rosettafold_pymol.py

from pymol import cmd
import requests


# from gradio_client import Client


def color_plddt(selection="all"):
    """
    AUTHOR
    Jinyuan Sun
    https://github.com/JinyuanSun/PymolFold/tree/main
    MIT License

    DESCRIPTION
    Colors Predicted Structures by pLDDT

    USAGE
    color_plddt sele

    PARAMETERS

    sele (string)
    The name of the selection/object to color by pLDDT. Default: all
    """
    # Alphafold color scheme for plddt
    cmd.set_color("high_lddt_c", [0, 0.325490196078431, 0.843137254901961])
    cmd.set_color(
        "normal_lddt_c", [0.341176470588235, 0.792156862745098, 0.976470588235294]
    )
    cmd.set_color("medium_lddt_c", [1, 0.858823529411765, 0.070588235294118])
    cmd.set_color("low_lddt_c", [1, 0.494117647058824, 0.270588235294118])

    # test the scale of predicted_lddt (0~1 or 0~100 ) as b-factors
    cmd.select("test_b_scale", f"b>1 and ({selection})")
    b_scale = cmd.count_atoms("test_b_scale")

    if b_scale > 0:
        cmd.select("high_lddt", f"({selection}) and (b >90 or b =90)")
        cmd.select("normal_lddt", f"({selection}) and ((b <90 and b >70) or (b =70))")
        cmd.select("medium_lddt", f"({selection}) and ((b <70 and b >50) or (b=50))")
        cmd.select("low_lddt", f"({selection}) and ((b <50 and b >0 ) or (b=0))")
    else:
        cmd.select("high_lddt", f"({selection}) and (b >.90 or b =.90)")
        cmd.select(
            "normal_lddt", f"({selection}) and ((b <.90 and b >.70) or (b =.70))"
        )
        cmd.select("medium_lddt", f"({selection}) and ((b <.70 and b >.50) or (b=.50))")
        cmd.select("low_lddt", f"({selection}) and ((b <.50 and b >0 ) or (b=0))")

    cmd.delete("test_b_scale")

    # set color based on plddt values
    cmd.color("high_lddt_c", "high_lddt")
    cmd.color("normal_lddt_c", "normal_lddt")
    cmd.color("medium_lddt_c", "medium_lddt")
    cmd.color("low_lddt_c", "low_lddt")

    # set background color
    cmd.bg_color("white")


def query_rosettafold2(
    sequence: str,
    jobname: str,
    sym: str = "X",
    order: int = 1,
    msa_concat_mode: str = "diag",
    msa_method: str = "single_sequence",
    pair_mode: str = "unpaired_paired",
    collapse_identical: bool = True,
    num_recycles: int = 0,
    use_mlm: bool = True,
    use_dropout: bool = True,
    max_msa: int = 16,
    random_seed: int = 0,
    num_models: int = 0,
):
    """
    AUTHOR
    Simon Duerr
    https://twitter.com/simonduerr


    DESCRIPTION
    Predict a structure using rosettafold2

    USAGE
    rosettafold2 sequence, jobname, [sym, order, msa_concat_mode, msa_method, pair_mode, collapse_identical, num_recycles, use_mlm, use_dropout, max_msa, random_seed, num_models]

    PARAMETERS

    sequence: (string)
    one letter amino acid codes that you want to predict

    jobname: string
    name of the pdbfile that will be outputted

    sym: string
    symmetry Default: X

    order:
    Default 1,

    msa_concat_mode:
    MSA concatenation mode Default:"diag" Options: "diag", "repeat", "default"

    msa_method:
    MSA method Default:"single_sequence" Options: "mmseqs2", "single_sequence"

    pair_mode:
    Pair mode Default:"unpaired_paired" Options: "unpaired_paired", "paired", "unpaired"

    collapse_identical:
    Collapse identical sequences Default:True

    num_recycles:
    Number of recycles Default:0 Options: 0, 1, 3, 6, 12, 24

    use_mlm:
    Use MLM Default:True

    use_dropout:
    Use dropout Default:True

    max_msa:
    Max MSA Default:16

    random_seed:
    Random seed Default:0

    num_models:
    Number of models Default:0
    """
    response = requests.post(
        "http://localhost:7860/run/rosettafold2/",
        json={
            "data": [
                sequence,  # str  in 'sequence' Textbox component
                jobname,  # str  in 'jobname' Textbox component
                sym,  # str  in 'sym' Textbox component
                order,  # int | float (numeric value between 1 and 12) in 'order' Slider component
                "diag",  # str (Option from: ['diag', 'repeat', 'default']) in 'msa_concat_mode' Dropdown component
                "single_sequence",  # str (Option from: ['mmseqs2', 'single_sequence', 'custom_a3m']) in 'msa_method' Dropdown component
                "unpaired_paired",  # str (Option from: ['unpaired_paired', 'paired', 'unpaired']) in 'pair_mode' Dropdown component
                True,  # bool  in 'collapse_identical' Checkbox component
                0,  # int (Option from: ['0', '1', '3', '6', '12', '24']) in 'num_recycles' Dropdown component
                True,  # bool  in 'use_mlm' Checkbox component
                True,  # bool  in 'use_dropout' Checkbox component
                16,  # int (Option from: ['16', '32', '64', '128', '256', '512']) in 'max_msa' Dropdown component
                0,  # int  in 'random_seed' Textbox component
                1,  # int (Option from: ['1', '2', '4', '8', '16', '32']) in 'num_models' Dropdown component
            ]
        },
    ).json()
    print(response)
    try:
        data = response["data"]
    except KeyError:
        print(response["error"])
        return None
    with open(f"{jobname}.pdb", "w") as out:
        out.writelines(data)
    cmd.load(f"{jobname}.pdb")


cmd.extend("rosettafold2", query_rosettafold2)
cmd.extend("color_plddt", color_plddt)