|
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 |
|
|
|
|
|
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): |
|
|
|
|
|
|
|
|
|
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] |
|
|
|
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"""<!DOCTYPE html><html> {output} </html>""" |
|
return f"""<iframe style="width: 100%; height:420px" name="result" allow="midi; geolocation; microphone; camera; |
|
display-capture; encrypted-media;" sandbox="allow-modals allow-forms |
|
allow-scripts allow-same-origin allow-popups |
|
allow-top-navigation-by-user-activation allow-downloads" allowfullscreen="" |
|
allowpaymentrequest="" frameborder="0" srcdoc='{x}'></iframe>""", pd.DataFrame(data, columns=['index','element','x','y','z','Adaptability']) |
|
|
|
|
|
callback = gr.CSVLogger() |
|
|
|
with gr.Blocks() as demo: |
|
gr.Markdown("# Protein Adaptability Prediction") |
|
|
|
|
|
|
|
|
|
inp = gr.Textbox(placeholder="PDB Code or upload file below", label="Input structure") |
|
pdb_file = gr.File(label="PDB File Upload") |
|
|
|
|
|
|
|
|
|
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) |
