view plotWheels/helical_wheel.py @ 1:9b276485c94a draft

planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c

import matplotlib

matplotlib.use("Agg")

import matplotlib.lines as lines
import matplotlib.patches as patches
import matplotlib.pyplot as plt

# from mpl_toolkits.mplot3d import Axes3D
import numpy as np
from scipy.stats.kde import gaussian_kde

from plotWheels.core import load_scale
from plotWheels.descriptors import PeptideDescriptor


def helical_wheel(
    sequence,
    colorcoding="rainbow",
    text_color=None,
    lineweights=True,
    filename=None,
    seq=False,
    moment=False,
    seqRange=1,
    t_size=32,
    rot=float(90),
    dpi=150,
    numbering=True,
):
    """A function to project a given peptide sequence onto a helical wheel plot. It can be useful to illustrate the
    properties of alpha-helices, like positioning of charged and hydrophobic residues along the sequence.

    :param sequence: {str} the peptide sequence for which the helical wheel should be drawn
    :param colorcoding: {str} the color coding to be used, available: *rainbow*, *charge*, *polar*, *simple*,
        *amphipathic*, *custom_input*, *none*
    :param lineweights: {boolean} defines whether connection lines decrease in thickness along the sequence
    :param filename: {str} filename  where to save the plot. *default = None* --> show the plot
    :param seq: {bool} whether the amino acid sequence should be plotted as a title
    :param moment: {bool} whether the Eisenberg hydrophobic moment should be calculated and plotted
    :param seqRange: {int} starting value of residue location in sequence
    :param t_size: {int} text size
    :param rot: {float} rotation by radians --> converted to degrees.
    :param dpi: {int} dpi parameter for saved files
    :return: a helical wheel projection plot of the given sequence (interactively or in **filename**)
    :Example:

    >>> helical_wheel('GLFDIVKKVVGALG')
    >>> helical_wheel('KLLKLLKKLLKLLK', colorcoding='charge')
    >>> helical_wheel('AKLWLKAGRGFGRG', colorcoding='none', lineweights=False)
    >>> helical_wheel('ACDEFGHIKLMNPQRSTVWY')

    .. image:: ../docs/static/wheel1.png
        :height: 300px
    .. image:: ../docs/static/wheel2.png
        :height: 300px
    .. image:: ../docs/static/wheel3.png
        :height: 300px
    .. image:: ../docs/static/wheel4.png
        :height: 300px

    .. versionadded:: v2.1.5
    """
    # color mappings
    aa = [
        "A",
        "C",
        "D",
        "E",
        "F",
        "G",
        "H",
        "I",
        "K",
        "L",
        "M",
        "N",
        "P",
        "Q",
        "R",
        "S",
        "T",
        "V",
        "W",
        "Y",
    ]
    if colorcoding == type(str):
        f_rainbow = [
            "#3e3e28",
            "#ffcc33",
            "#b30047",
            "#b30047",
            "#ffcc33",
            "#3e3e28",
            "#80d4ff",
            "#ffcc33",
            "#0047b3",
            "#ffcc33",
            "#ffcc33",
            "#b366ff",
            "#29a329",
            "#b366ff",
            "#0047b3",
            "#ff66cc",
            "#ff66cc",
            "#ffcc33",
            "#ffcc33",
            "#ffcc33",
        ]
        f_charge = [
            "#000000",
            "#000000",
            "#ff4d94",
            "#ff4d94",
            "#000000",
            "#000000",
            "#80d4ff",
            "#000000",
            "#80d4ff",
            "#000000",
            "#000000",
            "#000000",
            "#000000",
            "#000000",
            "#80d4ff",
            "#000000",
            "#000000",
            "#000000",
            "#000000",
            "#000000",
        ]
        f_polar = [
            "#000000",
            "#000000",
            "#80d4ff",
            "#80d4ff",
            "#000000",
            "#000000",
            "#80d4ff",
            "#000000",
            "#80d4ff",
            "#000000",
            "#000000",
            "#80d4ff",
            "#000000",
            "#80d4ff",
            "#80d4ff",
            "#80d4ff",
            "#80d4ff",
            "#000000",
            "#000000",
            "#000000",
        ]
        f_simple = [
            "#ffcc33",
            "#ffcc33",
            "#0047b3",
            "#0047b3",
            "#ffcc33",
            "#7f7f7f",
            "#0047b3",
            "#ffcc33",
            "#0047b3",
            "#ffcc33",
            "#ffcc33",
            "#0047b3",
            "#ffcc33",
            "#0047b3",
            "#0047b3",
            "#0047b3",
            "#0047b3",
            "#ffcc33",
            "#ffcc33",
            "#ffcc33",
        ]
        f_none = ["#ffffff"] * 20
        f_amphi = [
            "#ffcc33",
            "#29a329",
            "#b30047",
            "#b30047",
            "#f79318",
            "#80d4ff",
            "#0047b3",
            "#ffcc33",
            "#0047b3",
            "#ffcc33",
            "#ffcc33",
            "#80d4ff",
            "#29a329",
            "#80d4ff",
            "#0047b3",
            "#80d4ff",
            "#80d4ff",
            "#ffcc33",
            "#f79318",
            "#f79318",
        ]
        t_rainbow = [
            "w",
            "k",
            "w",
            "w",
            "k",
            "w",
            "k",
            "k",
            "w",
            "k",
            "k",
            "k",
            "k",
            "k",
            "w",
            "k",
            "k",
            "k",
            "k",
            "k",
        ]
        t_charge = [
            "w",
            "w",
            "k",
            "k",
            "w",
            "w",
            "k",
            "w",
            "k",
            "w",
            "w",
            "w",
            "w",
            "w",
            "k",
            "w",
            "w",
            "w",
            "w",
            "w",
        ]
        t_polar = [
            "w",
            "w",
            "k",
            "k",
            "w",
            "w",
            "k",
            "w",
            "k",
            "w",
            "w",
            "k",
            "w",
            "k",
            "k",
            "k",
            "k",
            "w",
            "w",
            "w",
        ]
        t_simple = [
            "k",
            "k",
            "w",
            "w",
            "k",
            "w",
            "w",
            "k",
            "w",
            "k",
            "k",
            "k",
            "k",
            "w",
            "w",
            "w",
            "w",
            "k",
            "k",
            "k",
        ]
        t_none = ["k"] * 20
        t_amphi = [
            "k",
            "k",
            "w",
            "w",
            "w",
            "k",
            "w",
            "k",
            "w",
            "k",
            "k",
            "k",
            "w",
            "k",
            "w",
            "k",
            "k",
            "k",
            "w",
            "w",
        ]
        d_eisberg = load_scale("eisenberg")[1]  # eisenberg hydrophobicity values for HM
    else:
        f_custom = colorcoding
        t_custom = text_color
        d_eisberg = load_scale("eisenberg")[1]

    if lineweights:
        lw = np.arange(0.1, 5.5, 5.0 / (len(sequence) - 1))  # line thickness array
        lw = lw[::-1]  # inverse order
    else:
        lw = [2.0] * (len(sequence) - 1)
    # check which color coding to use
    if colorcoding == type(str):
        if colorcoding == "rainbow":
            df = dict(zip(aa, f_rainbow))
            dt = dict(zip(aa, t_rainbow))
        elif colorcoding == "charge":
            df = dict(zip(aa, f_charge))
            dt = dict(zip(aa, t_charge))
        elif colorcoding == "polar":
            df = dict(zip(aa, f_polar))
            dt = dict(zip(aa, t_polar))
        elif colorcoding == "simple":
            df = dict(zip(aa, f_simple))
            dt = dict(zip(aa, t_simple))
        elif colorcoding == "none":
            df = dict(zip(aa, f_none))
            dt = dict(zip(aa, t_none))
        elif colorcoding == "amphipathic":
            df = dict(zip(aa, f_amphi))
            dt = dict(zip(aa, t_amphi))
        else:
            print("Unknown color coding, 'rainbow' used instead")
            df = dict(zip(aa, f_rainbow))
            dt = dict(zip(aa, t_rainbow))
    else:
        df = dict(zip(aa, f_custom))
        dt = dict(zip(aa, t_custom))

    # degree to radian
    deg = np.arange(float(len(sequence))) * -100.0
    deg = [d + rot for d in deg]  # start at 270 degree in unit circle (on top)
    rad = np.radians(deg)

    # dict for coordinates and eisenberg values
    d_hydro = dict(zip(rad, [0.0] * len(rad)))

    # create figure
    fig = plt.figure(frameon=False, figsize=(10, 10))
    ax = fig.add_subplot(111)
    old = None
    hm = list()

    # iterate over sequence
    for i, r in enumerate(rad):
        new = (np.cos(r), np.sin(r))  # new AA coordinates
        if i < 18:
            # plot the connecting lines
            if old is not None:
                line = lines.Line2D(
                    (old[0], new[0]),
                    (old[1], new[1]),
                    transform=ax.transData,
                    color="k",
                    linewidth=lw[i - 1],
                )
                line.set_zorder(1)  # 1 = level behind circles
                ax.add_line(line)
        elif 17 < i < 36:
            line = lines.Line2D(
                (old[0], new[0]),
                (old[1], new[1]),
                transform=ax.transData,
                color="k",
                linewidth=lw[i - 1],
            )
            line.set_zorder(1)  # 1 = level behind circles
            ax.add_line(line)
            new = (np.cos(r) * 1.2, np.sin(r) * 1.2)
        elif i == 36:
            line = lines.Line2D(
                (old[0], new[0]),
                (old[1], new[1]),
                transform=ax.transData,
                color="k",
                linewidth=lw[i - 1],
            )
            line.set_zorder(1)  # 1 = level behind circles
            ax.add_line(line)
            new = (np.cos(r) * 1.4, np.sin(r) * 1.4)
        else:
            new = (np.cos(r) * 1.4, np.sin(r) * 1.4)

        # plot circles
        circ = patches.Circle(
            new,
            radius=0.125,
            transform=ax.transData,
            edgecolor="k",
            facecolor=df[sequence[i]],
        )
        circ.set_zorder(2)  # level in front of lines
        ax.add_patch(circ)

        # check if N- or C-terminus and add subscript, then plot AA letter
        if numbering:
            size = t_size
            if i == 0:
                ax.text(
                    new[0],
                    new[1],
                    sequence[i] + "$_N$",
                    va="center",
                    ha="center",
                    transform=ax.transData,
                    size=size,
                    color=dt[sequence[i]],
                    fontweight="bold",
                )
            elif i == len(sequence) - 1:
                ax.text(
                    new[0],
                    new[1],
                    sequence[i] + "$_C$",
                    va="center",
                    ha="center",
                    transform=ax.transData,
                    size=size,
                    color=dt[sequence[i]],
                    fontweight="bold",
                )
            else:
                seqRange += 1
                ax.text(
                    new[0],
                    new[1],
                    sequence[i] + "$_{" + str(seqRange) + "}$",
                    va="center",
                    ha="center",
                    transform=ax.transData,
                    size=size,
                    color=dt[sequence[i]],
                    fontweight="bold",
                )

            eb = d_eisberg[sequence[i]][0]  # eisenberg value for this AA
            hm.append(
                [eb * new[0], eb * new[1]]
            )  # save eisenberg hydrophobicity vector value to later calculate HM

            old = (np.cos(r), np.sin(r))  # save as previous coordinates

        else:
            size = t_size
            if i == 0:
                ax.text(
                    new[0],
                    new[1],
                    sequence[i] + "$_N$",
                    va="center",
                    ha="center",
                    transform=ax.transData,
                    size=size,
                    color=dt[sequence[i]],
                    fontweight="bold",
                )
            elif i == len(sequence) - 1:
                ax.text(
                    new[0],
                    new[1],
                    sequence[i] + "$_C$",
                    va="center",
                    ha="center",
                    transform=ax.transData,
                    size=size,
                    color=dt[sequence[i]],
                    fontweight="bold",
                )
            else:
                ax.text(
                    new[0],
                    new[1],
                    sequence[i],
                    va="center",
                    ha="center",
                    transform=ax.transData,
                    size=size,
                    color=dt[sequence[i]],
                    fontweight="bold",
                )

            eb = d_eisberg[sequence[i]][0]  # eisenberg value for this AA
            hm.append(
                [eb * new[0], eb * new[1]]
            )  # save eisenberg hydrophobicity vector value to later calculate HM

            old = (np.cos(r), np.sin(r))  # save as previous coordinates

    # draw hydrophobic moment arrow if moment option
    if moment:
        v_hm = np.sum(np.array(hm), 0)
        x = 0.0333 * v_hm[0]
        y = 0.0333 * v_hm[1]
        ax.arrow(
            0.0,
            0.0,
            x,
            y,
            head_width=0.04,
            head_length=0.03,
            transform=ax.transData,
            color="k",
            linewidth=6.0,
        )
        desc = PeptideDescriptor(sequence)  # calculate hydrophobic moment
        desc.calculate_moment()
        if (
            abs(x) < 0.2 and y > 0.0
        ):  # right positioning of HM text so arrow does not cover it
            z = -0.2
        else:
            z = 0.2
        plt.text(
            0.0,
            z,
            str(round(desc.descriptor[0][0], 3)),
            fontdict={"fontsize": 20, "fontweight": "bold", "ha": "center"},
        )

    # plot shape
    if len(sequence) < 19:
        ax.set_xlim(-1.2, 1.2)
        ax.set_ylim(-1.2, 1.2)
    else:
        ax.set_xlim(-1.4, 1.4)
        ax.set_ylim(-1.4, 1.4)
    ax.spines["right"].set_visible(False)
    ax.spines["top"].set_visible(False)
    ax.spines["left"].set_visible(False)
    ax.spines["bottom"].set_visible(False)
    cur_axes = plt.gca()
    cur_axes.axes.get_xaxis().set_visible(False)
    cur_axes.axes.get_yaxis().set_visible(False)
    plt.tight_layout()

    if seq:
        plt.title(sequence, fontweight="bold", fontsize=20)

    # show or save plot
    if filename:
        plt.savefig(filename, dpi=dpi)
    else:
        plt.show()