113 lines
No EOL
3.7 KiB
Python
113 lines
No EOL
3.7 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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'''
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Created on Mon June 14 2021
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@author: tanu
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'''
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# FIXME: import dirs.py to get the basic dir paths available
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#=======================================================================
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# TASK
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# Input:
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# Output:
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#=======================================================================
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#%% load libraries
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import os, sys
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import pandas as pd
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#import numpy as np
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#from varname import nameof
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import argparse
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DEBUG = False
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#=======================================================================
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#%% specify input and curr dir
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homedir = os.path.expanduser('~')
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# set working dir
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os.getcwd()
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os.chdir(homedir + '/git/LSHTM_analysis/scripts')
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os.getcwd()
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from reference_dict import oneletter_aa_dict
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#=======================================================================
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#%%
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def get_aa_prop(df, col1 = 'aap1', col2 = 'aap2', col3 = 'aap_taylor', col4 = 'aap_kd', col5 = 'aap_polarity', col6 = 'aap_calcprop'):
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"""Add amino acid properties for wt and mutant residues
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@df: df containing one letter aa code for wt and mutant respectively
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@type: pandas df
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@col1: column adding 7 aa categories (no overlap; acidic, basic, amidic, hydrophobic, hydroxylic, aromatic, sulphur)
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@type: str
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@col2: column adding 9 aa categories (overlap; acidic, basic, polar, hydrophobic, hydrophilic, small, aromatic, aliphatic, special)
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@type: str
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@col3: column adding 8 aa categories (overlap; acidic, basic, polar, hydrophobic, small, aromatic, aliphatic, special)
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@type: str
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@col4: column adding 3 aa categories (no overlap, hydrophobic, neutral and hydrophilic according to KD scale)
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@type: str
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@col5: column adding 4 aa categories (no overlap, acidic, basic, neutral, non-polar)
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@type: str
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@col6: column adding 4 aa categories (neg, pos, polar, non-polar)
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@type: str
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returns df: with 6 added columns. If column names clash, the function column
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name will override original column
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@rtype: pandas df
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"""
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lookup_dict_p1 = dict()
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lookup_dict_p2 = dict()
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lookup_dict_taylor = dict()
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lookup_dict_kd = dict()
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lookup_dict_polarity = dict()
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lookup_dict_calcprop = dict()
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for k, v in oneletter_aa_dict.items():
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lookup_dict_p1[k] = v['aa_prop1']
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lookup_dict_p2[k] = v['aa_prop2']
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lookup_dict_taylor[k] = v['aa_taylor']
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lookup_dict_kd[k] = v['aa_prop_water']
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lookup_dict_polarity[k] = v['aa_prop_polarity']
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lookup_dict_calcprop[k] = v['aa_calcprop']
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#if DEBUG:
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# print('Key:', k, 'value:', v
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# , '\n============================================================'
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# , '\nlook up dict:\n')
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df['wt_aap1'] = df['wild_type'].map(lookup_dict_p1)
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df['mut_aap1'] = df['mutant_type'].map(lookup_dict_p1)
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df['wt_aap2'] = df['wild_type'].map(lookup_dict_p2)
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df['mut_aap2'] = df['mutant_type'].map(lookup_dict_p2)
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df['wt_aap_taylor'] = df['wild_type'].map(lookup_dict_taylor)
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df['mut_aap_taylor'] = df['mutant_type'].map(lookup_dict_taylor)
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df['wt_aap_kd'] = df['wild_type'].map(lookup_dict_kd)
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df['mut_aap_kd'] = df['mutant_type'].map(lookup_dict_kd)
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df['wt_aap_polarity'] = df['wild_type'].map(lookup_dict_polarity)
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df['mut_aap_polarity'] = df['mutant_type'].map(lookup_dict_polarity)
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df['wt_aa_calcprop'] = df['wild_type'].map(lookup_dict_calcprop)
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df['mut_aa_calcprop'] = df['mutant_type'].map(lookup_dict_calcprop)
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return df
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#======================================== |