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import gradio as gr
import urllib
import re
import sys
import warnings
import torch
import torch.nn as nn
import ipywidgets as widgets
from ipywidgets import interact, fixed
from utils.helpers import *
from utils.voxelization import processStructures
from utils.model import Model
import numpy as np
import os
def update(inp, file, mode):
try:
pdb_file = file.name
except:
print("using pdbfile")
try:
pdb_file = inp
if (
re.match(
"[OPQ][0-9][A-Z0-9]{3}[0-9]|[A-NR-Z][0-9]([A-Z][A-Z0-9]{2}[0-9]){1,2}",
pdb_file,
).group()
== pdb_file
):
urllib.request.urlretrieve(
f"https://alphafold.ebi.ac.uk/files/AF-{pdb_file}-F1-model_v2.pdb",
f"files/{pdb_file}.pdb",
)
except AttributeError:
if len(inp) == 4:
pdb_file = inp
urllib.request.urlretrieve(
f"http://files.rcsb.org/download/{pdb_file.lower()}.pdb1",
f"files/{pdb_file}.pdb",
)
else:
return "pdb code must be 4 letters or Uniprot code does not match", ""
if mode == "All residues":
ids = get_all_protein_resids(
f"files/{pdb_file}.pdb",
)
else:
ids = get_all_metalbinding_resids(f"files/{pdb_file}.pdb")
voxels, prot_centers, prot_N, prots = processStructures(pdb_file, ids)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
voxels.to(device)
print(voxels.shape)
model = Model()
model.to(device)
model.load_state_dict(torch.load("weights/metal_0.5A_v3_d0.2_16Abox.pth"))
model.eval()
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
output = model(voxels)
print(output.shape)
prot_v = np.vstack(prot_centers)
output_v = output.flatten().cpu().detach().numpy()
bb = get_bb(prot_v)
gridres = 0.5
grid, box_N = create_grid_fromBB(bb, voxelSize=gridres)
probability_values = get_probability_mean(grid, prot_v, output_v)
print(probability_values.shape)
write_cubefile(
bb,
probability_values,
box_N,
outname=f"output/metal_{pdb_file}.cube",
gridres=gridres,
)
message = find_unique_sites(
probability_values,
grid,
writeprobes=True,
probefile=f"output/probes_{pdb_file}.pdb",
threshold=7,
p=0.15,
)
return message, molecule(
f"files/{pdb_file}.pdb",
f"output/probes_{pdb_file}.pdb",
f"output/metal_{pdb_file}.cube",
)
def test():
x = """<!DOCTYPE html>
<html>
<head>
<meta http-equiv="content-type" content="text/html; charset=UTF-8" />
</head>
<body>
<script src="https://3Dmol.org/build/3Dmol-min.js" async></script> <div style="height: 400px; width: 400px; position: relative;" class="viewer_3Dmoljs" data-pdb="2POR" data-backgroundcolor="0xffffff" data-style="stick" ></div>
</body></html>"""
return f"""<iframe style="width: 100%; height: 480px" 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>"""
def read_mol(molpath):
with open(molpath, "r") as fp:
lines = fp.readlines()
mol = ""
for l in lines:
mol += l
return mol
def molecule(pdb, probes, cube):
mol = read_mol(pdb)
probes = read_mol(probes)
cubefile = read_mol(cube)
x = (
"""<!DOCTYPE html>
<html>
<head>
<meta http-equiv="content-type" content="text/html; charset=UTF-8" />
<style>
body{
font-family:sans-serif
}
.mol-container {
width: 100%;
height: 400px;
position: relative;
}
.slider{
width:80%;
margin:0 auto
}
.slidercontainer{
display:flex;
}
.slidercontainer > * + * {
margin-left: 0.5rem;
}
#isovalue{
text-align:right}
</style>
<script src="https://3Dmol.csb.pitt.edu/build/3Dmol-min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/rangeslider.js/2.3.3/rangeslider.min.js" integrity="sha512-BUlWdwDeJo24GIubM+z40xcj/pjw7RuULBkxOTc+0L9BaGwZPwiwtbiSVzv31qR7TWx7bs6OPTE5IyfLOorboQ==" crossorigin="anonymous" referrerpolicy="no-referrer"></script>
</head>
<body>
<div class="slidercontainer">
<span>Isovalue </span>
<span id="isovalue">0.5</span>
<input class="slider" type="range" id="rangeslider" min="0" max="1" step="0.025" value=0.5>
</div>
<div id="container" class="mol-container"></div>
<script>
let viewer = null;
let voldata = null;
$(document).ready(function () {
let element = $("#container");
let config = { backgroundColor: "white" };
viewer = $3Dmol.createViewer( element, config );
viewer.ui.initiateUI();
let data = `"""
+ mol
+ """`
viewer.addModel( data, "pdb" );
let cubefile = `"""
+ cubefile
+ """`
voldata = new $3Dmol.VolumeData(cubefile, "cube");
viewer.addIsosurface(voldata, { isoval: 0.7 , color: "blue", alpha: 0.85, smoothness: 1 });
viewer.getModel(0).setStyle({}, {cartoon: {color: "grayCarbon"}});
let probes =`"""
+ probes
+ """`
viewer.addModel(probes, "pdb");
viewer.getModel(1).setStyle({ "resn": "ZN" }, { "sphere": { }});
viewer.getModel(1).setHoverable({}, true,
function (atom, viewer, event, container) {
if (!atom.label) {
atom.label = viewer.addLabel("ZN p=" + atom.pdbline.substring(55, 60), { position: atom, backgroundColor: "mintcream", fontColor: "black" });
}
},
function (atom, viewer) {
if (atom.label) {
viewer.removeLabel(atom.label);
delete atom.label;
}
}
);
viewer.zoomTo();
viewer.render();
viewer.zoom(0.8, 2000);
});
</script>
<script>
$("#rangeslider").rangeslider().on("change", function (el) {
isoval = parseFloat(el.target.value);
$("#isovalue").text(el.target.value)
viewer.addIsosurface(voldata, { isoval: parseFloat(el.target.value), color: "blue", alpha: 0.85, smoothness: 1 });
viewer.render();
});
</script>
</body></html>"""
)
return f"""<iframe style="width: 100%; height: 480px" 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>"""
metal3d = gr.Blocks()
with metal3d:
gr.Markdown("# Metal3D")
gr.Markdown(
"""
Details about implementation and code available here:
>Duerr, Levy and Roethlisberger, Predicting zinc ion location using deep learning, BioRxiv, 2022 "
"""
)
with gr.Group():
inp = gr.Textbox(
placeholder="PDB Code or Uniprot identifier", label="Input molecule"
)
gr.Markdown("or upload a file")
file = gr.File(file_count="single", type="file")
mode = gr.Radio(
["All metalbinding residues (ASP, CYS, GLU, HIS)", "All residues"],
label="Residues to use for prediction",
)
btn = gr.Button("Run")
gr.Markdown("# Output")
out = gr.Textbox(label="status")
mol = gr.HTML()
btn.click(fn=update, inputs=[inp, file, mode], outputs=[out, mol])
metal3d.launch()
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