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import re
from pathlib import Path
import gradio as gr
from evodiff.pretrained import OA_DM_38M, D3PM_UNIFORM_38M, MSA_OA_DM_MAXSUB
from evodiff.generate import generate_oaardm, generate_d3pm
from evodiff.generate_msa import generate_query_oadm_msa_simple
from evodiff.conditional_generation import inpaint_simple, generate_scaffold
import py3Dmol
from colabfold.download import download_alphafold_params
from colabfold.batch import run
def a3m_file(file):
return "tmp.a3m"
def predict_protein(sequence):
download_alphafold_params("alphafold2_ptm", Path("."))
results = run(
queries=[('evodiff_protein', sequence, None)],
result_dir='evodiff_protein',
use_templates=False,
num_relax=0,
msa_mode="mmseqs2_uniref_env",
model_type="alphafold2_ptm",
num_models=1,
num_recycles=1,
model_order=[1],
is_complex=False,
data_dir=Path("."),
keep_existing_results=False,
rank_by="auto",
stop_at_score=float(100),
zip_results=False,
user_agent="colabfold/google-colab-main"
)
return f"evodiff_protein/evodiff_protein_unrelaxed_rank_001_alphafold2_ptm_model_1_seed_000.pdb"
def display_pdb(path_to_pdb):
'''
#function to display pdb in py3dmol
SOURCE: https://huggingface.co/spaces/merle/PROTEIN_GENERATOR/blob/main/app.py
'''
pdb = open(path_to_pdb, "r").read()
view = py3Dmol.view(width=500, height=500)
view.addModel(pdb, "pdb")
view.setStyle({'model': -1}, {"cartoon": {'colorscheme':{'prop':'b','gradient':'roygb','min':0,'max':1}}})#'linear', 'min': 0, 'max': 1, 'colors': ["#ff9ef0","#a903fc",]}}})
view.zoomTo()
output = view._make_html().replace("'", '"')
print(view._make_html())
x = f"""<!DOCTYPE html><html></center> {output} </center></html>""" # do not use ' in this input
return f"""<iframe height="500px" width="100%" 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>"""
'''
return f"""<iframe style="width: 100%; height:700px" 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 make_uncond_seq(seq_len, model_type, pred_structure):
if model_type == "EvoDiff-Seq-OADM 38M":
checkpoint = OA_DM_38M()
model, collater, tokenizer, scheme = checkpoint
tokeinzed_sample, generated_sequence = generate_oaardm(model, tokenizer, int(seq_len), batch_size=1, device='cpu')
if model_type == "EvoDiff-D3PM-Uniform 38M":
checkpoint = D3PM_UNIFORM_38M(return_all=True)
model, collater, tokenizer, scheme, timestep, Q_bar, Q = checkpoint
tokeinzed_sample, generated_sequence = generate_d3pm(model, tokenizer, Q, Q_bar, timestep, int(seq_len), batch_size=1, device='cpu')
if pred_structure:
path_to_pdb = predict_protein(generated_sequence)
molhtml = display_pdb(path_to_pdb)
return generated_sequence, molhtml
else:
return generated_sequence, None
def make_cond_seq(seq_len, msa_file, n_sequences, model_type, pred_structure):
if model_type == "EvoDiff-MSA":
checkpoint = MSA_OA_DM_MAXSUB()
model, collater, tokenizer, scheme = checkpoint
tokeinzed_sample, generated_sequence = generate_query_oadm_msa_simple(msa_file.name, model, tokenizer, int(n_sequences), seq_length=int(seq_len), device='cpu', selection_type='random')
if pred_structure:
path_to_pdb = predict_protein(generated_sequence)
molhtml = display_pdb(path_to_pdb)
return generated_sequence, molhtml
else:
return generated_sequence, None
def make_inpainted_idrs(sequence, start_idx, end_idx, model_type, pred_structure):
if model_type == "EvoDiff-Seq":
checkpoint = OA_DM_38M()
model, collater, tokenizer, scheme = checkpoint
sample, entire_sequence, generated_idr = inpaint_simple(model, sequence, int(start_idx), int(end_idx), tokenizer=tokenizer, device='cpu')
generated_idr_output = {
"original_sequence": sequence,
"generated_sequence": entire_sequence,
"original_region": sequence[start_idx:end_idx],
"generated_region": generated_idr
}
if pred_structure:
path_to_pdb = predict_protein(entire_sequence)
molhtml = display_pdb(path_to_pdb)
return generated_idr_output, molhtml
else:
return generated_idr_output, None
def make_scaffold_motifs(pdb_code, start_idx, end_idx, scaffold_length, model_type, pred_structure):
if model_type == "EvoDiff-Seq":
checkpoint = OA_DM_38M()
model, collater, tokenizer, scheme = checkpoint
data_top_dir = './'
start_idx = list(map(int, start_idx.strip('][').split(', ')))
end_idx = list(map(int, end_idx.strip('][').split(', ')))
generated_sequence, new_start_idx, new_end_idx = generate_scaffold(model, pdb_code, start_idx, end_idx, scaffold_length, data_top_dir, tokenizer, device='cpu')
generated_scaffold_output = {
"generated_sequence": generated_sequence,
"new_start_index": new_start_idx,
"new_end_index": new_end_idx
}
if pred_structure:
# path_to_pdb = predict_protein(generated_sequence)
path_to_pdb = f"scaffolding-pdbs/{pdb_code}.pdb"
molhtml = display_pdb(path_to_pdb)
return generated_scaffold_output, molhtml
else:
return generated_scaffold_output, None
usg_app = gr.Interface(
fn=make_uncond_seq,
inputs=[
gr.Slider(10, 100, step=1, label = "Sequence Length"),
gr.Dropdown(["EvoDiff-Seq-OADM 38M", "EvoDiff-D3PM-Uniform 38M"], value="EvoDiff-Seq-OADM 38M", type="value", label = "Model"),
gr.Checkbox(value=False, label = "Predict Structure?", visible=False)
],
outputs=[
"text",
gr.HTML()
],
title = "Unconditional sequence generation",
description="Generate a sequence with `EvoDiff-Seq-OADM 38M` (smaller/faster) or `EvoDiff-D3PM-Uniform 38M` (larger/slower) models."
)
csg_app = gr.Interface(
fn=make_cond_seq,
inputs=[
gr.Slider(10, 100, label = "Sequence Length"),
gr.File(file_types=["a3m"], label = "MSA File"),
gr.Number(value=1, placeholder=1, precision=0, label = "Number of Sequences")
gr.Dropdown(["EvoDiff-MSA"], value="EvoDiff-MSA", type="value", label = "Model"),
gr.Checkbox(value=False, label = "Predict Structure?", visible=False)
],
outputs=[
"text",
gr.HTML()
],
# examples=[["https://github.com/microsoft/evodiff/raw/main/examples/example_files/bfd_uniclust_hits.a3m"]],
title = "Conditional sequence generation",
description="Evolutionary guided sequence generation with the `EvoDiff-MSA` model."
)
idr_app = gr.Interface(
fn=make_inpainted_idrs,
inputs=[
gr.Textbox(placeholder="DQTERTVRSFEGRRTAPYLDSRNVLTIGYGHLLNRPGANKSWEGRLTSALPREFKQRLTELAASQLHETDVRLATARAQALYGSGAYFESVPVSLNDLWFDSVFNLGERKLLNWSGLRTKLESRDWGAAAKDLGRHTFGREPVSRRMAESMRMRRGIDLNHYNI", label = "Sequence"),
gr.Number(value=20, placeholder=20, precision=0, label = "Start Index"),
gr.Number(value=50, placeholder=50, precision=0, label = "End Index"),
gr.Dropdown(["EvoDiff-Seq"], value="EvoDiff-Seq", type="value", label = "Model"),
gr.Checkbox(value=False, label = "Predict Structure?", visible=False)
],
outputs=[
"text",
gr.HTML()
],
title = "Inpainting IDRs",
description="Inpaining a new region inside a given sequence using the `EvoDiff-Seq` model."
)
scaffold_app = gr.Interface(
fn=make_scaffold_motifs,
inputs=[
gr.Textbox(placeholder="1prw", label = "PDB Code"),
gr.Textbox(value="[15, 51]", placeholder="[15, 51]", label = "Start Index (as list)"),
gr.Textbox(value="[34, 70]", placeholder="[34, 70]", label = "End Index (as list)"),
gr.Number(value=75, placeholder=75, precision=0, label = "Scaffold Length"),
gr.Dropdown(["EvoDiff-Seq", "EvoDiff-MSA"], value="EvoDiff-Seq", type="value", label = "Model"),
gr.Checkbox(value=False, label = "Predict Structure?", visible=False)
],
outputs=[
"text",
gr.HTML()
],
title = "Scaffolding functional motifs",
description="Scaffolding a new functional motif inside a given PDB structure using the `EvoDiff-Seq` model."
)
with gr.Blocks() as edapp:
with gr.Row():
gr.Markdown(
"""
# EvoDiff
## Generation of protein sequences and evolutionary alignments via discrete diffusion models
Created By: Microsoft Research [Sarah Alamdari, Nitya Thakkar, Rianne van den Berg, Alex X. Lu, Nicolo Fusi, ProfileAva P. Amini, and Kevin K. Yang]
Spaces App By: Tuple, The Cloud Genomics Company [Colby T. Ford]
"""
)
with gr.Row():
gr.TabbedInterface([usg_app, csg_app, idr_app, scaffold_app],
["Unconditional sequence generation",
"Conditional generation",
"Inpainting IDRs",
"Scaffolding functional motifs"])
if __name__ == "__main__":
edapp.launch() |