import spaces import gradio as gr from gradio_molecule3d import Molecule3D from gradio_cofoldinginput import CofoldingInput import os import re import urllib.request import yaml #from msa import run_mmseqs2 CCD_URL = "https://huggingface.co/boltz-community/boltz-1/resolve/main/ccd.pkl" MODEL_URL = "https://huggingface.co/boltz-community/boltz-1/resolve/main/boltz1.ckpt" cache = "/home/user/.boltz" os.makedirs(cache) ccd = f"{cache}/ccd.pkl" if not os.path.exists(ccd): print( f"Downloading the CCD dictionary to {ccd}. You may " ) urllib.request.urlretrieve(CCD_URL, str(ccd)) # Download model model =f"{cache}/boltz1.ckpt" if not os.path.exists(model): print( f"Downloading the model weights to {model}" ) urllib.request.urlretrieve(MODEL_URL, str(model)) @spaces.GPU(duration=120) def predict(jobname, inputs, recycling_steps, sampling_steps, diffusion_samples): try: jobname = re.sub(r'[<>:"/\\|?*]', '_', jobname) if jobname == "": raise gr.Error("Job name empty or only invalid characters. Choose a plaintext name.") os.makedirs(jobname, exist_ok=True) """format Gradio Component: # {"chains": [ # { # "class": "DNA", # "sequence": "ATGCGT", # "chain": "A" # } # ], "covMods":[] # } """ #sequences_for_msa = [] output = { "sequences": [] } representations = [] for chain in inputs["chains"]: entity_type = chain["class"].lower() sequence_data = { entity_type: { "id": chain["chain"], } } if entity_type in ["protein", "dna", "rna"]: sequence_data[entity_type]["sequence"] = chain["sequence"] if entity_type == "protein": #sequences_for_msa.append(chain["sequence"]) if chain["msa"] == False: sequence_data[entity_type]["msa"] = f"empty" representations.append({"model":0, "chain":chain["chain"], "style":"cartoon"}) if entity_type == "ligand": if "sdf" in chain.keys(): if chain["sdf"]!="" and chain["name"]=="": raise gr.Error("Sorry, no SDF support yet.") if "name" in chain.keys() and len(chain["name"])==3: sequence_data[entity_type]["ccd"] = chain["name"] elif "smiles" in chain.keys(): sequence_data[entity_type]["smiles"] = chain["smiles"] else: raise gr.Error("No ligand found, or not in the right format. CCD codes have 3 letters") representations.append({"model":0, "chain":chain["chain"], "style":"stick", "color":"greenCarbon"}) if len(inputs["covMods"])>0: raise gr.Error("Sorry, covMods not supported yet. Coming soon. ") output["sequences"].append(sequence_data) # Convert the output to YAML yaml_file_path = f"{jobname}/{jobname}.yaml" # Write the YAML output to the file with open(yaml_file_path, "w") as file: yaml.dump(output, file, sort_keys=False, default_flow_style=False) os.system(f"cat {yaml_file_path}") #a3m_lines_mmseqs2 = run_mmseqs2( # sequences_for_msa, # f"./{jobname}", # use_templates=False, # ) #with open(f"{jobname}/msa.a3m", "w+") as fp: # fp.writelines(a3m_lines_mmseqs2) os.system(f"boltz predict {jobname}/{jobname}.yaml --use_msa_server --out_dir {jobname} --recycling_steps {recycling_steps} --sampling_steps {sampling_steps} --diffusion_samples {diffusion_samples} --override --output_format pdb") print(os.listdir(jobname)) print(os.listdir(f"{jobname}/boltz_results_{jobname}/predictions/{jobname}/")) return Molecule3D(f"{jobname}/boltz_results_{jobname}/predictions/{jobname}/{jobname}_model_0.pdb", label="Output", reps=representations) except Exception as e: raise gr.Error(f"failed with error:{e}") with gr.Blocks() as blocks: gr.Markdown("# Boltz-1") gr.Markdown("""Open GUI for running [Boltz-1 model](https://github.com/jwohlwend/boltz/)
Key components: - MMSeqs2 Webserver [Mirdita et al.](https://www.nature.com/articles/s41592-022-01488-1) - Boltz-1 Model [Wohlwend et al.](https://github.com/jwohlwend/boltz/) - Gradio Custom Components [Molecule3D](https://huggingface.co/spaces/simonduerr/gradio_molecule3d)/[Cofolding Input](https://huggingface.co/spaces/simonduerr/gradio_cofoldinginput) by myself - [3dmol.js Rego & Koes](https://academic.oup.com/bioinformatics/article/31/8/1322/213186) Note: This is an alpha: Some things like covalent modifications or using sdf files don't work yet. You can a Docker image of this on your local infrastructure easily using: `docker run -it -p 7860:7860 --platform=linux/amd64 --gpus all registry.hf.space/simonduerr-boltz-1:latest python app.py` """) with gr.Tab("Main"): jobname = gr.Textbox(label="Jobname") inp = CofoldingInput(label="Input") out = Molecule3D(label="Output") with gr.Tab("Settings"): recycling_steps =gr.Slider(value=3, minimum=0, label="Recycling steps") sampling_steps = gr.Slider(value=200, minimum=0, label="Sampling steps") diffusion_samples = gr.Slider(value=1, label="Diffusion samples") gr.Examples([ ["TOP7",{"chains": [{"class": "protein", "msa":True,"sequence": "MGDIQVQVNIDDNGKNFDYTYTVTTESELQKVLNELMDYIKKQGAKRVRISITARTKKEAEKFAAILIKVFAELGYNDINVTFDGDTVTVEGQLEGGSLEHHHHHH","chain": "A"}], "covMods":[]}], ["ApixacabanBinderSmiles", {"chains": [{"class": "protein", "msa":True,"sequence": "SVKSEYAEAAAVGQEAVAVFNTMKAAFQNGDKEAVAQYLARLASLYTRHEELLNRILEKARREGNKEAVTLMNEFTATFQTGKSIFNAMVAAFKNGDDDSFESYLQALEKVTAKGETLADQIAKAL","chain": "A"}, {"class":"ligand", "smiles":"COc1ccc(cc1)n2c3c(c(n2)C(=O)N)CCN(C3=O)c4ccc(cc4)N5CCCCC5=O", "sdf":"","name":"","chain": "B"}], "covMods":[]}], ["ApixacabanBinderCCD", {"chains": [{"class": "protein","msa":True,"sequence": "SVKSEYAEAAAVGQEAVAVFNTMKAAFQNGDKEAVAQYLARLASLYTRHEELLNRILEKARREGNKEAVTLMNEFTATFQTGKSIFNAMVAAFKNGDDDSFESYLQALEKVTAKGETLADQIAKAL","chain": "A"}, {"class":"ligand", "name":"GG2", "sdf":"","chain": "B"}], "covMods":[]}] ], inputs = [jobname, inp] ) btn = gr.Button("predict") btn.click(fn=predict, inputs=[jobname,inp, recycling_steps, sampling_steps, diffusion_samples], outputs=[out], api_name="predict") blocks.launch(ssr_mode=False)