
352 |
-
" model=class_model\n",
|
353 |
-
" del class_model\n",
|
354 |
-
" \n",
|
355 |
-
" if full == True:\n",
|
356 |
-
" return model, tokenizer \n",
|
357 |
-
" \n",
|
358 |
-
" # Print number of trainable parameters\n",
|
359 |
-
" model_parameters = filter(lambda p: p.requires_grad, model.parameters())\n",
|
360 |
-
" params = sum([np.prod(p.size()) for p in model_parameters])\n",
|
361 |
-
" print(\"T5_Classfier\\nTrainable Parameter: \"+ str(params)) \n",
|
362 |
-
"\n",
|
363 |
-
" if custom_lora:\n",
|
364 |
-
" #the linear CustomLoRAConfig allows better quality predictions, but more memory is needed\n",
|
365 |
-
" # Add model modification lora\n",
|
366 |
-
" config = CustomLoRAConfig()\n",
|
367 |
-
" \n",
|
368 |
-
" # Add LoRA layers\n",
|
369 |
-
" model = modify_with_lora(model, config)\n",
|
370 |
-
" \n",
|
371 |
-
" # Freeze Embeddings and Encoder (except LoRA)\n",
|
372 |
-
" for (param_name, param) in model.shared.named_parameters():\n",
|
373 |
-
" param.requires_grad = False\n",
|
374 |
-
" for (param_name, param) in model.encoder.named_parameters():\n",
|
375 |
-
" param.requires_grad = False \n",
|
376 |
-
" \n",
|
377 |
-
" for (param_name, param) in model.named_parameters():\n",
|
378 |
-
" if re.fullmatch(config.trainable_param_names, param_name):\n",
|
379 |
-
" param.requires_grad = True\n",
|
380 |
-
"\n",
|
381 |
-
" else:\n",
|
382 |
-
" # lora modification\n",
|
383 |
-
" peft_config = LoraConfig(\n",
|
384 |
-
" r=4, lora_alpha=1, bias=\"all\", target_modules=[\"q\",\"k\",\"v\",\"o\"]\n",
|
385 |
-
" )\n",
|
386 |
-
" \n",
|
387 |
-
" model = inject_adapter_in_model(peft_config, model)\n",
|
388 |
-
" \n",
|
389 |
-
" # Unfreeze the prediction head\n",
|
390 |
-
" for (param_name, param) in model.classifier.named_parameters():\n",
|
391 |
-
" param.requires_grad = True \n",
|
392 |
-
"\n",
|
393 |
-
" # Print trainable Parameter \n",
|
394 |
-
" model_parameters = filter(lambda p: p.requires_grad, model.parameters())\n",
|
395 |
-
" params = sum([np.prod(p.size()) for p in model_parameters])\n",
|
396 |
-
" print(\"T5_LoRA_Classfier\\nTrainable Parameter: \"+ str(params) + \"\\n\")\n",
|
397 |
-
" \n",
|
398 |
-
" return model, tokenizer"
|
399 |
-
]
|
400 |
-
},
|
401 |
-
{
|
402 |
-
"cell_type": "code",
|
403 |
-
"execution_count": 14,
|
404 |
-
"id": "7ba720bc-a003-4984-a965-cb2f42344e85",
|
405 |
-
"metadata": {},
|
406 |
-
"outputs": [],
|
407 |
-
"source": [
|
408 |
-
"class EsmForTokenClassificationCustom(EsmPreTrainedModel):\n",
|
409 |
-
" _keys_to_ignore_on_load_unexpected = [r\"pooler\"]\n",
|
410 |
-
" _keys_to_ignore_on_load_missing = [r\"position_ids\", r\"cnn\", r\"ffn\", r\"transformer\"]\n",
|
411 |
-
"\n",
|
412 |
-
" def __init__(self, config):\n",
|
413 |
-
" super().__init__(config)\n",
|
414 |
-
" self.num_labels = config.num_labels\n",
|
415 |
-
" self.esm = EsmModel(config, add_pooling_layer=False)\n",
|
416 |
-
" self.dropout = nn.Dropout(config.hidden_dropout_prob)\n",
|
417 |
-
"\n",
|
418 |
-
" if cnn_head:\n",
|
419 |
-
" self.cnn = nn.Conv1d(config.hidden_size, 512, kernel_size=3, padding=1)\n",
|
420 |
-
" self.classifier = nn.Linear(512, config.num_labels)\n",
|
421 |
-
" elif ffn_head:\n",
|
422 |
-
" # Multi-layer feed-forward network (FFN) as an alternative head\n",
|
423 |
-
" self.ffn = nn.Sequential(\n",
|
424 |
-
" nn.Linear(config.hidden_size, 512),\n",
|
425 |
-
" nn.ReLU(),\n",
|
426 |
-
" nn.Linear(512, 256),\n",
|
427 |
-
" nn.ReLU(),\n",
|
428 |
-
" nn.Linear(256, config.num_labels)\n",
|
429 |
-
" )\n",
|
430 |
-
" elif transformer_head:\n",
|
431 |
-
" # Transformer layer as an alternative head\n",
|
432 |
-
" encoder_layer = nn.TransformerEncoderLayer(d_model=config.hidden_size, nhead=8)\n",
|
433 |
-
" self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=1)\n",
|
434 |
-
" self.classifier = nn.Linear(config.hidden_size, config.num_labels)\n",
|
435 |
-
" else:\n",
|
436 |
-
" # Default classification head\n",
|
437 |
-
" self.classifier = nn.Linear(config.hidden_size, config.num_labels)\n",
|
438 |
-
"\n",
|
439 |
-
" self.init_weights()\n",
|
440 |
-
"\n",
|
441 |
-
" def forward(\n",
|
442 |
-
" self,\n",
|
443 |
-
" input_ids: Optional[torch.LongTensor] = None,\n",
|
444 |
-
" attention_mask: Optional[torch.Tensor] = None,\n",
|
445 |
-
" position_ids: Optional[torch.LongTensor] = None,\n",
|
446 |
-
" head_mask: Optional[torch.Tensor] = None,\n",
|
447 |
-
" inputs_embeds: Optional[torch.FloatTensor] = None,\n",
|
448 |
-
" labels: Optional[torch.LongTensor] = None,\n",
|
449 |
-
" output_attentions: Optional[bool] = None,\n",
|
450 |
-
" output_hidden_states: Optional[bool] = None,\n",
|
451 |
-
" return_dict: Optional[bool] = None,\n",
|
452 |
-
" ) -> Union[Tuple, TokenClassifierOutput]:\n",
|
453 |
-
" return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n",
|
454 |
-
" outputs = self.esm(\n",
|
455 |
-
" input_ids,\n",
|
456 |
-
" attention_mask=attention_mask,\n",
|
457 |
-
" position_ids=position_ids,\n",
|
458 |
-
" head_mask=head_mask,\n",
|
459 |
-
" inputs_embeds=inputs_embeds,\n",
|
460 |
-
" output_attentions=output_attentions,\n",
|
461 |
-
" output_hidden_states=output_hidden_states,\n",
|
462 |
-
" return_dict=return_dict,\n",
|
463 |
-
" )\n",
|
464 |
-
" \n",
|
465 |
-
" sequence_output = outputs[0]\n",
|
466 |
-
" sequence_output = self.dropout(sequence_output)\n",
|
467 |
-
"\n",
|
468 |
-
" if cnn_head:\n",
|
469 |
-
" sequence_output = sequence_output.transpose(1, 2)\n",
|
470 |
-
" sequence_output = self.cnn(sequence_output)\n",
|
471 |
-
" sequence_output = sequence_output.transpose(1, 2)\n",
|
472 |
-
" logits = self.classifier(sequence_output)\n",
|
473 |
-
" elif ffn_head:\n",
|
474 |
-
" logits = self.ffn(sequence_output)\n",
|
475 |
-
" elif transformer_head:\n",
|
476 |
-
" # Apply transformer encoder for the transformer head\n",
|
477 |
-
" sequence_output = self.transformer_encoder(sequence_output)\n",
|
478 |
-
" logits = self.classifier(sequence_output)\n",
|
479 |
-
" else:\n",
|
480 |
-
" logits = self.classifier(sequence_output)\n",
|
481 |
-
"\n",
|
482 |
-
" loss = None\n",
|
483 |
-
" if labels is not None:\n",
|
484 |
-
" loss_fct = CrossEntropyLoss()\n",
|
485 |
-
" active_loss = attention_mask.view(-1) == 1\n",
|
486 |
-
" active_logits = logits.view(-1, self.num_labels)\n",
|
487 |
-
" active_labels = torch.where(\n",
|
488 |
-
" active_loss, labels.view(-1), torch.tensor(-100).type_as(labels)\n",
|
489 |
-
" )\n",
|
490 |
-
" valid_logits = active_logits[active_labels != -100]\n",
|
491 |
-
" valid_labels = active_labels[active_labels != -100]\n",
|
492 |
-
" valid_labels = valid_labels.type(torch.LongTensor).to('cuda:0')\n",
|
493 |
-
" loss = loss_fct(valid_logits, valid_labels)\n",
|
494 |
-
"\n",
|
495 |
-
" if not return_dict:\n",
|
496 |
-
" output = (logits,) + outputs[2:]\n",
|
497 |
-
" return ((loss,) + output) if loss is not None else output\n",
|
498 |
-
"\n",
|
499 |
-
" return TokenClassifierOutput(\n",
|
500 |
-
" loss=loss,\n",
|
501 |
-
" logits=logits,\n",
|
502 |
-
" hidden_states=outputs.hidden_states,\n",
|
503 |
-
" attentions=outputs.attentions,\n",
|
504 |
-
" )\n",
|
505 |
-
"\n",
|
506 |
-
" def _init_weights(self, module):\n",
|
507 |
-
" if isinstance(module, nn.Linear) or isinstance(module, nn.Conv1d):\n",
|
508 |
-
" module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)\n",
|
509 |
-
" if module.bias is not None:\n",
|
510 |
-
" module.bias.data.zero_()\n",
|
511 |
-
"\n",
|
512 |
-
"# based on transformers DataCollatorForTokenClassification\n",
|
513 |
-
"@dataclass\n",
|
514 |
-
"class DataCollatorForTokenClassificationESM(DataCollatorMixin):\n",
|
515 |
-
" \"\"\"\n",
|
516 |
-
" Data collator that will dynamically pad the inputs received, as well as the labels.\n",
|
517 |
-
" Args:\n",
|
518 |
-
" tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):\n",
|
519 |
-
" The tokenizer used for encoding the data.\n",
|
520 |
-
" padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):\n",
|
521 |
-
" Select a strategy to pad the returned sequences (according to the model's padding side and padding index)\n",
|
522 |
-
" among:\n",
|
523 |
-
" - `True` or `'longest'` (default): Pad to the longest sequence in the batch (or no padding if only a single\n",
|
524 |
-
" sequence is provided).\n",
|
525 |
-
" - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n",
|
526 |
-
" acceptable input length for the model if that argument is not provided.\n",
|
527 |
-
" - `False` or `'do_not_pad'`: No padding (i.e., can output a batch with sequences of different lengths).\n",
|
528 |
-
" max_length (`int`, *optional*):\n",
|
529 |
-
" Maximum length of the returned list and optionally padding length (see above).\n",
|
530 |
-
" pad_to_multiple_of (`int`, *optional*):\n",
|
531 |
-
" If set will pad the sequence to a multiple of the provided value.\n",
|
532 |
-
" This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=\n",
|
533 |
-
" 7.5 (Volta).\n",
|
534 |
-
" label_pad_token_id (`int`, *optional*, defaults to -100):\n",
|
535 |
-
" The id to use when padding the labels (-100 will be automatically ignore by PyTorch loss functions).\n",
|
536 |
-
" return_tensors (`str`):\n",
|
537 |
-
" The type of Tensor to return. Allowable values are \"np\", \"pt\" and \"tf\".\n",
|
538 |
-
" \"\"\"\n",
|
539 |
-
"\n",
|
540 |
-
" tokenizer: PreTrainedTokenizerBase\n",
|
541 |
-
" padding: Union[bool, str, PaddingStrategy] = True\n",
|
542 |
-
" max_length: Optional[int] = None\n",
|
543 |
-
" pad_to_multiple_of: Optional[int] = None\n",
|
544 |
-
" label_pad_token_id: int = -100\n",
|
545 |
-
" return_tensors: str = \"pt\"\n",
|
546 |
-
"\n",
|
547 |
-
" def torch_call(self, features):\n",
|
548 |
-
" import torch\n",
|
549 |
-
"\n",
|
550 |
-
" label_name = \"label\" if \"label\" in features[0].keys() else \"labels\"\n",
|
551 |
-
" labels = [feature[label_name] for feature in features] if label_name in features[0].keys() else None\n",
|
552 |
-
"\n",
|
553 |
-
" no_labels_features = [{k: v for k, v in feature.items() if k != label_name} for feature in features]\n",
|
554 |
-
"\n",
|
555 |
-
" batch = self.tokenizer.pad(\n",
|
556 |
-
" no_labels_features,\n",
|
557 |
-
" padding=self.padding,\n",
|
558 |
-
" max_length=self.max_length,\n",
|
559 |
-
" pad_to_multiple_of=self.pad_to_multiple_of,\n",
|
560 |
-
" return_tensors=\"pt\",\n",
|
561 |
-
" )\n",
|
562 |
-
"\n",
|
563 |
-
" if labels is None:\n",
|
564 |
-
" return batch\n",
|
565 |
-
"\n",
|
566 |
-
" sequence_length = batch[\"input_ids\"].shape[1]\n",
|
567 |
-
" padding_side = self.tokenizer.padding_side\n",
|
568 |
-
"\n",
|
569 |
-
" def to_list(tensor_or_iterable):\n",
|
570 |
-
" if isinstance(tensor_or_iterable, torch.Tensor):\n",
|
571 |
-
" return tensor_or_iterable.tolist()\n",
|
572 |
-
" return list(tensor_or_iterable)\n",
|
573 |
-
"\n",
|
574 |
-
" if padding_side == \"right\":\n",
|
575 |
-
" batch[label_name] = [\n",
|
576 |
-
" # to_list(label) + [self.label_pad_token_id] * (sequence_length - len(label)) for label in labels\n",
|
577 |
-
" # changed to pad the special tokens at the beginning and end of the sequence\n",
|
578 |
-
" [self.label_pad_token_id] + to_list(label) + [self.label_pad_token_id] * (sequence_length - len(label)-1) for label in labels\n",
|
579 |
-
" ]\n",
|
580 |
-
" else:\n",
|
581 |
-
" batch[label_name] = [\n",
|
582 |
-
" [self.label_pad_token_id] * (sequence_length - len(label)) + to_list(label) for label in labels\n",
|
583 |
-
" ]\n",
|
584 |
-
"\n",
|
585 |
-
" batch[label_name] = torch.tensor(batch[label_name], dtype=torch.float)\n",
|
586 |
-
" return batch\n",
|
587 |
-
"\n",
|
588 |
-
"def _torch_collate_batch(examples, tokenizer, pad_to_multiple_of: Optional[int] = None):\n",
|
589 |
-
" \"\"\"Collate `examples` into a batch, using the information in `tokenizer` for padding if necessary.\"\"\"\n",
|
590 |
-
" import torch\n",
|
591 |
-
"\n",
|
592 |
-
" # Tensorize if necessary.\n",
|
593 |
-
" if isinstance(examples[0], (list, tuple, np.ndarray)):\n",
|
594 |
-
" examples = [torch.tensor(e, dtype=torch.long) for e in examples]\n",
|
595 |
-
"\n",
|
596 |
-
" length_of_first = examples[0].size(0)\n",
|
597 |
-
"\n",
|
598 |
-
" # Check if padding is necessary.\n",
|
599 |
-
"\n",
|
600 |
-
" are_tensors_same_length = all(x.size(0) == length_of_first for x in examples)\n",
|
601 |
-
" if are_tensors_same_length and (pad_to_multiple_of is None or length_of_first % pad_to_multiple_of == 0):\n",
|
602 |
-
" return torch.stack(examples, dim=0)\n",
|
603 |
-
"\n",
|
604 |
-
" # If yes, check if we have a `pad_token`.\n",
|
605 |
-
" if tokenizer._pad_token is None:\n",
|
606 |
-
" raise ValueError(\n",
|
607 |
-
" \"You are attempting to pad samples but the tokenizer you are using\"\n",
|
608 |
-
" f\" ({tokenizer.__class__.__name__}) does not have a pad token.\"\n",
|
609 |
-
" )\n",
|
610 |
-
"\n",
|
611 |
-
" # Creating the full tensor and filling it with our data.\n",
|
612 |
-
" max_length = max(x.size(0) for x in examples)\n",
|
613 |
-
" if pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):\n",
|
614 |
-
" max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of\n",
|
615 |
-
" result = examples[0].new_full([len(examples), max_length], tokenizer.pad_token_id)\n",
|
616 |
-
" for i, example in enumerate(examples):\n",
|
617 |
-
" if tokenizer.padding_side == \"right\":\n",
|
618 |
-
" result[i, : example.shape[0]] = example\n",
|
619 |
-
" else:\n",
|
620 |
-
" result[i, -example.shape[0] :] = example\n",
|
621 |
-
" return result\n",
|
622 |
-
"\n",
|
623 |
-
"def tolist(x):\n",
|
624 |
-
" if isinstance(x, list):\n",
|
625 |
-
" return x\n",
|
626 |
-
" elif hasattr(x, \"numpy\"): # Checks for TF tensors without needing the import\n",
|
627 |
-
" x = x.numpy()\n",
|
628 |
-
" return x.tolist()"
|
629 |
-
]
|
630 |
-
},
|
631 |
-
{
|
632 |
-
"cell_type": "code",
|
633 |
-
"execution_count": 16,
|
634 |
-
"id": "ea511812-1244-4e51-b63c-b4da7822f0b7",
|
635 |
-
"metadata": {},
|
636 |
-
"outputs": [],
|
637 |
-
"source": [
|
638 |
-
"#load ESM2 models\n",
|
639 |
-
"def load_esm_model_classification(checkpoint, num_labels, half_precision, full=False, deepspeed=True):\n",
|
640 |
-
" \n",
|
641 |
-
" tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
|
642 |
-
"\n",
|
643 |
-
" \n",
|
644 |
-
" if half_precision and deepspeed:\n",
|
645 |
-
" model = EsmForTokenClassificationCustom.from_pretrained(checkpoint, \n",
|
646 |
-
" num_labels = num_labels, \n",
|
647 |
-
" ignore_mismatched_sizes=True,\n",
|
648 |
-
" torch_dtype = torch.float16)\n",
|
649 |
-
" else:\n",
|
650 |
-
" model = EsmForTokenClassificationCustom.from_pretrained(checkpoint, \n",
|
651 |
-
" num_labels = num_labels,\n",
|
652 |
-
" ignore_mismatched_sizes=True)\n",
|
653 |
-
" \n",
|
654 |
-
" if full == True:\n",
|
655 |
-
" return model, tokenizer \n",
|
656 |
-
" \n",
|
657 |
-
" peft_config = LoraConfig(\n",
|
658 |
-
" r=4, lora_alpha=1, bias=\"all\", target_modules=[\"query\",\"key\",\"value\",\"dense\"]\n",
|
659 |
-
" )\n",
|
660 |
-
" \n",
|
661 |
-
" model = inject_adapter_in_model(peft_config, model)\n",
|
662 |
-
"\n",
|
663 |
-
" #model.gradient_checkpointing_enable()\n",
|
664 |
-
" \n",
|
665 |
-
" # Unfreeze the prediction head\n",
|
666 |
-
" for (param_name, param) in model.classifier.named_parameters():\n",
|
667 |
-
" param.requires_grad = True \n",
|
668 |
-
" \n",
|
669 |
-
" return model, tokenizer"
|
670 |
-
]
|
671 |
-
},
|
672 |
-
{
|
673 |
-
"cell_type": "code",
|
674 |
-
"execution_count": 22,
|
675 |
-
"id": "8941bbbb-57c5-4f3d-89d9-12b2d306e7a1",
|
676 |
-
"metadata": {},
|
677 |
-
"outputs": [],
|
678 |
-
"source": [
|
679 |
-
"checkpoint='../Pretrained/Rostlab/prot_t5_xl_uniref50'\n",
|
680 |
-
"best_model_path='../refined_models/ChallengeFinetuning/Rostlab/prot_t5_xl_uniref50/manual_checkpoint/cpt.pth'\n",
|
681 |
-
"full=False\n",
|
682 |
-
"deepspeed=False\n",
|
683 |
-
"mixed=False \n",
|
684 |
-
"num_labels=2"
|
685 |
-
]
|
686 |
-
},
|
687 |
-
{
|
688 |
-
"cell_type": "code",
|
689 |
-
"execution_count": null,
|
690 |
-
"id": "4f007331-34d4-4c1d-9311-e91db23d9ed5",
|
691 |
-
"metadata": {},
|
692 |
-
"outputs": [],
|
693 |
-
"source": [
|
694 |
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"/home/frohlkin/Projects/PLM/Publication/hf_webpage/pretrained"
|
695 |
-
]
|
696 |
-
},
|
697 |
-
{
|
698 |
-
"cell_type": "code",
|
699 |
-
"execution_count": 24,
|
700 |
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"id": "18d4ad06-b195-4cc6-a3c8-fa3e761838dc",
|
701 |
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"metadata": {},
|
702 |
-
"outputs": [
|
703 |
-
{
|
704 |
-
"name": "stdout",
|
705 |
-
"output_type": "stream",
|
706 |
-
"text": [
|
707 |
-
"../Pretrained/Rostlab/prot_t5_xl_uniref50 2 False False False\n",
|
708 |
-
"T5_Classfier\n",
|
709 |
-
"Trainable Parameter: 1209716226\n",
|
710 |
-
"T5_LoRA_Classfier\n",
|
711 |
-
"Trainable Parameter: 4082178\n",
|
712 |
-
"\n"
|
713 |
-
]
|
714 |
-
},
|
715 |
-
{
|
716 |
-
"data": {
|
717 |
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"text/plain": [
|
718 |
-
"<All keys matched successfully>"
|
719 |
-
]
|
720 |
-
},
|
721 |
-
"execution_count": 24,
|
722 |
-
"metadata": {},
|
723 |
-
"output_type": "execute_result"
|
724 |
-
}
|
725 |
-
],
|
726 |
-
"source": [
|
727 |
-
"print(checkpoint, num_labels, mixed, full, deepspeed)\n",
|
728 |
-
" \n",
|
729 |
-
"# Determine model type and load accordingly\n",
|
730 |
-
"if \"esm\" in checkpoint:\n",
|
731 |
-
" model, tokenizer = load_esm_model_classification(checkpoint, num_labels, mixed, full, deepspeed)\n",
|
732 |
-
"else:\n",
|
733 |
-
" model, tokenizer = load_T5_model_classification(checkpoint, num_labels, mixed, full, deepspeed)\n",
|
734 |
-
"\n",
|
735 |
-
"# Load the best model state\n",
|
736 |
-
"state_dict = torch.load(best_model_path, weights_only=True)\n",
|
737 |
-
"model.load_state_dict(state_dict)"
|
738 |
-
]
|
739 |
-
},
|
740 |
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{
|
741 |
-
"cell_type": "code",
|
742 |
-
"execution_count": 30,
|
743 |
-
"id": "4e215923-dfe2-4426-aedf-5cb81f7f0db2",
|
744 |
-
"metadata": {},
|
745 |
-
"outputs": [],
|
746 |
-
"source": [
|
747 |
-
"test_one_letter_sequence='AWYAAK'\n",
|
748 |
-
"max_length=1500"
|
749 |
-
]
|
750 |
-
},
|
751 |
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{
|
752 |
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"cell_type": "code",
|
753 |
-
"execution_count": 40,
|
754 |
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"id": "7174ea02-ed51-46f5-84c0-6bcd760670d4",
|
755 |
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"metadata": {},
|
756 |
-
"outputs": [
|
757 |
-
{
|
758 |
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"data": {
|
759 |
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"text/plain": [
|
760 |
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"(7,)"
|
761 |
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]
|
762 |
-
},
|
763 |
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"execution_count": 40,
|
764 |
-
"metadata": {},
|
765 |
-
"output_type": "execute_result"
|
766 |
-
}
|
767 |
-
],
|
768 |
-
"source": [
|
769 |
-
"def create_dataset(tokenizer,seqs,labels,checkpoint):\n",
|
770 |
-
" \n",
|
771 |
-
" tokenized = tokenizer(seqs, max_length=max_length, padding=False, truncation=True)\n",
|
772 |
-
" dataset = Dataset.from_dict(tokenized)\n",
|
773 |
-
" \n",
|
774 |
-
" if (\"esm\" in checkpoint) or (\"ProstT5\" in checkpoint):\n",
|
775 |
-
" labels = [l[:max_length-2] for l in labels] \n",
|
776 |
-
" else:\n",
|
777 |
-
" labels = [l[:max_length-1] for l in labels] \n",
|
778 |
-
" \n",
|
779 |
-
" dataset = dataset.add_column(\"labels\", labels)\n",
|
780 |
-
" \n",
|
781 |
-
" return dataset\n",
|
782 |
-
" \n",
|
783 |
-
"def convert_predictions(input_logits):\n",
|
784 |
-
" all_probs = []\n",
|
785 |
-
" for logits in input_logits:\n",
|
786 |
-
" logits = logits.reshape(-1, 2)\n",
|
787 |
-
"\n",
|
788 |
-
" # Mask out irrelevant regions\n",
|
789 |
-
" # Compute probabilities for class 1\n",
|
790 |
-
" probabilities_class1 = expit(logits[:, 1] - logits[:, 0])\n",
|
791 |
-
" \n",
|
792 |
-
" all_probs.append(probabilities_class1)\n",
|
793 |
-
" \n",
|
794 |
-
" return np.concatenate(all_probs)\n",
|
795 |
-
" \n",
|
796 |
-
" \n",
|
797 |
-
"dummy_labels=[np.zeros(len(test_one_letter_sequence))]\n",
|
798 |
-
"# Replace uncommon amino acids with \"X\"\n",
|
799 |
-
"test_one_letter_sequence = test_one_letter_sequence.replace(\"O\", \"X\").replace(\"B\", \"X\").replace(\"U\", \"X\").replace(\"Z\", \"X\").replace(\"J\", \"X\")\n",
|
800 |
-
"\n",
|
801 |
-
"# Add spaces between each amino acid for ProtT5 and ProstT5 models\n",
|
802 |
-
"if \"Rostlab\" in checkpoint:\n",
|
803 |
-
" test_one_letter_sequence = \" \".join(test_one_letter_sequence)\n",
|
804 |
-
"\n",
|
805 |
-
"# Add <AA2fold> for ProstT5 model input format\n",
|
806 |
-
"if \"ProstT5\" in checkpoint:\n",
|
807 |
-
" test_one_letter_sequence = \"<AA2fold> \" + test_one_letter_sequence\n",
|
808 |
-
" \n",
|
809 |
-
"test_dataset=create_dataset(tokenizer,[test_one_letter_sequence],dummy_labels,checkpoint)\n",
|
810 |
-
"\n",
|
811 |
-
"if (\"esm\" in checkpoint) or (\"ProstT5\" in checkpoint):\n",
|
812 |
-
" data_collator = DataCollatorForTokenClassificationESM(tokenizer)\n",
|
813 |
-
"else:\n",
|
814 |
-
" data_collator = DataCollatorForTokenClassification(tokenizer)\n",
|
815 |
-
"\n",
|
816 |
-
"test_loader = DataLoader(test_dataset, batch_size=1, collate_fn=data_collator)\n",
|
817 |
-
"\n",
|
818 |
-
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
|
819 |
-
"model.to(device)\n",
|
820 |
-
"for batch in test_loader:\n",
|
821 |
-
" input_ids = batch['input_ids'].to(device)\n",
|
822 |
-
" attention_mask = batch['attention_mask'].to(device)\n",
|
823 |
-
" labels = batch['labels'] # Ensure to get labels from batch\n",
|
824 |
-
"\n",
|
825 |
-
" outputs = model(input_ids, attention_mask=attention_mask)\n",
|
826 |
-
" logits = outputs.logits.detach().cpu().numpy()\n",
|
827 |
-
"\n",
|
828 |
-
"logits=convert_predictions(logits)\n",
|
829 |
-
"logits.shape\n",
|
830 |
-
"\n",
|
831 |
-
"def normalize_scores(scores):\n",
|
832 |
-
" min_score = np.min(scores)\n",
|
833 |
-
" max_score = np.max(scores)\n",
|
834 |
-
" return (scores - min_score) / (max_score - min_score) if max_score > min_score else scores\n",
|
835 |
-
"\n",
|
836 |
-
"normalized_scores = normalize_scores(logits)\n",
|
837 |
-
"\n",
|
838 |
-
"normalized_scores.shape"
|
839 |
-
]
|
840 |
-
},
|
841 |
-
{
|
842 |
-
"cell_type": "code",
|
843 |
-
"execution_count": null,
|
844 |
-
"id": "58b5ae4d-9e8e-4d07-ab46-76d23cc29016",
|
845 |
-
"metadata": {},
|
846 |
-
"outputs": [],
|
847 |
-
"source": []
|
848 |
-
}
|
849 |
-
],
|
850 |
-
"metadata": {
|
851 |
-
"kernelspec": {
|
852 |
-
"display_name": "Python [conda env:LLM] *",
|
853 |
-
"language": "python",
|
854 |
-
"name": "conda-env-LLM-py"
|
855 |
-
},
|
856 |
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"language_info": {
|
857 |
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"codemirror_mode": {
|
858 |
-
"name": "ipython",
|
859 |
-
"version": 3
|
860 |
-
},
|
861 |
-
"file_extension": ".py",
|
862 |
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"mimetype": "text/x-python",
|
863 |
-
"name": "python",
|
864 |
-
"nbconvert_exporter": "python",
|
865 |
-
"pygments_lexer": "ipython3",
|
866 |
-
"version": "3.12.2"
|
867 |
-
}
|
868 |
-
},
|
869 |
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"nbformat": 4,
|
870 |
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"nbformat_minor": 5
|
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}
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requirements.txt
CHANGED
@@ -10,5 +10,4 @@ sentencepiece
|
|
10 |
huggingface_hub>=0.15.0
|
11 |
requests
|
12 |
gradio_molecule3d
|
13 |
-
biopython>=1.81
|
14 |
-
matplotlib
|
|
|
10 |
huggingface_hub>=0.15.0
|
11 |
requests
|
12 |
gradio_molecule3d
|
13 |
+
biopython>=1.81
|
|
test.ipynb
ADDED
@@ -0,0 +1,846 @@
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|
|
|
1 |
+
{
|
2 |
+
"cells": [
|
3 |
+
{
|
4 |
+
"cell_type": "code",
|
5 |
+
"execution_count": 3,
|
6 |
+
"id": "1f8ea359-674c-4263-9c2a-7a8e7e464249",
|
7 |
+
"metadata": {},
|
8 |
+
"outputs": [
|
9 |
+
{
|
10 |
+
"name": "stdout",
|
11 |
+
"output_type": "stream",
|
12 |
+
"text": [
|
13 |
+
"* Running on local URL: http://127.0.0.1:7862\n",
|
14 |
+
"\n",
|
15 |
+
"To create a public link, set `share=True` in `launch()`.\n"
|
16 |
+
]
|
17 |
+
},
|
18 |
+
{
|
19 |
+
"data": {
|
20 |
+
"text/html": [
|
21 |
+
"<div><iframe src=\"http://127.0.0.1:7862/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
|
22 |
+
],
|
23 |
+
"text/plain": [
|
24 |
+
"<IPython.core.display.HTML object>"
|
25 |
+
]
|
26 |
+
},
|
27 |
+
"metadata": {},
|
28 |
+
"output_type": "display_data"
|
29 |
+
},
|
30 |
+
{
|
31 |
+
"data": {
|
32 |
+
"text/plain": []
|
33 |
+
},
|
34 |
+
"execution_count": 3,
|
35 |
+
"metadata": {},
|
36 |
+
"output_type": "execute_result"
|
37 |
+
}
|
38 |
+
],
|
39 |
+
"source": [
|
40 |
+
"import gradio as gr\n",
|
41 |
+
"import requests\n",
|
42 |
+
"from Bio.PDB import PDBParser\n",
|
43 |
+
"from gradio_molecule3d import Molecule3D\n",
|
44 |
+
"import numpy as np\n",
|
45 |
+
"\n",
|
46 |
+
"# Function to fetch a PDB file from RCSB PDB\n",
|
47 |
+
"def fetch_pdb(pdb_id):\n",
|
48 |
+
" pdb_url = f'https://files.rcsb.org/download/{pdb_id}.pdb'\n",
|
49 |
+
" pdb_path = f'{pdb_id}.pdb'\n",
|
50 |
+
" response = requests.get(pdb_url)\n",
|
51 |
+
" if response.status_code == 200:\n",
|
52 |
+
" with open(pdb_path, 'wb') as f:\n",
|
53 |
+
" f.write(response.content)\n",
|
54 |
+
" return pdb_path\n",
|
55 |
+
" else:\n",
|
56 |
+
" return None\n",
|
57 |
+
"\n",
|
58 |
+
"# Function to process the PDB file and return random predictions\n",
|
59 |
+
"def process_pdb(pdb_id, segment):\n",
|
60 |
+
" pdb_path = fetch_pdb(pdb_id)\n",
|
61 |
+
" if not pdb_path:\n",
|
62 |
+
" return \"Failed to fetch PDB file\", None, None\n",
|
63 |
+
"\n",
|
64 |
+
" parser = PDBParser(QUIET=True)\n",
|
65 |
+
" structure = parser.get_structure('protein', pdb_path)\n",
|
66 |
+
" \n",
|
67 |
+
" try:\n",
|
68 |
+
" chain = structure[0][segment]\n",
|
69 |
+
" except KeyError:\n",
|
70 |
+
" return \"Invalid Chain ID\", None, None\n",
|
71 |
+
"\n",
|
72 |
+
" sequence = [residue.get_resname() for residue in chain if residue.id[0] == ' ']\n",
|
73 |
+
" random_scores = np.random.rand(len(sequence))\n",
|
74 |
+
"\n",
|
75 |
+
" result_str = \"\\n\".join(\n",
|
76 |
+
" f\"{seq} {res.id[1]} {score:.2f}\" \n",
|
77 |
+
" for seq, res, score in zip(sequence, chain, random_scores)\n",
|
78 |
+
" )\n",
|
79 |
+
"\n",
|
80 |
+
" # Save the predictions to a file\n",
|
81 |
+
" prediction_file = f\"{pdb_id}_predictions.txt\"\n",
|
82 |
+
" with open(prediction_file, \"w\") as f:\n",
|
83 |
+
" f.write(result_str)\n",
|
84 |
+
" \n",
|
85 |
+
" return result_str, pdb_path, prediction_file\n",
|
86 |
+
"\n",
|
87 |
+
"#reps = [{\"model\": 0, \"style\": \"cartoon\", \"color\": \"spectrum\"}]\n",
|
88 |
+
"\n",
|
89 |
+
"reps = [\n",
|
90 |
+
" {\n",
|
91 |
+
" \"model\": 0,\n",
|
92 |
+
" \"style\": \"cartoon\",\n",
|
93 |
+
" \"color\": \"whiteCarbon\",\n",
|
94 |
+
" \"residue_range\": \"\",\n",
|
95 |
+
" \"around\": 0,\n",
|
96 |
+
" \"byres\": False,\n",
|
97 |
+
" },\n",
|
98 |
+
" {\n",
|
99 |
+
" \"model\": 0,\n",
|
100 |
+
" \"chain\": \"A\",\n",
|
101 |
+
" \"resname\": \"HIS\",\n",
|
102 |
+
" \"style\": \"stick\",\n",
|
103 |
+
" \"color\": \"red\"\n",
|
104 |
+
" }\n",
|
105 |
+
" ]\n",
|
106 |
+
"\n",
|
107 |
+
"\n",
|
108 |
+
"# Gradio UI\n",
|
109 |
+
"with gr.Blocks() as demo:\n",
|
110 |
+
" gr.Markdown(\"# Protein Binding Site Prediction (Random Scores)\")\n",
|
111 |
+
"\n",
|
112 |
+
" with gr.Row():\n",
|
113 |
+
" pdb_input = gr.Textbox(value=\"2IWI\", label=\"PDB ID\", placeholder=\"Enter PDB ID here...\")\n",
|
114 |
+
" segment_input = gr.Textbox(value=\"A\", label=\"Chain ID\", placeholder=\"Enter Chain ID here...\")\n",
|
115 |
+
" visualize_btn = gr.Button(\"Visualize Structure\")\n",
|
116 |
+
" prediction_btn = gr.Button(\"Predict Random Binding Site Scores\")\n",
|
117 |
+
"\n",
|
118 |
+
" molecule_output = Molecule3D(label=\"Protein Structure\", reps=reps)\n",
|
119 |
+
" predictions_output = gr.Textbox(label=\"Binding Site Predictions\")\n",
|
120 |
+
" download_output = gr.File(label=\"Download Predictions\")\n",
|
121 |
+
"\n",
|
122 |
+
" visualize_btn.click(fetch_pdb, inputs=[pdb_input], outputs=molecule_output)\n",
|
123 |
+
" prediction_btn.click(process_pdb, inputs=[pdb_input, segment_input], outputs=[predictions_output, molecule_output, download_output])\n",
|
124 |
+
"\n",
|
125 |
+
" gr.Markdown(\"## Examples\")\n",
|
126 |
+
" gr.Examples(\n",
|
127 |
+
" examples=[\n",
|
128 |
+
" [\"2IWI\", \"A\"],\n",
|
129 |
+
" [\"7RPZ\", \"B\"],\n",
|
130 |
+
" [\"3TJN\", \"C\"]\n",
|
131 |
+
" ],\n",
|
132 |
+
" inputs=[pdb_input, segment_input],\n",
|
133 |
+
" outputs=[predictions_output, molecule_output, download_output]\n",
|
134 |
+
" )\n",
|
135 |
+
"\n",
|
136 |
+
"demo.launch()"
|
137 |
+
]
|
138 |
+
},
|
139 |
+
{
|
140 |
+
"cell_type": "code",
|
141 |
+
"execution_count": null,
|
142 |
+
"id": "bd50ff2e-ed03-498e-8af2-73c0fb8ea07e",
|
143 |
+
"metadata": {},
|
144 |
+
"outputs": [],
|
145 |
+
"source": []
|
146 |
+
},
|
147 |
+
{
|
148 |
+
"cell_type": "raw",
|
149 |
+
"id": "88affe12-7c48-4bd6-9e46-32cdffa729fe",
|
150 |
+
"metadata": {},
|
151 |
+
"source": [
|
152 |
+
"import gradio as gr\n",
|
153 |
+
"from gradio_molecule3d import Molecule3D\n",
|
154 |
+
"\n",
|
155 |
+
"\n",
|
156 |
+
"example = Molecule3D().example_value()\n",
|
157 |
+
"\n",
|
158 |
+
"\n",
|
159 |
+
"reps = [\n",
|
160 |
+
" {\n",
|
161 |
+
" \"model\": 0,\n",
|
162 |
+
" \"style\": \"cartoon\",\n",
|
163 |
+
" \"color\": \"whiteCarbon\",\n",
|
164 |
+
" \"residue_range\": \"\",\n",
|
165 |
+
" \"around\": 0,\n",
|
166 |
+
" \"byres\": False,\n",
|
167 |
+
" },\n",
|
168 |
+
" {\n",
|
169 |
+
" \"model\": 0,\n",
|
170 |
+
" \"chain\": \"A\",\n",
|
171 |
+
" \"resname\": \"HIS\",\n",
|
172 |
+
" \"style\": \"stick\",\n",
|
173 |
+
" \"color\": \"red\"\n",
|
174 |
+
" }\n",
|
175 |
+
" ]\n",
|
176 |
+
"\n",
|
177 |
+
"\n",
|
178 |
+
"\n",
|
179 |
+
"def predict(x):\n",
|
180 |
+
" print(\"predict function\", x)\n",
|
181 |
+
" print(x.name)\n",
|
182 |
+
" return x\n",
|
183 |
+
"\n",
|
184 |
+
"with gr.Blocks() as demo:\n",
|
185 |
+
" gr.Markdown(\"# Molecule3D\")\n",
|
186 |
+
" inp = Molecule3D(label=\"Molecule3D\", reps=reps)\n",
|
187 |
+
" out = Molecule3D(label=\"Output\", reps=reps)\n",
|
188 |
+
"\n",
|
189 |
+
" btn = gr.Button(\"Predict\")\n",
|
190 |
+
" gr.Markdown(\"\"\" \n",
|
191 |
+
" You can configure the default rendering of the molecule by adding a list of representations\n",
|
192 |
+
" <pre>\n",
|
193 |
+
" reps = [\n",
|
194 |
+
" {\n",
|
195 |
+
" \"model\": 0,\n",
|
196 |
+
" \"style\": \"cartoon\",\n",
|
197 |
+
" \"color\": \"whiteCarbon\",\n",
|
198 |
+
" \"residue_range\": \"\",\n",
|
199 |
+
" \"around\": 0,\n",
|
200 |
+
" \"byres\": False,\n",
|
201 |
+
" },\n",
|
202 |
+
" {\n",
|
203 |
+
" \"model\": 0,\n",
|
204 |
+
" \"chain\": \"A\",\n",
|
205 |
+
" \"resname\": \"HIS\",\n",
|
206 |
+
" \"style\": \"stick\",\n",
|
207 |
+
" \"color\": \"red\"\n",
|
208 |
+
" }\n",
|
209 |
+
" ]\n",
|
210 |
+
" </pre>\n",
|
211 |
+
" \"\"\")\n",
|
212 |
+
" btn.click(predict, inputs=inp, outputs=out)\n",
|
213 |
+
"\n",
|
214 |
+
"\n",
|
215 |
+
"if __name__ == \"__main__\":\n",
|
216 |
+
" demo.launch()"
|
217 |
+
]
|
218 |
+
},
|
219 |
+
{
|
220 |
+
"cell_type": "code",
|
221 |
+
"execution_count": null,
|
222 |
+
"id": "d27cc368-26a0-42c2-a68a-8833de7bb4a0",
|
223 |
+
"metadata": {},
|
224 |
+
"outputs": [],
|
225 |
+
"source": []
|
226 |
+
},
|
227 |
+
{
|
228 |
+
"cell_type": "raw",
|
229 |
+
"id": "2b970adb-3152-427f-bb58-b92974ff406e",
|
230 |
+
"metadata": {},
|
231 |
+
"source": [
|
232 |
+
"import gradio as gr\n",
|
233 |
+
"import os\n",
|
234 |
+
"import requests\n",
|
235 |
+
"from Bio.PDB import PDBParser, PDBIO\n",
|
236 |
+
"import biotite.structure.io as bsio\n",
|
237 |
+
"\n",
|
238 |
+
"def read_mol(pdb_path):\n",
|
239 |
+
" \"\"\"Read PDB file and return its content as a string\"\"\"\n",
|
240 |
+
" with open(pdb_path, 'r') as f:\n",
|
241 |
+
" return f.read()\n",
|
242 |
+
"\n",
|
243 |
+
"# Function to fetch or upload the PDB file\n",
|
244 |
+
"def get_pdb(pdb_code=\"\", filepath=\"\"):\n",
|
245 |
+
" if pdb_code and len(pdb_code) == 4:\n",
|
246 |
+
" pdb_file = f\"{pdb_code}.pdb\"\n",
|
247 |
+
" if not os.path.exists(pdb_file):\n",
|
248 |
+
" os.system(f\"wget -qnc https://files.rcsb.org/view/{pdb_code}.pdb\")\n",
|
249 |
+
" return pdb_file\n",
|
250 |
+
" elif filepath is not None:\n",
|
251 |
+
" return filepath\n",
|
252 |
+
" else:\n",
|
253 |
+
" return None\n",
|
254 |
+
"\n",
|
255 |
+
"def molecule(input_pdb):\n",
|
256 |
+
" mol = read_mol(input_pdb) # Read PDB file content\n",
|
257 |
+
" \n",
|
258 |
+
" html_content = f\"\"\"\n",
|
259 |
+
" <!DOCTYPE html>\n",
|
260 |
+
" <html>\n",
|
261 |
+
" <head> \n",
|
262 |
+
" <meta http-equiv=\"content-type\" content=\"text/html; charset=UTF-8\" />\n",
|
263 |
+
" <style>\n",
|
264 |
+
" .mol-container {{\n",
|
265 |
+
" width: 100%;\n",
|
266 |
+
" height: 700px;\n",
|
267 |
+
" position: relative;\n",
|
268 |
+
" }}\n",
|
269 |
+
" </style>\n",
|
270 |
+
" <script src=\"https://cdnjs.cloudflare.com/ajax/libs/jquery/3.6.3/jquery.min.js\"></script>\n",
|
271 |
+
" <script src=\"https://3Dmol.csb.pitt.edu/build/3Dmol-min.js\"></script>\n",
|
272 |
+
" </head>\n",
|
273 |
+
" <body>\n",
|
274 |
+
" <div id=\"container\" class=\"mol-container\"></div>\n",
|
275 |
+
" <script>\n",
|
276 |
+
" let pdb = `{mol}`; // Use template literal to properly escape PDB content\n",
|
277 |
+
" $(document).ready(function () {{\n",
|
278 |
+
" let element = $(\"#container\");\n",
|
279 |
+
" let config = {{ backgroundColor: \"white\" }};\n",
|
280 |
+
" let viewer = $3Dmol.createViewer(element, config);\n",
|
281 |
+
" viewer.addModel(pdb, \"pdb\");\n",
|
282 |
+
" viewer.getModel(0).setStyle({{}}, {{ cartoon: {{ colorscheme:\"whiteCarbon\" }} }});\n",
|
283 |
+
" viewer.zoomTo();\n",
|
284 |
+
" viewer.render();\n",
|
285 |
+
" viewer.zoom(0.8, 2000);\n",
|
286 |
+
" }});\n",
|
287 |
+
" </script>\n",
|
288 |
+
" </body>\n",
|
289 |
+
" </html>\n",
|
290 |
+
" \"\"\"\n",
|
291 |
+
" \n",
|
292 |
+
" # Return the HTML content within an iframe safely encoded for special characters\n",
|
293 |
+
" return f'<iframe width=\"100%\" height=\"700\" srcdoc=\"{html_content.replace(chr(34), \""\").replace(chr(39), \"'\")}\"></iframe>'\n",
|
294 |
+
"\n",
|
295 |
+
"# Gradio function to update the visualization\n",
|
296 |
+
"def update(inp, file):\n",
|
297 |
+
" pdb_path = get_pdb(inp, file)\n",
|
298 |
+
" if pdb_path:\n",
|
299 |
+
" return molecule(pdb_path)\n",
|
300 |
+
" else:\n",
|
301 |
+
" return \"Invalid input. Please provide a valid PDB code or upload a PDB file.\"\n",
|
302 |
+
"\n",
|
303 |
+
"# Gradio UI\n",
|
304 |
+
"demo = gr.Blocks()\n",
|
305 |
+
"with demo:\n",
|
306 |
+
" gr.Markdown(\"# PDB Viewer using 3Dmol.js\")\n",
|
307 |
+
" with gr.Row():\n",
|
308 |
+
" with gr.Column():\n",
|
309 |
+
" inp = gr.Textbox(\n",
|
310 |
+
" placeholder=\"PDB Code or upload file below\", label=\"Input structure\"\n",
|
311 |
+
" )\n",
|
312 |
+
" file = gr.File(file_count=\"single\")\n",
|
313 |
+
" btn = gr.Button(\"View structure\")\n",
|
314 |
+
" mol = gr.HTML()\n",
|
315 |
+
" btn.click(fn=update, inputs=[inp, file], outputs=mol)\n",
|
316 |
+
"\n",
|
317 |
+
"# Launch the Gradio interface \n",
|
318 |
+
"demo.launch(debug=True)"
|
319 |
+
]
|
320 |
+
},
|
321 |
+
{
|
322 |
+
"cell_type": "code",
|
323 |
+
"execution_count": null,
|
324 |
+
"id": "ee215c16-a1fb-450f-bb93-37aaee6fb3f1",
|
325 |
+
"metadata": {},
|
326 |
+
"outputs": [],
|
327 |
+
"source": []
|
328 |
+
},
|
329 |
+
{
|
330 |
+
"cell_type": "raw",
|
331 |
+
"id": "050aa2e8-2dbe-4a28-8692-58ca7c50fccd",
|
332 |
+
"metadata": {},
|
333 |
+
"source": [
|
334 |
+
"import gradio as gr\n",
|
335 |
+
"import os\n",
|
336 |
+
"import requests\n",
|
337 |
+
"import numpy as np\n",
|
338 |
+
"from Bio.PDB import PDBParser\n",
|
339 |
+
"\n",
|
340 |
+
"def read_mol(pdb_path):\n",
|
341 |
+
" \"\"\"Read PDB file and return its content as a string\"\"\"\n",
|
342 |
+
" with open(pdb_path, 'r') as f:\n",
|
343 |
+
" return f.read()\n",
|
344 |
+
"\n",
|
345 |
+
"# Function to fetch a PDB file from RCSB PDB\n",
|
346 |
+
"def fetch_pdb(pdb_id):\n",
|
347 |
+
" pdb_url = f'https://files.rcsb.org/download/{pdb_id}.pdb'\n",
|
348 |
+
" pdb_path = f'{pdb_id}.pdb'\n",
|
349 |
+
" response = requests.get(pdb_url)\n",
|
350 |
+
" if response.status_code == 200:\n",
|
351 |
+
" with open(pdb_path, 'wb') as f:\n",
|
352 |
+
" f.write(response.content)\n",
|
353 |
+
" return molecule(pdb_path)\n",
|
354 |
+
" else:\n",
|
355 |
+
" return None\n",
|
356 |
+
"\n",
|
357 |
+
"# Function to process the PDB file and return random predictions\n",
|
358 |
+
"def process_pdb(pdb_id, segment):\n",
|
359 |
+
" pdb_path = fetch_pdb(pdb_id)\n",
|
360 |
+
" if not pdb_path:\n",
|
361 |
+
" return \"Failed to fetch PDB file\", None, None\n",
|
362 |
+
" \n",
|
363 |
+
" parser = PDBParser(QUIET=True)\n",
|
364 |
+
" structure = parser.get_structure('protein', pdb_path)\n",
|
365 |
+
" \n",
|
366 |
+
" try:\n",
|
367 |
+
" chain = structure[0][segment]\n",
|
368 |
+
" except KeyError:\n",
|
369 |
+
" return \"Invalid Chain ID\", None, None\n",
|
370 |
+
" \n",
|
371 |
+
" sequence = [residue.get_resname() for residue in chain if residue.id[0] == ' ']\n",
|
372 |
+
" random_scores = np.random.rand(len(sequence))\n",
|
373 |
+
" result_str = \"\\n\".join(\n",
|
374 |
+
" f\"{seq} {res.id[1]} {score:.2f}\" \n",
|
375 |
+
" for seq, res, score in zip(sequence, chain, random_scores)\n",
|
376 |
+
" )\n",
|
377 |
+
" \n",
|
378 |
+
" # Save the predictions to a file\n",
|
379 |
+
" prediction_file = f\"{pdb_id}_predictions.txt\"\n",
|
380 |
+
" with open(prediction_file, \"w\") as f:\n",
|
381 |
+
" f.write(result_str)\n",
|
382 |
+
" \n",
|
383 |
+
" return result_str, molecule(pdb_path), prediction_file\n",
|
384 |
+
"\n",
|
385 |
+
"def molecule(input_pdb):\n",
|
386 |
+
" mol = read_mol(input_pdb) # Read PDB file content\n",
|
387 |
+
" \n",
|
388 |
+
" html_content = f\"\"\"\n",
|
389 |
+
" <!DOCTYPE html>\n",
|
390 |
+
" <html>\n",
|
391 |
+
" <head> \n",
|
392 |
+
" <meta http-equiv=\"content-type\" content=\"text/html; charset=UTF-8\" />\n",
|
393 |
+
" <style>\n",
|
394 |
+
" .mol-container {{\n",
|
395 |
+
" width: 100%;\n",
|
396 |
+
" height: 700px;\n",
|
397 |
+
" position: relative;\n",
|
398 |
+
" }}\n",
|
399 |
+
" </style>\n",
|
400 |
+
" <script src=\"https://cdnjs.cloudflare.com/ajax/libs/jquery/3.6.3/jquery.min.js\"></script>\n",
|
401 |
+
" <script src=\"https://3Dmol.csb.pitt.edu/build/3Dmol-min.js\"></script>\n",
|
402 |
+
" </head>\n",
|
403 |
+
" <body>\n",
|
404 |
+
" <div id=\"container\" class=\"mol-container\"></div>\n",
|
405 |
+
" <script>\n",
|
406 |
+
" let pdb = `{mol}`; // Use template literal to properly escape PDB content\n",
|
407 |
+
" $(document).ready(function () {{\n",
|
408 |
+
" let element = $(\"#container\");\n",
|
409 |
+
" let config = {{ backgroundColor: \"white\" }};\n",
|
410 |
+
" let viewer = $3Dmol.createViewer(element, config);\n",
|
411 |
+
" viewer.addModel(pdb, \"pdb\");\n",
|
412 |
+
" \n",
|
413 |
+
" // Set cartoon representation with white carbon color scheme\n",
|
414 |
+
" viewer.getModel(0).setStyle({{}}, {{ cartoon: {{ colorscheme:\"whiteCarbon\" }} }});\n",
|
415 |
+
" \n",
|
416 |
+
" // Highlight specific histidine residues in red stick representation\n",
|
417 |
+
" viewer.getModel(0).setStyle(\n",
|
418 |
+
" {{\"resn\": \"HIS\"}}, \n",
|
419 |
+
" {{\"stick\": {{\"color\": \"red\"}}}}\n",
|
420 |
+
" );\n",
|
421 |
+
" \n",
|
422 |
+
" viewer.zoomTo();\n",
|
423 |
+
" viewer.render();\n",
|
424 |
+
" viewer.zoom(0.8, 2000);\n",
|
425 |
+
" }});\n",
|
426 |
+
" </script>\n",
|
427 |
+
" </body>\n",
|
428 |
+
" </html>\n",
|
429 |
+
" \"\"\"\n",
|
430 |
+
" \n",
|
431 |
+
" # Return the HTML content within an iframe safely encoded for special characters\n",
|
432 |
+
" return f'<iframe width=\"100%\" height=\"700\" srcdoc=\"{html_content.replace(chr(34), \""\").replace(chr(39), \"'\")}\"></iframe>'\n",
|
433 |
+
"\n",
|
434 |
+
"# Gradio UI\n",
|
435 |
+
"with gr.Blocks() as demo:\n",
|
436 |
+
" gr.Markdown(\"# Protein Binding Site Prediction (Random Scores)\")\n",
|
437 |
+
" with gr.Row():\n",
|
438 |
+
" pdb_input = gr.Textbox(value=\"2IWI\", label=\"PDB ID\", placeholder=\"Enter PDB ID here...\")\n",
|
439 |
+
" segment_input = gr.Textbox(value=\"A\", label=\"Chain ID\", placeholder=\"Enter Chain ID here...\")\n",
|
440 |
+
" visualize_btn = gr.Button(\"Visualize Structure\")\n",
|
441 |
+
" prediction_btn = gr.Button(\"Predict Random Binding Site Scores\")\n",
|
442 |
+
" \n",
|
443 |
+
" # Use HTML output instead of Molecule3D\n",
|
444 |
+
" molecule_output = gr.HTML(label=\"Protein Structure\")\n",
|
445 |
+
" predictions_output = gr.Textbox(label=\"Binding Site Predictions\")\n",
|
446 |
+
" download_output = gr.File(label=\"Download Predictions\")\n",
|
447 |
+
" \n",
|
448 |
+
" visualize_btn.click(fetch_pdb, inputs=[pdb_input], outputs=molecule_output)\n",
|
449 |
+
" prediction_btn.click(process_pdb, inputs=[pdb_input, segment_input], outputs=[predictions_output, molecule_output, download_output])\n",
|
450 |
+
" \n",
|
451 |
+
" gr.Markdown(\"## Examples\")\n",
|
452 |
+
" gr.Examples(\n",
|
453 |
+
" examples=[\n",
|
454 |
+
" [\"2IWI\", \"A\"],\n",
|
455 |
+
" [\"7RPZ\", \"B\"],\n",
|
456 |
+
" [\"3TJN\", \"C\"]\n",
|
457 |
+
" ],\n",
|
458 |
+
" inputs=[pdb_input, segment_input],\n",
|
459 |
+
" outputs=[predictions_output, molecule_output, download_output]\n",
|
460 |
+
" )\n",
|
461 |
+
"\n",
|
462 |
+
"demo.launch(debug=True)"
|
463 |
+
]
|
464 |
+
},
|
465 |
+
{
|
466 |
+
"cell_type": "code",
|
467 |
+
"execution_count": null,
|
468 |
+
"id": "9a5facd9-855c-4b35-8dd3-2c0c8c7dd356",
|
469 |
+
"metadata": {},
|
470 |
+
"outputs": [],
|
471 |
+
"source": []
|
472 |
+
},
|
473 |
+
{
|
474 |
+
"cell_type": "raw",
|
475 |
+
"id": "a762170f-92a9-473d-b18d-53607a780e3b",
|
476 |
+
"metadata": {},
|
477 |
+
"source": [
|
478 |
+
"import gradio as gr\n",
|
479 |
+
"import requests\n",
|
480 |
+
"from Bio.PDB import PDBParser\n",
|
481 |
+
"import numpy as np\n",
|
482 |
+
"import os\n",
|
483 |
+
"\n",
|
484 |
+
"def read_mol(pdb_path):\n",
|
485 |
+
" \"\"\"Read PDB file and return its content as a string\"\"\"\n",
|
486 |
+
" with open(pdb_path, 'r') as f:\n",
|
487 |
+
" return f.read()\n",
|
488 |
+
"\n",
|
489 |
+
"# Function to fetch a PDB file from RCSB PDB\n",
|
490 |
+
"def fetch_pdb(pdb_id):\n",
|
491 |
+
" pdb_url = f'https://files.rcsb.org/download/{pdb_id}.pdb'\n",
|
492 |
+
" pdb_path = f'{pdb_id}.pdb'\n",
|
493 |
+
" response = requests.get(pdb_url)\n",
|
494 |
+
" if response.status_code == 200:\n",
|
495 |
+
" with open(pdb_path, 'wb') as f:\n",
|
496 |
+
" f.write(response.content)\n",
|
497 |
+
" return pdb_path\n",
|
498 |
+
" else:\n",
|
499 |
+
" return None\n",
|
500 |
+
"\n",
|
501 |
+
"# Function to process the PDB file and return random predictions\n",
|
502 |
+
"def process_pdb(pdb_id, segment):\n",
|
503 |
+
" pdb_path = fetch_pdb(pdb_id)\n",
|
504 |
+
" if not pdb_path:\n",
|
505 |
+
" return \"Failed to fetch PDB file\", None, None\n",
|
506 |
+
" parser = PDBParser(QUIET=True)\n",
|
507 |
+
" structure = parser.get_structure('protein', pdb_path)\n",
|
508 |
+
" \n",
|
509 |
+
" try:\n",
|
510 |
+
" chain = structure[0][segment]\n",
|
511 |
+
" except KeyError:\n",
|
512 |
+
" return \"Invalid Chain ID\", None, None\n",
|
513 |
+
" sequence = [residue.get_resname() for residue in chain if residue.id[0] == ' ']\n",
|
514 |
+
" random_scores = np.random.rand(len(sequence))\n",
|
515 |
+
" result_str = \"\\n\".join(\n",
|
516 |
+
" f\"{seq} {res.id[1]} {score:.2f}\" \n",
|
517 |
+
" for seq, res, score in zip(sequence, chain, random_scores)\n",
|
518 |
+
" )\n",
|
519 |
+
" # Save the predictions to a file\n",
|
520 |
+
" prediction_file = f\"{pdb_id}_predictions.txt\"\n",
|
521 |
+
" with open(prediction_file, \"w\") as f:\n",
|
522 |
+
" f.write(result_str)\n",
|
523 |
+
" \n",
|
524 |
+
" return result_str, molecule(pdb_path), prediction_file\n",
|
525 |
+
"\n",
|
526 |
+
"def molecule(input_pdb):\n",
|
527 |
+
" mol = read_mol(input_pdb) # Read PDB file content\n",
|
528 |
+
" \n",
|
529 |
+
" html_content = f\"\"\"\n",
|
530 |
+
" <!DOCTYPE html>\n",
|
531 |
+
" <html>\n",
|
532 |
+
" <head> \n",
|
533 |
+
" <meta http-equiv=\"content-type\" content=\"text/html; charset=UTF-8\" />\n",
|
534 |
+
" <style>\n",
|
535 |
+
" .mol-container {{\n",
|
536 |
+
" width: 100%;\n",
|
537 |
+
" height: 700px;\n",
|
538 |
+
" position: relative;\n",
|
539 |
+
" }}\n",
|
540 |
+
" </style>\n",
|
541 |
+
" <script src=\"https://cdnjs.cloudflare.com/ajax/libs/jquery/3.6.3/jquery.min.js\"></script>\n",
|
542 |
+
" <script src=\"https://3Dmol.csb.pitt.edu/build/3Dmol-min.js\"></script>\n",
|
543 |
+
" </head>\n",
|
544 |
+
" <body>\n",
|
545 |
+
" <div id=\"container\" class=\"mol-container\"></div>\n",
|
546 |
+
" <script>\n",
|
547 |
+
" let pdb = `{mol}`; // Use template literal to properly escape PDB content\n",
|
548 |
+
" $(document).ready(function () {{\n",
|
549 |
+
" let element = $(\"#container\");\n",
|
550 |
+
" let config = {{ backgroundColor: \"white\" }};\n",
|
551 |
+
" let viewer = $3Dmol.createViewer(element, config);\n",
|
552 |
+
" viewer.addModel(pdb, \"pdb\");\n",
|
553 |
+
" \n",
|
554 |
+
" // Set cartoon representation with white carbon color scheme\n",
|
555 |
+
" viewer.getModel(0).setStyle({{}}, {{ cartoon: {{ colorscheme:\"whiteCarbon\" }} }});\n",
|
556 |
+
" \n",
|
557 |
+
" // Highlight specific histidine residues in red stick representation\n",
|
558 |
+
" viewer.getModel(0).setStyle(\n",
|
559 |
+
" {{\"resn\": \"HIS\"}}, \n",
|
560 |
+
" {{\"stick\": {{\"color\": \"red\"}}}}\n",
|
561 |
+
" );\n",
|
562 |
+
" \n",
|
563 |
+
" viewer.zoomTo();\n",
|
564 |
+
" viewer.render();\n",
|
565 |
+
" viewer.zoom(0.8, 2000);\n",
|
566 |
+
" }});\n",
|
567 |
+
" </script>\n",
|
568 |
+
" </body>\n",
|
569 |
+
" </html>\n",
|
570 |
+
" \"\"\"\n",
|
571 |
+
" \n",
|
572 |
+
" # Return the HTML content within an iframe safely encoded for special characters\n",
|
573 |
+
" return f'<iframe width=\"100%\" height=\"700\" srcdoc=\"{html_content.replace(chr(34), \""\").replace(chr(39), \"'\")}\"></iframe>'\n",
|
574 |
+
"\n",
|
575 |
+
"# Gradio UI\n",
|
576 |
+
"with gr.Blocks() as demo:\n",
|
577 |
+
" gr.Markdown(\"# Protein Binding Site Prediction (Random Scores)\")\n",
|
578 |
+
" with gr.Row():\n",
|
579 |
+
" pdb_input = gr.Textbox(value=\"2IWI\", label=\"PDB ID\", placeholder=\"Enter PDB ID here...\")\n",
|
580 |
+
" segment_input = gr.Textbox(value=\"A\", label=\"Chain ID\", placeholder=\"Enter Chain ID here...\")\n",
|
581 |
+
" visualize_btn = gr.Button(\"Visualize Structure\")\n",
|
582 |
+
" prediction_btn = gr.Button(\"Predict Random Binding Site Scores\")\n",
|
583 |
+
" \n",
|
584 |
+
" molecule_output = gr.HTML(label=\"Protein Structure\")\n",
|
585 |
+
" predictions_output = gr.Textbox(label=\"Binding Site Predictions\")\n",
|
586 |
+
" download_output = gr.File(label=\"Download Predictions\")\n",
|
587 |
+
" \n",
|
588 |
+
" # Update to explicitly use molecule() function for visualization\n",
|
589 |
+
" visualize_btn.click(\n",
|
590 |
+
" fn=lambda pdb_id: molecule(fetch_pdb(pdb_id)), \n",
|
591 |
+
" inputs=[pdb_input], \n",
|
592 |
+
" outputs=molecule_output\n",
|
593 |
+
" )\n",
|
594 |
+
" \n",
|
595 |
+
" prediction_btn.click(process_pdb, inputs=[pdb_input, segment_input], outputs=[predictions_output, molecule_output, download_output])\n",
|
596 |
+
" \n",
|
597 |
+
" gr.Markdown(\"## Examples\")\n",
|
598 |
+
" gr.Examples(\n",
|
599 |
+
" examples=[\n",
|
600 |
+
" [\"2IWI\", \"A\"],\n",
|
601 |
+
" [\"7RPZ\", \"B\"],\n",
|
602 |
+
" [\"3TJN\", \"C\"]\n",
|
603 |
+
" ],\n",
|
604 |
+
" inputs=[pdb_input, segment_input],\n",
|
605 |
+
" outputs=[predictions_output, molecule_output, download_output]\n",
|
606 |
+
" )\n",
|
607 |
+
"\n",
|
608 |
+
"demo.launch()"
|
609 |
+
]
|
610 |
+
},
|
611 |
+
{
|
612 |
+
"cell_type": "code",
|
613 |
+
"execution_count": null,
|
614 |
+
"id": "15527a58-c449-4da0-8fab-3baaede15e41",
|
615 |
+
"metadata": {},
|
616 |
+
"outputs": [],
|
617 |
+
"source": []
|
618 |
+
},
|
619 |
+
{
|
620 |
+
"cell_type": "code",
|
621 |
+
"execution_count": 2,
|
622 |
+
"id": "9ef3e330-cb88-4c29-b84a-2f8652883cfc",
|
623 |
+
"metadata": {},
|
624 |
+
"outputs": [
|
625 |
+
{
|
626 |
+
"name": "stdout",
|
627 |
+
"output_type": "stream",
|
628 |
+
"text": [
|
629 |
+
"* Running on local URL: http://127.0.0.1:7860\n",
|
630 |
+
"\n",
|
631 |
+
"To create a public link, set `share=True` in `launch()`.\n"
|
632 |
+
]
|
633 |
+
},
|
634 |
+
{
|
635 |
+
"data": {
|
636 |
+
"text/html": [
|
637 |
+
"<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
|
638 |
+
],
|
639 |
+
"text/plain": [
|
640 |
+
"<IPython.core.display.HTML object>"
|
641 |
+
]
|
642 |
+
},
|
643 |
+
"metadata": {},
|
644 |
+
"output_type": "display_data"
|
645 |
+
},
|
646 |
+
{
|
647 |
+
"data": {
|
648 |
+
"text/plain": []
|
649 |
+
},
|
650 |
+
"execution_count": 2,
|
651 |
+
"metadata": {},
|
652 |
+
"output_type": "execute_result"
|
653 |
+
}
|
654 |
+
],
|
655 |
+
"source": [
|
656 |
+
"import gradio as gr\n",
|
657 |
+
"import requests\n",
|
658 |
+
"from Bio.PDB import PDBParser\n",
|
659 |
+
"import numpy as np\n",
|
660 |
+
"import os\n",
|
661 |
+
"from gradio_molecule3d import Molecule3D\n",
|
662 |
+
"\n",
|
663 |
+
"def read_mol(pdb_path):\n",
|
664 |
+
" \"\"\"Read PDB file and return its content as a string\"\"\"\n",
|
665 |
+
" with open(pdb_path, 'r') as f:\n",
|
666 |
+
" return f.read()\n",
|
667 |
+
"\n",
|
668 |
+
"def fetch_pdb(pdb_id):\n",
|
669 |
+
" pdb_url = f'https://files.rcsb.org/download/{pdb_id}.pdb'\n",
|
670 |
+
" pdb_path = f'{pdb_id}.pdb'\n",
|
671 |
+
" response = requests.get(pdb_url)\n",
|
672 |
+
" if response.status_code == 200:\n",
|
673 |
+
" with open(pdb_path, 'wb') as f:\n",
|
674 |
+
" f.write(response.content)\n",
|
675 |
+
" return pdb_path\n",
|
676 |
+
" else:\n",
|
677 |
+
" return None\n",
|
678 |
+
"\n",
|
679 |
+
"def process_pdb(pdb_id, segment):\n",
|
680 |
+
" pdb_path = fetch_pdb(pdb_id)\n",
|
681 |
+
" if not pdb_path:\n",
|
682 |
+
" return \"Failed to fetch PDB file\", None, None\n",
|
683 |
+
" parser = PDBParser(QUIET=True)\n",
|
684 |
+
" structure = parser.get_structure('protein', pdb_path)\n",
|
685 |
+
" \n",
|
686 |
+
" try:\n",
|
687 |
+
" chain = structure[0][segment]\n",
|
688 |
+
" except KeyError:\n",
|
689 |
+
" return \"Invalid Chain ID\", None, None\n",
|
690 |
+
" sequence = [residue.get_resname() for residue in chain if residue.id[0] == ' ']\n",
|
691 |
+
" random_scores = np.random.rand(len(sequence))\n",
|
692 |
+
" result_str = \"\\n\".join(\n",
|
693 |
+
" f\"{seq} {res.id[1]} {score:.2f}\" \n",
|
694 |
+
" for seq, res, score in zip(sequence, chain, random_scores)\n",
|
695 |
+
" )\n",
|
696 |
+
" # Save the predictions to a file\n",
|
697 |
+
" prediction_file = f\"{pdb_id}_predictions.txt\"\n",
|
698 |
+
" with open(prediction_file, \"w\") as f:\n",
|
699 |
+
" f.write(result_str)\n",
|
700 |
+
" \n",
|
701 |
+
" return result_str, molecule(pdb_path, random_scores), prediction_file\n",
|
702 |
+
"\n",
|
703 |
+
"def molecule(input_pdb, scores=None):\n",
|
704 |
+
" mol = read_mol(input_pdb) # Read PDB file content\n",
|
705 |
+
" \n",
|
706 |
+
" # Prepare high-scoring residues script if scores are provided\n",
|
707 |
+
" high_score_script = \"\"\n",
|
708 |
+
" if scores is not None:\n",
|
709 |
+
" high_score_script = \"\"\"\n",
|
710 |
+
" // Highlight residues with high scores\n",
|
711 |
+
" let highScoreResidues = [{}];\n",
|
712 |
+
" viewer.getModel(0).setStyle(\n",
|
713 |
+
" {{\"resi\": highScoreResidues}}, \n",
|
714 |
+
" {{\"stick\": {{\"color\": \"red\"}}}}\n",
|
715 |
+
" );\n",
|
716 |
+
" \"\"\".format(\n",
|
717 |
+
" \", \".join(str(i+1) for i, score in enumerate(scores) if score > 0.8)\n",
|
718 |
+
" )\n",
|
719 |
+
" \n",
|
720 |
+
" html_content = f\"\"\"\n",
|
721 |
+
" <!DOCTYPE html>\n",
|
722 |
+
" <html>\n",
|
723 |
+
" <head> \n",
|
724 |
+
" <meta http-equiv=\"content-type\" content=\"text/html; charset=UTF-8\" />\n",
|
725 |
+
" <style>\n",
|
726 |
+
" .mol-container {{\n",
|
727 |
+
" width: 100%;\n",
|
728 |
+
" height: 700px;\n",
|
729 |
+
" position: relative;\n",
|
730 |
+
" }}\n",
|
731 |
+
" </style>\n",
|
732 |
+
" <script src=\"https://cdnjs.cloudflare.com/ajax/libs/jquery/3.6.3/jquery.min.js\"></script>\n",
|
733 |
+
" <script src=\"https://3Dmol.csb.pitt.edu/build/3Dmol-min.js\"></script>\n",
|
734 |
+
" </head>\n",
|
735 |
+
" <body>\n",
|
736 |
+
" <div id=\"container\" class=\"mol-container\"></div>\n",
|
737 |
+
" <script>\n",
|
738 |
+
" let pdb = `{mol}`; // Use template literal to properly escape PDB content\n",
|
739 |
+
" $(document).ready(function () {{\n",
|
740 |
+
" let element = $(\"#container\");\n",
|
741 |
+
" let config = {{ backgroundColor: \"white\" }};\n",
|
742 |
+
" let viewer = $3Dmol.createViewer(element, config);\n",
|
743 |
+
" viewer.addModel(pdb, \"pdb\");\n",
|
744 |
+
" \n",
|
745 |
+
" // Set cartoon representation with white carbon color scheme\n",
|
746 |
+
" viewer.getModel(0).setStyle({{}}, {{ cartoon: {{ colorscheme:\"whiteCarbon\" }} }});\n",
|
747 |
+
" \n",
|
748 |
+
" {high_score_script}\n",
|
749 |
+
" \n",
|
750 |
+
" viewer.zoomTo();\n",
|
751 |
+
" viewer.render();\n",
|
752 |
+
" viewer.zoom(0.8, 2000);\n",
|
753 |
+
" }});\n",
|
754 |
+
" </script>\n",
|
755 |
+
" </body>\n",
|
756 |
+
" </html>\n",
|
757 |
+
" \"\"\"\n",
|
758 |
+
" \n",
|
759 |
+
" # Return the HTML content within an iframe safely encoded for special characters\n",
|
760 |
+
" return f'<iframe width=\"100%\" height=\"700\" srcdoc=\"{html_content.replace(chr(34), \""\").replace(chr(39), \"'\")}\"></iframe>'\n",
|
761 |
+
"\n",
|
762 |
+
"reps = [\n",
|
763 |
+
" {\n",
|
764 |
+
" \"model\": 0,\n",
|
765 |
+
" \"style\": \"cartoon\",\n",
|
766 |
+
" \"color\": \"whiteCarbon\",\n",
|
767 |
+
" \"residue_range\": \"\",\n",
|
768 |
+
" \"around\": 0,\n",
|
769 |
+
" \"byres\": False,\n",
|
770 |
+
" }\n",
|
771 |
+
" ]\n",
|
772 |
+
"# Gradio UI\n",
|
773 |
+
"with gr.Blocks() as demo:\n",
|
774 |
+
" gr.Markdown(\"# Protein Binding Site Prediction (Random Scores)\")\n",
|
775 |
+
" with gr.Row():\n",
|
776 |
+
" pdb_input = gr.Textbox(value=\"2IWI\", label=\"PDB ID\", placeholder=\"Enter PDB ID here...\")\n",
|
777 |
+
" segment_input = gr.Textbox(value=\"A\", label=\"Chain ID\", placeholder=\"Enter Chain ID here...\")\n",
|
778 |
+
" visualize_btn = gr.Button(\"Visualize Structure\")\n",
|
779 |
+
" #prediction_btn = gr.Button(\"Predict Random Binding Site Scores\")\n",
|
780 |
+
"\n",
|
781 |
+
" molecule_output2 = Molecule3D(label=\"Protein Structure\", reps=reps)\n",
|
782 |
+
"\n",
|
783 |
+
" with gr.Row():\n",
|
784 |
+
" pdb_input = gr.Textbox(value=\"2IWI\", label=\"PDB ID\", placeholder=\"Enter PDB ID here...\")\n",
|
785 |
+
" segment_input = gr.Textbox(value=\"A\", label=\"Chain ID\", placeholder=\"Enter Chain ID here...\")\n",
|
786 |
+
" prediction_btn = gr.Button(\"Predict Random Binding Site Scores\")\n",
|
787 |
+
"\n",
|
788 |
+
" molecule_output = gr.HTML(label=\"Protein Structure\")\n",
|
789 |
+
" predictions_output = gr.Textbox(label=\"Binding Site Predictions\")\n",
|
790 |
+
" download_output = gr.File(label=\"Download Predictions\")\n",
|
791 |
+
" \n",
|
792 |
+
" #visualize_btn.click(\n",
|
793 |
+
" # fn=lambda pdb_id: molecule(fetch_pdb(pdb_id)), \n",
|
794 |
+
" # inputs=[pdb_input], \n",
|
795 |
+
" # outputs=molecule_output\n",
|
796 |
+
" #)\n",
|
797 |
+
" visualize_btn.click(fetch_pdb, inputs=[pdb_input], outputs=molecule_output2)\n",
|
798 |
+
" \n",
|
799 |
+
" \n",
|
800 |
+
" prediction_btn.click(process_pdb, inputs=[pdb_input, segment_input], outputs=[predictions_output, molecule_output, download_output])\n",
|
801 |
+
" \n",
|
802 |
+
" gr.Markdown(\"## Examples\")\n",
|
803 |
+
" gr.Examples(\n",
|
804 |
+
" examples=[\n",
|
805 |
+
" [\"2IWI\", \"A\"],\n",
|
806 |
+
" [\"7RPZ\", \"B\"],\n",
|
807 |
+
" [\"3TJN\", \"C\"]\n",
|
808 |
+
" ],\n",
|
809 |
+
" inputs=[pdb_input, segment_input],\n",
|
810 |
+
" outputs=[predictions_output, molecule_output, download_output]\n",
|
811 |
+
" )\n",
|
812 |
+
"\n",
|
813 |
+
"demo.launch()"
|
814 |
+
]
|
815 |
+
},
|
816 |
+
{
|
817 |
+
"cell_type": "code",
|
818 |
+
"execution_count": null,
|
819 |
+
"id": "14605615-8610-4d9e-841b-db7618cde844",
|
820 |
+
"metadata": {},
|
821 |
+
"outputs": [],
|
822 |
+
"source": []
|
823 |
+
}
|
824 |
+
],
|
825 |
+
"metadata": {
|
826 |
+
"kernelspec": {
|
827 |
+
"display_name": "Python (LLM)",
|
828 |
+
"language": "python",
|
829 |
+
"name": "llm"
|
830 |
+
},
|
831 |
+
"language_info": {
|
832 |
+
"codemirror_mode": {
|
833 |
+
"name": "ipython",
|
834 |
+
"version": 3
|
835 |
+
},
|
836 |
+
"file_extension": ".py",
|
837 |
+
"mimetype": "text/x-python",
|
838 |
+
"name": "python",
|
839 |
+
"nbconvert_exporter": "python",
|
840 |
+
"pygments_lexer": "ipython3",
|
841 |
+
"version": "3.12.7"
|
842 |
+
}
|
843 |
+
},
|
844 |
+
"nbformat": 4,
|
845 |
+
"nbformat_minor": 5
|
846 |
+
}
|
test2.ipynb
ADDED
@@ -0,0 +1,1193 @@
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1 |
+
{
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2 |
+
"cells": [
|
3 |
+
{
|
4 |
+
"cell_type": "code",
|
5 |
+
"execution_count": 2,
|
6 |
+
"id": "f3b7f6b0-6685-4a5c-9529-45e0ca905a3b",
|
7 |
+
"metadata": {},
|
8 |
+
"outputs": [
|
9 |
+
{
|
10 |
+
"name": "stdout",
|
11 |
+
"output_type": "stream",
|
12 |
+
"text": [
|
13 |
+
"* Running on local URL: http://127.0.0.1:7860\n",
|
14 |
+
"\n",
|
15 |
+
"To create a public link, set `share=True` in `launch()`.\n"
|
16 |
+
]
|
17 |
+
},
|
18 |
+
{
|
19 |
+
"data": {
|
20 |
+
"text/html": [
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+
"<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
|
22 |
+
],
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23 |
+
"text/plain": [
|
24 |
+
"<IPython.core.display.HTML object>"
|
25 |
+
]
|
26 |
+
},
|
27 |
+
"metadata": {},
|
28 |
+
"output_type": "display_data"
|
29 |
+
},
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30 |
+
{
|
31 |
+
"data": {
|
32 |
+
"text/plain": []
|
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+
},
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34 |
+
"execution_count": 2,
|
35 |
+
"metadata": {},
|
36 |
+
"output_type": "execute_result"
|
37 |
+
}
|
38 |
+
],
|
39 |
+
"source": [
|
40 |
+
"import gradio as gr\n",
|
41 |
+
"import requests\n",
|
42 |
+
"from Bio.PDB import PDBParser\n",
|
43 |
+
"import numpy as np\n",
|
44 |
+
"import os\n",
|
45 |
+
"from gradio_molecule3d import Molecule3D\n",
|
46 |
+
"\n",
|
47 |
+
"def read_mol(pdb_path):\n",
|
48 |
+
" \"\"\"Read PDB file and return its content as a string\"\"\"\n",
|
49 |
+
" with open(pdb_path, 'r') as f:\n",
|
50 |
+
" return f.read()\n",
|
51 |
+
"\n",
|
52 |
+
"def fetch_pdb(pdb_id):\n",
|
53 |
+
" pdb_url = f'https://files.rcsb.org/download/{pdb_id}.pdb'\n",
|
54 |
+
" pdb_path = f'{pdb_id}.pdb'\n",
|
55 |
+
" response = requests.get(pdb_url)\n",
|
56 |
+
" if response.status_code == 200:\n",
|
57 |
+
" with open(pdb_path, 'wb') as f:\n",
|
58 |
+
" f.write(response.content)\n",
|
59 |
+
" return pdb_path\n",
|
60 |
+
" else:\n",
|
61 |
+
" return None\n",
|
62 |
+
"\n",
|
63 |
+
"def process_pdb(pdb_id, segment):\n",
|
64 |
+
" pdb_path = fetch_pdb(pdb_id)\n",
|
65 |
+
" if not pdb_path:\n",
|
66 |
+
" return \"Failed to fetch PDB file\", None, None\n",
|
67 |
+
" \n",
|
68 |
+
" parser = PDBParser(QUIET=1)\n",
|
69 |
+
" structure = parser.get_structure('protein', pdb_path)\n",
|
70 |
+
" \n",
|
71 |
+
" try:\n",
|
72 |
+
" chain = structure[0][segment]\n",
|
73 |
+
" except KeyError:\n",
|
74 |
+
" return \"Invalid Chain ID\", None, None\n",
|
75 |
+
" \n",
|
76 |
+
" # Comprehensive amino acid mapping\n",
|
77 |
+
" aa_dict = {\n",
|
78 |
+
" 'ALA': 'A', 'CYS': 'C', 'ASP': 'D', 'GLU': 'E', 'PHE': 'F',\n",
|
79 |
+
" 'GLY': 'G', 'HIS': 'H', 'ILE': 'I', 'LYS': 'K', 'LEU': 'L',\n",
|
80 |
+
" 'MET': 'M', 'ASN': 'N', 'PRO': 'P', 'GLN': 'Q', 'ARG': 'R',\n",
|
81 |
+
" 'SER': 'S', 'THR': 'T', 'VAL': 'V', 'TRP': 'W', 'TYR': 'Y',\n",
|
82 |
+
" 'MSE': 'M', 'SEP': 'S', 'TPO': 'T', 'CSO': 'C', 'PTR': 'Y', 'HYP': 'P'\n",
|
83 |
+
" }\n",
|
84 |
+
" \n",
|
85 |
+
" # Exclude non-amino acid residues\n",
|
86 |
+
" sequence = [\n",
|
87 |
+
" residue for residue in chain \n",
|
88 |
+
" if residue.get_resname().strip() in aa_dict\n",
|
89 |
+
" ]\n",
|
90 |
+
" \n",
|
91 |
+
" random_scores = np.random.rand(len(sequence))\n",
|
92 |
+
" result_str = \"\\n\".join(\n",
|
93 |
+
" f\"{aa_dict[res.get_resname()]} {res.id[1]} {score:.2f}\" \n",
|
94 |
+
" for res, score in zip(sequence, random_scores)\n",
|
95 |
+
" )\n",
|
96 |
+
" \n",
|
97 |
+
" # Save the predictions to a file\n",
|
98 |
+
" prediction_file = f\"{pdb_id}_predictions.txt\"\n",
|
99 |
+
" with open(prediction_file, \"w\") as f:\n",
|
100 |
+
" f.write(result_str)\n",
|
101 |
+
" \n",
|
102 |
+
" return result_str, molecule(pdb_path, random_scores, segment), prediction_file\n",
|
103 |
+
"\n",
|
104 |
+
"def molecule(input_pdb, scores=None, segment='A'):\n",
|
105 |
+
" mol = read_mol(input_pdb) # Read PDB file content\n",
|
106 |
+
" \n",
|
107 |
+
" # Prepare high-scoring residues script if scores are provided\n",
|
108 |
+
" high_score_script = \"\"\n",
|
109 |
+
" if scores is not None:\n",
|
110 |
+
" high_score_script = \"\"\"\n",
|
111 |
+
" // Reset all styles first\n",
|
112 |
+
" viewer.getModel(0).setStyle({}, {});\n",
|
113 |
+
" \n",
|
114 |
+
" // Show only the selected chain\n",
|
115 |
+
" viewer.getModel(0).setStyle(\n",
|
116 |
+
" {\"chain\": \"%s\"}, \n",
|
117 |
+
" { cartoon: {colorscheme:\"whiteCarbon\"} }\n",
|
118 |
+
" );\n",
|
119 |
+
" \n",
|
120 |
+
" // Highlight high-scoring residues only for the selected chain\n",
|
121 |
+
" let highScoreResidues = [%s];\n",
|
122 |
+
" viewer.getModel(0).setStyle(\n",
|
123 |
+
" {\"chain\": \"%s\", \"resi\": highScoreResidues}, \n",
|
124 |
+
" {\"stick\": {\"color\": \"red\"}}\n",
|
125 |
+
" );\n",
|
126 |
+
" \"\"\" % (segment, \n",
|
127 |
+
" \", \".join(str(i+1) for i, score in enumerate(scores) if score > 0.8),\n",
|
128 |
+
" segment)\n",
|
129 |
+
" \n",
|
130 |
+
" html_content = f\"\"\"\n",
|
131 |
+
" <!DOCTYPE html>\n",
|
132 |
+
" <html>\n",
|
133 |
+
" <head> \n",
|
134 |
+
" <meta http-equiv=\"content-type\" content=\"text/html; charset=UTF-8\" />\n",
|
135 |
+
" <style>\n",
|
136 |
+
" .mol-container {{\n",
|
137 |
+
" width: 100%;\n",
|
138 |
+
" height: 700px;\n",
|
139 |
+
" position: relative;\n",
|
140 |
+
" }}\n",
|
141 |
+
" </style>\n",
|
142 |
+
" <script src=\"https://cdnjs.cloudflare.com/ajax/libs/jquery/3.6.3/jquery.min.js\"></script>\n",
|
143 |
+
" <script src=\"https://3Dmol.csb.pitt.edu/build/3Dmol-min.js\"></script>\n",
|
144 |
+
" </head>\n",
|
145 |
+
" <body>\n",
|
146 |
+
" <div id=\"container\" class=\"mol-container\"></div>\n",
|
147 |
+
" <script>\n",
|
148 |
+
" let pdb = `{mol}`; // Use template literal to properly escape PDB content\n",
|
149 |
+
" $(document).ready(function () {{\n",
|
150 |
+
" let element = $(\"#container\");\n",
|
151 |
+
" let config = {{ backgroundColor: \"white\" }};\n",
|
152 |
+
" let viewer = $3Dmol.createViewer(element, config);\n",
|
153 |
+
" viewer.addModel(pdb, \"pdb\");\n",
|
154 |
+
" \n",
|
155 |
+
" // Reset all styles and show only selected chain\n",
|
156 |
+
" viewer.getModel(0).setStyle(\n",
|
157 |
+
" {{\"chain\": \"{segment}\"}}, \n",
|
158 |
+
" {{ cartoon: {{ colorscheme:\"whiteCarbon\" }} }}\n",
|
159 |
+
" );\n",
|
160 |
+
" \n",
|
161 |
+
" {high_score_script}\n",
|
162 |
+
" \n",
|
163 |
+
" viewer.zoomTo();\n",
|
164 |
+
" viewer.render();\n",
|
165 |
+
" viewer.zoom(0.8, 2000);\n",
|
166 |
+
" }});\n",
|
167 |
+
" </script>\n",
|
168 |
+
" </body>\n",
|
169 |
+
" </html>\n",
|
170 |
+
" \"\"\"\n",
|
171 |
+
" \n",
|
172 |
+
" # Return the HTML content within an iframe safely encoded for special characters\n",
|
173 |
+
" return f'<iframe width=\"100%\" height=\"700\" srcdoc=\"{html_content.replace(chr(34), \""\").replace(chr(39), \"'\")}\"></iframe>'\n",
|
174 |
+
"\n",
|
175 |
+
"reps = [\n",
|
176 |
+
" {\n",
|
177 |
+
" \"model\": 0,\n",
|
178 |
+
" \"style\": \"cartoon\",\n",
|
179 |
+
" \"color\": \"whiteCarbon\",\n",
|
180 |
+
" \"residue_range\": \"\",\n",
|
181 |
+
" \"around\": 0,\n",
|
182 |
+
" \"byres\": False,\n",
|
183 |
+
" }\n",
|
184 |
+
" ]\n",
|
185 |
+
"# Gradio UI\n",
|
186 |
+
"with gr.Blocks() as demo:\n",
|
187 |
+
" gr.Markdown(\"# Protein Binding Site Prediction (Random Scores)\")\n",
|
188 |
+
" with gr.Row():\n",
|
189 |
+
" pdb_input = gr.Textbox(value=\"2IWI\", label=\"PDB ID\", placeholder=\"Enter PDB ID here...\")\n",
|
190 |
+
" visualize_btn = gr.Button(\"Visualize Structure\")\n",
|
191 |
+
"\n",
|
192 |
+
" molecule_output2 = Molecule3D(label=\"Protein Structure\", reps=reps)\n",
|
193 |
+
"\n",
|
194 |
+
" with gr.Row():\n",
|
195 |
+
" pdb_input = gr.Textbox(value=\"2IWI\", label=\"PDB ID\", placeholder=\"Enter PDB ID here...\")\n",
|
196 |
+
" segment_input = gr.Textbox(value=\"A\", label=\"Chain ID\", placeholder=\"Enter Chain ID here...\")\n",
|
197 |
+
" prediction_btn = gr.Button(\"Predict Random Binding Site Scores\")\n",
|
198 |
+
"\n",
|
199 |
+
" molecule_output = gr.HTML(label=\"Protein Structure\")\n",
|
200 |
+
" predictions_output = gr.Textbox(label=\"Binding Site Predictions\")\n",
|
201 |
+
" download_output = gr.File(label=\"Download Predictions\")\n",
|
202 |
+
" \n",
|
203 |
+
" visualize_btn.click(fetch_pdb, inputs=[pdb_input], outputs=molecule_output2)\n",
|
204 |
+
" \n",
|
205 |
+
" prediction_btn.click(process_pdb, inputs=[pdb_input, segment_input], outputs=[predictions_output, molecule_output, download_output])\n",
|
206 |
+
" \n",
|
207 |
+
" gr.Markdown(\"## Examples\")\n",
|
208 |
+
" gr.Examples(\n",
|
209 |
+
" examples=[\n",
|
210 |
+
" [\"2IWI\", \"A\"],\n",
|
211 |
+
" [\"7RPZ\", \"B\"],\n",
|
212 |
+
" [\"3TJN\", \"C\"]\n",
|
213 |
+
" ],\n",
|
214 |
+
" inputs=[pdb_input, segment_input],\n",
|
215 |
+
" outputs=[predictions_output, molecule_output, download_output]\n",
|
216 |
+
" )\n",
|
217 |
+
"\n",
|
218 |
+
"demo.launch()"
|
219 |
+
]
|
220 |
+
},
|
221 |
+
{
|
222 |
+
"cell_type": "code",
|
223 |
+
"execution_count": 6,
|
224 |
+
"id": "28f8f28c-48d3-4e35-9766-3de9882179b5",
|
225 |
+
"metadata": {},
|
226 |
+
"outputs": [
|
227 |
+
{
|
228 |
+
"name": "stdout",
|
229 |
+
"output_type": "stream",
|
230 |
+
"text": [
|
231 |
+
"* Running on local URL: http://127.0.0.1:7864\n",
|
232 |
+
"\n",
|
233 |
+
"To create a public link, set `share=True` in `launch()`.\n"
|
234 |
+
]
|
235 |
+
},
|
236 |
+
{
|
237 |
+
"data": {
|
238 |
+
"text/html": [
|
239 |
+
"<div><iframe src=\"http://127.0.0.1:7864/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
|
240 |
+
],
|
241 |
+
"text/plain": [
|
242 |
+
"<IPython.core.display.HTML object>"
|
243 |
+
]
|
244 |
+
},
|
245 |
+
"metadata": {},
|
246 |
+
"output_type": "display_data"
|
247 |
+
},
|
248 |
+
{
|
249 |
+
"data": {
|
250 |
+
"text/plain": []
|
251 |
+
},
|
252 |
+
"execution_count": 6,
|
253 |
+
"metadata": {},
|
254 |
+
"output_type": "execute_result"
|
255 |
+
}
|
256 |
+
],
|
257 |
+
"source": [
|
258 |
+
"import gradio as gr\n",
|
259 |
+
"import requests\n",
|
260 |
+
"from Bio.PDB import PDBParser\n",
|
261 |
+
"import numpy as np\n",
|
262 |
+
"import os\n",
|
263 |
+
"from gradio_molecule3d import Molecule3D\n",
|
264 |
+
"\n",
|
265 |
+
"def read_mol(pdb_path):\n",
|
266 |
+
" \"\"\"Read PDB file and return its content as a string\"\"\"\n",
|
267 |
+
" with open(pdb_path, 'r') as f:\n",
|
268 |
+
" return f.read()\n",
|
269 |
+
"\n",
|
270 |
+
"def fetch_pdb(pdb_id):\n",
|
271 |
+
" pdb_url = f'https://files.rcsb.org/download/{pdb_id}.pdb'\n",
|
272 |
+
" pdb_path = f'{pdb_id}.pdb'\n",
|
273 |
+
" response = requests.get(pdb_url)\n",
|
274 |
+
" if response.status_code == 200:\n",
|
275 |
+
" with open(pdb_path, 'wb') as f:\n",
|
276 |
+
" f.write(response.content)\n",
|
277 |
+
" return pdb_path\n",
|
278 |
+
" else:\n",
|
279 |
+
" return None\n",
|
280 |
+
"\n",
|
281 |
+
"def process_pdb(pdb_id, segment):\n",
|
282 |
+
" pdb_path = fetch_pdb(pdb_id)\n",
|
283 |
+
" if not pdb_path:\n",
|
284 |
+
" return \"Failed to fetch PDB file\", None, None\n",
|
285 |
+
" \n",
|
286 |
+
" parser = PDBParser(QUIET=1)\n",
|
287 |
+
" structure = parser.get_structure('protein', pdb_path)\n",
|
288 |
+
" \n",
|
289 |
+
" try:\n",
|
290 |
+
" chain = structure[0][segment]\n",
|
291 |
+
" except KeyError:\n",
|
292 |
+
" return \"Invalid Chain ID\", None, None\n",
|
293 |
+
" \n",
|
294 |
+
" # Comprehensive amino acid mapping\n",
|
295 |
+
" aa_dict = {\n",
|
296 |
+
" 'ALA': 'A', 'CYS': 'C', 'ASP': 'D', 'GLU': 'E', 'PHE': 'F',\n",
|
297 |
+
" 'GLY': 'G', 'HIS': 'H', 'ILE': 'I', 'LYS': 'K', 'LEU': 'L',\n",
|
298 |
+
" 'MET': 'M', 'ASN': 'N', 'PRO': 'P', 'GLN': 'Q', 'ARG': 'R',\n",
|
299 |
+
" 'SER': 'S', 'THR': 'T', 'VAL': 'V', 'TRP': 'W', 'TYR': 'Y',\n",
|
300 |
+
" 'MSE': 'M', 'SEP': 'S', 'TPO': 'T', 'CSO': 'C', 'PTR': 'Y', 'HYP': 'P'\n",
|
301 |
+
" }\n",
|
302 |
+
" \n",
|
303 |
+
" # Exclude non-amino acid residues\n",
|
304 |
+
" sequence = [\n",
|
305 |
+
" residue for residue in chain \n",
|
306 |
+
" if residue.get_resname().strip() in aa_dict\n",
|
307 |
+
" ]\n",
|
308 |
+
" \n",
|
309 |
+
" random_scores = np.random.rand(len(sequence))\n",
|
310 |
+
" result_str = \"\\n\".join(\n",
|
311 |
+
" f\"{aa_dict[res.get_resname()]} {res.id[1]} {score:.2f}\" \n",
|
312 |
+
" for res, score in zip(sequence, random_scores)\n",
|
313 |
+
" )\n",
|
314 |
+
" \n",
|
315 |
+
" # Save the predictions to a file\n",
|
316 |
+
" prediction_file = f\"{pdb_id}_predictions.txt\"\n",
|
317 |
+
" with open(prediction_file, \"w\") as f:\n",
|
318 |
+
" f.write(result_str)\n",
|
319 |
+
" \n",
|
320 |
+
" return result_str, molecule(pdb_path, random_scores, segment), prediction_file\n",
|
321 |
+
"\n",
|
322 |
+
"def molecule(input_pdb, scores=None, segment='A'):\n",
|
323 |
+
" mol = read_mol(input_pdb) # Read PDB file content\n",
|
324 |
+
" \n",
|
325 |
+
" # Prepare high-scoring residues script if scores are provided\n",
|
326 |
+
" high_score_script = \"\"\n",
|
327 |
+
" if scores is not None:\n",
|
328 |
+
" high_score_script = \"\"\"\n",
|
329 |
+
" // Reset all styles first\n",
|
330 |
+
" viewer.getModel(0).setStyle({}, {});\n",
|
331 |
+
" \n",
|
332 |
+
" // Show only the selected chain\n",
|
333 |
+
" viewer.getModel(0).setStyle(\n",
|
334 |
+
" {\"chain\": \"%s\"}, \n",
|
335 |
+
" { cartoon: {colorscheme:\"whiteCarbon\"} }\n",
|
336 |
+
" );\n",
|
337 |
+
" \n",
|
338 |
+
" // Highlight high-scoring residues only for the selected chain\n",
|
339 |
+
" let highScoreResidues = [%s];\n",
|
340 |
+
" viewer.getModel(0).setStyle(\n",
|
341 |
+
" {\"chain\": \"%s\", \"resi\": highScoreResidues}, \n",
|
342 |
+
" {\"stick\": {\"color\": \"red\"}}\n",
|
343 |
+
" );\n",
|
344 |
+
" \"\"\" % (segment, \n",
|
345 |
+
" \", \".join(str(i+1) for i, score in enumerate(scores) if score > 0.8),\n",
|
346 |
+
" segment)\n",
|
347 |
+
" \n",
|
348 |
+
" html_content = f\"\"\"\n",
|
349 |
+
" <!DOCTYPE html>\n",
|
350 |
+
" <html>\n",
|
351 |
+
" <head> \n",
|
352 |
+
" <meta http-equiv=\"content-type\" content=\"text/html; charset=UTF-8\" />\n",
|
353 |
+
" <style>\n",
|
354 |
+
" .mol-container {{\n",
|
355 |
+
" width: 100%;\n",
|
356 |
+
" height: 700px;\n",
|
357 |
+
" position: relative;\n",
|
358 |
+
" }}\n",
|
359 |
+
" </style>\n",
|
360 |
+
" <script src=\"https://cdnjs.cloudflare.com/ajax/libs/jquery/3.6.3/jquery.min.js\"></script>\n",
|
361 |
+
" <script src=\"https://3Dmol.csb.pitt.edu/build/3Dmol-min.js\"></script>\n",
|
362 |
+
" </head>\n",
|
363 |
+
" <body>\n",
|
364 |
+
" <div id=\"container\" class=\"mol-container\"></div>\n",
|
365 |
+
" <script>\n",
|
366 |
+
" let pdb = `{mol}`; // Use template literal to properly escape PDB content\n",
|
367 |
+
" $(document).ready(function () {{\n",
|
368 |
+
" let element = $(\"#container\");\n",
|
369 |
+
" let config = {{ backgroundColor: \"white\" }};\n",
|
370 |
+
" let viewer = $3Dmol.createViewer(element, config);\n",
|
371 |
+
" viewer.addModel(pdb, \"pdb\");\n",
|
372 |
+
" \n",
|
373 |
+
" // Reset all styles and show only selected chain\n",
|
374 |
+
" viewer.getModel(0).setStyle(\n",
|
375 |
+
" {{\"chain\": \"{segment}\"}}, \n",
|
376 |
+
" {{ cartoon: {{ colorscheme:\"whiteCarbon\" }} }}\n",
|
377 |
+
" );\n",
|
378 |
+
" \n",
|
379 |
+
" {high_score_script}\n",
|
380 |
+
" \n",
|
381 |
+
" // Add hover functionality\n",
|
382 |
+
" viewer.setHoverable(\n",
|
383 |
+
" {{}}, \n",
|
384 |
+
" true, \n",
|
385 |
+
" function(atom, viewer, event, container) {{\n",
|
386 |
+
" if (!atom.label) {{\n",
|
387 |
+
" atom.label = viewer.addLabel(\n",
|
388 |
+
" atom.resn + \":\" + atom.atom, \n",
|
389 |
+
" {{\n",
|
390 |
+
" position: atom, \n",
|
391 |
+
" backgroundColor: 'mintcream', \n",
|
392 |
+
" fontColor: 'black',\n",
|
393 |
+
" fontSize: 12,\n",
|
394 |
+
" padding: 2\n",
|
395 |
+
" }}\n",
|
396 |
+
" );\n",
|
397 |
+
" }}\n",
|
398 |
+
" }},\n",
|
399 |
+
" function(atom, viewer) {{\n",
|
400 |
+
" if (atom.label) {{\n",
|
401 |
+
" viewer.removeLabel(atom.label);\n",
|
402 |
+
" delete atom.label;\n",
|
403 |
+
" }}\n",
|
404 |
+
" }}\n",
|
405 |
+
" );\n",
|
406 |
+
" \n",
|
407 |
+
" viewer.zoomTo();\n",
|
408 |
+
" viewer.render();\n",
|
409 |
+
" viewer.zoom(0.8, 2000);\n",
|
410 |
+
" }});\n",
|
411 |
+
" </script>\n",
|
412 |
+
" </body>\n",
|
413 |
+
" </html>\n",
|
414 |
+
" \"\"\"\n",
|
415 |
+
" \n",
|
416 |
+
" # Return the HTML content within an iframe safely encoded for special characters\n",
|
417 |
+
" return f'<iframe width=\"100%\" height=\"700\" srcdoc=\"{html_content.replace(chr(34), \""\").replace(chr(39), \"'\")}\"></iframe>'\n",
|
418 |
+
"\n",
|
419 |
+
"reps = [\n",
|
420 |
+
" {\n",
|
421 |
+
" \"model\": 0,\n",
|
422 |
+
" \"style\": \"cartoon\",\n",
|
423 |
+
" \"color\": \"whiteCarbon\",\n",
|
424 |
+
" \"residue_range\": \"\",\n",
|
425 |
+
" \"around\": 0,\n",
|
426 |
+
" \"byres\": False,\n",
|
427 |
+
" }\n",
|
428 |
+
" ]\n",
|
429 |
+
"\n",
|
430 |
+
"# Gradio UI\n",
|
431 |
+
"with gr.Blocks() as demo:\n",
|
432 |
+
" gr.Markdown(\"# Protein Binding Site Prediction (Random Scores)\")\n",
|
433 |
+
" with gr.Row():\n",
|
434 |
+
" pdb_input = gr.Textbox(value=\"2IWI\", label=\"PDB ID\", placeholder=\"Enter PDB ID here...\")\n",
|
435 |
+
" visualize_btn = gr.Button(\"Visualize Structure\")\n",
|
436 |
+
"\n",
|
437 |
+
" molecule_output2 = Molecule3D(label=\"Protein Structure\", reps=reps)\n",
|
438 |
+
"\n",
|
439 |
+
" with gr.Row():\n",
|
440 |
+
" pdb_input = gr.Textbox(value=\"2IWI\", label=\"PDB ID\", placeholder=\"Enter PDB ID here...\")\n",
|
441 |
+
" segment_input = gr.Textbox(value=\"A\", label=\"Chain ID\", placeholder=\"Enter Chain ID here...\")\n",
|
442 |
+
" prediction_btn = gr.Button(\"Predict Random Binding Site Scores\")\n",
|
443 |
+
"\n",
|
444 |
+
" molecule_output = gr.HTML(label=\"Protein Structure\")\n",
|
445 |
+
" predictions_output = gr.Textbox(label=\"Binding Site Predictions\")\n",
|
446 |
+
" download_output = gr.File(label=\"Download Predictions\")\n",
|
447 |
+
" \n",
|
448 |
+
" visualize_btn.click(fetch_pdb, inputs=[pdb_input], outputs=molecule_output2)\n",
|
449 |
+
" \n",
|
450 |
+
" prediction_btn.click(process_pdb, inputs=[pdb_input, segment_input], outputs=[predictions_output, molecule_output, download_output])\n",
|
451 |
+
" \n",
|
452 |
+
" gr.Markdown(\"## Examples\")\n",
|
453 |
+
" gr.Examples(\n",
|
454 |
+
" examples=[\n",
|
455 |
+
" [\"2IWI\", \"A\"],\n",
|
456 |
+
" [\"7RPZ\", \"B\"],\n",
|
457 |
+
" [\"3TJN\", \"C\"]\n",
|
458 |
+
" ],\n",
|
459 |
+
" inputs=[pdb_input, segment_input],\n",
|
460 |
+
" outputs=[predictions_output, molecule_output, download_output]\n",
|
461 |
+
" )\n",
|
462 |
+
"\n",
|
463 |
+
"demo.launch()"
|
464 |
+
]
|
465 |
+
},
|
466 |
+
{
|
467 |
+
"cell_type": "code",
|
468 |
+
"execution_count": null,
|
469 |
+
"id": "517a2fe7-419f-4d0b-a9ed-62a22c1c1284",
|
470 |
+
"metadata": {},
|
471 |
+
"outputs": [],
|
472 |
+
"source": []
|
473 |
+
},
|
474 |
+
{
|
475 |
+
"cell_type": "code",
|
476 |
+
"execution_count": 11,
|
477 |
+
"id": "d62be1b5-762e-4b69-aed4-e4ba2a44482f",
|
478 |
+
"metadata": {},
|
479 |
+
"outputs": [
|
480 |
+
{
|
481 |
+
"name": "stdout",
|
482 |
+
"output_type": "stream",
|
483 |
+
"text": [
|
484 |
+
"* Running on local URL: http://127.0.0.1:7867\n",
|
485 |
+
"\n",
|
486 |
+
"To create a public link, set `share=True` in `launch()`.\n"
|
487 |
+
]
|
488 |
+
},
|
489 |
+
{
|
490 |
+
"data": {
|
491 |
+
"text/html": [
|
492 |
+
"<div><iframe src=\"http://127.0.0.1:7867/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
|
493 |
+
],
|
494 |
+
"text/plain": [
|
495 |
+
"<IPython.core.display.HTML object>"
|
496 |
+
]
|
497 |
+
},
|
498 |
+
"metadata": {},
|
499 |
+
"output_type": "display_data"
|
500 |
+
},
|
501 |
+
{
|
502 |
+
"data": {
|
503 |
+
"text/plain": []
|
504 |
+
},
|
505 |
+
"execution_count": 11,
|
506 |
+
"metadata": {},
|
507 |
+
"output_type": "execute_result"
|
508 |
+
}
|
509 |
+
],
|
510 |
+
"source": [
|
511 |
+
"import gradio as gr\n",
|
512 |
+
"import requests\n",
|
513 |
+
"from Bio.PDB import PDBParser\n",
|
514 |
+
"import numpy as np\n",
|
515 |
+
"import os\n",
|
516 |
+
"from gradio_molecule3d import Molecule3D\n",
|
517 |
+
"\n",
|
518 |
+
"def read_mol(pdb_path):\n",
|
519 |
+
" \"\"\"Read PDB file and return its content as a string\"\"\"\n",
|
520 |
+
" with open(pdb_path, 'r') as f:\n",
|
521 |
+
" return f.read()\n",
|
522 |
+
"\n",
|
523 |
+
"def fetch_pdb(pdb_id):\n",
|
524 |
+
" pdb_url = f'https://files.rcsb.org/download/{pdb_id}.pdb'\n",
|
525 |
+
" pdb_path = f'{pdb_id}.pdb'\n",
|
526 |
+
" response = requests.get(pdb_url)\n",
|
527 |
+
" if response.status_code == 200:\n",
|
528 |
+
" with open(pdb_path, 'wb') as f:\n",
|
529 |
+
" f.write(response.content)\n",
|
530 |
+
" return pdb_path\n",
|
531 |
+
" else:\n",
|
532 |
+
" return None\n",
|
533 |
+
"\n",
|
534 |
+
"def process_pdb(pdb_id, segment):\n",
|
535 |
+
" pdb_path = fetch_pdb(pdb_id)\n",
|
536 |
+
" if not pdb_path:\n",
|
537 |
+
" return \"Failed to fetch PDB file\", None, None\n",
|
538 |
+
" \n",
|
539 |
+
" parser = PDBParser(QUIET=1)\n",
|
540 |
+
" structure = parser.get_structure('protein', pdb_path)\n",
|
541 |
+
" \n",
|
542 |
+
" try:\n",
|
543 |
+
" chain = structure[0][segment]\n",
|
544 |
+
" except KeyError:\n",
|
545 |
+
" return \"Invalid Chain ID\", None, None\n",
|
546 |
+
" \n",
|
547 |
+
" # Comprehensive amino acid mapping\n",
|
548 |
+
" aa_dict = {\n",
|
549 |
+
" 'ALA': 'A', 'CYS': 'C', 'ASP': 'D', 'GLU': 'E', 'PHE': 'F',\n",
|
550 |
+
" 'GLY': 'G', 'HIS': 'H', 'ILE': 'I', 'LYS': 'K', 'LEU': 'L',\n",
|
551 |
+
" 'MET': 'M', 'ASN': 'N', 'PRO': 'P', 'GLN': 'Q', 'ARG': 'R',\n",
|
552 |
+
" 'SER': 'S', 'THR': 'T', 'VAL': 'V', 'TRP': 'W', 'TYR': 'Y',\n",
|
553 |
+
" 'MSE': 'M', 'SEP': 'S', 'TPO': 'T', 'CSO': 'C', 'PTR': 'Y', 'HYP': 'P'\n",
|
554 |
+
" }\n",
|
555 |
+
" \n",
|
556 |
+
" # Exclude non-amino acid residues\n",
|
557 |
+
" sequence = [\n",
|
558 |
+
" residue for residue in chain \n",
|
559 |
+
" if residue.get_resname().strip() in aa_dict\n",
|
560 |
+
" ]\n",
|
561 |
+
" \n",
|
562 |
+
" random_scores = np.random.rand(len(sequence))\n",
|
563 |
+
" result_str = \"\\n\".join(\n",
|
564 |
+
" f\"{aa_dict[res.get_resname()]} {res.id[1]} {score:.2f}\" \n",
|
565 |
+
" for res, score in zip(sequence, random_scores)\n",
|
566 |
+
" )\n",
|
567 |
+
" \n",
|
568 |
+
" # Save the predictions to a file\n",
|
569 |
+
" prediction_file = f\"{pdb_id}_predictions.txt\"\n",
|
570 |
+
" with open(prediction_file, \"w\") as f:\n",
|
571 |
+
" f.write(result_str)\n",
|
572 |
+
" \n",
|
573 |
+
" return result_str, molecule(pdb_path, random_scores, segment), prediction_file\n",
|
574 |
+
"\n",
|
575 |
+
"def molecule(input_pdb, scores=None, segment='A'):\n",
|
576 |
+
" mol = read_mol(input_pdb) # Read PDB file content\n",
|
577 |
+
" \n",
|
578 |
+
" # Prepare high-scoring residues script if scores are provided\n",
|
579 |
+
" high_score_script = \"\"\n",
|
580 |
+
" if scores is not None:\n",
|
581 |
+
" high_score_script = \"\"\"\n",
|
582 |
+
" // Reset all styles first\n",
|
583 |
+
" viewer.getModel(0).setStyle({}, {});\n",
|
584 |
+
" \n",
|
585 |
+
" // Show only the selected chain\n",
|
586 |
+
" viewer.getModel(0).setStyle(\n",
|
587 |
+
" {\"chain\": \"%s\"}, \n",
|
588 |
+
" { cartoon: {colorscheme:\"whiteCarbon\"} }\n",
|
589 |
+
" );\n",
|
590 |
+
" \n",
|
591 |
+
" // Highlight high-scoring residues only for the selected chain\n",
|
592 |
+
" let highScoreResidues = [%s];\n",
|
593 |
+
" viewer.getModel(0).setStyle(\n",
|
594 |
+
" {\"chain\": \"%s\", \"resi\": highScoreResidues}, \n",
|
595 |
+
" {\"stick\": {\"color\": \"red\"}}\n",
|
596 |
+
" );\n",
|
597 |
+
"\n",
|
598 |
+
" // Highlight high-scoring residues only for the selected chain\n",
|
599 |
+
" let highScoreResidues2 = [%s];\n",
|
600 |
+
" viewer.getModel(0).setStyle(\n",
|
601 |
+
" {\"chain\": \"%s\", \"resi\": highScoreResidues2}, \n",
|
602 |
+
" {\"stick\": {\"color\": \"orange\"}}\n",
|
603 |
+
" );\n",
|
604 |
+
" \"\"\" % (segment, \n",
|
605 |
+
" \", \".join(str(i+1) for i, score in enumerate(scores) if score > 0.8),\n",
|
606 |
+
" segment,\n",
|
607 |
+
" \", \".join(str(i+1) for i, score in enumerate(scores) if (score > 0.5) and (score < 0.8)),\n",
|
608 |
+
" segment)\n",
|
609 |
+
" \n",
|
610 |
+
" html_content = f\"\"\"\n",
|
611 |
+
" <!DOCTYPE html>\n",
|
612 |
+
" <html>\n",
|
613 |
+
" <head> \n",
|
614 |
+
" <meta http-equiv=\"content-type\" content=\"text/html; charset=UTF-8\" />\n",
|
615 |
+
" <style>\n",
|
616 |
+
" .mol-container {{\n",
|
617 |
+
" width: 100%;\n",
|
618 |
+
" height: 700px;\n",
|
619 |
+
" position: relative;\n",
|
620 |
+
" }}\n",
|
621 |
+
" </style>\n",
|
622 |
+
" <script src=\"https://cdnjs.cloudflare.com/ajax/libs/jquery/3.6.3/jquery.min.js\"></script>\n",
|
623 |
+
" <script src=\"https://3Dmol.csb.pitt.edu/build/3Dmol-min.js\"></script>\n",
|
624 |
+
" </head>\n",
|
625 |
+
" <body>\n",
|
626 |
+
" <div id=\"container\" class=\"mol-container\"></div>\n",
|
627 |
+
" <script>\n",
|
628 |
+
" let pdb = `{mol}`; // Use template literal to properly escape PDB content\n",
|
629 |
+
" $(document).ready(function () {{\n",
|
630 |
+
" let element = $(\"#container\");\n",
|
631 |
+
" let config = {{ backgroundColor: \"white\" }};\n",
|
632 |
+
" let viewer = $3Dmol.createViewer(element, config);\n",
|
633 |
+
" viewer.addModel(pdb, \"pdb\");\n",
|
634 |
+
" \n",
|
635 |
+
" // Reset all styles and show only selected chain\n",
|
636 |
+
" viewer.getModel(0).setStyle(\n",
|
637 |
+
" {{\"chain\": \"{segment}\"}}, \n",
|
638 |
+
" {{ cartoon: {{ colorscheme:\"whiteCarbon\" }} }}\n",
|
639 |
+
" );\n",
|
640 |
+
" \n",
|
641 |
+
" {high_score_script}\n",
|
642 |
+
" \n",
|
643 |
+
" // Add hover functionality\n",
|
644 |
+
" viewer.setHoverable(\n",
|
645 |
+
" {{}}, \n",
|
646 |
+
" true, \n",
|
647 |
+
" function(atom, viewer, event, container) {{\n",
|
648 |
+
" if (!atom.label) {{\n",
|
649 |
+
" atom.label = viewer.addLabel(\n",
|
650 |
+
" atom.resn + \":\" + atom.atom, \n",
|
651 |
+
" {{\n",
|
652 |
+
" position: atom, \n",
|
653 |
+
" backgroundColor: 'mintcream', \n",
|
654 |
+
" fontColor: 'black',\n",
|
655 |
+
" fontSize: 12,\n",
|
656 |
+
" padding: 2\n",
|
657 |
+
" }}\n",
|
658 |
+
" );\n",
|
659 |
+
" }}\n",
|
660 |
+
" }},\n",
|
661 |
+
" function(atom, viewer) {{\n",
|
662 |
+
" if (atom.label) {{\n",
|
663 |
+
" viewer.removeLabel(atom.label);\n",
|
664 |
+
" delete atom.label;\n",
|
665 |
+
" }}\n",
|
666 |
+
" }}\n",
|
667 |
+
" );\n",
|
668 |
+
" \n",
|
669 |
+
" viewer.zoomTo();\n",
|
670 |
+
" viewer.render();\n",
|
671 |
+
" viewer.zoom(0.8, 2000);\n",
|
672 |
+
" }});\n",
|
673 |
+
" </script>\n",
|
674 |
+
" </body>\n",
|
675 |
+
" </html>\n",
|
676 |
+
" \"\"\"\n",
|
677 |
+
" \n",
|
678 |
+
" # Return the HTML content within an iframe safely encoded for special characters\n",
|
679 |
+
" return f'<iframe width=\"100%\" height=\"700\" srcdoc=\"{html_content.replace(chr(34), \""\").replace(chr(39), \"'\")}\"></iframe>'\n",
|
680 |
+
"\n",
|
681 |
+
"reps = [\n",
|
682 |
+
" {\n",
|
683 |
+
" \"model\": 0,\n",
|
684 |
+
" \"style\": \"cartoon\",\n",
|
685 |
+
" \"color\": \"whiteCarbon\",\n",
|
686 |
+
" \"residue_range\": \"\",\n",
|
687 |
+
" \"around\": 0,\n",
|
688 |
+
" \"byres\": False,\n",
|
689 |
+
" }\n",
|
690 |
+
" ]\n",
|
691 |
+
"\n",
|
692 |
+
"# Gradio UI\n",
|
693 |
+
"with gr.Blocks() as demo:\n",
|
694 |
+
" gr.Markdown(\"# Protein Binding Site Prediction (Random Scores)\")\n",
|
695 |
+
" with gr.Row():\n",
|
696 |
+
" pdb_input = gr.Textbox(value=\"2IWI\", label=\"PDB ID\", placeholder=\"Enter PDB ID here...\")\n",
|
697 |
+
" visualize_btn = gr.Button(\"Visualize Structure\")\n",
|
698 |
+
"\n",
|
699 |
+
" molecule_output2 = Molecule3D(label=\"Protein Structure\", reps=reps)\n",
|
700 |
+
"\n",
|
701 |
+
" with gr.Row():\n",
|
702 |
+
" pdb_input = gr.Textbox(value=\"2IWI\", label=\"PDB ID\", placeholder=\"Enter PDB ID here...\")\n",
|
703 |
+
" segment_input = gr.Textbox(value=\"A\", label=\"Chain ID\", placeholder=\"Enter Chain ID here...\")\n",
|
704 |
+
" prediction_btn = gr.Button(\"Predict Random Binding Site Scores\")\n",
|
705 |
+
"\n",
|
706 |
+
" molecule_output = gr.HTML(label=\"Protein Structure\")\n",
|
707 |
+
" predictions_output = gr.Textbox(label=\"Binding Site Predictions\")\n",
|
708 |
+
" download_output = gr.File(label=\"Download Predictions\")\n",
|
709 |
+
" \n",
|
710 |
+
" visualize_btn.click(fetch_pdb, inputs=[pdb_input], outputs=molecule_output2)\n",
|
711 |
+
" \n",
|
712 |
+
" prediction_btn.click(process_pdb, inputs=[pdb_input, segment_input], outputs=[predictions_output, molecule_output, download_output])\n",
|
713 |
+
" \n",
|
714 |
+
" gr.Markdown(\"## Examples\")\n",
|
715 |
+
" gr.Examples(\n",
|
716 |
+
" examples=[\n",
|
717 |
+
" [\"2IWI\", \"A\"],\n",
|
718 |
+
" [\"7RPZ\", \"B\"],\n",
|
719 |
+
" [\"3TJN\", \"C\"]\n",
|
720 |
+
" ],\n",
|
721 |
+
" inputs=[pdb_input, segment_input],\n",
|
722 |
+
" outputs=[predictions_output, molecule_output, download_output]\n",
|
723 |
+
" )\n",
|
724 |
+
"\n",
|
725 |
+
"demo.launch()"
|
726 |
+
]
|
727 |
+
},
|
728 |
+
{
|
729 |
+
"cell_type": "code",
|
730 |
+
"execution_count": null,
|
731 |
+
"id": "30f35243-852f-4771-9a4b-5cdd198552b5",
|
732 |
+
"metadata": {},
|
733 |
+
"outputs": [],
|
734 |
+
"source": []
|
735 |
+
},
|
736 |
+
{
|
737 |
+
"cell_type": "code",
|
738 |
+
"execution_count": null,
|
739 |
+
"id": "5eca6754-4aa1-463f-881a-25d2a0d6bb5b",
|
740 |
+
"metadata": {},
|
741 |
+
"outputs": [],
|
742 |
+
"source": [
|
743 |
+
"import gradio as gr\n",
|
744 |
+
"import requests\n",
|
745 |
+
"from Bio.PDB import PDBParser\n",
|
746 |
+
"import numpy as np\n",
|
747 |
+
"import os\n",
|
748 |
+
"from gradio_molecule3d import Molecule3D\n",
|
749 |
+
"\n",
|
750 |
+
"\n",
|
751 |
+
"from model_loader import load_model\n",
|
752 |
+
"\n",
|
753 |
+
"import torch\n",
|
754 |
+
"import torch.nn as nn\n",
|
755 |
+
"import torch.nn.functional as F\n",
|
756 |
+
"from torch.utils.data import DataLoader\n",
|
757 |
+
"\n",
|
758 |
+
"import re\n",
|
759 |
+
"import pandas as pd\n",
|
760 |
+
"import copy\n",
|
761 |
+
"\n",
|
762 |
+
"import transformers, datasets\n",
|
763 |
+
"from transformers import AutoTokenizer\n",
|
764 |
+
"from transformers import DataCollatorForTokenClassification\n",
|
765 |
+
"\n",
|
766 |
+
"from datasets import Dataset\n",
|
767 |
+
"\n",
|
768 |
+
"from scipy.special import expit\n",
|
769 |
+
"\n",
|
770 |
+
"# Load model and move to device\n",
|
771 |
+
"checkpoint = 'ThorbenF/prot_t5_xl_uniref50'\n",
|
772 |
+
"max_length = 1500\n",
|
773 |
+
"model, tokenizer = load_model(checkpoint, max_length)\n",
|
774 |
+
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
|
775 |
+
"model.to(device)\n",
|
776 |
+
"model.eval()\n",
|
777 |
+
"\n",
|
778 |
+
"def normalize_scores(scores):\n",
|
779 |
+
" min_score = np.min(scores)\n",
|
780 |
+
" max_score = np.max(scores)\n",
|
781 |
+
" return (scores - min_score) / (max_score - min_score) if max_score > min_score else scores\n",
|
782 |
+
" \n",
|
783 |
+
"def read_mol(pdb_path):\n",
|
784 |
+
" \"\"\"Read PDB file and return its content as a string\"\"\"\n",
|
785 |
+
" with open(pdb_path, 'r') as f:\n",
|
786 |
+
" return f.read()\n",
|
787 |
+
"\n",
|
788 |
+
"def fetch_pdb(pdb_id):\n",
|
789 |
+
" pdb_url = f'https://files.rcsb.org/download/{pdb_id}.pdb'\n",
|
790 |
+
" pdb_path = f'{pdb_id}.pdb'\n",
|
791 |
+
" response = requests.get(pdb_url)\n",
|
792 |
+
" if response.status_code == 200:\n",
|
793 |
+
" with open(pdb_path, 'wb') as f:\n",
|
794 |
+
" f.write(response.content)\n",
|
795 |
+
" return pdb_path\n",
|
796 |
+
" else:\n",
|
797 |
+
" return None\n",
|
798 |
+
"\n",
|
799 |
+
"def process_pdb(pdb_id, segment):\n",
|
800 |
+
" pdb_path = fetch_pdb(pdb_id)\n",
|
801 |
+
" if not pdb_path:\n",
|
802 |
+
" return \"Failed to fetch PDB file\", None, None\n",
|
803 |
+
" \n",
|
804 |
+
" parser = PDBParser(QUIET=1)\n",
|
805 |
+
" structure = parser.get_structure('protein', pdb_path)\n",
|
806 |
+
" \n",
|
807 |
+
" try:\n",
|
808 |
+
" chain = structure[0][segment]\n",
|
809 |
+
" except KeyError:\n",
|
810 |
+
" return \"Invalid Chain ID\", None, None\n",
|
811 |
+
" \n",
|
812 |
+
" # Comprehensive amino acid mapping\n",
|
813 |
+
" aa_dict = {\n",
|
814 |
+
" 'ALA': 'A', 'CYS': 'C', 'ASP': 'D', 'GLU': 'E', 'PHE': 'F',\n",
|
815 |
+
" 'GLY': 'G', 'HIS': 'H', 'ILE': 'I', 'LYS': 'K', 'LEU': 'L',\n",
|
816 |
+
" 'MET': 'M', 'ASN': 'N', 'PRO': 'P', 'GLN': 'Q', 'ARG': 'R',\n",
|
817 |
+
" 'SER': 'S', 'THR': 'T', 'VAL': 'V', 'TRP': 'W', 'TYR': 'Y',\n",
|
818 |
+
" 'MSE': 'M', 'SEP': 'S', 'TPO': 'T', 'CSO': 'C', 'PTR': 'Y', 'HYP': 'P'\n",
|
819 |
+
" }\n",
|
820 |
+
" \n",
|
821 |
+
" # Exclude non-amino acid residues\n",
|
822 |
+
" sequence = [\n",
|
823 |
+
" residue for residue in chain \n",
|
824 |
+
" if residue.get_resname().strip() in aa_dict\n",
|
825 |
+
" ]\n",
|
826 |
+
" \n",
|
827 |
+
" # Prepare input for model prediction\n",
|
828 |
+
" input_ids = tokenizer(\" \".join(sequence), return_tensors=\"pt\").input_ids.to(device)\n",
|
829 |
+
" with torch.no_grad():\n",
|
830 |
+
" outputs = model(input_ids).logits.detach().cpu().numpy().squeeze()\n",
|
831 |
+
"\n",
|
832 |
+
" # Calculate scores and normalize them\n",
|
833 |
+
" scores = expit(outputs[:, 1] - outputs[:, 0])\n",
|
834 |
+
" normalized_scores = normalize_scores(scores)\n",
|
835 |
+
"\n",
|
836 |
+
" result_str = \"\\n\".join(\n",
|
837 |
+
" f\"{aa_dict[res.get_resname()]} {res.id[1]} {score:.2f}\" \n",
|
838 |
+
" for res, score in zip(sequence, normalized_scores)\n",
|
839 |
+
" )\n",
|
840 |
+
" \n",
|
841 |
+
" # Save the predictions to a file\n",
|
842 |
+
" prediction_file = f\"{pdb_id}_predictions.txt\"\n",
|
843 |
+
" with open(prediction_file, \"w\") as f:\n",
|
844 |
+
" f.write(result_str)\n",
|
845 |
+
" \n",
|
846 |
+
" return result_str, molecule(pdb_path, random_scores, segment), prediction_file\n",
|
847 |
+
"\n",
|
848 |
+
"def molecule(input_pdb, scores=None, segment='A'):\n",
|
849 |
+
" mol = read_mol(input_pdb) # Read PDB file content\n",
|
850 |
+
" \n",
|
851 |
+
" # Prepare high-scoring residues script if scores are provided\n",
|
852 |
+
" high_score_script = \"\"\n",
|
853 |
+
" if scores is not None:\n",
|
854 |
+
" high_score_script = \"\"\"\n",
|
855 |
+
" // Reset all styles first\n",
|
856 |
+
" viewer.getModel(0).setStyle({}, {});\n",
|
857 |
+
" \n",
|
858 |
+
" // Show only the selected chain\n",
|
859 |
+
" viewer.getModel(0).setStyle(\n",
|
860 |
+
" {\"chain\": \"%s\"}, \n",
|
861 |
+
" { cartoon: {colorscheme:\"whiteCarbon\"} }\n",
|
862 |
+
" );\n",
|
863 |
+
" \n",
|
864 |
+
" // Highlight high-scoring residues only for the selected chain\n",
|
865 |
+
" let highScoreResidues = [%s];\n",
|
866 |
+
" viewer.getModel(0).setStyle(\n",
|
867 |
+
" {\"chain\": \"%s\", \"resi\": highScoreResidues}, \n",
|
868 |
+
" {\"stick\": {\"color\": \"red\"}}\n",
|
869 |
+
" );\n",
|
870 |
+
"\n",
|
871 |
+
" // Highlight high-scoring residues only for the selected chain\n",
|
872 |
+
" let highScoreResidues2 = [%s];\n",
|
873 |
+
" viewer.getModel(0).setStyle(\n",
|
874 |
+
" {\"chain\": \"%s\", \"resi\": highScoreResidues2}, \n",
|
875 |
+
" {\"stick\": {\"color\": \"orange\"}}\n",
|
876 |
+
" );\n",
|
877 |
+
" \"\"\" % (segment, \n",
|
878 |
+
" \", \".join(str(i+1) for i, score in enumerate(scores) if score > 0.8),\n",
|
879 |
+
" segment,\n",
|
880 |
+
" \", \".join(str(i+1) for i, score in enumerate(scores) if (score > 0.5) and (score < 0.8)),\n",
|
881 |
+
" segment)\n",
|
882 |
+
" \n",
|
883 |
+
" html_content = f\"\"\"\n",
|
884 |
+
" <!DOCTYPE html>\n",
|
885 |
+
" <html>\n",
|
886 |
+
" <head> \n",
|
887 |
+
" <meta http-equiv=\"content-type\" content=\"text/html; charset=UTF-8\" />\n",
|
888 |
+
" <style>\n",
|
889 |
+
" .mol-container {{\n",
|
890 |
+
" width: 100%;\n",
|
891 |
+
" height: 700px;\n",
|
892 |
+
" position: relative;\n",
|
893 |
+
" }}\n",
|
894 |
+
" </style>\n",
|
895 |
+
" <script src=\"https://cdnjs.cloudflare.com/ajax/libs/jquery/3.6.3/jquery.min.js\"></script>\n",
|
896 |
+
" <script src=\"https://3Dmol.csb.pitt.edu/build/3Dmol-min.js\"></script>\n",
|
897 |
+
" </head>\n",
|
898 |
+
" <body>\n",
|
899 |
+
" <div id=\"container\" class=\"mol-container\"></div>\n",
|
900 |
+
" <script>\n",
|
901 |
+
" let pdb = `{mol}`; // Use template literal to properly escape PDB content\n",
|
902 |
+
" $(document).ready(function () {{\n",
|
903 |
+
" let element = $(\"#container\");\n",
|
904 |
+
" let config = {{ backgroundColor: \"white\" }};\n",
|
905 |
+
" let viewer = $3Dmol.createViewer(element, config);\n",
|
906 |
+
" viewer.addModel(pdb, \"pdb\");\n",
|
907 |
+
" \n",
|
908 |
+
" // Reset all styles and show only selected chain\n",
|
909 |
+
" viewer.getModel(0).setStyle(\n",
|
910 |
+
" {{\"chain\": \"{segment}\"}}, \n",
|
911 |
+
" {{ cartoon: {{ colorscheme:\"whiteCarbon\" }} }}\n",
|
912 |
+
" );\n",
|
913 |
+
" \n",
|
914 |
+
" {high_score_script}\n",
|
915 |
+
" \n",
|
916 |
+
" // Add hover functionality\n",
|
917 |
+
" viewer.setHoverable(\n",
|
918 |
+
" {{}}, \n",
|
919 |
+
" true, \n",
|
920 |
+
" function(atom, viewer, event, container) {{\n",
|
921 |
+
" if (!atom.label) {{\n",
|
922 |
+
" atom.label = viewer.addLabel(\n",
|
923 |
+
" atom.resn + \":\" + atom.atom, \n",
|
924 |
+
" {{\n",
|
925 |
+
" position: atom, \n",
|
926 |
+
" backgroundColor: 'mintcream', \n",
|
927 |
+
" fontColor: 'black',\n",
|
928 |
+
" fontSize: 12,\n",
|
929 |
+
" padding: 2\n",
|
930 |
+
" }}\n",
|
931 |
+
" );\n",
|
932 |
+
" }}\n",
|
933 |
+
" }},\n",
|
934 |
+
" function(atom, viewer) {{\n",
|
935 |
+
" if (atom.label) {{\n",
|
936 |
+
" viewer.removeLabel(atom.label);\n",
|
937 |
+
" delete atom.label;\n",
|
938 |
+
" }}\n",
|
939 |
+
" }}\n",
|
940 |
+
" );\n",
|
941 |
+
" \n",
|
942 |
+
" viewer.zoomTo();\n",
|
943 |
+
" viewer.render();\n",
|
944 |
+
" viewer.zoom(0.8, 2000);\n",
|
945 |
+
" }});\n",
|
946 |
+
" </script>\n",
|
947 |
+
" </body>\n",
|
948 |
+
" </html>\n",
|
949 |
+
" \"\"\"\n",
|
950 |
+
" \n",
|
951 |
+
" # Return the HTML content within an iframe safely encoded for special characters\n",
|
952 |
+
" return f'<iframe width=\"100%\" height=\"700\" srcdoc=\"{html_content.replace(chr(34), \""\").replace(chr(39), \"'\")}\"></iframe>'\n",
|
953 |
+
"\n",
|
954 |
+
"reps = [\n",
|
955 |
+
" {\n",
|
956 |
+
" \"model\": 0,\n",
|
957 |
+
" \"style\": \"cartoon\",\n",
|
958 |
+
" \"color\": \"whiteCarbon\",\n",
|
959 |
+
" \"residue_range\": \"\",\n",
|
960 |
+
" \"around\": 0,\n",
|
961 |
+
" \"byres\": False,\n",
|
962 |
+
" }\n",
|
963 |
+
" ]\n",
|
964 |
+
"\n",
|
965 |
+
"# Gradio UI\n",
|
966 |
+
"with gr.Blocks() as demo:\n",
|
967 |
+
" gr.Markdown(\"# Protein Binding Site Prediction (Random Scores)\")\n",
|
968 |
+
" with gr.Row():\n",
|
969 |
+
" pdb_input = gr.Textbox(value=\"2IWI\", label=\"PDB ID\", placeholder=\"Enter PDB ID here...\")\n",
|
970 |
+
" visualize_btn = gr.Button(\"Visualize Structure\")\n",
|
971 |
+
"\n",
|
972 |
+
" molecule_output2 = Molecule3D(label=\"Protein Structure\", reps=reps)\n",
|
973 |
+
"\n",
|
974 |
+
" with gr.Row():\n",
|
975 |
+
" pdb_input = gr.Textbox(value=\"2IWI\", label=\"PDB ID\", placeholder=\"Enter PDB ID here...\")\n",
|
976 |
+
" segment_input = gr.Textbox(value=\"A\", label=\"Chain ID\", placeholder=\"Enter Chain ID here...\")\n",
|
977 |
+
" prediction_btn = gr.Button(\"Predict Random Binding Site Scores\")\n",
|
978 |
+
"\n",
|
979 |
+
" molecule_output = gr.HTML(label=\"Protein Structure\")\n",
|
980 |
+
" predictions_output = gr.Textbox(label=\"Binding Site Predictions\")\n",
|
981 |
+
" download_output = gr.File(label=\"Download Predictions\")\n",
|
982 |
+
" \n",
|
983 |
+
" visualize_btn.click(fetch_pdb, inputs=[pdb_input], outputs=molecule_output2)\n",
|
984 |
+
" \n",
|
985 |
+
" prediction_btn.click(process_pdb, inputs=[pdb_input, segment_input], outputs=[predictions_output, molecule_output, download_output])\n",
|
986 |
+
" \n",
|
987 |
+
" gr.Markdown(\"## Examples\")\n",
|
988 |
+
" gr.Examples(\n",
|
989 |
+
" examples=[\n",
|
990 |
+
" [\"2IWI\", \"A\"],\n",
|
991 |
+
" [\"7RPZ\", \"B\"],\n",
|
992 |
+
" [\"3TJN\", \"C\"]\n",
|
993 |
+
" ],\n",
|
994 |
+
" inputs=[pdb_input, segment_input],\n",
|
995 |
+
" outputs=[predictions_output, molecule_output, download_output]\n",
|
996 |
+
" )\n",
|
997 |
+
"\n",
|
998 |
+
"demo.launch()"
|
999 |
+
]
|
1000 |
+
},
|
1001 |
+
{
|
1002 |
+
"cell_type": "code",
|
1003 |
+
"execution_count": null,
|
1004 |
+
"id": "95046d1c-ec7c-4e3e-8a98-1802cb09a25b",
|
1005 |
+
"metadata": {},
|
1006 |
+
"outputs": [],
|
1007 |
+
"source": []
|
1008 |
+
},
|
1009 |
+
{
|
1010 |
+
"cell_type": "code",
|
1011 |
+
"execution_count": null,
|
1012 |
+
"id": "a37cbe6f-d57f-41e5-8ae1-38258da39d47",
|
1013 |
+
"metadata": {},
|
1014 |
+
"outputs": [],
|
1015 |
+
"source": [
|
1016 |
+
"import gradio as gr\n",
|
1017 |
+
"from model_loader import load_model\n",
|
1018 |
+
"\n",
|
1019 |
+
"import torch\n",
|
1020 |
+
"import torch.nn as nn\n",
|
1021 |
+
"import torch.nn.functional as F\n",
|
1022 |
+
"from torch.utils.data import DataLoader\n",
|
1023 |
+
"\n",
|
1024 |
+
"import re\n",
|
1025 |
+
"import numpy as np\n",
|
1026 |
+
"import os\n",
|
1027 |
+
"import pandas as pd\n",
|
1028 |
+
"import copy\n",
|
1029 |
+
"\n",
|
1030 |
+
"import transformers, datasets\n",
|
1031 |
+
"from transformers import AutoTokenizer\n",
|
1032 |
+
"from transformers import DataCollatorForTokenClassification\n",
|
1033 |
+
"\n",
|
1034 |
+
"from datasets import Dataset\n",
|
1035 |
+
"\n",
|
1036 |
+
"from scipy.special import expit\n",
|
1037 |
+
"\n",
|
1038 |
+
"import requests\n",
|
1039 |
+
"\n",
|
1040 |
+
"from gradio_molecule3d import Molecule3D\n",
|
1041 |
+
"\n",
|
1042 |
+
"# Biopython imports\n",
|
1043 |
+
"from Bio.PDB import PDBParser, Select, PDBIO\n",
|
1044 |
+
"from Bio.PDB.DSSP import DSSP\n",
|
1045 |
+
"from Bio.PDB import PDBList\n",
|
1046 |
+
"\n",
|
1047 |
+
"from matplotlib import cm # For color mapping\n",
|
1048 |
+
"from matplotlib.colors import Normalize\n",
|
1049 |
+
"\n",
|
1050 |
+
"# Load model and move to device\n",
|
1051 |
+
"checkpoint = 'ThorbenF/prot_t5_xl_uniref50'\n",
|
1052 |
+
"max_length = 1500\n",
|
1053 |
+
"model, tokenizer = load_model(checkpoint, max_length)\n",
|
1054 |
+
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
|
1055 |
+
"model.to(device)\n",
|
1056 |
+
"model.eval()\n",
|
1057 |
+
"\n",
|
1058 |
+
"# Function to fetch a PDB file\n",
|
1059 |
+
"def fetch_pdb(pdb_id):\n",
|
1060 |
+
" pdb_url = f'https://files.rcsb.org/download/{pdb_id}.pdb'\n",
|
1061 |
+
" pdb_path = f'pdb_files/{pdb_id}.pdb'\n",
|
1062 |
+
" os.makedirs('pdb_files', exist_ok=True)\n",
|
1063 |
+
" response = requests.get(pdb_url)\n",
|
1064 |
+
" if response.status_code == 200:\n",
|
1065 |
+
" with open(pdb_path, 'wb') as f:\n",
|
1066 |
+
" f.write(response.content)\n",
|
1067 |
+
" return pdb_path\n",
|
1068 |
+
" return None\n",
|
1069 |
+
"\n",
|
1070 |
+
"\n",
|
1071 |
+
"def normalize_scores(scores):\n",
|
1072 |
+
" min_score = np.min(scores)\n",
|
1073 |
+
" max_score = np.max(scores)\n",
|
1074 |
+
" return (scores - min_score) / (max_score - min_score) if max_score > min_score else scores\n",
|
1075 |
+
"\n",
|
1076 |
+
"def process_pdb(pdb_id, segment):\n",
|
1077 |
+
" pdb_path = fetch_pdb(pdb_id)\n",
|
1078 |
+
" if not pdb_path:\n",
|
1079 |
+
" return \"Failed to fetch PDB file\", None, None\n",
|
1080 |
+
" \n",
|
1081 |
+
" parser = PDBParser(QUIET=1)\n",
|
1082 |
+
" structure = parser.get_structure('protein', pdb_path)\n",
|
1083 |
+
" chain = structure[0][segment]\n",
|
1084 |
+
" \n",
|
1085 |
+
" # Comprehensive amino acid mapping\n",
|
1086 |
+
" aa_dict = {\n",
|
1087 |
+
" 'ALA': 'A', 'CYS': 'C', 'ASP': 'D', 'GLU': 'E', 'PHE': 'F',\n",
|
1088 |
+
" 'GLY': 'G', 'HIS': 'H', 'ILE': 'I', 'LYS': 'K', 'LEU': 'L',\n",
|
1089 |
+
" 'MET': 'M', 'ASN': 'N', 'PRO': 'P', 'GLN': 'Q', 'ARG': 'R',\n",
|
1090 |
+
" 'SER': 'S', 'THR': 'T', 'VAL': 'V', 'TRP': 'W', 'TYR': 'Y',\n",
|
1091 |
+
" 'MSE': 'M', 'SEP': 'S', 'TPO': 'T', 'CSO': 'C', 'PTR': 'Y', 'HYP': 'P'\n",
|
1092 |
+
" }\n",
|
1093 |
+
" \n",
|
1094 |
+
" # Exclude non-amino acid residues\n",
|
1095 |
+
" sequence = \"\".join(\n",
|
1096 |
+
" aa_dict[residue.get_resname().strip()] \n",
|
1097 |
+
" for residue in chain \n",
|
1098 |
+
" if residue.get_resname().strip() in aa_dict\n",
|
1099 |
+
" )\n",
|
1100 |
+
" \n",
|
1101 |
+
" # Prepare input for model prediction\n",
|
1102 |
+
" input_ids = tokenizer(\" \".join(sequence), return_tensors=\"pt\").input_ids.to(device)\n",
|
1103 |
+
" with torch.no_grad():\n",
|
1104 |
+
" outputs = model(input_ids).logits.detach().cpu().numpy().squeeze()\n",
|
1105 |
+
"\n",
|
1106 |
+
" # Calculate scores and normalize them\n",
|
1107 |
+
" scores = expit(outputs[:, 1] - outputs[:, 0])\n",
|
1108 |
+
" normalized_scores = normalize_scores(scores)\n",
|
1109 |
+
" \n",
|
1110 |
+
" # Prepare the result string, including only amino acid residues\n",
|
1111 |
+
" result_str = \"\\n\".join([\n",
|
1112 |
+
" f\"{res.get_resname()} {res.id[1]} {sequence[i]} {normalized_scores[i]:.2f}\" \n",
|
1113 |
+
" for i, res in enumerate(chain) if res.get_resname().strip() in aa_dict\n",
|
1114 |
+
" ])\n",
|
1115 |
+
" \n",
|
1116 |
+
" # Save predictions to file\n",
|
1117 |
+
" with open(f\"{pdb_id}_predictions.txt\", \"w\") as f:\n",
|
1118 |
+
" f.write(result_str)\n",
|
1119 |
+
" \n",
|
1120 |
+
" return result_str, pdb_path, f\"{pdb_id}_predictions.txt\"\n",
|
1121 |
+
"\n",
|
1122 |
+
"reps = [{\"model\": 0, \"style\": \"cartoon\", \"color\": \"spectrum\"}]\n",
|
1123 |
+
"\n",
|
1124 |
+
"# Gradio UI\n",
|
1125 |
+
"with gr.Blocks() as demo:\n",
|
1126 |
+
" gr.Markdown(\"# Protein Binding Site Prediction\")\n",
|
1127 |
+
"\n",
|
1128 |
+
" with gr.Row():\n",
|
1129 |
+
" pdb_input = gr.Textbox(value=\"2IWI\",\n",
|
1130 |
+
" label=\"PDB ID\",\n",
|
1131 |
+
" placeholder=\"Enter PDB ID here...\")\n",
|
1132 |
+
" segment_input = gr.Textbox(value=\"A\",\n",
|
1133 |
+
" label=\"Chain ID (Segment)\",\n",
|
1134 |
+
" placeholder=\"Enter Chain ID here...\")\n",
|
1135 |
+
" visualize_btn = gr.Button(\"Visualize Sructure\")\n",
|
1136 |
+
" prediction_btn = gr.Button(\"Predict Ligand Binding Site\")\n",
|
1137 |
+
"\n",
|
1138 |
+
" molecule_output = Molecule3D(label=\"Protein Structure\", reps=reps)\n",
|
1139 |
+
" predictions_output = gr.Textbox(label=\"Binding Site Predictions\")\n",
|
1140 |
+
" download_output = gr.File(label=\"Download Predictions\")\n",
|
1141 |
+
"\n",
|
1142 |
+
" visualize_btn.click(fetch_pdb, inputs=[pdb_input], outputs=molecule_output)\n",
|
1143 |
+
" prediction_btn.click(\n",
|
1144 |
+
" process_pdb, \n",
|
1145 |
+
" inputs=[pdb_input, segment_input], \n",
|
1146 |
+
" outputs=[predictions_output, molecule_output, download_output]\n",
|
1147 |
+
" )\n",
|
1148 |
+
"\n",
|
1149 |
+
" gr.Markdown(\"## Examples\")\n",
|
1150 |
+
" gr.Examples(\n",
|
1151 |
+
" examples=[\n",
|
1152 |
+
" [\"2IWI\"],\n",
|
1153 |
+
" [\"7RPZ\"],\n",
|
1154 |
+
" [\"3TJN\"]\n",
|
1155 |
+
" ],\n",
|
1156 |
+
" inputs=[pdb_input, segment_input], \n",
|
1157 |
+
" outputs=[predictions_output, molecule_output, download_output]\n",
|
1158 |
+
" )\n",
|
1159 |
+
"\n",
|
1160 |
+
"demo.launch(share=True)"
|
1161 |
+
]
|
1162 |
+
},
|
1163 |
+
{
|
1164 |
+
"cell_type": "code",
|
1165 |
+
"execution_count": null,
|
1166 |
+
"id": "4c61bac4-4f2e-4f4a-aa1f-30dca209747c",
|
1167 |
+
"metadata": {},
|
1168 |
+
"outputs": [],
|
1169 |
+
"source": []
|
1170 |
+
}
|
1171 |
+
],
|
1172 |
+
"metadata": {
|
1173 |
+
"kernelspec": {
|
1174 |
+
"display_name": "Python (LLM)",
|
1175 |
+
"language": "python",
|
1176 |
+
"name": "llm"
|
1177 |
+
},
|
1178 |
+
"language_info": {
|
1179 |
+
"codemirror_mode": {
|
1180 |
+
"name": "ipython",
|
1181 |
+
"version": 3
|
1182 |
+
},
|
1183 |
+
"file_extension": ".py",
|
1184 |
+
"mimetype": "text/x-python",
|
1185 |
+
"name": "python",
|
1186 |
+
"nbconvert_exporter": "python",
|
1187 |
+
"pygments_lexer": "ipython3",
|
1188 |
+
"version": "3.12.7"
|
1189 |
+
}
|
1190 |
+
},
|
1191 |
+
"nbformat": 4,
|
1192 |
+
"nbformat_minor": 5
|
1193 |
+
}
|