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def compositionValues(aa1, aa2): | |
compositionValues = {'S': 1.42, 'R': 0.65, 'L': 0, 'P': 0.39, 'T': 0.71, 'A': 0, 'V': 0, 'G': 0.74, | |
'I': 0, 'F': 0, 'Y': 0.20, 'C': 2.75, 'H': 0.58, 'Q': 0.89, 'N': 1.33, 'K': 0.33, | |
'D': 1.38, 'E': 0.92, 'M': 0, 'W': 0.13} | |
dif = round((compositionValues[aa1] - compositionValues[aa2]), 2) | |
return (dif) | |
def polarityValues(aa1, aa2): | |
polarityValues = {'S': 9.2, 'R': 10.5, 'L': 4.9, 'P': 8.0, 'T': 8.6, 'A': 8.1, 'V': 5.9, 'G': 9.0, | |
'I': 5.2, 'F': 5.2, 'Y': 6.2, 'C': 5.5, 'H': 10.4, 'Q': 10.5, 'N': 11.6, 'K': 11.3, | |
'D': 13.0, 'E': 12.3, 'M': 5.7, 'W': 5.4} | |
dif = round((polarityValues[aa1] - polarityValues[aa2]), 2) | |
return (dif) | |
def volumeValues(aa1, aa2): | |
volumeValues = {'S': 32, 'R': 124, 'L': 111, 'P': 32.5, 'T': 61, 'A': 31, 'V': 84, 'G': 3, | |
'I': 111, 'F': 132, 'Y': 136, 'C': 55, 'H': 96, 'Q': 85, 'N': 56, 'K': 119, | |
'D': 54, 'E': 83, 'M': 105, 'W': 170} | |
dif = round((volumeValues[aa1] - volumeValues[aa2]), 2) | |
return (dif) | |
def add_physicochemical(df): | |
grantham_dict = { | |
('A', 'A'): '0', | |
('A', 'C'): '195', | |
('A', 'D'): '126', | |
('A', 'E'): '107', | |
('A', 'F'): '113', | |
('A', 'G'): '60', | |
('A', 'H'): '86', | |
('A', 'I'): '94', | |
('A', 'K'): '106', | |
('A', 'L'): '96', | |
('A', 'M'): '84', | |
('A', 'N'): '111', | |
('A', 'P'): '27', | |
('A', 'Q'): '91', | |
('A', 'R'): '112', | |
('A', 'S'): '99', | |
('A', 'T'): '58', | |
('A', 'V'): '64', | |
('A', 'W'): '148', | |
('A', 'Y'): '112', | |
('C', 'A'): '195', | |
('C', 'C'): '0', | |
('C', 'D'): '154', | |
('C', 'E'): '170', | |
('C', 'F'): '205', | |
('C', 'G'): '159', | |
('C', 'H'): '174', | |
('C', 'I'): '198', | |
('C', 'K'): '202', | |
('C', 'L'): '198', | |
('C', 'M'): '196', | |
('C', 'N'): '139', | |
('C', 'P'): '169', | |
('C', 'Q'): '154', | |
('C', 'R'): '180', | |
('C', 'S'): '112', | |
('C', 'T'): '149', | |
('C', 'V'): '192', | |
('C', 'W'): '215', | |
('C', 'Y'): '194', | |
('D', 'A'): '126', | |
('D', 'C'): '154', | |
('D', 'D'): '0', | |
('D', 'E'): '45', | |
('D', 'F'): '177', | |
('D', 'G'): '94', | |
('D', 'H'): '81', | |
('D', 'I'): '168', | |
('D', 'K'): '101', | |
('D', 'L'): '172', | |
('D', 'M'): '160', | |
('D', 'N'): '23', | |
('D', 'P'): '108', | |
('D', 'Q'): '61', | |
('D', 'R'): '96', | |
('D', 'S'): '65', | |
('D', 'T'): '85', | |
('D', 'V'): '152', | |
('D', 'W'): '181', | |
('D', 'Y'): '160', | |
('E', 'A'): '107', | |
('E', 'C'): '170', | |
('E', 'D'): '45', | |
('E', 'E'): '0', | |
('E', 'F'): '140', | |
('E', 'G'): '98', | |
('E', 'H'): '40', | |
('E', 'I'): '134', | |
('E', 'K'): '56', | |
('E', 'L'): '138', | |
('E', 'M'): '126', | |
('E', 'N'): '42', | |
('E', 'P'): '93', | |
('E', 'Q'): '29', | |
('E', 'R'): '54', | |
('E', 'S'): '80', | |
('E', 'T'): '65', | |
('E', 'V'): '121', | |
('E', 'W'): '152', | |
('E', 'Y'): '122', | |
('F', 'A'): '113', | |
('F', 'C'): '205', | |
('F', 'D'): '177', | |
('F', 'E'): '140', | |
('F', 'F'): '0', | |
('F', 'G'): '153', | |
('F', 'H'): '100', | |
('F', 'I'): '21', | |
('F', 'K'): '102', | |
('F', 'L'): '22', | |
('F', 'M'): '28', | |
('F', 'N'): '158', | |
('F', 'P'): '114', | |
('F', 'Q'): '116', | |
('F', 'R'): '97', | |
('F', 'S'): '155', | |
('F', 'T'): '103', | |
('F', 'V'): '50', | |
('F', 'W'): '40', | |
('F', 'Y'): '22', | |
('G', 'A'): '60', | |
('G', 'C'): '159', | |
('G', 'D'): '94', | |
('G', 'E'): '98', | |
('G', 'F'): '153', | |
('G', 'G'): '0', | |
('G', 'H'): '98', | |
('G', 'I'): '135', | |
('G', 'K'): '127', | |
('G', 'L'): '138', | |
('G', 'M'): '127', | |
('G', 'N'): '80', | |
('G', 'P'): '42', | |
('G', 'Q'): '87', | |
('G', 'R'): '125', | |
('G', 'S'): '56', | |
('G', 'T'): '59', | |
('G', 'V'): '109', | |
('G', 'W'): '184', | |
('G', 'Y'): '147', | |
('H', 'A'): '86', | |
('H', 'C'): '174', | |
('H', 'D'): '81', | |
('H', 'E'): '40', | |
('H', 'F'): '100', | |
('H', 'G'): '98', | |
('H', 'H'): '0', | |
('H', 'I'): '94', | |
('H', 'K'): '32', | |
('H', 'L'): '99', | |
('H', 'M'): '87', | |
('H', 'N'): '68', | |
('H', 'P'): '77', | |
('H', 'Q'): '24', | |
('H', 'R'): '29', | |
('H', 'S'): '89', | |
('H', 'T'): '47', | |
('H', 'V'): '84', | |
('H', 'W'): '115', | |
('H', 'Y'): '83', | |
('I', 'A'): '94', | |
('I', 'C'): '198', | |
('I', 'D'): '168', | |
('I', 'E'): '134', | |
('I', 'F'): '21', | |
('I', 'G'): '135', | |
('I', 'H'): '94', | |
('I', 'I'): '0', | |
('I', 'K'): '102', | |
('I', 'L'): '5', | |
('I', 'M'): '10', | |
('I', 'N'): '149', | |
('I', 'P'): '95', | |
('I', 'Q'): '109', | |
('I', 'R'): '97', | |
('I', 'S'): '142', | |
('I', 'T'): '89', | |
('I', 'V'): '29', | |
('I', 'W'): '61', | |
('I', 'Y'): '33', | |
('K', 'A'): '106', | |
('K', 'C'): '202', | |
('K', 'D'): '101', | |
('K', 'E'): '56', | |
('K', 'F'): '102', | |
('K', 'G'): '127', | |
('K', 'H'): '32', | |
('K', 'I'): '102', | |
('K', 'K'): '0', | |
('K', 'L'): '107', | |
('K', 'M'): '95', | |
('K', 'N'): '94', | |
('K', 'P'): '103', | |
('K', 'Q'): '53', | |
('K', 'R'): '26', | |
('K', 'S'): '121', | |
('K', 'T'): '78', | |
('K', 'V'): '97', | |
('K', 'W'): '110', | |
('K', 'Y'): '85', | |
('L', 'A'): '96', | |
('L', 'C'): '198', | |
('L', 'D'): '172', | |
('L', 'E'): '138', | |
('L', 'F'): '22', | |
('L', 'G'): '138', | |
('L', 'H'): '99', | |
('L', 'I'): '5', | |
('L', 'K'): '107', | |
('L', 'L'): '0', | |
('L', 'M'): '15', | |
('L', 'N'): '153', | |
('L', 'P'): '98', | |
('L', 'Q'): '113', | |
('L', 'R'): '102', | |
('L', 'S'): '145', | |
('L', 'T'): '92', | |
('L', 'V'): '32', | |
('L', 'W'): '61', | |
('L', 'Y'): '36', | |
('M', 'A'): '84', | |
('M', 'C'): '196', | |
('M', 'D'): '160', | |
('M', 'E'): '126', | |
('M', 'F'): '28', | |
('M', 'G'): '127', | |
('M', 'H'): '87', | |
('M', 'I'): '10', | |
('M', 'K'): '95', | |
('M', 'L'): '15', | |
('M', 'M'): '0', | |
('M', 'N'): '142', | |
('M', 'P'): '87', | |
('M', 'Q'): '101', | |
('M', 'R'): '91', | |
('M', 'S'): '135', | |
('M', 'T'): '81', | |
('M', 'V'): '21', | |
('M', 'W'): '67', | |
('M', 'Y'): '36', | |
('N', 'A'): '111', | |
('N', 'C'): '139', | |
('N', 'D'): '23', | |
('N', 'E'): '42', | |
('N', 'F'): '158', | |
('N', 'G'): '80', | |
('N', 'H'): '68', | |
('N', 'I'): '149', | |
('N', 'K'): '94', | |
('N', 'L'): '153', | |
('N', 'M'): '142', | |
('N', 'N'): '0', | |
('N', 'P'): '91', | |
('N', 'Q'): '46', | |
('N', 'R'): '86', | |
('N', 'S'): '46', | |
('N', 'T'): '65', | |
('N', 'V'): '133', | |
('N', 'W'): '174', | |
('N', 'Y'): '143', | |
('P', 'A'): '27', | |
('P', 'C'): '169', | |
('P', 'D'): '108', | |
('P', 'E'): '93', | |
('P', 'F'): '114', | |
('P', 'G'): '42', | |
('P', 'H'): '77', | |
('P', 'I'): '95', | |
('P', 'K'): '103', | |
('P', 'L'): '98', | |
('P', 'M'): '87', | |
('P', 'N'): '91', | |
('P', 'P'): '0', | |
('P', 'Q'): '76', | |
('P', 'R'): '103', | |
('P', 'S'): '74', | |
('P', 'T'): '38', | |
('P', 'V'): '68', | |
('P', 'W'): '147', | |
('P', 'Y'): '110', | |
('Q', 'A'): '91', | |
('Q', 'C'): '154', | |
('Q', 'D'): '61', | |
('Q', 'E'): '29', | |
('Q', 'F'): '116', | |
('Q', 'G'): '87', | |
('Q', 'H'): '24', | |
('Q', 'I'): '109', | |
('Q', 'K'): '53', | |
('Q', 'L'): '113', | |
('Q', 'M'): '101', | |
('Q', 'N'): '46', | |
('Q', 'P'): '76', | |
('Q', 'Q'): '0', | |
('Q', 'R'): '43', | |
('Q', 'S'): '68', | |
('Q', 'T'): '42', | |
('Q', 'V'): '96', | |
('Q', 'W'): '130', | |
('Q', 'Y'): '99', | |
('R', 'A'): '112', | |
('R', 'C'): '180', | |
('R', 'D'): '96', | |
('R', 'E'): '54', | |
('R', 'F'): '97', | |
('R', 'G'): '125', | |
('R', 'H'): '29', | |
('R', 'I'): '97', | |
('R', 'K'): '26', | |
('R', 'L'): '102', | |
('R', 'M'): '91', | |
('R', 'N'): '86', | |
('R', 'P'): '103', | |
('R', 'Q'): '43', | |
('R', 'R'): '0', | |
('R', 'S'): '110', | |
('R', 'T'): '71', | |
('R', 'V'): '96', | |
('R', 'W'): '101', | |
('R', 'Y'): '77', | |
('S', 'A'): '99', | |
('S', 'C'): '112', | |
('S', 'D'): '65', | |
('S', 'E'): '80', | |
('S', 'F'): '155', | |
('S', 'G'): '56', | |
('S', 'H'): '89', | |
('S', 'I'): '142', | |
('S', 'K'): '121', | |
('S', 'L'): '145', | |
('S', 'M'): '135', | |
('S', 'N'): '46', | |
('S', 'P'): '74', | |
('S', 'Q'): '68', | |
('S', 'R'): '110', | |
('S', 'S'): '0', | |
('S', 'T'): '58', | |
('S', 'V'): '124', | |
('S', 'W'): '177', | |
('S', 'Y'): '144', | |
('T', 'A'): '58', | |
('T', 'C'): '149', | |
('T', 'D'): '85', | |
('T', 'E'): '65', | |
('T', 'F'): '103', | |
('T', 'G'): '59', | |
('T', 'H'): '47', | |
('T', 'I'): '89', | |
('T', 'K'): '78', | |
('T', 'L'): '92', | |
('T', 'M'): '81', | |
('T', 'N'): '65', | |
('T', 'P'): '38', | |
('T', 'Q'): '42', | |
('T', 'R'): '71', | |
('T', 'S'): '58', | |
('T', 'T'): '0', | |
('T', 'V'): '69', | |
('T', 'W'): '128', | |
('T', 'Y'): '92', | |
('V', 'A'): '64', | |
('V', 'C'): '192', | |
('V', 'D'): '152', | |
('V', 'E'): '121', | |
('V', 'F'): '50', | |
('V', 'G'): '109', | |
('V', 'H'): '84', | |
('V', 'I'): '29', | |
('V', 'K'): '97', | |
('V', 'L'): '32', | |
('V', 'M'): '21', | |
('V', 'N'): '133', | |
('V', 'P'): '68', | |
('V', 'Q'): '96', | |
('V', 'R'): '96', | |
('V', 'S'): '124', | |
('V', 'T'): '69', | |
('V', 'V'): '0', | |
('V', 'W'): '88', | |
('V', 'Y'): '55', | |
('W', 'A'): '148', | |
('W', 'C'): '215', | |
('W', 'D'): '181', | |
('W', 'E'): '152', | |
('W', 'F'): '40', | |
('W', 'G'): '184', | |
('W', 'H'): '115', | |
('W', 'I'): '61', | |
('W', 'K'): '110', | |
('W', 'L'): '61', | |
('W', 'M'): '67', | |
('W', 'N'): '174', | |
('W', 'P'): '147', | |
('W', 'Q'): '130', | |
('W', 'R'): '101', | |
('W', 'S'): '177', | |
('W', 'T'): '128', | |
('W', 'V'): '88', | |
('W', 'W'): '0', | |
('W', 'Y'): '37', | |
('Y', 'A'): '112', | |
('Y', 'C'): '194', | |
('Y', 'D'): '160', | |
('Y', 'E'): '122', | |
('Y', 'F'): '22', | |
('Y', 'G'): '147', | |
('Y', 'H'): '83', | |
('Y', 'I'): '33', | |
('Y', 'K'): '85', | |
('Y', 'L'): '36', | |
('Y', 'M'): '36', | |
('Y', 'N'): '143', | |
('Y', 'P'): '110', | |
('Y', 'Q'): '99', | |
('Y', 'R'): '77', | |
('Y', 'S'): '144', | |
('Y', 'T'): '92', | |
('Y', 'V'): '55', | |
('Y', 'W'): '37', | |
('Y', 'Y'): '0' | |
} | |
for i in df.index: | |
try: | |
df.at[i, 'composition'] = compositionValues(df.at[i, 'wt'], df.at[i, 'mut']) | |
df.at[i, 'polarity'] = polarityValues(df.at[i, 'wt'], df.at[i, 'mut']) | |
df.at[i, 'volume'] = volumeValues(df.at[i, 'wt'], df.at[i, 'mut']) | |
df.at[i, 'granthamScore'] = grantham_dict[df.at[i, 'wt'], df.at[i, 'mut']] | |
except: | |
KeyError | |
df.at[i, 'composition'] = 'nan' | |
df.at[i, 'polarity'] = 'nan' | |
df.at[i, 'volume'] = 'nan' | |
df.at[i, 'granthamScore'] = 'nan' | |
return df | |