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import gradio as gr |
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import py3Dmol |
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from Bio.PDB import * |
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import numpy as np |
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from Bio.PDB import PDBParser |
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import pandas as pd |
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import torch |
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import os |
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from MDmodel import GNN_MD |
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import h5py |
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from transformMD import GNNTransformMD |
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resid_hover = """function(atom,viewer) {{ |
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if(!atom.label) {{ |
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atom.label = viewer.addLabel('{0}:'+atom.atom+atom.serial, |
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{{position: atom, backgroundColor: 'mintcream', fontColor:'black'}}); |
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}} |
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}}""" |
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hover_func = """ |
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function(atom,viewer) { |
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if(!atom.label) { |
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atom.label = viewer.addLabel(atom.interaction, |
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{position: atom, backgroundColor: 'black', fontColor:'white'}); |
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} |
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}""" |
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unhover_func = """ |
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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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atom_mapping = {0:'H', 1:'C', 2:'N', 3:'O', 4:'F', 5:'P', 6:'S', 7:'CL', 8:'BR', 9:'I', 10: 'UNK'} |
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model = GNN_MD(11, 64) |
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state_dict = torch.load( |
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"best_weights_rep0.pt", |
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map_location=torch.device("cpu"), |
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)["model_state_dict"] |
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model.load_state_dict(state_dict) |
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model = model.to('cpu') |
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model.eval() |
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def get_pdb(pdb_code="", filepath=""): |
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try: |
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return filepath.name |
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except AttributeError as e: |
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if pdb_code is None or pdb_code == "": |
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return None |
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else: |
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os.system(f"wget -qnc https://files.rcsb.org/view/{pdb_code}.pdb") |
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return f"{pdb_code}.pdb" |
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def get_offset(pdb): |
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pdb_multiline = pdb.split("\n") |
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for line in pdb_multiline: |
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if line.startswith("ATOM"): |
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return int(line[22:27]) |
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def predict(pdb_code, pdb_file): |
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path_to_pdb = get_pdb(pdb_code=pdb_code, filepath=pdb_file) |
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mdh5_file = "inference_for_md.hdf5" |
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md_H5File = h5py.File(mdh5_file) |
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column_names = ["x", "y", "z", "element"] |
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atoms_protein = pd.DataFrame(columns = column_names) |
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cutoff = md_H5File["11GS"]["molecules_begin_atom_index"][:][-1] |
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atoms_protein["x"] = md_H5File["11GS"]["atoms_coordinates_ref"][:][:cutoff, 0] |
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atoms_protein["y"] = md_H5File["11GS"]["atoms_coordinates_ref"][:][:cutoff, 1] |
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atoms_protein["z"] = md_H5File["11GS"]["atoms_coordinates_ref"][:][:cutoff, 2] |
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atoms_protein["element"] = md_H5File["11GS"]["atoms_element"][:][:cutoff] |
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item = {} |
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item["scores"] = 0 |
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item["id"] = "11GS" |
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item["atoms_protein"] = atoms_protein |
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transform = GNNTransformMD() |
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data_item = transform(item) |
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adaptability = model(data_item) |
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adaptability = adaptability.detach().numpy() |
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data = [] |
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for i in range(adaptability.shape[0]): |
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data.append([i, atom_mapping[atoms_protein.iloc[i, atoms_protein.columns.get_loc("element")] - 1], atoms_protein.iloc[i, atoms_protein.columns.get_loc("x")],atoms_protein.iloc[i, atoms_protein.columns.get_loc("y")],atoms_protein.iloc[i, atoms_protein.columns.get_loc("z")],adaptability[i]]) |
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topN = 100 |
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topN_ind = np.argsort(adaptability)[::-1][:topN] |
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pdb = open(path_to_pdb, "r").read() |
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view = py3Dmol.view(width=600, height=400) |
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view.setBackgroundColor('white') |
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view.addModel(pdb, "pdb") |
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view.setStyle({'stick': {'colorscheme': {'prop': 'resi', 'C': 'turquoise'}}}) |
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for i in range(topN): |
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view.addSphere({'center':{'x':atoms_protein.iloc[topN_ind[i], atoms_protein.columns.get_loc("x")], 'y':atoms_protein.iloc[topN_ind[i], atoms_protein.columns.get_loc("y")],'z':atoms_protein.iloc[topN_ind[i], atoms_protein.columns.get_loc("z")]},'radius':adaptability[topN_ind[i]]/1.5,'color':'orange','alpha':0.75}) |
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view.zoomTo() |
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output = view._make_html().replace("'", '"') |
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x = f"""<!DOCTYPE html><html> {output} </html>""" |
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return f"""<iframe style="width: 100%; height:420px" name="result" allow="midi; geolocation; microphone; camera; |
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display-capture; encrypted-media;" sandbox="allow-modals allow-forms |
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allow-scripts allow-same-origin allow-popups |
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allow-top-navigation-by-user-activation allow-downloads" allowfullscreen="" |
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allowpaymentrequest="" frameborder="0" srcdoc='{x}'></iframe>""", pd.DataFrame(data, columns=['index','element','x','y','z','Adaptability']) |
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callback = gr.CSVLogger() |
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def run(): |
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with gr.Blocks() as demo: |
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gr.Markdown("# Protein Adaptability Prediction") |
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inp = gr.Textbox(placeholder="PDB Code or upload file below", label="Input structure") |
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pdb_file = gr.File(label="PDB File Upload") |
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single_btn = gr.Button(label="Run") |
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with gr.Row(): |
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html = gr.HTML() |
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with gr.Row(): |
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dataframe = gr.Dataframe() |
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single_btn.click(fn=predict, inputs=[inp, pdb_file], outputs=[html, dataframe]) |
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demo.launch(server_name="0.0.0.0", server_port=7860) |
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if __name__ == "__main__": |
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run() |
