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2 class PDPOPrediction:
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3 def __init__(self, Folder = 'location', mdl='',seq_file = 'fasta_file.fasta',ttable=11):
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4 import pickle
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5 import pandas as pd
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6 from Bio import SeqIO
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7 import os
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8 from pathlib import Path
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9 self.data = {}
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10 self.df_output = None
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11 self.seqfile = seq_file
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12 self.__location__ = os.path.realpath(os.path.join(os.getcwd(), Folder))
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13
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14 with open(os.path.join(self.__location__,mdl), 'rb') as m:
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15 self.model = pickle.load(m)
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16 if mdl == 'SVM4311':
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17 with open(os.path.join(__location__,'d4311_SCALER'),'rb') as sl:
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18 self.scaler = pickle.load(sl)
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19 self.name = mdl
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20 elif mdl == 'ANN7185':
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21 with open(os.path.join(__location__,'d7185_SCALER'),'rb') as sc:
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22 self.scaler = pickle.load(sc)
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23 self.name = mdl
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24
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25 for seq in SeqIO.parse(os.path.join(self.__location__,self.seqfile), 'fasta'):
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26 DNA_seq = seq.seq
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27 AA_seq = DNA_seq.translate(table=ttable)
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28 descr_seq = seq.description.replace(' ','')
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29 self.data[descr_seq]=[DNA_seq._data,AA_seq._data]
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30 self.df = pd.DataFrame({'ID':list(self.data.keys()),
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31 'DNAseq':[elem[0] for elem in self.data.values()],
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32 'AAseq':[elem[1] for elem in self.data.values()]})
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33 self.df = self.df.set_index('ID')
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34
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35 def Datastructure(self):
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36 import pandas as pd
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37 import pickle
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38 from Bio.SeqUtils.ProtParam import ProteinAnalysis
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39 from local_ctd import CalculateCTD
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40 from local_AAComposition import CalculateAAComposition, CalculateDipeptideComposition
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41
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42 def count_orf(orf_seq):
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43 dic = {'DNA-A': 0, 'DNA-C': 0, 'DNA-T': 0, 'DNA-G': 0, 'DNA-GC': 0}
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44 for letter in range(len(orf_seq)):
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45 for k in range(0, 4):
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46 if orf_seq[letter] in list(dic.keys())[k][-1]:
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47 dic[list(dic.keys())[k]] += 1
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48 dic['DNA-GC'] = ((dic['DNA-C'] + dic['DNA-G']) / (
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49 dic['DNA-A'] + dic['DNA-C'] + dic['DNA-T'] + dic['DNA-G'])) * 100
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50 return dic
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51
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52 def count_aa(aa_seq):
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53 dic = {'G': 0, 'A': 0, 'L': 0, 'V': 0, 'I': 0, 'P': 0, 'F': 0, 'S': 0, 'T': 0, 'C': 0,
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54 'Y': 0, 'N': 0, 'Q': 0, 'D': 0, 'E': 0, 'R': 0, 'K': 0, 'H': 0, 'W': 0, 'M': 0}
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55 for letter in range(len(aa_seq)):
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56 if aa_seq[letter] in dic.keys():
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57 dic[aa_seq[letter]] += 1
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58 return dic
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59
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60 def sec_st_fr(aa_seq):
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61 from Bio.SeqUtils.ProtParam import ProteinAnalysis
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62 st_dic = {'Helix': 0, 'Turn': 0, 'Sheet': 0}
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63 stu = ProteinAnalysis(aa_seq).secondary_structure_fraction()
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64 st_dic['Helix'] = stu[0]
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65 st_dic['Turn'] = stu[1]
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66 st_dic['Sheet'] = stu[2]
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67 return st_dic
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68
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69 self.feat={"SVM4311": ["DNA-A", "DNA-T", "DNA-G", "DNA-GC", "AA_Len", "G", "A", "S", "T", "N", "Turn", "Sheet",
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70 "_PolarizabilityC1", "_PolarizabilityC3", "_SolventAccessibilityC1", "_SecondaryStrC1",
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71 "_SecondaryStrC2", "_SecondaryStrC3", "_ChargeC2", "_ChargeC3", "_PolarityC1", "_NormalizedVDWVC1",
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72 "_NormalizedVDWVC3", "_HydrophobicityC2", "_HydrophobicityC3", "_SecondaryStrT23",
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73 "_NormalizedVDWVT13", "_PolarizabilityD1001", "_SolventAccessibilityD1001", "_SolventAccessibilityD2001",
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74 "_SolventAccessibilityD3001", "_SecondaryStrD1025", "_ChargeD1075","_ChargeD2001", "_ChargeD2025",
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75 "_ChargeD3025", "_ChargeD3050", "_PolarityD1075", "_PolarityD3025","_NormalizedVDWVD1001",
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76 "_NormalizedVDWVD3050", "_HydrophobicityD2001", "DG", "DT", "GD"],
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77 "ANN7185": ["DNA-GC", "AA_Len", "Aromaticity", "IsoelectricPoint", "G", "A", "L", "V", "I", "P", "F",
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78 "S", "T", "C", "Y", "N", "Q", "D", "E", "R", "K", "H", "W", "M", "Turn", "Sheet", "_PolarizabilityC1",
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79 "_PolarizabilityC2", "_PolarizabilityC3", "_SolventAccessibilityC1", "_SolventAccessibilityC2",
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80 "_SecondaryStrC1", "_SecondaryStrC3", "_ChargeC1", "_ChargeC2", "_ChargeC3", "_PolarityC2",
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81 "_NormalizedVDWVC2", "_NormalizedVDWVC3", "_HydrophobicityC1", "_HydrophobicityC2", "_SecondaryStrT13",
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82 "_SecondaryStrT23", "_ChargeT12", "_ChargeT13", "_HydrophobicityT12", "_PolarizabilityD1001",
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83 "_PolarizabilityD1025", "_PolarizabilityD1050", "_PolarizabilityD2001", "_PolarizabilityD3025",
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84 "_PolarizabilityD3050", "_PolarizabilityD3075", "_SolventAccessibilityD1050", "_SolventAccessibilityD2001",
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85 "_SolventAccessibilityD2025", "_SolventAccessibilityD2050", "_SolventAccessibilityD3025",
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86 "_SolventAccessibilityD3050", "_SolventAccessibilityD3100", "_SecondaryStrD1025", "_SecondaryStrD1050",
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87 "_SecondaryStrD1075", "_SecondaryStrD2001", "_SecondaryStrD2050", "_SecondaryStrD2075", "_ChargeD1050",
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88 "_ChargeD1075", "_ChargeD1100", "_ChargeD2025", "_ChargeD3025", "_ChargeD3050", "_PolarityD2050",
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89 "_PolarityD3050", "_NormalizedVDWVD1001", "_NormalizedVDWVD1050", "_NormalizedVDWVD2001", "_NormalizedVDWVD2025",
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90 "_HydrophobicityD3001", "_HydrophobicityD3075", "AD", "AW", "AY", "RC", "RT", "NA", "NE",
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91 "NG", "NP", "DE", "DQ", "DG", "DT", "DY", "CG", "CL", "CY", "CV", "EN", "QA", "QR", "QE",
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92 "QI", "GA", "GR", "GD", "GQ", "GG", "GH", "GL", "GF", "GP", "GT", "GY", "HA", "HC", "HI",
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93 "HK", "HP", "IC", "IG", "IS", "IT", "IW", "LA", "LR", "LH", "LI", "LK", "LP", "KQ", "KH",
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94 "KS", "KT", "MQ", "MG", "MI", "FA", "FR", "FS", "FY", "PC", "PE", "PG", "PH", "PM", "PF",
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95 "PT", "SA", "SD", "SC", "SQ", "SW", "TA", "TC", "TM", "WL", "WV", "YE", "YG", "YH", "YI",
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96 "YL", "YK", "YM", "YS"]}
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97
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98 self.df_output = self.df.copy()
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99 self.df_output.drop(['DNAseq','AAseq'],axis=1,inplace=True)
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100 dna_feat = {}
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101 aa_len = {}
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102 aroma_dic = {}
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103 iso_dic = {}
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104 aa_content = {}
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105 st_dic_master = {}
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106 CTD_dic = {}
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107 dp = {}
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108 for i in range(len(self.df)):
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109 i_name = self.df.index[i]
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110 dna_feat[i_name] = count_orf(self.df.iloc[i]['DNAseq'])
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111 aa_len[i_name] = len(self.df.iloc[i]['AAseq'])
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112 aroma_dic[i_name] = ProteinAnalysis(self.df.iloc[i]['AAseq']).aromaticity()
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113 iso_dic[i_name] = ProteinAnalysis(self.df.iloc[i]['AAseq']).isoelectric_point()
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114 aa_content[i_name] = count_aa(self.df.iloc[i]['AAseq'])
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115 st_dic_master[i_name] = sec_st_fr(self.df.iloc[i]['AAseq'])
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116 CTD_dic[i_name] = CalculateCTD(self.df.iloc[i]['AAseq'])
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117 dp[i_name] = CalculateDipeptideComposition(self.df.iloc[i]['AAseq'])
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118 for j in self.df.index:
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119 self.df.loc[j, dna_feat[j].keys()] = dna_feat[j].values() #dic with multiple values
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120 self.df.loc[j, 'AA_Len'] = int(aa_len[j]) #dic with one value
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121 self.df.loc[j, 'Aromaticity'] = aroma_dic[j]
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122 self.df.loc[j, 'IsoelectricPoint'] = iso_dic[j]
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123 self.df.loc[j, aa_content[j].keys()] = aa_content[j].values()
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124 self.df.loc[j, st_dic_master[j].keys()] = st_dic_master[j].values()
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125 self.df.loc[j, CTD_dic[j].keys()] = CTD_dic[j].values()
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126 self.df.loc[j, dp[j].keys()] = dp[j].values()
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127 self.df.drop(['DNAseq','AAseq'],axis=1,inplace=True)
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128
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129 def Prediction(self):
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130 import os
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131 import pickle
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132 import json
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133 import pandas as pd
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134 import numpy as np
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135 from pathlib import Path
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136 ft_scaler = pd.DataFrame(self.scaler.transform(self.df.iloc[:, :]), index=self.df.index,columns=self.df.columns)
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137 ft_scaler = ft_scaler.drop(columns=[col for col in self.df if col not in self.feat[self.name]], axis=1)
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138 scores = self.model.predict_proba(ft_scaler)
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139 pos_scores = np.empty((self.df.shape[0], 0), float)
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140 for x in scores:
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141 pos_scores = np.append(pos_scores, round(x[1]*100))
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142 self.df_output.reset_index(inplace=True)
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143 self.df_output['{} DPO Prediction (%)'.format(self.name)]= pos_scores
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144 #self.df_output = self.df_output.sort_values(by='{} DPO Prediction (%)'.format(self.name), ascending=False)
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145 self.df_output.to_html('output.html', index=False, justify='center')
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146
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147 if __name__ == '__main__':
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148 import os
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149 import sys
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150 __location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
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151
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152 model = sys.argv[1]
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153 fasta_file = sys.argv[2]
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154
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155 PDPO = PDPOPrediction(__location__,model,fasta_file)
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156 PDPO.Datastructure()
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157 PDPO.Prediction()
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158
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