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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() |