Mercurial > repos > cpt > cpt_helical_wheel

annotate plotWheels/descriptors.py @ 1:9b276485c94a draft

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planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
author cpt
date Mon, 05 Jun 2023 02:44:43 +0000
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1 # -*- coding: utf-8 -*-
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2 """
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3 .. currentmodule:: modlamp.descriptors
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4
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5 .. moduleauthor:: modlab Alex Mueller ETH Zurich <alex.mueller@pharma.ethz.ch>
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6
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7 This module incorporates different classes to calculate peptide descriptor values. The following classes are available:
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8
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9 ============================= ============================================================================
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10 Class Characteristics
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11 ============================= ============================================================================
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12 :py:class:`GlobalDescriptor` Global one-dimensional peptide descriptors calculated from the AA sequence.
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13 :py:class:`PeptideDescriptor` AA scale based global or convoluted descriptors (auto-/cross-correlated).
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14 ============================= ============================================================================
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15
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16 .. seealso:: :class:`modlamp.core.BaseDescriptor` from which the classes in :mod:`modlamp.descriptors` inherit.
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17 """
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18
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19 import sys
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20
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21 import numpy as np
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22 from scipy import stats
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23 from sklearn.externals.joblib import Parallel, delayed
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24
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25 from plotWheels.core import (
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26 BaseDescriptor,
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27 load_scale,
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28 count_aas,
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29 aa_weights,
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30 aa_energies,
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31 aa_formulas,
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32 )
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33
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34 __author__ = "Alex Müller, Gisela Gabernet"
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35 __docformat__ = "restructuredtext en"
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36
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37
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38 def _one_autocorr(seq, window, scale):
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39 """Private function used for calculating auto-correlated descriptors for 1 given sequence, window and an AA scale.
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40 This function is used by the :py:func:`calculate_autocorr` method of :py:class:`PeptideDescriptor`.
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41
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42 :param seq: {str} amino acid sequence to calculate descriptor for
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43 :param window: {int} correlation-window size
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44 :param scale: {str} amino acid scale to be used to calculate descriptor
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45 :return: {numpy.array} calculated descriptor data
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46 """
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47 try:
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48 m = list() # list of lists to store translated sequence values
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49 for l in range(len(seq)): # translate AA sequence into values
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50 m.append(scale[str(seq[l])])
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51 # auto-correlation in defined sequence window
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52 seqdesc = list()
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53 for dist in range(window): # for all correlation distances
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54 for val in range(
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55 len(scale["A"])
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56 ): # for all features of the descriptor scale
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57 valsum = list()
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58 cntr = 0.0
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59 for pos in range(len(seq)): # for every position in the sequence
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60 if (pos + dist) < len(
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61 seq
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62 ): # check if corr distance is possible at that sequence position
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63 cntr += 1 # counter to scale sum
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64 valsum.append(m[pos][val] * m[pos + dist][val])
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65 seqdesc.append(
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66 sum(valsum) / cntr
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67 ) # append scaled correlation distance values
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68 return seqdesc
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69 except ZeroDivisionError:
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70 print(
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71 "ERROR!\nThe chosen correlation window % i is larger than the sequence %s !"
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72 % (window, seq)
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73 )
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74
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75
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76 def _one_crosscorr(seq, window, scale):
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77 """Private function used for calculating cross-correlated descriptors for 1 given sequence, window and an AA scale.
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78 This function is used by the :py:func:`calculate_crosscorr` method of :py:class:`PeptideDescriptor`.
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79
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80 :param seq: {str} amino acid sequence to calculate descriptor for
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81 :param window: {int} correlation-window size
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82 :param scale: {str} amino acid scale to be used to calculate descriptor
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83 :return: {numpy.array} calculated descriptor data
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84 """
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85 try:
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86 m = list() # list of lists to store translated sequence values
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87 for l in range(len(seq)): # translate AA sequence into values
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88 m.append(scale[str(seq[l])])
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89 # auto-correlation in defined sequence window
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90 seqdesc = list()
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91 for val in range(len(scale["A"])): # for all features of the descriptor scale
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92 for cc in range(len(scale["A"])): # for every feature cross correlation
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93 if (val + cc) < len(
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94 scale["A"]
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95 ): # check if corr distance is in range of the num of features
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96 for dist in range(window): # for all correlation distances
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97 cntr = float()
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98 valsum = list()
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99 for pos in range(
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100 len(seq)
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101 ): # for every position in the sequence
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102 if (pos + dist) < len(
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103 seq
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104 ): # check if corr distance is possible at that sequence pos
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105 cntr += 1 # counter to scale sum
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106 valsum.append(m[pos][val] * m[pos + dist][val + cc])
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107 seqdesc.append(
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108 sum(valsum) / cntr
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109 ) # append scaled correlation distance values
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110 return seqdesc
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111 except ZeroDivisionError:
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112 print(
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113 "ERROR!\nThe chosen correlation window % i is larger than the sequence %s !"
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114 % (window, seq)
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115 )
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116
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117
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118 def _one_arc(seq, modality, scale):
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119 """Privat function used for calculating arc descriptors for one sequence and AA scale. This function is used by
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120 :py:func:`calculate_arc` method method of :py:class:`PeptideDescriptor`.
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121
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122 :param seq: {str} amino acid sequence to calculate descriptor for
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123 :param scale: {str} amino acid scale to be used to calculate descriptor
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124 :return: {numpy.array} calculated descriptor data
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125 """
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126 desc_mat = []
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127 for aa in seq:
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128 desc_mat.append(scale[aa])
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129 desc_mat = np.asarray(desc_mat)
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130
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131 # Check descriptor dimension
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132 desc_dim = desc_mat.shape[1]
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133
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134 # list to store descriptor values for all windows
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135 allwindows_arc = []
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136
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137 if len(seq) > 18:
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138 window = 18
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139 # calculates number of windows in sequence
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140 num_windows = len(seq) - window
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141 else:
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142 window = len(seq)
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143 num_windows = 1
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144
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145 # loop through all windows
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146 for j in range(num_windows):
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147 # slices descriptor matrix into current window
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148 window_mat = desc_mat[j : j + window, :]
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149
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150 # defines order of amino acids in helical projection
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151 order = [0, 11, 4, 15, 8, 1, 12, 5, 16, 9, 2, 13, 6, 17, 10, 3, 14, 7]
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152
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153 # orders window descriptor matrix into helical projection order
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154 ordered = []
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155 for pos in order:
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156 try:
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157 ordered.append(window_mat[pos, :])
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158 except:
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159 # for sequences of len < 18 adding dummy vector with 2s, length of descriptor dimensions
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160 ordered.append([2] * desc_dim)
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161 ordered = np.asarray(ordered)
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162
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163 window_arc = []
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164
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165 # loop through pharmacophoric features
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166 for m in range(desc_dim):
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167 all_arcs = (
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168 []
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169 ) # stores all arcs that can be found of a pharmacophoric feature
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170 arc = 0
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171
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172 for n in range(
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173 18
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174 ): # for all positions in helix, regardless of sequence length
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175 if (
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176 ordered[n, m] == 0
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177 ): # if position does not contain pharmacophoric feature
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178 all_arcs.append(arc) # append previous arc to all arcs list
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179 arc = 0 # arc is initialized
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180 elif (
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181 ordered[n, m] == 1
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182 ): # if position contains pharmacophoric feature(PF), elongate arc by 20°
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183 arc += 20
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184 elif ordered[n, m] == 2: # if position doesn't contain amino acid:
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185 if (
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186 ordered[n - 1, m] == 1
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187 ): # if previous position contained PF add 10°
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188 arc += 10
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189 elif (
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190 ordered[n - 1, m] == 0
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191 ): # if previous position didn't contain PF don't add anything
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192 arc += 0
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193 elif (
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194 ordered[n - 2, m] == 1
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195 ): # if previous position is empty then check second previous for PF
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196 arc += 10
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197 if (
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198 n == 17
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199 ): # if we are at the last position check for position n=0 instead of next position.
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200 if ordered[0, m] == 1: # if it contains PF add 10° extra
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201 arc += 10
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202 else: # if next position contains PF add 10° extra
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203 if ordered[n + 1, m] == 1:
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204 arc += 10
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205 elif ordered[n + 1, m] == 0:
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206 arc += 0
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207 else: # if next position is empty check for 2nd next position
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208 if n == 16:
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209 if ordered[0, m] == 1:
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210 arc += 10
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211 else:
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212 if ordered[n + 2, m] == 1:
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213 arc += 10
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214
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215 all_arcs.append(arc)
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216 if not arc == 360:
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217 arc0 = all_arcs.pop() + all_arcs[0] # join first and last arc together
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218 all_arcs = [arc0] + all_arcs[1:]
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219
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220 window_arc.append(
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221 np.max(all_arcs)
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222 ) # append to window arcs the maximum arc of this PF
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223 allwindows_arc.append(window_arc) # append all PF arcs of this window
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224
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225 allwindows_arc = np.asarray(allwindows_arc)
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226
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227 if modality == "max":
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228 final_arc = np.max(
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229 allwindows_arc, axis=0
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230 ) # calculate maximum / mean arc along all windows
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231 elif modality == "mean":
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232 final_arc = np.mean(allwindows_arc, axis=0)
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233 else:
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234 print('modality is unknown, please choose between "max" and "mean"\n.')
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235 sys.exit()
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236 return final_arc
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237
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238
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239 def _charge(seq, ph=7.0, amide=False):
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240 """Calculates charge of a single sequence. The method used is first described by Bjellqvist. In the case of
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241 amidation, the value for the 'Cterm' pKa is 15 (and Cterm is added to the pos_pks dictionary.
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242 The pKa scale is extracted from: http://www.hbcpnetbase.com/ (CRC Handbook of Chemistry and Physics, 96th ed).
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243
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244 **pos_pks** = {'Nterm': 9.38, 'K': 10.67, 'R': 12.10, 'H': 6.04}
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245
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246 **neg_pks** = {'Cterm': 2.15, 'D': 3.71, 'E': 4.15, 'C': 8.14, 'Y': 10.10}
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247
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248 :param ph: {float} pH at which to calculate peptide charge.
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249 :param amide: {boolean} whether the sequences have an amidated C-terminus.
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250 :return: {array} descriptor values in the attribute :py:attr:`descriptor
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251 """
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252
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253 if amide:
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254 pos_pks = {"Nterm": 9.38, "K": 10.67, "R": 12.10, "H": 6.04}
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255 neg_pks = {"Cterm": 15.0, "D": 3.71, "E": 4.15, "C": 8.14, "Y": 10.10}
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256 else:
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257 pos_pks = {"Nterm": 9.38, "K": 10.67, "R": 12.10, "H": 6.04}
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258 neg_pks = {"Cterm": 2.15, "D": 3.71, "E": 4.15, "C": 8.14, "Y": 10.10}
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259
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260 aa_content = count_aas(seq, scale="absolute")
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261 aa_content["Nterm"] = 1.0
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262 aa_content["Cterm"] = 1.0
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263 pos_charge = 0.0
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264 for aa, pK in pos_pks.items():
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265 c_r = 10 ** (pK - ph)
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266 partial_charge = c_r / (c_r + 1.0)
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267 pos_charge += aa_content[aa] * partial_charge
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268 neg_charge = 0.0
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269 for aa, pK in neg_pks.items():
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270 c_r = 10 ** (ph - pK)
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271 partial_charge = c_r / (c_r + 1.0)
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272 neg_charge += aa_content[aa] * partial_charge
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273 return round(pos_charge - neg_charge, 3)
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274
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275
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276 class GlobalDescriptor(BaseDescriptor):
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277 """
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278 Base class for global, non-amino acid scale dependant descriptors. The following descriptors can be calculated by
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279 the **methods** linked below:
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280
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281 - `Sequence Length <modlamp.html#modlamp.descriptors.GlobalDescriptor.length>`_
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282 - `Molecular Formula <modlamp.html#modlamp.descriptors.GlobalDescriptor.formula>`_
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283 - `Molecular Weight <modlamp.html#modlamp.descriptors.GlobalDescriptor.calculate_MW>`_
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284 - `Sequence Charge <modlamp.html#modlamp.descriptors.GlobalDescriptor.calculate_charge>`_
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285 - `Charge Density <modlamp.html#modlamp.descriptors.GlobalDescriptor.charge_density>`_
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286 - `Isoelectric Point <modlamp.html#modlamp.descriptors.GlobalDescriptor.isoelectric_point>`_
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287 - `Instability Index <modlamp.html#modlamp.descriptors.GlobalDescriptor.instability_index>`_
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288 - `Aromaticity <modlamp.html#modlamp.descriptors.GlobalDescriptor.aromaticity>`_
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289 - `Aliphatic Index <modlamp.html#modlamp.descriptors.GlobalDescriptor.aliphatic_index>`_
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290 - `Boman Index <modlamp.html#modlamp.descriptors.GlobalDescriptor.boman_index>`_
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291 - `Hydrophobic Ratio <modlamp.html#modlamp.descriptors.GlobalDescriptor.hydrophobic_ratio>`_
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292 - `all of the above <modlamp.html#modlamp.descriptors.GlobalDescriptor.calculate_all>`_
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293 """
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294
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295 def length(self, append=False):
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296 """
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297 Method to calculate the length (total AA count) of every sequence in the attribute :py:attr:`sequences`.
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298
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299 :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the
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300 attribute :py:attr:`descriptor`.
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301 :return: array of sequence lengths in the attribute :py:attr:`descriptor`
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302 :Example:
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303
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304 >>> desc = GlobalDescriptor(['AFDGHLKI','KKLQRSDLLRTK','KKLASCNNIPPR'])
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305 >>> desc.length()
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306 >>> desc.descriptor
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307 array([[ 8.], [12.], [12.]])
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308 """
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309 desc = []
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310 for seq in self.sequences:
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311 desc.append(float(len(seq.strip())))
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312 desc = np.asarray(desc).reshape(len(desc), 1)
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313 if append:
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314 self.descriptor = np.hstack((self.descriptor, np.array(desc)))
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315 self.featurenames.append("Length")
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316 else:
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317 self.descriptor = np.array(desc)
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318 self.featurenames = ["Length"]
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319
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320 def formula(self, amide=False, append=False):
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321 """Method to calculate the molecular formula of every sequence in the attribute :py:attr:`sequences`.
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322
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323 :param amide: {boolean} whether the sequences are C-terminally amidated.
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324 :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the
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325 attribute :py:attr:`descriptor`.
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326 :return: array of molecular formulas {str} in the attribute :py:attr:`descriptor`
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327 :Example:
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328
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329 >>> desc = GlobalDescriptor(['KADSFLSADGHSADFSLDKKLKERL', 'ERTILSDFPQWWFASLDFLNC', 'ACDEFGHIKLMNPQRSTVWY'])
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330 >>> desc.formula(amide=True)
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331 >>> for v in desc.descriptor:
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332 ... print(v[0])
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333 C122 H197 N35 O39
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334 C121 H168 N28 O33 S
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335 C106 H157 N29 O30 S2
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336
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337 .. seealso:: :py:func:`modlamp.core.aa_formulas()`
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338
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339 .. versionadded:: v2.7.6
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340 """
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341 desc = []
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342 formulas = aa_formulas()
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343 for seq in self.sequences:
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344 f = {"C": 0, "H": 0, "N": 0, "O": 0, "S": 0}
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345 for aa in seq: # loop over all AAs
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346 for k in f.keys():
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347 f[k] += formulas[aa][k]
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348
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349 # substract H2O for every peptide bond
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350 f["H"] -= 2 * (len(seq) - 1)
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351 f["O"] -= len(seq) - 1
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352
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353 if amide: # add C-terminal amide --> replace OH with NH2
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354 f["O"] -= 1
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355 f["H"] += 1
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356 f["N"] += 1
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357
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358 if f["S"] != 0:
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359 val = "C%s H%s N%s O%s %s%s" % (
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360 f["C"],
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361 f["H"],
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362 f["N"],
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363 f["O"],
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364 "S",
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365 f["S"],
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366 )
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367 else:
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368 val = "C%s H%s N%s O%s" % (f["C"], f["H"], f["N"], f["O"])
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369
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370 desc.append([val])
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371
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372 if append:
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373 self.descriptor = np.hstack((self.descriptor, np.array(desc)))
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374 self.featurenames.append("Formula")
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375 else:
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376 self.descriptor = np.array(desc)
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377 self.featurenames = ["Formula"]
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378
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379 def calculate_MW(self, amide=False, append=False):
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380 """Method to calculate the molecular weight [g/mol] of every sequence in the attribute :py:attr:`sequences`.
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381
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382 :param amide: {boolean} whether the sequences are C-terminally amidated (subtracts 0.95 from the MW).
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383 :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the
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384 attribute :py:attr:`descriptor`.
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385 :return: array of descriptor values in the attribute :py:attr:`descriptor`
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386 :Example:
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387
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388 >>> desc = GlobalDescriptor('IAESFKGHIPL')
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389 >>> desc.calculate_MW(amide=True)
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390 >>> desc.descriptor
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391 array([[ 1210.43]])
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392
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393 .. seealso:: :py:func:`modlamp.core.aa_weights()`
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394
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395 .. versionchanged:: v2.1.5 amide option added
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396 """
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397 desc = []
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398 weights = aa_weights()
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399 for seq in self.sequences:
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400 mw = []
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401 for aa in seq: # sum over aa weights
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402 mw.append(weights[aa])
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403 desc.append(
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404 round(sum(mw) - 18.015 * (len(seq) - 1), 2)
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405 ) # sum over AA MW and subtract H20 MW for every
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406 # peptide bond
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407 desc = np.asarray(desc).reshape(len(desc), 1)
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408 if (
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409 amide
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410 ): # if sequences are amidated, subtract 0.98 from calculated MW (OH - NH2)
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411 desc = [d - 0.98 for d in desc]
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412 if append:
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413 self.descriptor = np.hstack((self.descriptor, np.array(desc)))
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414 self.featurenames.append("MW")
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415 else:
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416 self.descriptor = np.array(desc)
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417 self.featurenames = ["MW"]
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418
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419 def calculate_charge(self, ph=7.0, amide=False, append=False):
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420 """Method to overall charge of every sequence in the attribute :py:attr:`sequences`.
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421
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422 The method used is first described by Bjellqvist. In the case of amidation, the value for the 'Cterm' pKa is 15
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423 (and Cterm is added to the pos_pKs dictionary.
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424 The pKa scale is extracted from: http://www.hbcpnetbase.com/ (CRC Handbook of Chemistry and Physics, 96th ed).
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425
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426 **pos_pKs** = {'Nterm': 9.38, 'K': 10.67, 'R': 12.10, 'H': 6.04}
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427
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428 **neg_pKs** = {'Cterm': 2.15, 'D': 3.71, 'E': 4.15, 'C': 8.14, 'Y': 10.10}
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429
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430 :param ph: {float} ph at which to calculate peptide charge.
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431 :param amide: {boolean} whether the sequences have an amidated C-terminus.
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432 :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the
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433 attribute :py:attr:`descriptor`.
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434 :return: array of descriptor values in the attribute :py:attr:`descriptor`
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435 :Example:
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436
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437 >>> desc = GlobalDescriptor('KLAKFGKRSELVALSG')
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438 >>> desc.calculate_charge(ph=7.4, amide=True)
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439 >>> desc.descriptor
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440 array([[ 3.989]])
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441 """
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442
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443 desc = []
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diff changeset
444 for seq in self.sequences:
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445 desc.append(
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parents:
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446 _charge(seq, ph, amide)
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447 ) # calculate charge with helper function
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448 desc = np.asarray(desc).reshape(len(desc), 1)
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449 if append:
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450 self.descriptor = np.hstack((self.descriptor, np.array(desc)))
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451 self.featurenames.append("Charge")
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452 else:
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453 self.descriptor = np.array(desc)
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454 self.featurenames = ["Charge"]
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455
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456 def charge_density(self, ph=7.0, amide=False, append=False):
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457 """Method to calculate the charge density (charge / MW) of every sequences in the attributes :py:attr:`sequences`
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458
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459 :param ph: {float} pH at which to calculate peptide charge.
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460 :param amide: {boolean} whether the sequences have an amidated C-terminus.
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461 :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the
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462 attribute :py:attr:`descriptor`.
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463 :return: array of descriptor values in the attribute :py:attr:`descriptor`.
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464 :Example:
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465
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466 >>> desc = GlobalDescriptor('GNSDLLIEQRTLLASDEF')
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467 >>> desc.charge_density(ph=6, amide=True)
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468 >>> desc.descriptor
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469 array([[-0.00097119]])
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470 """
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471 self.calculate_charge(ph, amide)
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diff changeset
472 charges = self.descriptor
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cpt
parents:
diff changeset
473 self.calculate_MW(amide)
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cpt
parents:
diff changeset
474 masses = self.descriptor
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cpt
parents:
diff changeset
475 desc = charges / masses
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cpt
parents:
diff changeset
476 desc = np.asarray(desc).reshape(len(desc), 1)
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cpt
parents:
diff changeset
477 if append:
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cpt
parents:
diff changeset
478 self.descriptor = np.hstack((self.descriptor, np.array(desc)))
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cpt
parents:
diff changeset
479 self.featurenames.append("ChargeDensity")
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cpt
parents:
diff changeset
480 else:
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cpt
parents:
diff changeset
481 self.descriptor = np.array(desc)
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cpt
parents:
diff changeset
482 self.featurenames = ["ChargeDensity"]
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cpt
parents:
diff changeset
483
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cpt
parents:
diff changeset
484 def isoelectric_point(self, amide=False, append=False):
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cpt
parents:
diff changeset
485 """
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cpt
parents:
diff changeset
486 Method to calculate the isoelectric point of every sequence in the attribute :py:attr:`sequences`.
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cpt
parents:
diff changeset
487 The pK scale is extracted from: http://www.hbcpnetbase.com/ (CRC Handbook of Chemistry and Physics, 96th ed).
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cpt
parents:
diff changeset
488
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cpt
parents:
diff changeset
489 **pos_pKs** = {'Nterm': 9.38, 'K': 10.67, 'R': 12.10, 'H': 6.04}
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cpt
parents:
diff changeset
490
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cpt
parents:
diff changeset
491 **neg_pKs** = {'Cterm': 2.15, 'D': 3.71, 'E': 4.15, 'C': 8.14, 'Y': 10.10}
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cpt
parents:
diff changeset
492
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parents:
diff changeset
493 :param amide: {boolean} whether the sequences have an amidated C-terminus.
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parents:
diff changeset
494 :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the
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cpt
parents:
diff changeset
495 attribute :py:attr:`descriptor`.
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cpt
parents:
diff changeset
496 :return: array of descriptor values in the attribute :py:attr:`descriptor`
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cpt
parents:
diff changeset
497 :Example:
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cpt
parents:
diff changeset
498
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cpt
parents:
diff changeset
499 >>> desc = GlobalDescriptor('KLFDIKFGHIPQRST')
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cpt
parents:
diff changeset
500 >>> desc.isoelectric_point()
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cpt
parents:
diff changeset
501 >>> desc.descriptor
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cpt
parents:
diff changeset
502 array([[ 10.6796875]])
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cpt
parents:
diff changeset
503 """
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cpt
parents:
diff changeset
504 ph, ph1, ph2 = float(), float(), float()
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cpt
parents:
diff changeset
505 desc = []
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cpt
parents:
diff changeset
506 for seq in self.sequences:
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cpt
parents:
diff changeset
507
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cpt
parents:
diff changeset
508 # Bracket between ph1 and ph2
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cpt
parents:
diff changeset
509 ph = 7.0
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cpt
parents:
diff changeset
510 charge = _charge(seq, ph, amide)
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cpt
parents:
diff changeset
511 if charge > 0.0:
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cpt
parents:
diff changeset
512 ph1 = ph
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cpt
parents:
diff changeset
513 charge1 = charge
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cpt
parents:
diff changeset
514 while charge1 > 0.0:
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
515 ph = ph1 + 1.0
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cpt
parents:
diff changeset
516 charge = _charge(seq, ph, amide)
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cpt
parents:
diff changeset
517 if charge > 0.0:
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cpt
parents:
diff changeset
518 ph1 = ph
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cpt
parents:
diff changeset
519 charge1 = charge
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cpt
parents:
diff changeset
520 else:
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cpt
parents:
diff changeset
521 ph2 = ph
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cpt
parents:
diff changeset
522 break
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cpt
parents:
diff changeset
523 else:
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cpt
parents:
diff changeset
524 ph2 = ph
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cpt
parents:
diff changeset
525 charge2 = charge
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cpt
parents:
diff changeset
526 while charge2 < 0.0:
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
527 ph = ph2 - 1.0
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cpt
parents:
diff changeset
528 charge = _charge(seq, ph, amide)
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cpt
parents:
diff changeset
529 if charge < 0.0:
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cpt
parents:
diff changeset
530 ph2 = ph
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cpt
parents:
diff changeset
531 charge2 = charge
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cpt
parents:
diff changeset
532 else:
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cpt
parents:
diff changeset
533 ph1 = ph
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
534 break
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cpt
parents:
diff changeset
535 # Bisection
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cpt
parents:
diff changeset
536 while ph2 - ph1 > 0.0001 and charge != 0.0:
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
537 ph = (ph1 + ph2) / 2.0
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
538 charge = _charge(seq, ph, amide)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
539 if charge > 0.0:
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
540 ph1 = ph
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cpt
parents:
diff changeset
541 else:
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
542 ph2 = ph
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
543 desc.append(ph)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
544 desc = np.asarray(desc).reshape(len(desc), 1)
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cpt
parents:
diff changeset
545 if append:
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
546 self.descriptor = np.hstack((self.descriptor, np.array(desc)))
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
547 self.featurenames.append("pI")
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
548 else:
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
549 self.descriptor = np.array(desc)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
550 self.featurenames = ["pI"]
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cpt
parents:
diff changeset
551
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cpt
parents:
diff changeset
552 def instability_index(self, append=False):
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
553 """
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
554 Method to calculate the instability of every sequence in the attribute :py:attr:`sequences`.
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
555 The instability index is a prediction of protein stability based on the amino acid composition.
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
556 ([1] K. Guruprasad, B. V Reddy, M. W. Pandit, Protein Eng. 1990, 4, 155–161.)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
557
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
558 :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
559 attribute :py:attr:`descriptor`.
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
560 :return: array of descriptor values in the attribute :py:attr:`descriptor`
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
561 :Example:
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cpt
parents:
diff changeset
562
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cpt
parents:
diff changeset
563 >>> desc = GlobalDescriptor('LLASMNDLLAKRST')
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
564 >>> desc.instability_index()
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
565 >>> desc.descriptor
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
566 array([[ 63.95714286]])
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
567 """
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
568
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cpt
parents:
diff changeset
569 desc = []
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
570 dimv = load_scale("instability")[1]
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
571 for seq in self.sequences:
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
572 stabindex = float()
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
573 for i in range(len(seq) - 1):
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
574 stabindex += dimv[seq[i]][seq[i + 1]]
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
575 desc.append((10.0 / len(seq)) * stabindex)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
576 desc = np.asarray(desc).reshape(len(desc), 1)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
577 if append:
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
578 self.descriptor = np.hstack((self.descriptor, np.array(desc)))
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
579 self.featurenames.append("InstabilityInd")
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
580 else:
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
581 self.descriptor = np.array(desc)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
582 self.featurenames = ["InstabilityInd"]
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
583
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
584 def aromaticity(self, append=False):
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
585 """
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
586 Method to calculate the aromaticity of every sequence in the attribute :py:attr:`sequences`.
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
587 According to Lobry, 1994, it is simply the relative frequency of Phe+Trp+Tyr.
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
588
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
589 :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
590 attribute :py:attr:`descriptor`.
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
591 :return: array of descriptor values in the attribute :py:attr:`descriptor`
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
592 :Example:
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parents:
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593
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594 >>> desc = GlobalDescriptor('GLFYWRFFLQRRFLYWW')
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parents:
diff changeset
595 >>> desc.aromaticity()
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parents:
diff changeset
596 >>> desc.descriptor
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parents:
diff changeset
597 array([[ 0.52941176]])
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parents:
diff changeset
598 """
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parents:
diff changeset
599 desc = []
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parents:
diff changeset
600 for seq in self.sequences:
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parents:
diff changeset
601 f = seq.count("F")
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parents:
diff changeset
602 w = seq.count("W")
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parents:
diff changeset
603 y = seq.count("Y")
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cpt
parents:
diff changeset
604 desc.append(float(f + w + y) / len(seq))
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parents:
diff changeset
605 desc = np.asarray(desc).reshape(len(desc), 1)
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parents:
diff changeset
606 if append:
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parents:
diff changeset
607 self.descriptor = np.hstack((self.descriptor, np.array(desc)))
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parents:
diff changeset
608 self.featurenames.append("Aromaticity")
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parents:
diff changeset
609 else:
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parents:
diff changeset
610 self.descriptor = np.array(desc)
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parents:
diff changeset
611 self.featurenames = ["Aromaticity"]
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parents:
diff changeset
612
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parents:
diff changeset
613 def aliphatic_index(self, append=False):
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parents:
diff changeset
614 """
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parents:
diff changeset
615 Method to calculate the aliphatic index of every sequence in the attribute :py:attr:`sequences`.
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parents:
diff changeset
616 According to Ikai, 1980, the aliphatic index is a measure of thermal stability of proteins and is dependant
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parents:
diff changeset
617 on the relative volume occupied by aliphatic amino acids (A,I,L & V).
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parents:
diff changeset
618 ([1] A. Ikai, J. Biochem. 1980, 88, 1895–1898.)
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parents:
diff changeset
619
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parents:
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620 :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the
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cpt
parents:
diff changeset
621 attribute :py:attr:`descriptor`.
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parents:
diff changeset
622 :return: array of descriptor values in the attribute :py:attr:`descriptor`
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parents:
diff changeset
623 :Example:
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parents:
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624
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parents:
diff changeset
625 >>> desc = GlobalDescriptor('KWLKYLKKLAKLVK')
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parents:
diff changeset
626 >>> desc.aliphatic_index()
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parents:
diff changeset
627 >>> desc.descriptor
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parents:
diff changeset
628 array([[ 139.28571429]])
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parents:
diff changeset
629 """
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parents:
diff changeset
630 desc = []
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parents:
diff changeset
631 aa_dict = aa_weights()
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cpt
parents:
diff changeset
632 for seq in self.sequences:
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cpt
parents:
diff changeset
633 d = {aa: seq.count(aa) for aa in aa_dict.keys()} # count aa
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parents:
diff changeset
634 d = {
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parents:
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635 k: (float(d[k]) / len(seq)) * 100 for k in d.keys()
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cpt
parents:
diff changeset
636 } # get mole percent of all AA
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parents:
diff changeset
637 desc.append(
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cpt
parents:
diff changeset
638 d["A"] + 2.9 * d["V"] + 3.9 * (d["I"] + d["L"])
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cpt
parents:
diff changeset
639 ) # formula for calculating the AI (Ikai, 1980)
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cpt
parents:
diff changeset
640 desc = np.asarray(desc).reshape(len(desc), 1)
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cpt
parents:
diff changeset
641 if append:
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cpt
parents:
diff changeset
642 self.descriptor = np.hstack((self.descriptor, np.array(desc)))
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cpt
parents:
diff changeset
643 self.featurenames.append("AliphaticInd")
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cpt
parents:
diff changeset
644 else:
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parents:
diff changeset
645 self.descriptor = np.array(desc)
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cpt
parents:
diff changeset
646 self.featurenames = ["AliphaticInd"]
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cpt
parents:
diff changeset
647
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parents:
diff changeset
648 def boman_index(self, append=False):
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cpt
parents:
diff changeset
649 """Method to calculate the boman index of every sequence in the attribute :py:attr:`sequences`.
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parents:
diff changeset
650 According to Boman, 2003, the boman index is a measure for protein-protein interactions and is calculated by
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cpt
parents:
diff changeset
651 summing over all amino acid free energy of transfer [kcal/mol] between water and cyclohexane,[2] followed by
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parents:
diff changeset
652 dividing by sequence length.
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parents:
diff changeset
653 ([1] H. G. Boman, D. Wade, I. a Boman, B. Wåhlin, R. B. Merrifield, *FEBS Lett*. **1989**, *259*, 103–106.
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parents:
diff changeset
654 [2] A. Radzick, R. Wolfenden, *Biochemistry* **1988**, *27*, 1664–1670.)
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cpt
parents:
diff changeset
655
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parents:
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656 .. seealso:: :py:func:`modlamp.core.aa_energies()`
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cpt
parents:
diff changeset
657
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parents:
diff changeset
658 :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the
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cpt
parents:
diff changeset
659 attribute :py:attr:`descriptor`.
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cpt
parents:
diff changeset
660 :return: array of descriptor values in the attribute :py:attr:`descriptor`
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cpt
parents:
diff changeset
661 :Example:
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cpt
parents:
diff changeset
662
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parents:
diff changeset
663 >>> desc = GlobalDescriptor('GLFDIVKKVVGALGSL')
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cpt
parents:
diff changeset
664 >>> desc.boman_index()
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cpt
parents:
diff changeset
665 >>> desc.descriptor
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cpt
parents:
diff changeset
666 array([[-1.011875]])
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cpt
parents:
diff changeset
667 """
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cpt
parents:
diff changeset
668 d = aa_energies()
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cpt
parents:
diff changeset
669 desc = []
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cpt
parents:
diff changeset
670 for seq in self.sequences:
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cpt
parents:
diff changeset
671 val = []
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cpt
parents:
diff changeset
672 for a in seq:
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cpt
parents:
diff changeset
673 val.append(d[a])
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cpt
parents:
diff changeset
674 desc.append(sum(val) / len(val))
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cpt
parents:
diff changeset
675 desc = np.asarray(desc).reshape(len(desc), 1)
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cpt
parents:
diff changeset
676 if append:
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cpt
parents:
diff changeset
677 self.descriptor = np.hstack((self.descriptor, np.array(desc)))
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cpt
parents:
diff changeset
678 self.featurenames.append("BomanInd")
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cpt
parents:
diff changeset
679 else:
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parents:
diff changeset
680 self.descriptor = np.array(desc)
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cpt
parents:
diff changeset
681 self.featurenames = ["BomanInd"]
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cpt
parents:
diff changeset
682
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parents:
diff changeset
683 def hydrophobic_ratio(self, append=False):
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cpt
parents:
diff changeset
684 """
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cpt
parents:
diff changeset
685 Method to calculate the hydrophobic ratio of every sequence in the attribute :py:attr:`sequences`, which is the
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cpt
parents:
diff changeset
686 relative frequency of the amino acids **A,C,F,I,L,M & V**.
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cpt
parents:
diff changeset
687
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cpt
parents:
diff changeset
688 :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the
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cpt
parents:
diff changeset
689 attribute :py:attr:`descriptor`.
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cpt
parents:
diff changeset
690 :return: array of descriptor values in the attribute :py:attr:`descriptor`
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cpt
parents:
diff changeset
691 :Example:
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cpt
parents:
diff changeset
692
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parents:
diff changeset
693 >>> desc = GlobalDescriptor('VALLYWRTVLLAIII')
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cpt
parents:
diff changeset
694 >>> desc.hydrophobic_ratio()
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cpt
parents:
diff changeset
695 >>> desc.descriptor
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cpt
parents:
diff changeset
696 array([[ 0.73333333]])
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cpt
parents:
diff changeset
697 """
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parents:
diff changeset
698 desc = []
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parents:
diff changeset
699 aa_dict = aa_weights()
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cpt
parents:
diff changeset
700 for seq in self.sequences:
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cpt
parents:
diff changeset
701 pa = {aa: seq.count(aa) for aa in aa_dict.keys()} # count aa
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cpt
parents:
diff changeset
702 # formula for calculating the AI (Ikai, 1980):
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cpt
parents:
diff changeset
703 desc.append(
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
704 (pa["A"] + pa["C"] + pa["F"] + pa["I"] + pa["L"] + pa["M"] + pa["V"])
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cpt
parents:
diff changeset
705 / float(len(seq))
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cpt
parents:
diff changeset
706 )
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cpt
parents:
diff changeset
707 desc = np.asarray(desc).reshape(len(desc), 1)
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cpt
parents:
diff changeset
708 if append:
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cpt
parents:
diff changeset
709 self.descriptor = np.hstack((self.descriptor, np.array(desc)))
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parents:
diff changeset
710 self.featurenames.append("HydrophRatio")
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cpt
parents:
diff changeset
711 else:
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cpt
parents:
diff changeset
712 self.descriptor = np.array(desc)
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cpt
parents:
diff changeset
713 self.featurenames = ["HydrophRatio"]
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cpt
parents:
diff changeset
714
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cpt
parents:
diff changeset
715 def calculate_all(self, ph=7.4, amide=True):
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cpt
parents:
diff changeset
716 """Method combining all global descriptors and appending them into the feature matrix in the attribute
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cpt
parents:
diff changeset
717 :py:attr:`descriptor`.
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cpt
parents:
diff changeset
718
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cpt
parents:
diff changeset
719 :param ph: {float} pH at which to calculate peptide charge
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cpt
parents:
diff changeset
720 :param amide: {boolean} whether the sequences have an amidated C-terminus.
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cpt
parents:
diff changeset
721 :return: array of descriptor values in the attribute :py:attr:`descriptor`
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cpt
parents:
diff changeset
722 :Example:
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cpt
parents:
diff changeset
723
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cpt
parents:
diff changeset
724 >>> desc = GlobalDescriptor('AFGHFKLKKLFIFGHERT')
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cpt
parents:
diff changeset
725 >>> desc.calculate_all(amide=True)
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cpt
parents:
diff changeset
726 >>> desc.featurenames
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cpt
parents:
diff changeset
727 ['Length', 'MW', 'ChargeDensity', 'pI', 'InstabilityInd', 'Aromaticity', 'AliphaticInd', 'BomanInd', 'HydRatio']
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cpt
parents:
diff changeset
728 >>> desc.descriptor
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cpt
parents:
diff changeset
729 array([[ 18., 2.17559000e+03, 1.87167619e-03, 1.16757812e+01, ... 1.10555556e+00, 4.44444444e-01]])
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cpt
parents:
diff changeset
730 >>> desc.save_descriptor('/path/to/outputfile.csv') # save the descriptor data (with feature names header)
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cpt
parents:
diff changeset
731 """
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cpt
parents:
diff changeset
732
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cpt
parents:
diff changeset
733 # This is a strange way of doing it. However, the append=True option excludes length and charge, no idea why!
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cpt
parents:
diff changeset
734 fn = []
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cpt
parents:
diff changeset
735 self.length() # sequence length
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cpt
parents:
diff changeset
736 l = self.descriptor
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cpt
parents:
diff changeset
737 fn.extend(self.featurenames)
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cpt
parents:
diff changeset
738 self.calculate_MW(amide=amide) # molecular weight
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cpt
parents:
diff changeset
739 mw = self.descriptor
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cpt
parents:
diff changeset
740 fn.extend(self.featurenames)
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cpt
parents:
diff changeset
741 self.calculate_charge(ph=ph, amide=amide) # net charge
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cpt
parents:
diff changeset
742 c = self.descriptor
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cpt
parents:
diff changeset
743 fn.extend(self.featurenames)
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cpt
parents:
diff changeset
744 self.charge_density(ph=ph, amide=amide) # charge density
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cpt
parents:
diff changeset
745 cd = self.descriptor
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cpt
parents:
diff changeset
746 fn.extend(self.featurenames)
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cpt
parents:
diff changeset
747 self.isoelectric_point(amide=amide) # pI
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cpt
parents:
diff changeset
748 pi = self.descriptor
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cpt
parents:
diff changeset
749 fn.extend(self.featurenames)
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cpt
parents:
diff changeset
750 self.instability_index() # instability index
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cpt
parents:
diff changeset
751 si = self.descriptor
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cpt
parents:
diff changeset
752 fn.extend(self.featurenames)
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cpt
parents:
diff changeset
753 self.aromaticity() # global aromaticity
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cpt
parents:
diff changeset
754 ar = self.descriptor
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cpt
parents:
diff changeset
755 fn.extend(self.featurenames)
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cpt
parents:
diff changeset
756 self.aliphatic_index() # aliphatic index
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parents:
diff changeset
757 ai = self.descriptor
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cpt
parents:
diff changeset
758 fn.extend(self.featurenames)
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cpt
parents:
diff changeset
759 self.boman_index() # Boman index
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cpt
parents:
diff changeset
760 bi = self.descriptor
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cpt
parents:
diff changeset
761 fn.extend(self.featurenames)
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cpt
parents:
diff changeset
762 self.hydrophobic_ratio() # Hydrophobic ratio
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cpt
parents:
diff changeset
763 hr = self.descriptor
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cpt
parents:
diff changeset
764 fn.extend(self.featurenames)
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cpt
parents:
diff changeset
765
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cpt
parents:
diff changeset
766 self.descriptor = np.concatenate((l, mw, c, cd, pi, si, ar, ai, bi, hr), axis=1)
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cpt
parents:
diff changeset
767 self.featurenames = fn
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cpt
parents:
diff changeset
768
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cpt
parents:
diff changeset
769
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cpt
parents:
diff changeset
770 class PeptideDescriptor(BaseDescriptor):
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cpt
parents:
diff changeset
771 """Base class for peptide descriptors. The following **amino acid descriptor scales** are available for descriptor
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cpt
parents:
diff changeset
772 calculation:
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cpt
parents:
diff changeset
773
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cpt
parents:
diff changeset
774 - **AASI** (An amino acid selectivity index scale for helical antimicrobial peptides, *[1] D. Juretić, D. Vukicević, N. Ilić, N. Antcheva, A. Tossi, J. Chem. Inf. Model. 2009, 49, 2873–2882.*)
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cpt
parents:
diff changeset
775 - **ABHPRK** (modlabs inhouse physicochemical feature scale (Acidic, Basic, Hydrophobic, Polar, aRomatic, Kink-inducer)
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cpt
parents:
diff changeset
776 - **argos** (Argos hydrophobicity amino acid scale, *[2] Argos, P., Rao, J. K. M. & Hargrave, P. A., Eur. J. Biochem. 2005, 128, 565–575.*)
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cpt
parents:
diff changeset
777 - **bulkiness** (Amino acid side chain bulkiness scale, *[3] J. M. Zimmerman, N. Eliezer, R. Simha, J. Theor. Biol. 1968, 21, 170–201.*)
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cpt
parents:
diff changeset
778 - **charge_phys** (Amino acid charge at pH 7.0 - Hystidine charge +0.1.)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
779 - **charge_acid** (Amino acid charge at acidic pH - Hystidine charge +1.0.)
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cpt
parents:
diff changeset
780 - **cougar** (modlabs inhouse selection of global peptide descriptors)
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cpt
parents:
diff changeset
781 - **eisenberg** (the Eisenberg hydrophobicity consensus amino acid scale, *[4] D. Eisenberg, R. M. Weiss, T. C. Terwilliger, W. Wilcox, Faraday Symp. Chem. Soc. 1982, 17, 109.*)
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cpt
parents:
diff changeset
782 - **Ez** (potential that assesses energies of insertion of amino acid side chains into lipid bilayers, *[5] A. Senes, D. C. Chadi, P. B. Law, R. F. S. Walters, V. Nanda, W. F. DeGrado, J. Mol. Biol. 2007, 366, 436–448.*)
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cpt
parents:
diff changeset
783 - **flexibility** (amino acid side chain flexibilitiy scale, *[6] R. Bhaskaran, P. K. Ponnuswamy, Int. J. Pept. Protein Res. 1988, 32, 241–255.*)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
784 - **grantham** (amino acid side chain composition, polarity and molecular volume, *[8] Grantham, R. Science. 185, 862–864 (1974).*)
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cpt
parents:
diff changeset
785 - **gravy** (GRAVY hydrophobicity amino acid scale, *[9] J. Kyte, R. F. Doolittle, J. Mol. Biol. 1982, 157, 105–132.*)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
786 - **hopp-woods** (Hopp-Woods amino acid hydrophobicity scale,*[10] T. P. Hopp, K. R. Woods, Proc. Natl. Acad. Sci. 1981, 78, 3824–3828.*)
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cpt
parents:
diff changeset
787 - **ISAECI** (Isotropic Surface Area (ISA) and Electronic Charge Index (ECI) of amino acid side chains, *[11] E. R. Collantes, W. J. Dunn, J. Med. Chem. 1995, 38, 2705–2713.*)
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cpt
parents:
diff changeset
788 - **janin** (Janin hydrophobicity amino acid scale, *[12] J. L. Cornette, K. B. Cease, H. Margalit, J. L. Spouge, J. A. Berzofsky, C. DeLisi, J. Mol. Biol. 1987, 195, 659–685.*)
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cpt
parents:
diff changeset
789 - **kytedoolittle** (Kyte & Doolittle hydrophobicity amino acid scale, *[13] J. Kyte, R. F. Doolittle, J. Mol. Biol. 1982, 157, 105–132.*)
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cpt
parents:
diff changeset
790 - **levitt_alpha** (Levitt amino acid alpha-helix propensity scale, extracted from http://web.expasy.org/protscale. *[14] M. Levitt, Biochemistry 1978, 17, 4277-4285.*)
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cpt
parents:
diff changeset
791 - **MSS** (A graph-theoretical index that reflects topological shape and size of amino acid side chains, *[15] C. Raychaudhury, A. Banerjee, P. Bag, S. Roy, J. Chem. Inf. Comput. Sci. 1999, 39, 248–254.*)
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cpt
parents:
diff changeset
792 - **MSW** (Amino acid scale based on a PCA of the molecular surface based WHIM descriptor (MS-WHIM), extended to natural amino acids, *[16] A. Zaliani, E. Gancia, J. Chem. Inf. Comput. Sci 1999, 39, 525–533.*)
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cpt
parents:
diff changeset
793 - **pepArc** (modlabs pharmacophoric feature scale, dimensions are: hydrophobicity, polarity, positive charge, negative charge, proline.)
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cpt
parents:
diff changeset
794 - **pepcats** (modlabs pharmacophoric feature based PEPCATS scale, *[17] C. P. Koch, A. M. Perna, M. Pillong, N. K. Todoroff, P. Wrede, G. Folkers, J. A. Hiss, G. Schneider, PLoS Comput. Biol. 2013, 9, e1003088.*)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
795 - **polarity** (Amino acid polarity scale, *[18] J. M. Zimmerman, N. Eliezer, R. Simha, J. Theor. Biol. 1968, 21, 170–201.*)
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cpt
parents:
diff changeset
796 - **PPCALI** (modlabs inhouse scale derived from a PCA of 143 amino acid property scales, *[19] C. P. Koch, A. M. Perna, M. Pillong, N. K. Todoroff, P. Wrede, G. Folkers, J. A. Hiss, G. Schneider, PLoS Comput. Biol. 2013, 9, e1003088.*)
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cpt
parents:
diff changeset
797 - **refractivity** (Relative amino acid refractivity values, *[20] T. L. McMeekin, M. Wilensky, M. L. Groves, Biochem. Biophys. Res. Commun. 1962, 7, 151–156.*)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
798 - **t_scale** (A PCA derived scale based on amino acid side chain properties calculated with 6 different probes of the GRID program, *[21] M. Cocchi, E. Johansson, Quant. Struct. Act. Relationships 1993, 12, 1–8.*)
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cpt
parents:
diff changeset
799 - **TM_tend** (Amino acid transmembrane propensity scale, extracted from http://web.expasy.org/protscale, *[22] Zhao, G., London E. Protein Sci. 2006, 15, 1987-2001.*)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
800 - **z3** (The original three dimensional Z-scale, *[23] S. Hellberg, M. Sjöström, B. Skagerberg, S. Wold, J. Med. Chem. 1987, 30, 1126–1135.*)
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cpt
parents:
diff changeset
801 - **z5** (The extended five dimensional Z-scale, *[24] M. Sandberg, L. Eriksson, J. Jonsson, M. Sjöström, S. Wold, J. Med. Chem. 1998, 41, 2481–2491.*)
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cpt
parents:
diff changeset
802
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
803 Further, amino acid scale independent methods can be calculated with help of the :class:`GlobalDescriptor` class.
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cpt
parents:
diff changeset
804
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parents:
diff changeset
805 """
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parents:
diff changeset
806
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cpt
parents:
diff changeset
807 def __init__(self, seqs, scalename="Eisenberg"):
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parents:
diff changeset
808 """
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cpt
parents:
diff changeset
809 :param seqs: a .fasta file with sequences, a list of sequences or a single sequence as string to calculate the
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cpt
parents:
diff changeset
810 descriptor values for.
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cpt
parents:
diff changeset
811 :param scalename: {str} name of the amino acid scale (one of the given list above) used to calculate the
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cpt
parents:
diff changeset
812 descriptor values
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cpt
parents:
diff changeset
813 :return: initialized attributes :py:attr:`sequences`, :py:attr:`names` and dictionary :py:attr:`scale` with
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
814 amino acid scale values of the scale name in :py:attr:`scalename`.
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cpt
parents:
diff changeset
815 :Example:
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cpt
parents:
diff changeset
816
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cpt
parents:
diff changeset
817 >>> AMP = PeptideDescriptor('KLLKLLKKLLKLLK','pepcats')
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cpt
parents:
diff changeset
818 >>> AMP.sequences
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cpt
parents:
diff changeset
819 ['KLLKLLKKLLKLLK']
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cpt
parents:
diff changeset
820 >>> seqs = PeptideDescriptor('/Path/to/file.fasta', 'eisenberg') # load sequences from .fasta file
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cpt
parents:
diff changeset
821 >>> seqs.sequences
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cpt
parents:
diff changeset
822 ['AFDGHLKI','KKLQRSDLLRTK','KKLASCNNIPPR'...]
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cpt
parents:
diff changeset
823 """
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cpt
parents:
diff changeset
824 super(PeptideDescriptor, self).__init__(seqs)
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cpt
parents:
diff changeset
825 self.scalename, self.scale = load_scale(scalename.lower())
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cpt
parents:
diff changeset
826 self.all_moms = list() # for passing hydrophobic moments to calculate_profile
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cpt
parents:
diff changeset
827 self.all_globs = list() # for passing global to calculate_profile
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cpt
parents:
diff changeset
828
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cpt
parents:
diff changeset
829 def load_scale(self, scalename):
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cpt
parents:
diff changeset
830 """Method to load amino acid values from a given scale
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cpt
parents:
diff changeset
831
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parents:
diff changeset
832 :param scalename: {str} name of the amino acid scale to be loaded.
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cpt
parents:
diff changeset
833 :return: loaded amino acid scale values in a dictionary in the attribute :py:attr:`scale`.
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parents:
diff changeset
834
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parents:
diff changeset
835 .. seealso:: :func:`modlamp.core.load_scale()`
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parents:
diff changeset
836 """
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parents:
diff changeset
837 self.scalename, self.scale = load_scale(scalename.lower())
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cpt
parents:
diff changeset
838
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cpt
parents:
diff changeset
839 def calculate_autocorr(self, window, append=False):
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cpt
parents:
diff changeset
840 """Method for auto-correlating the amino acid values for a given descriptor scale
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cpt
parents:
diff changeset
841
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cpt
parents:
diff changeset
842 :param window: {int} correlation window for descriptor calculation in a sliding window approach
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
843 :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the
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cpt
parents:
diff changeset
844 attribute :py:attr:`descriptor`.
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cpt
parents:
diff changeset
845 :return: calculated descriptor numpy.array in the attribute :py:attr:`descriptor`.
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cpt
parents:
diff changeset
846 :Example:
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parents:
diff changeset
847
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parents:
diff changeset
848 >>> AMP = PeptideDescriptor('GLFDIVKKVVGALGSL','PPCALI')
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cpt
parents:
diff changeset
849 >>> AMP.calculate_autocorr(7)
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cpt
parents:
diff changeset
850 >>> AMP.descriptor
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parents:
diff changeset
851 array([[ 1.28442339e+00, 1.29025116e+00, 1.03240901e+00, .... ]])
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cpt
parents:
diff changeset
852 >>> AMP.descriptor.shape
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cpt
parents:
diff changeset
853 (1, 133)
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cpt
parents:
diff changeset
854
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cpt
parents:
diff changeset
855 .. versionchanged:: v.2.3.0
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parents:
diff changeset
856 """
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parents:
diff changeset
857 desc = Parallel(n_jobs=-1)(
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
858 delayed(_one_autocorr)(seq, window, self.scale) for seq in self.sequences
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cpt
parents:
diff changeset
859 )
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cpt
parents:
diff changeset
860
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cpt
parents:
diff changeset
861 if append:
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cpt
parents:
diff changeset
862 self.descriptor = np.hstack((self.descriptor, np.array(desc)))
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cpt
parents:
diff changeset
863 else:
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parents:
diff changeset
864 self.descriptor = np.array(desc)
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cpt
parents:
diff changeset
865
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cpt
parents:
diff changeset
866 def calculate_crosscorr(self, window, append=False):
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cpt
parents:
diff changeset
867 """Method for cross-correlating the amino acid values for a given descriptor scale
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cpt
parents:
diff changeset
868
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cpt
parents:
diff changeset
869 :param window: {int} correlation window for descriptor calculation in a sliding window approach
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cpt
parents:
diff changeset
870 :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the
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cpt
parents:
diff changeset
871 attribute :py:attr:`descriptor`.
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
872 :return: calculated descriptor numpy.array in the attribute :py:attr:`descriptor`.
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cpt
parents:
diff changeset
873 :Example:
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cpt
parents:
diff changeset
874
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parents:
diff changeset
875 >>> AMP = PeptideDescriptor('GLFDIVKKVVGALGSL','pepcats')
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cpt
parents:
diff changeset
876 >>> AMP.calculate_crosscorr(7)
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cpt
parents:
diff changeset
877 >>> AMP.descriptor
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cpt
parents:
diff changeset
878 array([[ 0.6875 , 0.46666667, 0.42857143, 0.61538462, 0.58333333, ... ]])
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cpt
parents:
diff changeset
879 >>> AMP.descriptor.shape
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cpt
parents:
diff changeset
880 (1, 147)
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parents:
diff changeset
881 """
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cpt
parents:
diff changeset
882 desc = Parallel(n_jobs=-1)(
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
883 delayed(_one_crosscorr)(seq, window, self.scale) for seq in self.sequences
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cpt
parents:
diff changeset
884 )
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cpt
parents:
diff changeset
885
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cpt
parents:
diff changeset
886 if append:
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cpt
parents:
diff changeset
887 self.descriptor = np.hstack((self.descriptor, np.array(desc)))
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cpt
parents:
diff changeset
888 else:
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parents:
diff changeset
889 self.descriptor = np.array(desc)
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cpt
parents:
diff changeset
890
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cpt
parents:
diff changeset
891 def calculate_moment(self, window=1000, angle=100, modality="max", append=False):
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cpt
parents:
diff changeset
892 """Method for calculating the maximum or mean moment of the amino acid values for a given descriptor scale and
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cpt
parents:
diff changeset
893 window.
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cpt
parents:
diff changeset
894
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cpt
parents:
diff changeset
895 :param window: {int} amino acid window in which to calculate the moment. If the sequence is shorter than the
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cpt
parents:
diff changeset
896 window, the length of the sequence is taken. So if the default window of 1000 is chosen, for all sequences
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cpt
parents:
diff changeset
897 shorter than 1000, the **global** hydrophobic moment will be calculated. Otherwise, the maximal
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cpt
parents:
diff changeset
898 hydrophiobic moment for the chosen window size found in the sequence will be returned.
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cpt
parents:
diff changeset
899 :param angle: {int} angle in which to calculate the moment. **100** for alpha helices, **180** for beta sheets.
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cpt
parents:
diff changeset
900 :param modality: {'all', 'max' or 'mean'} Calculate respectively maximum or mean hydrophobic moment. If all,
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cpt
parents:
diff changeset
901 moments for all windows are returned.
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cpt
parents:
diff changeset
902 :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the
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cpt
parents:
diff changeset
903 attribute :py:attr:`descriptor`.
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cpt
parents:
diff changeset
904 :return: Calculated descriptor as a numpy.array in the attribute :py:attr:`descriptor` and all possible global
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cpt
parents:
diff changeset
905 values in :py:attr:`all_moms` (needed for the :py:func:`calculate_profile` method)
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parents:
diff changeset
906 :Example:
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parents:
diff changeset
907
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cpt
parents:
diff changeset
908 >>> AMP = PeptideDescriptor('GLFDIVKKVVGALGSL', 'eisenberg')
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cpt
parents:
diff changeset
909 >>> AMP.calculate_moment()
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cpt
parents:
diff changeset
910 >>> AMP.descriptor
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cpt
parents:
diff changeset
911 array([[ 0.48790226]])
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parents:
diff changeset
912 """
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parents:
diff changeset
913 if self.scale["A"] == list:
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parents:
diff changeset
914 print(
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parents:
diff changeset
915 "\n Descriptor moment calculation is only possible for one dimensional descriptors.\n"
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parents:
diff changeset
916 )
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parents:
diff changeset
917
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parents:
diff changeset
918 else:
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parents:
diff changeset
919 desc = []
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parents:
diff changeset
920 for seq in self.sequences:
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cpt
parents:
diff changeset
921 wdw = min(
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cpt
parents:
diff changeset
922 window, len(seq)
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cpt
parents:
diff changeset
923 ) # if sequence is shorter than window, take the whole sequence instead
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cpt
parents:
diff changeset
924 mtrx = []
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cpt
parents:
diff changeset
925 mwdw = []
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
926
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
927 for aa in range(len(seq)):
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
928 mtrx.append(self.scale[str(seq[aa])])
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
929
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
930 for i in range(len(mtrx) - wdw + 1):
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
931 mwdw.append(sum(mtrx[i : i + wdw], []))
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
932
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
933 mwdw = np.asarray(mwdw)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
934 rads = (
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
935 angle * (np.pi / 180) * np.asarray(range(wdw))
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
936 ) # calculate actual moment (radial)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
937 vcos = (mwdw * np.cos(rads)).sum(axis=1)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
938 vsin = (mwdw * np.sin(rads)).sum(axis=1)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
939 moms = np.sqrt(vsin**2 + vcos**2) / wdw
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
940
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
941 if modality == "max": # take window with maximal value
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
942 moment = np.max(moms)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
943 elif modality == "mean": # take average value over all windows
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
944 moment = np.mean(moms)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
945 elif modality == "all":
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
946 moment = moms
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
947 else:
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
948 print(
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
949 '\nERROR!\nModality parameter is wrong, please choose between "all", "max" and "mean".\n'
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
950 )
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
951 return
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
952 desc.append(moment)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
953 self.all_moms.append(moms)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
954
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
955 desc = np.asarray(desc).reshape(len(desc), 1) # final descriptor array
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
956
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
957 if append:
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
958 self.descriptor = np.hstack((self.descriptor, np.array(desc)))
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
959 else:
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
960 self.descriptor = np.array(desc)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
961
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
962 def calculate_global(self, window=1000, modality="max", append=False):
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
963 """Method for calculating a global / window averaging descriptor value of a given AA scale
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
964
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
965 :param window: {int} amino acid window in which to calculate the moment. If the sequence is shorter than the
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
966 window, the length of the sequence is taken.
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
967 :param modality: {'max' or 'mean'} Calculate respectively maximum or mean hydrophobic moment.
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
968 :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
969 attribute :py:attr:`descriptor`.
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
970 :return: Calculated descriptor as a numpy.array in the attribute :py:attr:`descriptor` and all possible global
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
971 values in :py:attr:`all_globs` (needed for the :py:func:`calculate_profile` method)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
972 :Example:
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
973
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
974 >>> AMP = PeptideDescriptor('GLFDIVKKVVGALGSL','eisenberg')
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
975 >>> AMP.calculate_global(window=1000, modality='max')
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
976 >>> AMP.descriptor
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
977 array([[ 0.44875]])
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
978 """
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
979 desc = list()
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
980 for n, seq in enumerate(self.sequences):
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
981 wdw = min(
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
982 window, len(seq)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
983 ) # if sequence is shorter than window, take the whole sequence instead
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
984 mtrx = []
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
985 mwdw = []
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
986
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
987 for l in range(len(seq)): # translate AA sequence into values
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
988 mtrx.append(self.scale[str(seq[l])])
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
989
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
990 for i in range(len(mtrx) - wdw + 1):
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
991 mwdw.append(
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
992 sum(mtrx[i : i + wdw], [])
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
993 ) # list of all the values for the different windows
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
994
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
995 mwdw = np.asarray(mwdw)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
996 glob = np.sum(mwdw, axis=1) / float(wdw)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
997 outglob = float()
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
998
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
999 if modality in ["max", "mean"]:
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1000 if modality == "max":
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1001 outglob = np.max(
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1002 glob
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cpt
parents:
diff changeset
1003 ) # returned moment will be the maximum of all windows
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1004 elif modality == "mean":
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1005 outglob = np.mean(
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1006 glob
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1007 ) # returned moment will be the mean of all windows
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cpt
parents:
diff changeset
1008 else:
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cpt
parents:
diff changeset
1009 print(
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1010 'Modality parameter is wrong, please choose between "max" and "mean"\n.'
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1011 )
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cpt
parents:
diff changeset
1012 return
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1013 desc.append(outglob)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1014 self.all_globs.append(glob)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1015
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1016 desc = np.asarray(desc).reshape(len(desc), 1)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1017 if append:
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1018 self.descriptor = np.hstack((self.descriptor, np.array(desc)))
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1019 else:
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1020 self.descriptor = np.array(desc)
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cpt
parents:
diff changeset
1021
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1022 def calculate_profile(self, prof_type="uH", window=7, append=False):
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1023 """Method for calculating hydrophobicity or hydrophobic moment profiles for given sequences and fitting for
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1024 slope and intercept. The hydrophobicity scale used is "eisenberg"
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cpt
parents:
diff changeset
1025
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cpt
parents:
diff changeset
1026 :param prof_type: prof_type of profile, available: 'H' for hydrophobicity or 'uH' for hydrophobic moment
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1027 :param window: {int} size of sliding window used (odd-numbered).
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1028 :param append: {boolean} whether the produced descriptor values should be appended to the existing ones in the
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1029 attribute :py:attr:`descriptor`.
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1030 :return: Fitted slope and intercept of calculated profile for every given sequence in the attribute
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1031 :py:attr:`descriptor`.
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cpt
parents:
diff changeset
1032 :Example:
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1033
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1034 >>> AMP = PeptideDescriptor('KLLKLLKKVVGALG','kytedoolittle')
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1035 >>> AMP.calculate_profile(prof_type='H')
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1036 >>> AMP.descriptor
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1037 array([[ 0.03731293, 0.19246599]])
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1038 """
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1039 if prof_type == "uH":
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1040 self.calculate_moment(window=window)
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1041 y_vals = self.all_moms
9b276485c94a planemo upload commit 94b0cd1fff0826c6db3e7dc0c91c0c5a8be8bb0c
cpt
parents:
diff changeset
1042 elif prof_type == "H":
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diff changeset
1043 self.calculate_global(window=window)
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parents:
diff changeset
1044 y_vals = self.all_globs
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diff changeset
1045 else:
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diff changeset
1046 print(
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1047 'prof_type parameter is unknown, choose "uH" for hydrophobic moment or "H" for hydrophobicity\n.'
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parents:
diff changeset
1048 )
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parents:
diff changeset
1049 sys.exit()
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parents:
diff changeset
1050
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diff changeset
1051 desc = list()
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parents:
diff changeset
1052 for n, seq in enumerate(self.sequences):
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parents:
diff changeset
1053 x_vals = range(len(seq))[int((window - 1) / 2) : -int((window - 1) / 2)]
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parents:
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1054 if len(seq) <= window:
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parents:
diff changeset
1055 slope, intercept, r_value, p_value, std_err = [0, 0, 0, 0, 0]
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parents:
diff changeset
1056 else:
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parents:
diff changeset
1057 slope, intercept, r_value, p_value, std_err = stats.linregress(
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parents:
diff changeset
1058 x_vals, y_vals[n]
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parents:
diff changeset
1059 )
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parents:
diff changeset
1060 desc.append([slope, intercept])
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diff changeset
1061
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1062 if append:
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parents:
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1063 self.descriptor = np.hstack((self.descriptor, np.array(desc)))
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1064 else:
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1065 self.descriptor = np.array(desc)
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diff changeset
1066
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1067 def calculate_arc(self, modality="max", append=False):
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1068 """Method for calculating property arcs as seen in the helical wheel plot. Use for binary amino acid scales only.
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1069
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1070 :param modality: modality of the arc to calculate, to choose between "max" and "mean".
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1071 :param append: if true, append to current descriptor stored in the descriptor attribute.
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1072 :return: calculated descriptor as numpy.array in the descriptor attribute.
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diff changeset
1073
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parents:
diff changeset
1074 :Example:
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parents:
diff changeset
1075
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1076 >>> arc = PeptideDescriptor("KLLKLLKKLLKLLK", scalename="peparc")
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1077 >>> arc.calculate_arc(modality="max", append=False)
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1078 >>> arc.descriptor
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parents:
diff changeset
1079 array([[200, 160, 160, 0, 0]])
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1080 """
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parents:
diff changeset
1081 desc = Parallel(n_jobs=-1)(
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parents:
diff changeset
1082 delayed(_one_arc)(seq, modality, self.scale) for seq in self.sequences
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parents:
diff changeset
1083 )
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parents:
diff changeset
1084
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parents:
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1085 # Converts each of the amino acids to descriptor vector
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parents:
diff changeset
1086 for seq in self.sequences:
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parents:
diff changeset
1087
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parents:
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1088 # desc_mat = []
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cpt
parents:
diff changeset
1089 # for aa in seq:
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cpt
parents:
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1090 # desc_mat.append(self.scale[aa])
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parents:
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1091 # desc_mat = np.asarray(desc_mat)
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diff changeset
1092 #
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parents:
diff changeset
1093 # # Check descriptor dimension
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parents:
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1094 # desc_dim = desc_mat.shape[1]
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parents:
diff changeset
1095 #
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parents:
diff changeset
1096 # # list to store descriptor values for all windows
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parents:
diff changeset
1097 # allwindows_arc = []
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parents:
diff changeset
1098 #
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parents:
diff changeset
1099 # if len(seq) > 18:
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parents:
diff changeset
1100 # window = 18
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parents:
diff changeset
1101 # # calculates number of windows in sequence
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parents:
diff changeset
1102 # num_windows = len(seq) - window
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parents:
diff changeset
1103 # else:
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parents:
diff changeset
1104 # window = len(seq)
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parents:
diff changeset
1105 # num_windows = 1
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cpt
parents:
diff changeset
1106 #
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parents:
diff changeset
1107 # # loop through all windows
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cpt
parents:
diff changeset
1108 # for j in range(num_windows):
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parents:
diff changeset
1109 # # slices descriptor matrix into current window
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cpt
parents:
diff changeset
1110 # window_mat = desc_mat[j:j + window, :]
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parents:
diff changeset
1111 #
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parents:
diff changeset
1112 # # defines order of amino acids in helical projection
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parents:
diff changeset
1113 # order = [0, 11, 4, 15, 8, 1, 12, 5, 16, 9, 2, 13, 6, 17, 10, 3, 14, 7]
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parents:
diff changeset
1114 #
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parents:
diff changeset
1115 # # orders window descriptor matrix into helical projection order
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parents:
diff changeset
1116 # ordered = []
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parents:
diff changeset
1117 # for pos in order:
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parents:
diff changeset
1118 # try:
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parents:
diff changeset
1119 # ordered.append(window_mat[pos, :])
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parents:
diff changeset
1120 # except:
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parents:
diff changeset
1121 # # for sequences of len < 18 adding dummy vector with 2s, length of descriptor dimensions
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cpt
parents:
diff changeset
1122 # ordered.append([2] * desc_dim)
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parents:
diff changeset
1123 # ordered = np.asarray(ordered)
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parents:
diff changeset
1124 #
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parents:
diff changeset
1125 # window_arc = []
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parents:
diff changeset
1126 #
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parents:
diff changeset
1127 # # loop through pharmacophoric features
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cpt
parents:
diff changeset
1128 # for m in range(desc_dim):
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parents:
diff changeset
1129 # all_arcs = [] # stores all arcs that can be found of a pharmacophoric feature
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parents:
diff changeset
1130 # arc = 0
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parents:
diff changeset
1131 #
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parents:
diff changeset
1132 # for n in range(18): # for all positions in helix, regardless of sequence length
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parents:
diff changeset
1133 # if ordered[n, m] == 0: # if position does not contain pharmacophoric feature
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cpt
parents:
diff changeset
1134 # all_arcs.append(arc) # append previous arc to all arcs list
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cpt
parents:
diff changeset
1135 # arc = 0 # arc is initialized
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cpt
parents:
diff changeset
1136 # elif ordered[n, m] == 1: # if position contains pharmacophoric feature(PF), elongate arc by 20°
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parents:
diff changeset
1137 # arc += 20
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parents:
diff changeset
1138 # elif ordered[n, m] == 2: # if position doesn't contain amino acid:
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cpt
parents:
diff changeset
1139 # if ordered[n - 1, m] == 1: # if previous position contained PF add 10°
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parents:
diff changeset
1140 # arc += 10
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parents:
diff changeset
1141 # elif ordered[n - 1, m] == 0: # if previous position didn't contain PF don't add anything
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parents:
diff changeset
1142 # arc += 0
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parents:
diff changeset
1143 # elif ordered[
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parents:
diff changeset
1144 # n - 2, m] == 1: # if previous position is empty then check second previous for PF
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parents:
diff changeset
1145 # arc += 10
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parents:
diff changeset
1146 # if n == 17: # if we are at the last position check for position n=0 instead of next position.
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cpt
parents:
diff changeset
1147 # if ordered[0, m] == 1: # if it contains PF add 10° extra
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parents:
diff changeset
1148 # arc += 10
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parents:
diff changeset
1149 # else: # if next position contains PF add 10° extra
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parents:
diff changeset
1150 # if ordered[n + 1, m] == 1:
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parents:
diff changeset
1151 # arc += 10
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diff changeset
1152 # elif ordered[n + 1, m] == 0:
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parents:
diff changeset
1153 # arc += 0
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parents:
diff changeset
1154 # else: # if next position is empty check for 2nd next position
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parents:
diff changeset
1155 # if n == 16:
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parents:
diff changeset
1156 # if ordered[0, m] == 1:
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parents:
diff changeset
1157 # arc += 10
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diff changeset
1158 # else:
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parents:
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1159 # if ordered[n + 2, m] == 1:
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1160 # arc += 10
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1161 #
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1162 # all_arcs.append(arc)
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1163 # if not arc == 360:
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1164 # arc0 = all_arcs.pop() + all_arcs[0] # join first and last arc together
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1165 # all_arcs = [arc0] + all_arcs[1:]
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1166 #
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1167 # window_arc.append(np.max(all_arcs)) # append to window arcs the maximum arc of this PF
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1168 # allwindows_arc.append(window_arc) # append all PF arcs of this window
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1169 #
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1170 # allwindows_arc = np.asarray(allwindows_arc)
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1171 #
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1172 # if modality == 'max':
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1173 # final_arc = np.max(allwindows_arc, axis=0) # calculate maximum / mean arc along all windows
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1174 # elif modality == 'mean':
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1175 # final_arc = np.mean(allwindows_arc, axis=0)
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1176 # else:
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1177 # print('modality is unknown, please choose between "max" and "mean"\n.')
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1178 # sys.exit()
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1179
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1180 if append:
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1181 self.descriptor = np.hstack((self.descriptor, np.array(desc)))
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1182 else:
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1183 self.descriptor = np.array(desc)