import gradio as gr import py3Dmol from Bio.PDB import * import numpy as np from Bio.PDB import PDBParser import pandas as pd import torch import os from MDmodel import GNN_MD import h5py from transformMD import GNNTransformMD # JavaScript functions resid_hover = """function(atom,viewer) {{ if(!atom.label) {{ atom.label = viewer.addLabel('{0}:'+atom.atom+atom.serial, {{position: atom, backgroundColor: 'mintcream', fontColor:'black'}}); }} }}""" hover_func = """ function(atom,viewer) { if(!atom.label) { atom.label = viewer.addLabel(atom.interaction, {position: atom, backgroundColor: 'black', fontColor:'white'}); } }""" unhover_func = """ function(atom,viewer) { if(atom.label) { viewer.removeLabel(atom.label); delete atom.label; } }""" atom_mapping = {0:'H', 1:'C', 2:'N', 3:'O', 4:'F', 5:'P', 6:'S', 7:'CL', 8:'BR', 9:'I', 10: 'UNK'} model = GNN_MD(11, 64) state_dict = torch.load( "best_weights_rep0.pt", map_location=torch.device("cpu"), )["model_state_dict"] model.load_state_dict(state_dict) model = model.to('cpu') model.eval() def get_pdb(pdb_code="", filepath=""): try: return filepath.name except AttributeError as e: if pdb_code is None or pdb_code == "": return None else: os.system(f"wget -qnc https://files.rcsb.org/view/{pdb_code}.pdb") return f"{pdb_code}.pdb" def get_offset(pdb): pdb_multiline = pdb.split("\n") for line in pdb_multiline: if line.startswith("ATOM"): return int(line[22:27]) def predict(pdb_code, pdb_file): #path_to_pdb = get_pdb(pdb_code=pdb_code, filepath=pdb_file) #pdb = open(path_to_pdb, "r").read() # switch to misato env if not running from container mdh5_file = "inference_for_md.hdf5" md_H5File = h5py.File(mdh5_file) column_names = ["x", "y", "z", "element"] atoms_protein = pd.DataFrame(columns = column_names) cutoff = md_H5File["11GS"]["molecules_begin_atom_index"][:][-1] # cutoff defines protein atoms atoms_protein["x"] = md_H5File["11GS"]["atoms_coordinates_ref"][:][:cutoff, 0] atoms_protein["y"] = md_H5File["11GS"]["atoms_coordinates_ref"][:][:cutoff, 1] atoms_protein["z"] = md_H5File["11GS"]["atoms_coordinates_ref"][:][:cutoff, 2] atoms_protein["element"] = md_H5File["11GS"]["atoms_element"][:][:cutoff] item = {} item["scores"] = 0 item["id"] = "11GS" item["atoms_protein"] = atoms_protein transform = GNNTransformMD() data_item = transform(item) adaptability = model(data_item) adaptability = adaptability.detach().numpy() data = [] for i in range(adaptability.shape[0]): 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]]) topN = 100 topN_ind = np.argsort(adaptability)[::-1][:topN] pdb = open(pdb_file.name, "r").read() view = py3Dmol.view(width=600, height=400) view.setBackgroundColor('black') view.addModel(pdb, "pdb") view.setStyle({'stick': {'colorscheme': {'prop': 'resi', 'C': 'turquoise'}}}) for i in range(topN): 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}) view.zoomTo() output = view._make_html().replace("'", '"') x = f""" {output} """ # do not use ' in this input return f"""""", pd.DataFrame(data, columns=['index','element','x','y','z','Adaptability']) callback = gr.CSVLogger() with gr.Blocks() as demo: gr.Markdown("# Protein Adaptability Prediction") #text_input = gr.Textbox() #text_output = gr.Textbox() #text_button = gr.Button("Flip") inp = gr.Textbox(placeholder="PDB Code or upload file below", label="Input structure") pdb_file = gr.File(label="PDB File Upload") #with gr.Row(): # helix = gr.ColorPicker(label="helix") # sheet = gr.ColorPicker(label="sheet") # loop = gr.ColorPicker(label="loop") single_btn = gr.Button(label="Run") with gr.Row(): html = gr.HTML() with gr.Row(): dataframe = gr.Dataframe() single_btn.click(fn=predict, inputs=[inp, pdb_file], outputs=[html, dataframe]) demo.launch(debug=True)