app.py

import gradio as gr

import numpy as np

import os

import matplotlib

matplotlib.use("Agg")
import matplotlib.pyplot as plt
from transformers import pipeline as pl

import numpy as np
import matplotlib.pyplot as plt
import sys

print(os.getcwd())
if "/home/user/app/alphafold" not in sys.path:
    sys.path.append("/home/user/app/alphafold")

from alphafold.common import protein
from alphafold.data import pipeline
from alphafold.data import templates
from alphafold.model import data
from alphafold.model import config
from alphafold.model import model


def update_seqs(choice):
    return gr.Textbox.update(choice)


def mk_mock_template(query_sequence):
    """create blank template"""
    ln = len(query_sequence)
    output_templates_sequence = "-" * ln
    templates_all_atom_positions = np.zeros(
        (ln, templates.residue_constants.atom_type_num, 3)
    )
    templates_all_atom_masks = np.zeros((ln, templates.residue_constants.atom_type_num))
    templates_aatype = templates.residue_constants.sequence_to_onehot(
        output_templates_sequence, templates.residue_constants.HHBLITS_AA_TO_ID
    )
    template_features = {
        "template_all_atom_positions": templates_all_atom_positions[None],
        "template_all_atom_masks": templates_all_atom_masks[None],
        "template_aatype": np.array(templates_aatype)[None],
        "template_domain_names": [f"none".encode()],
    }
    return template_features


def predict_structure(prefix, feature_dict, model_runners, random_seed=0):
    """Predicts structure using AlphaFold for the given sequence."""

    # Run the models.
    plddts = {}
    for model_name, model_runner in model_runners.items():
        processed_feature_dict = model_runner.process_features(
            feature_dict, random_seed=random_seed
        )
        prediction_result = model_runner.predict(processed_feature_dict)
        b_factors = (
            prediction_result["plddt"][:, None]
            * prediction_result["structure_module"]["final_atom_mask"]
        )
        unrelaxed_protein = protein.from_prediction(
            processed_feature_dict, prediction_result, b_factors
        )
        unrelaxed_pdb_path = f"{prefix}_unrelaxed_{model_name}.pdb"
        plddts[model_name] = prediction_result["plddt"]

        print(f"{model_name} {plddts[model_name].mean()}")

        with open(unrelaxed_pdb_path, "w") as f:
            f.write(protein.to_pdb(unrelaxed_protein))
    return plddts


def run_protgpt2(startsequence, length):
    protgpt2 = pl("text-generation", model="nferruz/ProtGPT2")
    sequences = protgpt2(
        startsequence,
        max_length=length,
        do_sample=True,
        top_k=950,
        repetition_penalty=1.2,
        num_return_sequences=5,
        eos_token_id=0,
    )
    return sequences


def run_alphafold(startsequence):
    model_runners = {}
    models = ["model_1"]  # ,"model_2","model_3","model_4","model_5"]
    for model_name in models:
        model_config = config.model_config(model_name)
        model_config.data.eval.num_ensemble = 1
        model_params = data.get_model_haiku_params(model_name=model_name, data_dir=".")
        model_runner = model.RunModel(model_config, model_params)
        model_runners[model_name] = model_runner
    query_sequence = startsequence.replace("\n", "")

    feature_dict = {
        **pipeline.make_sequence_features(
            sequence=query_sequence, description="none", num_res=len(query_sequence)
        ),
        **pipeline.make_msa_features(
            msas=[[query_sequence]], deletion_matrices=[[[0] * len(query_sequence)]]
        ),
        **mk_mock_template(query_sequence),
    }
    plddts = predict_structure("test", feature_dict, model_runners)
    return plddts["model_1"]


def update_protGPT2(inp, length):
    startsequence = inp
    seqlen = length
    generated_seqs = run_protgpt2(startsequence, seqlen)
    gen_seqs = [x["generated_text"] for x in generated_seqs]
    print(gen_seqs)
    return gr.Radio.update(gen_seqs)


def update(inp):
    print("Running AF on", inp)
    startsequence = inp
    plddts = run_alphafold(startsequence)
    print(plddts)
    x = np.arange(10)
    plt.style.use(["seaborn-ticks", "seaborn-talk"])
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.plot(plddts)
    ax.set_ylabel("predicted LDDT")
    ax.set_xlabel("positions")
    ax.set_title("pLDDT")

    return (
        molecule(
            f"test_unrelaxed_model_1.pdb",
        ),
        fig,
        f"{np.mean(plddts):.1f} ± {np.std(plddts):.1f}",
    )


def read_mol(molpath):
    with open(molpath, "r") as fp:
        lines = fp.readlines()
    mol = ""
    for l in lines:
        mol += l
    return mol


def molecule(pdb):
    mol = read_mol(pdb)
    x = (
        """<!DOCTYPE html>
        <html>
        <head>    
    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
     <link rel="stylesheet" href="https://unpkg.com/flowbite@1.4.5/dist/flowbite.min.css" />
    <style>
    body{
        font-family:sans-serif
    }
.mol-container {
  width: 100%;
  height: 800px;
  position: relative;
}
.space-x-2 > * + *{
    margin-left: 0.5rem;
}
.p-1{
    padding:0.5rem;
}
.flex{
    display:flex;
    align-items: center;
}
.w-4{
    width:1rem;
}
.h-4{
    height:1rem;
}
.mt-4{
    margin-top:1rem;
}
</style>
<script src="https://3Dmol.csb.pitt.edu/build/3Dmol-min.js"></script>
    </head>
    <body>  
   
    <div id="container" class="mol-container"></div>
    <div class="flex">
        <div class="px-4">
        <label for="sidechain" class="relative inline-flex items-center mb-4 cursor-pointer ">
            <input  id="sidechain"type="checkbox" class="sr-only peer">
            <div class="w-11 h-6 bg-gray-200 rounded-full peer peer-focus:ring-4 peer-focus:ring-blue-300 dark:peer-focus:ring-blue-800 dark:bg-gray-700 peer-checked:after:translate-x-full peer-checked:after:border-white after:absolute after:top-0.5 after:left-[2px] after:bg-white after:border-gray-300 after:border after:rounded-full after:h-5 after:w-5 after:transition-all dark:border-gray-600 peer-checked:bg-blue-600"></div>
            <span class="ml-3 text-sm font-medium text-gray-900 dark:text-gray-300">Show side chains</span>
          </label>
        </div>
 <button type="button" class="text-gray-900 bg-white hover:bg-gray-100 border border-gray-200 focus:ring-4 focus:outline-none focus:ring-gray-100 font-medium rounded-lg text-sm px-5 py-2.5 text-center inline-flex items-center dark:focus:ring-gray-600 dark:bg-gray-800 dark:border-gray-700 dark:text-white dark:hover:bg-gray-700 mr-2 mb-2" id="download">
                    <svg class="w-6 h-6 mr-2 -ml-1" fill="none" stroke="currentColor" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M4 16v1a3 3 0 003 3h10a3 3 0 003-3v-1m-4-4l-4 4m0 0l-4-4m4 4V4"></path></svg>
                    Download predicted structure
                  </button>
            </div>       
<div class="text-sm">
                            <div class="font-medium mt-4"><b>AlphaFold model confidence:</b></div>
                            <div class="flex space-x-2 py-1"><span class="w-4 h-4"
                                    style="background-color: rgb(0, 83, 214);">&nbsp;</span><span class="legendlabel">Very high
                                    (pLDDT &gt; 90)</span></div>
                            <div class="flex space-x-2 py-1"><span class="w-4 h-4"
                                    style="background-color: rgb(101, 203, 243);">&nbsp;</span><span class="legendlabel">Confident
                                    (90 &gt; pLDDT &gt; 70)</span></div>
                            <div class="flex space-x-2 py-1"><span class="w-4 h-4"
                                    style="background-color: rgb(255, 219, 19);">&nbsp;</span><span class="legendlabel">Low (70 &gt;
                                    pLDDT &gt; 50)</span></div>
                            <div class="flex space-x-2 py-1"><span class="w-4 h-4"
                                    style="background-color: rgb(255, 125, 69);">&nbsp;</span><span class="legendlabel">Very low
                                    (pLDDT &lt; 50)</span></div>
                            <div class="row column legendDesc"> AlphaFold produces a per-residue confidence
                                score (pLDDT) between 0 and 100. Some regions below 50 pLDDT may be unstructured in isolation.
                            </div>
                        </div>
            <script>
            let viewer = null;
            let voldata = null;
            $(document).ready(function () {

                let element = $("#container");
                let config = { backgroundColor: "white" };
                viewer = $3Dmol.createViewer( element, config );
                viewer.ui.initiateUI();
                let data = `"""
        + mol
        + """`  
                viewer.addModel( data, "pdb" );
                //AlphaFold code from https://gist.github.com/piroyon/30d1c1099ad488a7952c3b21a5bebc96
                let colorAlpha = function (atom) {
                    if (atom.b < 50) {
                        return "OrangeRed";
                    } else if (atom.b < 70) {
                        return "Gold";
                    } else if (atom.b < 90) {
                        return "MediumTurquoise";
                    } else {
                        return "Blue";
                    }
                };
                viewer.setStyle({}, { cartoon: { colorfunc: colorAlpha } });
                viewer.zoomTo();
                viewer.render();
                viewer.zoom(0.8, 2000);
                viewer.getModel(0).setHoverable({}, true,
                    function (atom, viewer, event, container) {
                        console.log(atom)
                        if (!atom.label) {
                            atom.label = viewer.addLabel(atom.resn+atom.resi+" pLDDT=" + atom.b, { position: atom, backgroundColor: "mintcream", fontColor: "black" });
                        }
                    },
                    function (atom, viewer) {
                        if (atom.label) {
                            viewer.removeLabel(atom.label);
                            delete atom.label;
                        }
                    }
                );
                $("#sidechain").change(function () {
                    if (this.checked) {
                        BB = ["C", "O", "N"]
                        viewer.setStyle( {"and": [{resn: ["GLY", "PRO"], invert: true},{atom: BB, invert: true},]},{stick: {colorscheme: "WhiteCarbon", radius: 0.3}, cartoon: { colorfunc: colorAlpha }});
                        viewer.render()
                    } else {
                        viewer.setStyle({cartoon: { colorfunc: colorAlpha }});
                        viewer.render()
                    }
                });
                $("#download").click(function () {
                    download("gradioFold_model1.pdb", data);
                })
        });

        function download(filename, text) {
            var element = document.createElement("a");
            element.setAttribute("href", "data:text/plain;charset=utf-8," + encodeURIComponent(text));
            element.setAttribute("download", filename);

            element.style.display = "none";
            document.body.appendChild(element);

            element.click();

            document.body.removeChild(element);
        }
        </script>
        </body></html>"""
    )

    return f"""<iframe style="width: 800px; height: 1200px" 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>"""


proteindream = gr.Blocks()

with proteindream:
    gr.Markdown("# GradioFold")
    gr.Markdown(
        """GradioFold is a web-based tool that combines a large language model trained on natural protein sequence (protGPT2) with structure prediction using AlphaFold. 
    Type a start sequence or provide a sequence with blanks that protGPT2 can complete."""
    )
    gr.Markdown("## protGPT2")
    gr.Markdown(
        """
        Enter a start sequence and have the language model complete it.
    """
    )
    with gr.Group():
        with gr.Row():
            inp = gr.Textbox(placeholder="M", label="Start sequence")
            length = gr.Number(value=50, label="Target sequence length")
        btn = gr.Button("Autocomplete sequences")

    seqs = [
        "MTAEADPAPLAANPPAPVRPIQFHDVSVRYEARPWLRALWDVASGSFIGLLGASGAGKSTCVDLLNGVRKPSSGERFVRGQPSRGRKGRFNRRVAMVFQDVRHQLFSRSVAREIAFGLENLPTSAAAIDRRVS",
        "MTAGIVAGGIAGGVAGYKAKKHRKAVKATMIAAGVSGGIGGGYIGEKFNRRLAKHEDRVRRSAPRHKKHSSYSKSSGEGGGILGKLFGR",
        "MTAVLVAIALEMQNPHRMALAAVLCGQFTVAVAAEPFAPEGVAEGLNPLGDLLAESPLLEVVSATLALLVALGTATSLSWISGPVSALPAPSFQSSETPYPQRPIERESFDQDSREEDPWDRL",
        "MTARVRNRSSSRSYVLDFADLADGQREVLLPESRGNASEVDLPAGTTVNVTIDVTASGTGTLTARTPDGADVVSNEYELTVERDTDLTRVETESPQVAAGETATVTGTAENVGTVAGEREVTAYVDGE",
        "MTAAGWREEGTPFARIARQLGRHVTSVRQAAGRVRQQMGLTSPDPADPPRSGPTPTIPIEQERA",
    ]
    seqChoice = gr.Radio(seqs, label="Generated sequences")
    btn.click(fn=update_protGPT2, inputs=[inp, length], outputs=seqChoice)
    gr.Markdown("## AlphaFold")
    gr.Markdown(
        "Select a generated sequence above for structure prediction using AlphaFold2."
    )
    with gr.Group():
        chosenSeq = gr.Textbox(label="Chosen sequence")
        btn2 = gr.Button("Predict structure")
    with gr.Group():
        meanpLDDT = gr.Textbox(label="Mean pLDDT of chosen sequence")
        with gr.Row():
            mol = gr.HTML()
            plot = gr.Plot(label="pLDDT")
    gr.Markdown(
        """## Acknowledgements
         This was a fun demo using Gradio, Huggingface and ColabFold. More information about the used algorithms can be found below. 

         All code is available on [Github]() and licensed under MIT license.

        - ProtGPT2: Ferruz et.al [BioRxiv](https://doi.org/10.1101/2022.03.09.483666) [Code](https://huggingface.co/nferruz/ProtGPT2)
        - AlphaFold2: Jumper et.al [Paper](https://doi.org/10.1038/s41586-021-03819-2) [Code](https://github.com/deepmind/alphafold) Model parameters released under CC BY 4.0
        - ColabFold: Mirdita et.al [Paper](https://doi.org/10.1101/2021.08.15.456425 ) [Code](https://github.com/sokrypton/ColabFold)

        Created by [@simonduerr](https://twitter.com/simonduerr)
    """
    )
    seqChoice.change(fn=update_seqs, inputs=seqChoice, outputs=chosenSeq)
    btn2.click(fn=update, inputs=seqChoice, outputs=[mol, plot, meanpLDDT])

proteindream.launch(share=False)