folding_studio_demo/app.py

"""Folding Studio Demo App."""

import logging

import gradio as gr
import pandas as pd
import plotly.graph_objects as go
from folding_studio_data_models import FoldingModel
from gradio_molecule3d import Molecule3D

from folding_studio_demo.correlate import (
    SCORE_COLUMN_NAMES,
    SCORE_COLUMNS,
    compute_correlation_data,
    fake_predict_and_correlate,
    get_score_description,
    make_regression_plot,
    plot_correlation_ranking,
)
from folding_studio_demo.predict import filter_predictions, predict, predict_comparison

logger = logging.getLogger(__name__)


MOLECULE_REPS = [
    {
        "model": 0,
        # "chain": "",
        # "resname": "",
        "style": "cartoon",
        "color": "alphafold",
        # "residue_range": "",
        "around": 0,
        "byres": False,
        # "visible": False,
        # "opacity": 0.5
    }
]


MODEL_CHOICES = [
    ("AlphaFold2", FoldingModel.AF2),
    ("OpenFold", FoldingModel.OPENFOLD),
    # ("SoloSeq", FoldingModel.SOLOSEQ),
    ("Boltz-1", FoldingModel.BOLTZ),
    ("Chai-1", FoldingModel.CHAI),
    ("Protenix", FoldingModel.PROTENIX),
]

DEFAULT_SEQ = "MALWMRLLPLLALLALWGPDPAAA"
MODEL_EXAMPLES = {
    FoldingModel.AF2: [
        ["Monomer", f">A\n{DEFAULT_SEQ}"],
        ["Multimer", f">A\n{DEFAULT_SEQ}\n>B\n{DEFAULT_SEQ}"],
    ],
    FoldingModel.OPENFOLD: [
        ["Monomer", f">A\n{DEFAULT_SEQ}"],
        ["Multimer", f">A\n{DEFAULT_SEQ}\n>B\n{DEFAULT_SEQ}"],
    ],
    FoldingModel.SOLOSEQ: [["Monomer", f">A\n{DEFAULT_SEQ}"]],
    FoldingModel.BOLTZ: [
        ["Monomer", f">A|protein\n{DEFAULT_SEQ}"],
        ["Multimer", f">A|protein\n{DEFAULT_SEQ}\n>B|protein\n{DEFAULT_SEQ}"],
    ],
    FoldingModel.CHAI: [
        ["Monomer", f">protein|name=A\n{DEFAULT_SEQ}"],
        ["Multimer", f">protein|name=A\n{DEFAULT_SEQ}\n>protein|name=B\n{DEFAULT_SEQ}"],
    ],
    FoldingModel.PROTENIX: [
        ["Monomer", f">A|protein\n{DEFAULT_SEQ}"],
        ["Multimer", f">A|protein\n{DEFAULT_SEQ}\n>B|protein\n{DEFAULT_SEQ}"],
    ],
}


def sequence_input(dropdown: gr.Dropdown | None = None) -> gr.Textbox:
    """Sequence input component.

    Returns:
        gr.Textbox: Sequence input component
    """
    with gr.Row(equal_height=True):
        with gr.Column():
            sequence = gr.Textbox(
                label="Protein Sequence",
                lines=2,
                placeholder="Enter a protein sequence or upload a FASTA file",
            )
            dummy = gr.Textbox(label="Complex type", visible=False)

            examples = gr.Examples(
                examples=MODEL_EXAMPLES[FoldingModel.BOLTZ],
                inputs=[dummy, sequence],
            )
        file_input = gr.File(
            label="Upload a FASTA file",
            file_types=[".fasta", ".fa"],
            scale=0,
        )

    if dropdown is not None:
        dropdown.change(
            fn=lambda x: gr.Dataset(samples=MODEL_EXAMPLES[x]),
            inputs=[dropdown],
            outputs=[examples.dataset],
        )

    def _process_file(file: gr.File | None) -> gr.Textbox:
        if file is None:
            return gr.Textbox()
        try:
            with open(file.name, "r") as f:
                content = f.read().strip()
            return gr.Textbox(value=content)
        except Exception as e:
            logger.error(f"Error reading file: {e}")
            return gr.Textbox()

    file_input.change(fn=_process_file, inputs=[file_input], outputs=[sequence])
    return sequence


def simple_prediction(api_key: str) -> None:
    """Simple prediction tab.

    Args:
        api_key (str): Folding Studio API key
    """
    gr.Markdown(
        """
        ## Predict a Protein Structure

        It will be run in the background and the results will be displayed in the output section.
        The output will contain the protein structure and the pLDDT plot.

        Select a model to run the inference with and enter a protein sequence or upload a FASTA file.
        """
    )
    with gr.Row():
        dropdown = gr.Dropdown(
            label="Model",
            choices=MODEL_CHOICES,
            scale=0,
            value=FoldingModel.BOLTZ,
        )
        with gr.Column():
            sequence = sequence_input(dropdown)

    predict_btn = gr.Button(
        "Predict",
        elem_classes="gradient-button",
        elem_id="predict-btn",
        variant="primary",
    )

    with gr.Row():
        mol_output = Molecule3D(label="Protein Structure", reps=MOLECULE_REPS)
        metrics_plot = gr.Plot(label="pLDDT")

    predict_btn.click(
        fn=predict,
        inputs=[sequence, api_key, dropdown],
        outputs=[mol_output, metrics_plot],
    )


def model_comparison(api_key: str) -> None:
    """Model comparison tab.

    Args:
        api_key (str): Folding Studio API key
    """
    gr.Markdown(
        """
        ## Compare Folding Models

        Select multiple models to compare their predictions on your protein sequence.
        You can either enter the sequence directly or upload a FASTA file.

        The selected models will run in parallel and generate:
        - 3D structures of your protein that you can visualize and compare
        - pLDDT confidence scores plotted for each residue
        
        """
    )
    with gr.Row():
        models = gr.CheckboxGroup(
            label="Model",
            choices=MODEL_CHOICES,
            scale=0,
            min_width=300,
            value=[FoldingModel.BOLTZ, FoldingModel.CHAI, FoldingModel.PROTENIX],
        )
        with gr.Column():
            sequence = sequence_input()

    predict_btn = gr.Button(
        "Compare Models",
        elem_classes=["gradient-button"],
        elem_id="compare-models-btn",
        variant="primary",
    )
    with gr.Row():
        af2_predictions = gr.CheckboxGroup(label="AlphaFold2", visible=False)
        openfold_predictions = gr.CheckboxGroup(label="OpenFold", visible=False)
        solo_predictions = gr.CheckboxGroup(label="SoloSeq", visible=False)
        chai_predictions = gr.CheckboxGroup(label="Chai", visible=False)
        protenix_predictions = gr.CheckboxGroup(label="Protenix", visible=False)
        boltz_predictions = gr.CheckboxGroup(label="Boltz", visible=False)
    with gr.Row():
        mol_outputs = Molecule3D(
            label="Protein Structure", reps=MOLECULE_REPS, height=1000
        )
        metrics_plot = gr.Plot(label="pLDDT")

    # Store the initial predictions
    prediction_outputs = gr.State()

    predict_btn.click(
        fn=predict_comparison,
        inputs=[sequence, api_key, models],
        outputs=[
            prediction_outputs,
            af2_predictions,
            openfold_predictions,
            solo_predictions,
            chai_predictions,
            boltz_predictions,
            protenix_predictions,
        ],
    ).then(
        fn=filter_predictions,
        inputs=[
            prediction_outputs,
            af2_predictions,
            openfold_predictions,
            solo_predictions,
            chai_predictions,
            boltz_predictions,
            protenix_predictions,
        ],
        outputs=[mol_outputs, metrics_plot],
    )

    # Handle checkbox changes
    for checkbox in [
        af2_predictions,
        openfold_predictions,
        solo_predictions,
        chai_predictions,
        boltz_predictions,
        protenix_predictions,
    ]:
        checkbox.change(
            fn=filter_predictions,
            inputs=[
                prediction_outputs,
                af2_predictions,
                openfold_predictions,
                solo_predictions,
                chai_predictions,
                boltz_predictions,
                protenix_predictions,
            ],
            outputs=[mol_outputs, metrics_plot],
        )


def create_antibody_discovery_tab():
    gr.Markdown("# Accelerating Antibody Discovery: In-Silico and Experimental Insights")
    gr.Markdown("""
        Hey there! 👋 Let's dive into how we're using AI to accelerate antibody drug discovery by looking at how protein folding models stack up against real lab data.

        We've got this fascinating dataset that shows how well different antibodies stick to a specific target (we measure this as KD in nM). 🧪
        For each antibody-target pair, we've recorded:
        - The antibody's light and heavy chain sequences (think of them as the antibody's building blocks) 🧬
        - The target (antigen) sequence 🎯
        - How strongly they bind together in the lab (the KD value, lower means stronger binding) 💪
        
        Here's where it gets interesting! We take these sequences and feed them into protein folding models
        that predict their 3D structures. The models tell us how confident they are about their predictions.
        By comparing these confidence scores with our lab results, we can figure out which model scores
        are actually good at predicting real binding strength! 🎯
        
        Why is this exciting for drug discovery? 🚀 Once we know which computational scores to trust,
        we can use them to quickly check thousands of potential antibodies without having to test each one
        in the lab. It's like having a high-speed screening tool! We can then focus our lab work on testing
        just the most promising candidates. This means we can find effective antibody drugs much faster than
        before! 🔬✨
    """)
    spr_data_with_scores = pd.read_csv("spr_af_scores_mapped.csv")
    spr_data_with_scores = spr_data_with_scores.rename(columns=SCORE_COLUMN_NAMES)
    prettified_columns = {
        "antibody_name": "Antibody Name",
        "KD (nM)": "KD (nM)",
        "antibody_vh_sequence": "Antibody VH Sequence",
        "antibody_vl_sequence": "Antibody VL Sequence",
        "antigen_sequence": "Antigen Sequence",
    }
    spr_data_with_scores = spr_data_with_scores.rename(columns=prettified_columns)
    columns = [
        "Antibody Name",
        "KD (nM)",
        "Antibody VH Sequence",
        "Antibody VL Sequence",
        "Antigen Sequence",
    ]
    # Display dataframe with floating point values rounded to 2 decimal places
    spr_data = gr.DataFrame(
        value=spr_data_with_scores[columns].round(2),
        label="Experimental Antibody-Antigen Binding Affinity Data",
    )

    gr.Markdown("# Prediction and correlation")

    with gr.Row():
        with gr.Column(min_width=150):
            gr.Markdown("Now, let's see how well the protein folding models can predict the binding affinity of these antibodies to the target antigen.")
        with gr.Column(min_width=150):
            fake_predict_btn = gr.Button(
                "Predict structures of all complexes",
                elem_classes="gradient-button",
                variant="primary",
            )
    prediction_dataframe = gr.Dataframe(
        label="Predicted Structures Data", visible=False
    )
    prediction_dataframe.change(
        fn=lambda x: gr.Dataframe(x, visible=True),
        inputs=[prediction_dataframe],
        outputs=[prediction_dataframe],
    )
    with gr.Row(visible=False) as correlation_row:
        with gr.Column(scale=0):
            with gr.Row():
                correlation_type = gr.Radio(
                    choices=["Spearman", "Pearson"],
                    value="Spearman",
                    label="Correlation Type",
                    interactive=True,
                    min_width=150,
                )
            with gr.Row():
                log_scale = gr.Checkbox(
                    label="Use log scale for KD",
                    value=False,
                    min_width=150,
                )
        with gr.Column():
            correlation_ranking_plot = gr.Plot(label="Correlation ranking")
    with gr.Row(visible=False) as regression_row:
        with gr.Column(scale=0):
            
            # User can select the columns to display in the correlation plot
            correlation_column = gr.Dropdown(
                label="Score data to display",
                choices=SCORE_COLUMNS,
                multiselect=False,
                value=SCORE_COLUMNS[0],
            )
            score_description = gr.Markdown(
                get_score_description(correlation_column.value)
            )
            correlation_column.change(
                fn=lambda x: get_score_description(x),
                inputs=correlation_column,
                outputs=score_description,
            )
        with gr.Column():
            regression_plot = gr.Plot(label="Correlation with binding affinity")

    fake_predict_btn.click(
        fn=lambda x: (
            *fake_predict_and_correlate(
                spr_data_with_scores, SCORE_COLUMNS, ["Antibody Name", "KD (nM)"]
            ),
            gr.Row(visible=True),
            gr.Row(visible=True)
        ),
        inputs=[correlation_type],
        outputs=[
            prediction_dataframe,
            correlation_ranking_plot,
            regression_plot,
            correlation_row,
            regression_row,
        ],
    )

    def update_plots_with_log(correlation_type, score, use_log):
        logger.info(f"Updating correlation plot for {correlation_type}")
        corr_data = compute_correlation_data(spr_data_with_scores, SCORE_COLUMNS)
        logger.info(f"Correlation data: {corr_data}")
        corr_ranking_plot = plot_correlation_ranking(corr_data, correlation_type, kd_col="KD (nM)" if not use_log else "log_kd")
        regression_plot = make_regression_plot(spr_data_with_scores, score, use_log)
        return regression_plot, corr_ranking_plot

    correlation_column.change(
        fn=update_plots_with_log,
        inputs=[correlation_type, correlation_column, log_scale],
        outputs=[regression_plot, correlation_ranking_plot],
    )

    correlation_type.change(
        fn=update_plots_with_log,
        inputs=[correlation_type, correlation_column, log_scale],
        outputs=[regression_plot, correlation_ranking_plot],
    )
    log_scale.change(
        fn=update_plots_with_log,
        inputs=[correlation_type, correlation_column, log_scale],
        outputs=[regression_plot, correlation_ranking_plot],
    )


def __main__():
    theme = gr.themes.Ocean(
        primary_hue="blue",
        secondary_hue="purple",
    )
    with gr.Blocks(theme=theme, title="Folding Studio Demo") as demo:
        gr.Markdown(
            """
            # Folding Studio: Harness the Power of Protein Folding 🧬

            Folding Studio is a platform for protein structure prediction.
            It uses the latest AI-powered folding models to predict the structure of a protein.

            Available models are : AlphaFold2, OpenFold, SoloSeq, Boltz-1, Chai and Protenix.

            ## API Key
            To use the Folding Studio API, you need to provide an API key.
            You can get your API key by asking to the Folding Studio team.
            """
        )
        api_key = gr.Textbox(label="Folding Studio API Key", type="password")
        gr.Markdown("## Demo Usage")
        with gr.Tab("🚀 Basic Folding"):
            simple_prediction(api_key)
        with gr.Tab("📊 Model Comparison"):
            model_comparison(api_key)
        with gr.Tab("🧪 Antibody Discovery Pipeline"):
            create_antibody_discovery_tab()

    demo.launch()