File size: 5,261 Bytes
b879795 a3a5fc1 b879795 a3a5fc1 eb1f61c a3a5fc1 3ed3216 a3a5fc1 b879795 a3a5fc1 b879795 a3a5fc1 b879795 a3a5fc1 b879795 a3a5fc1 b879795 dea771b ea795dd dea771b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 |
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('white')
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>""" # do not use ' in this input
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()
def run():
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(server_name="0.0.0.0", server_port=7860)
if __name__ == "__main__":
run() |