app.py

import gradio as gr
import numpy as np
import os, tempfile
import torch
import py3Dmol
from huggingface_hub import login
# import spaces

from esm.utils.structure.protein_chain import ProteinChain
from esm.models.esm3 import ESM3
from esm.sdk.api import (
    ESMProtein,
    GenerationConfig,
)

from gradio_molecule3d import Molecule3D


theme = gr.themes.Monochrome(
    primary_hue="gray",
)

## Function to get model from Hugging Face using token
# @spaces.GPU()
def get_model(model_name, token):
    login(token=token)

    if torch.cuda.is_available():
        model = ESM3.from_pretrained(model_name, device=torch.device("cuda"))
    else:
        model = ESM3.from_pretrained(model_name, device=torch.device("cpu"))

    # model = ESM3.from_pretrained(model_name, device=torch.device("cpu"))
    return model

## Function to get PDB data
def get_pdb(pdb_id, chain_id):
    pdb = ProteinChain.from_rcsb(pdb_id, chain_id)
    # return [pdb.sequence, render_pdb(pdb.to_pdb_string())]
    return pdb

## Function to generate rep for 3D structure
def make_reps(res_start=None, res_end=None, main_color="whiteCarbon", highlight_color="redCarbon", main_style="cartoon", highlight_style="cartoon"):
    residue_range = f"{res_start}-{res_end}" if res_start != res_end else ""

    return [
     {
        "model": 0,
        "chain": "",
        "resname": "",
        "style": main_style,
        "color": main_color,
        "residue_range": "",
        "around": 0,
        "byres": False,
        "visible": True
    },
    {
        "model": 0,
        "chain": "",
        "resname": "",
        "style": highlight_style,
        "color": highlight_color,
        "residue_range": residue_range,
        "around": 0,
        "byres": False,
        "visible": True
    }]

## Function to render 3D structure
def render_pdb(pdb_id, chain_id, res_start, res_end, pdb_string=None):
    if pdb_string is None:
        pdb_string = get_pdb(pdb_id, chain_id).to_pdb_string()
    ## Write to temporary file and read back in to get the 3D structure
    tmp_pdb = tempfile.NamedTemporaryFile(delete=False, prefix=f"{pdb_id}_chain{chain_id}_", suffix=".pdb")
    tmp_pdb.write(str.encode(pdb_string))

    return Molecule3D(tmp_pdb.name, reps=make_reps(res_start=res_start, res_end=res_end))

## Function for Scaffolding
def scaffold(model_name, token, pdb_id, chain_id, motif_start, motif_end, prompt_length, insert_size):
    pdb = get_pdb(pdb_id, chain_id)

    ## Get motif sequence and atom37 positions
    motif_inds = np.arange(motif_start, motif_end)
    motif_sequence = pdb[motif_inds].sequence
    motif_atom37_positions = pdb[motif_inds].atom37_positions

    ## Create sequence prompt
    sequence_prompt = ["_"]*prompt_length
    sequence_prompt[insert_size:insert_size+len(motif_sequence)] = list(motif_sequence)
    sequence_prompt = "".join(sequence_prompt)

    ## Create structure prompt
    structure_prompt = torch.full((prompt_length, 37, 3), np.nan)
    structure_prompt[insert_size:insert_size+len(motif_atom37_positions)] = torch.tensor(motif_atom37_positions)

    ## Create protein prompt and sequence generation config
    protein_prompt = ESMProtein(sequence=sequence_prompt, coordinates=structure_prompt)
    sequence_generation_config = GenerationConfig(track="sequence",
                                                  num_steps=sequence_prompt.count("_") // 2,
                                                  temperature=0.5)
    ## Generate sequence
    model = get_model(model_name, token)
    sequence_generation = model.generate(protein_prompt, sequence_generation_config)
    generated_sequence = sequence_generation.sequence

    ## Generate structure
    structure_prediction_config = GenerationConfig(
        track="structure", # We want ESM3 to generate tokens for the structure track
        num_steps=len(sequence_generation) // 8,
        temperature=0.7, 
    )
    structure_prediction_prompt = ESMProtein(sequence=sequence_generation.sequence)
    structure_prediction = model.generate(structure_prediction_prompt, structure_prediction_config)
    ## Convert the generated structure to a back into a ProteinChain object
    structure_prediction_chain = structure_prediction.to_protein_chain()
    motif_inds_in_generation = np.arange(insert_size, insert_size+len(motif_sequence))
    structure_prediction_chain.align(pdb, mobile_inds=motif_inds_in_generation, target_inds=motif_inds)
    # crmsd = structure_prediction_chain.rmsd(renal_dipep_chain, mobile_inds=motif_inds_in_generation, target_inds=motif_inds)

    structure_orig_highlight = render_pdb(pdb_id, chain_id, res_start=motif_start, res_end=motif_end)
    structure_new_highlight = render_pdb(pdb_id, chain_id, res_start=insert_size, res_end=insert_size+len(motif_sequence), 
                                         pdb_string=structure_prediction_chain.to_pdb_string())

    return [
        pdb.sequence,
        motif_sequence,
        structure_orig_highlight,
        # motif_atom37_positions,
        sequence_prompt,
        # structure_prompt,
        # protein_prompt
        generated_sequence,
        # structure_prediction,
        # structure_prediction_chain,
        structure_new_highlight
    ]

## Function for Secondary Structure Editing
def ss_edit(model_name, token, pdb_id, chain_id, region_start, region_end, shortened_region_length, shortening_ss8):
    pdb = get_pdb(pdb_id, chain_id)
    edit_region = np.arange(region_start, region_end)

    ## Construct a sequence prompt that masks the (shortened) helix-coil-helix region, but leaves the flanking regions unmasked
    sequence_prompt = pdb.sequence[:edit_region[0]] + "_" * shortened_region_length + pdb.sequence[edit_region[-1] + 1:]

    ## Construct a secondary structure prompt that retains the secondary structure of the flanking regions, and shortens the lengths of helices in the helix-coil-helix region
    ss8_prompt = shortening_ss8[:edit_region[0]] + (((shortened_region_length - 3) // 2) * "H" + "C"*3 + ((shortened_region_length - 3) // 2) * "H") + shortening_ss8[edit_region[-1] + 1:]
    
    ## Save original sequence and secondary structure
    original_sequence = pdb.sequence
    original_ss8 = shortening_ss8
    original_ss8_region = " "*edit_region[0] + shortening_ss8[edit_region[0]:edit_region[-1]+1]
    
    proposed_ss8_region = " "*edit_region[0] + ss8_prompt[edit_region[0]:edit_region[0]+shortened_region_length]

    ## Create protein prompt
    protein_prompt = ESMProtein(sequence=sequence_prompt, secondary_structure=ss8_prompt)

    ## Generatre sequence
    model = get_model(model_name, token)
    sequence_generation = model.generate(protein_prompt, GenerationConfig(track="sequence", num_steps=protein_prompt.sequence.count("_") // 2, temperature=0.5))
    
    ## Generate structure
    structure_prediction = model.generate(ESMProtein(sequence=sequence_generation.sequence), GenerationConfig(track="structure", num_steps=len(protein_prompt) // 4, temperature=0))
    structure_prediction_chain = structure_prediction.to_protein_chain()

    structure_orig_highlight = render_pdb(pdb_id, chain_id, res_start=region_start, res_end=region_end)
    structure_new_highlight = render_pdb(pdb_id, chain_id, res_start=region_start, res_end=region_end,
                                         pdb_string=structure_prediction_chain.to_pdb_string())

    return [
        original_sequence,
        original_ss8,
        original_ss8_region,
        structure_orig_highlight,
        sequence_prompt,
        ss8_prompt,
        proposed_ss8_region,
        # protein_prompt,
        sequence_generation,
        structure_new_highlight
        ]

## Function for SASA Editing
def sasa_edit(model_name, token, pdb_id, chain_id, span_start, span_end, n_samples):
    pdb = get_pdb(pdb_id, chain_id)

    structure_prompt = torch.full((len(pdb), 37, 3), torch.nan)
    structure_prompt[span_start:span_end] = torch.tensor(pdb[span_start:span_end].atom37_positions, dtype=torch.float32)   

    sasa_prompt = [None]*len(pdb)
    sasa_prompt[span_start:span_end] = [40.0]*(span_end - span_start)

    protein_prompt = ESMProtein(sequence="_"*len(pdb), coordinates=structure_prompt, sasa=sasa_prompt)

    model = get_model(model_name, token)

    generated_proteins = []
    for i in range(n_samples):
        ## Generate sequence
        sequence_generation = model.generate(protein_prompt, GenerationConfig(track="sequence", num_steps=len(protein_prompt) // 8, temperature=0.7))
        ## Fold Protein
        structure_prediction = model.generate(ESMProtein(sequence=sequence_generation.sequence), GenerationConfig(track="structure", num_steps=len(protein_prompt) // 32))
        generated_proteins.append(structure_prediction)

    ## Sort generations by ptm
    generated_proteins = sorted(generated_proteins, key=lambda x: x.ptm.item(), reverse=True)

    structure_orig_highlight = render_pdb(pdb_id, chain_id, res_start=span_start, res_end=span_end)
    structure_new_highlight = render_pdb(pdb_id, chain_id, res_start=span_start, res_end=span_end,
                                         pdb_string=generated_proteins[0].to_protein_chain().to_pdb_string())

    return [
        protein_prompt.sequence,
        structure_orig_highlight,
        # [seq.sequence for seq in sequence_generation],
        # [pro.sequence for pro in generated_proteins]
        structure_new_highlight
    ]


## Interface for main Scaffolding Example
scaffold_app = gr.Interface(
    fn=scaffold,
    inputs=[
        gr.Dropdown(label="Model Name", choices=["esm3_sm_open_v1"], value="esm3_sm_open_v1", allow_custom_value=True),
        gr.Textbox(value = "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", label="Hugging Face Token", type="password"),
        gr.Textbox(value="1ITU", label = "PDB Code"),
        gr.Textbox(value="A", label = "Chain"),
        gr.Number(value=123, label="Motif Start"),
        gr.Number(value=146, label="Motif End"),
        gr.Number(value=200, label="Prompt Length"),
        gr.Number(value=72, label="Insert Size")
        ],
    outputs=[
        gr.Textbox(label="Sequence"),
        gr.Textbox(label="Motif Sequence"),
        Molecule3D(label="Original Structure"),
        # gr.Textbox(label="Motif Positions")
        gr.Textbox(label="Sequence Prompt"),
        # gr.Textbox(label="Structure Prompt"),
        # gr.Textbox(label="Protein Prompt"),
        gr.Textbox(label="Generated Sequence"),
        Molecule3D(label="Generated Structure")
    ]
    )

## Interface for "Secondary Structure Editing Example: Helix Shortening"
ss_app = gr.Interface(
    fn=ss_edit,
    inputs=[
        gr.Dropdown(label="Model Name", choices=["esm3_sm_open_v1"], value="esm3_sm_open_v1", allow_custom_value=True),
        gr.Textbox(value = "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", label="Hugging Face Token", type="password"),
        gr.Textbox(value = "7XBQ", label="PDB ID"),
        gr.Textbox(value = "A", label="Chain ID"),
        gr.Number(value=38, label="Edit Region Start"),
        gr.Number(value=111, label="Edit Region End"),
        gr.Number(value=45, label="Shortened Region Length"),
        gr.Textbox(value="CCCSHHHHHHHHHHHTTCHHHHHHHHHHHHHTCSSCCCCHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHTTCHHHHHHHHHHHHHHHHHHHHHHHHHHHHIIIIIGGGCCSHHHHHHHHHHHHHHHHHHHHHCCHHHHHHHHHHHHHHHHHHHHHHHHHSCTTCHHHHHHHHHHHHHIIIIICCHHHHHHHHHHHHHHHHTTCTTCCSSHHHHHHHHHHHHHHHHHHHC", label="SS8 Shortening")
    ],
    outputs=[
        gr.Textbox(label="Original Sequence"),
        gr.Textbox(label="Original SS8"),
        gr.Textbox(label="Original SS8 Edit Region"),
        Molecule3D(label="Original Structure"),
        gr.Textbox(label="Sequence Prompt"),
        gr.Textbox(label="Edited SS8 Prompt"),
        gr.Textbox(label="Proposed SS8 of Edit Region"),
        # gr.Textbox(label="Protein Prompt"),
        gr.Textbox(label="Generated Sequence"),
        Molecule3D(label="Generated Structure")
    ]
    )

## Interface for "SASA Editing Example: Exposing a buried helix"
sasa_app = gr.Interface(
    fn=sasa_edit,
    inputs=[
        gr.Dropdown(label="Model Name", choices=["esm3_sm_open_v1"], value="esm3_sm_open_v1", allow_custom_value=True),
        gr.Textbox(value = "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", label="Hugging Face Token", type="password"),
        gr.Textbox(value = "1LBS", label="PDB ID"),
        gr.Textbox(value = "A", label="Chain ID"),
        gr.Number(value=105, label="Span Start"),
        gr.Number(value=116, label="Span End"),
        # gr.Textbox(value="CCSSCCCCSSCHHHHHHTEEETTBBTTBCSSEEEEECCTTCCHHHHHTTTHHHHHHHTTCEEEEECCTTTTCSCHHHHHHHHHHHHHHHHHHTTSCCEEEEEETHHHHHHHHHHHHCGGGGGTEEEEEEESCCTTCBGGGHHHHHTTCBCHHHHHTBTTCHHHHHHHHTTTTBCSSCEEEEECTTCSSSCCCCSSSTTSTTCCBTSEEEEHHHHHCTTCCCCSHHHHHBHHHHHHHHHHHHCTTSSCCGGGCCSTTCCCSBCTTSCHHHHHHHHSTHHHHHHHHHHSCCBSSCCCCCGGGGGGSTTCEETTEECCC", label="SS8 String")
        gr.Number(value=1, label="Number of Samples")
    ],
    outputs = [
        gr.Textbox(label="Protein Prompt"),
        Molecule3D(label="Original Structure"),
        # gr.Textbox(label="Generated Sequences"),
        # gr.Textbox(label="Generated Proteins")
        Molecule3D(label="Best Generated Structure")
    ]
)


protein_viewer = gr.Interface(
    fn=render_pdb,
    inputs=[
        gr.Textbox(value = "1LBS", label="PDB ID"),
        gr.Textbox(value = "A", label="Chain ID"),
        gr.Number(value=10, label="Residue Highlight Start"),
        gr.Number(value=20, label="Residue Highlight End")
    ],
    outputs=[
        Molecule3D(label="3D Structure")
    ]
)


## Main Interface
with gr.Blocks(theme=theme) as esm_app:
    with gr.Row():
        gr.Markdown(
            """
            # ESM3: A frontier language model for biology.
            Model Created By: [EvolutionaryScale](https://www.evolutionaryscale.ai)
            - Press Release: https://www.evolutionaryscale.ai/blog/esm3-release
            - GitHub: https://github.com/evolutionaryscale/esm
            - HuggingFace Model: https://huggingface.co/EvolutionaryScale/esm3-sm-open-v1

            Spaces App By: [[Colby T. Ford](https://colbyford.com)] from [Tuple, The Cloud Genomics Company](https://tuple.xyz) 

            NOTE: You will need to agree to EvolutionaryScale's [license agreement](https://huggingface.co/EvolutionaryScale/esm3-sm-open-v1) to use the model. Then, create and paste your HuggingFace token in the appropriate field.
            """
        )
    with gr.Row():
        gr.TabbedInterface([
            scaffold_app,
            ss_app,
            sasa_app,
            protein_viewer
            ],
            [
                "Scaffolding Example",
                "Secondary Structure Editing Example",
                "SASA Editing Example",
                "PDB Viewer"
            ])

if __name__ == "__main__":
    esm_app.launch()