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app.py
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app.py
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from transformers import AutoTokenizer, EsmForProteinFolding
from transformers.models.esm.openfold_utils.protein import to_pdb, Protein as OFProtein
from transformers.models.esm.openfold_utils.feats import atom14_to_atom37
from Bio import SeqIO
import gradio as gr
from gradio_molecule3d import Molecule3D
reps = [
{
"model": 0,
"chain": "",
"resname": "",
"style": "stick",
"color": "whiteCarbon",
"residue_range": "",
"around": 0,
"byres": False,
"visible": False
}
]
def read_mol(molpath):
with open(molpath, "r") as fp:
lines = fp.readlines()
mol = ""
for l in lines:
mol += l
return mol
def molecule(input_pdb):
mol = read_mol(input_pdb)
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: 600px;
position: relative;
}
.mol-container select{
background-image:None;
}
</style>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.6.3/jquery.min.js" integrity="sha512-STof4xm1wgkfm7heWqFJVn58Hm3EtS31XFaagaa8VMReCXAkQnJZ+jEy8PCC/iT18dFy95WcExNHFTqLyp72eQ==" crossorigin="anonymous" referrerpolicy="no-referrer"></script>
<script src="https://3Dmol.csb.pitt.edu/build/3Dmol-min.js"></script>
</head>
<body>
<div id="container" class="mol-container"></div>
<script>
let pdb = `"""
+ mol
+ """`
$(document).ready(function () {
let element = $("#container");
let config = { backgroundColor: "white" };
let viewer = $3Dmol.createViewer(element, config);
viewer.addModel(pdb, "pdb");
viewer.getModel(0).setStyle({}, { cartoon: { colorscheme:"whiteCarbon" } });
viewer.zoomTo();
viewer.render();
viewer.zoom(0.8, 2000);
})
</script>
</body></html>"""
)
return f"""<iframe style="width: 100%; height: 600px" 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 convert_outputs_to_pdb(outputs):
final_atom_positions = atom14_to_atom37(outputs["positions"][-1], outputs)
outputs = {k: v.to("cpu").numpy() for k, v in outputs.items()}
final_atom_positions = final_atom_positions.cpu().numpy()
final_atom_mask = outputs["atom37_atom_exists"]
pdbs = []
for i in range(outputs["aatype"].shape[0]):
aa = outputs["aatype"][i]
pred_pos = final_atom_positions[i]
mask = final_atom_mask[i]
resid = outputs["residue_index"][i] + 1
pred = OFProtein(
aatype=aa,
atom_positions=pred_pos,
atom_mask=mask,
residue_index=resid,
b_factors=outputs["plddt"][i],
chain_index=outputs["chain_index"][i] if "chain_index" in outputs else None,
)
pdbs.append(to_pdb(pred))
return pdbs
tokenizer = AutoTokenizer.from_pretrained("facebook/esmfold_v1")
model = EsmForProteinFolding.from_pretrained("facebook/esmfold_v1", low_cpu_mem_usage=True)
model = model.cuda()
model.esm = model.esm.half()
import torch
torch.backends.cuda.matmul.allow_tf32 = True
model.trunk.set_chunk_size(64)
def fold_protein(test_protein):
tokenized_input = tokenizer([test_protein], return_tensors="pt", add_special_tokens=False)['input_ids']
tokenized_input = tokenized_input.cuda()
with torch.no_grad():
output = model(tokenized_input)
pdb = convert_outputs_to_pdb(output)
with open("output_structure.pdb", "w") as f:
f.write("".join(pdb))
html = molecule("output_structure.pdb")
return html, "output_structure.pdb"
def fold_protein_wpdb(test_protein, pdb_path):
tokenized_input = tokenizer([test_protein], return_tensors="pt", add_special_tokens=False)['input_ids']
tokenized_input = tokenized_input.cuda()
with torch.no_grad():
output = model(tokenized_input)
pdb = convert_outputs_to_pdb(output)
with open(pdb_path, "w") as f:
f.write("".join(pdb))
html = molecule(pdb_path)
return html
def load_protein_sequences(fasta_file):
protein_sequences = {}
for record in SeqIO.parse(fasta_file, "fasta"):
protein_sequences[record.id] = str(record.seq)
return protein_sequences
def show_split(inputfile):
seqs = load_protein_sequences(inputfile)
htmls = []
for seq in seqs:
pdb_path = f'{seq.replace(" ", "_").replace(",","")}.pdb'
html = fold_protein_wpdb(seqs[seq], pdb_path)
x = f"""<h3>>{seq}</h3>
<br>
"""
htmls.append(x+html)
final = "\n<br>\n".join(htmls)
realhtml = "<div>\n"+final+"\n</div>"
return realhtml
iface = gr.Interface(
title="SingleProteinviz",
fn=fold_protein,
inputs=gr.Textbox(
label="Protein Sequence",
info="Find sequences examples below, and complete examples with images at: https://github.com/AstraBert/proteinviz/tree/main/examples.md; if you input a sequence, you're gonna get the static image and the 3D model to explore and play with",
lines=5,
value=f"Paste or write amino-acidic sequence here",
),
outputs=[gr.HTML(label="Protein 3D model"), Molecule3D(label="Molecular 3D model", reps=reps)],
examples=[
"MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH",
"MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHQYREQIKRVKDSDDVPMVLVGNKCDLAARTVESRQAQDLARSYGIPYIETSAKTRQGVEDAFYTLVREIRQHKLRKLNPPDESGPGCMSCKCVLS",
"MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG",
]
)
demo1 = gr.Interface(
title="BulkProteinviz",
fn=show_split,
inputs=gr.File(
label="FASTA File With Protein Sequences",
),
outputs= gr.HTML(label="Protein 3D models"),
examples = ["proteins.fasta"]
)
demo = gr.TabbedInterface([iface, demo1], ["Single Protein Structure Prediction", "Bulk Protein Structure Prediction"])
demo.launch(server_name="0.0.0.0", server_port=7860)