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added aa_prop.py and add_aa_prop.py to add aa properties for wt and mutant in a given file containing one letter code wt and mut cols as csv

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Tanushree Tunstall 2021-06-14 13:24:00 +01:00
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
'''
Created on Mon June 14 2021
@author: tanu
'''
# FIXME: import dirs.py to get the basic dir paths available
#=======================================================================
# TASK
# Input:
# Output:
#=======================================================================
#%% load libraries
import os, sys
import pandas as pd
#import numpy as np
#from varname import nameof
import argparse
DEBUG = False
#=======================================================================
#%% specify input and curr dir
homedir = os.path.expanduser('~')
# set working dir
os.getcwd()
os.chdir(homedir + '/git/LSHTM_analysis/scripts')
os.getcwd()
from reference_dict import oneletter_aa_dict
#=======================================================================
#%%
def get_aa_prop(df, col1 = 'aap1', col2 = 'aap2', col3 = 'aap_taylor', col4 = 'aap_kd', col5 = 'aap_polarity', col6 = 'aap_calcprop'):
"""Add amino acid properties for wt and mutant residues
@df: df containing one letter aa code for wt and mutant respectively
@type: pandas df
@col1: column adding 7 aa categories (no overlap; acidic, basic, amidic, hydrophobic, hydroxylic, aromatic, sulphur)
@type: str
@col2: column adding 9 aa categories (overlap; acidic, basic, polar, hydrophobic, hydrophilic, small, aromatic, aliphatic, special)
@type: str
@col3: column adding 8 aa categories (overlap; acidic, basic, polar, hydrophobic, small, aromatic, aliphatic, special)
@type: str
@col4: column adding 3 aa categories (no overlap, hydrophobic, neutral and hydrophilic according to KD scale)
@type: str
@col5: column adding 4 aa categories (no overlap, acidic, basic, neutral, non-polar)
@type: str
@col6: column adding 4 aa categories (neg, pos, polar, non-polar)
@type: str
returns df: with 6 added columns. If column names clash, the function column
name will override original column
@rtype: pandas df
"""
lookup_dict_p1 = dict()
lookup_dict_p2 = dict()
lookup_dict_taylor = dict()
lookup_dict_kd = dict()
lookup_dict_polarity = dict()
lookup_dict_calcprop = dict()
for k, v in oneletter_aa_dict.items():
lookup_dict_p1[k] = v['aa_prop1']
lookup_dict_p2[k] = v['aa_prop2']
lookup_dict_taylor[k] = v['aa_taylor']
lookup_dict_kd[k] = v['aa_prop_water']
lookup_dict_polarity[k] = v['aa_prop_polarity']
lookup_dict_calcprop[k] = v['aa_calcprop']
#if DEBUG:
# print('Key:', k, 'value:', v
# , '\n============================================================'
# , '\nlook up dict:\n')
df['wt_aap1'] = df['wild_type'].map(lookup_dict_p1)
df['mut_aap1'] = df['mutant_type'].map(lookup_dict_p1)
df['wt_aap2'] = df['wild_type'].map(lookup_dict_p2)
df['mut_aap2'] = df['mutant_type'].map(lookup_dict_p2)
df['wt_aap_taylor'] = df['wild_type'].map(lookup_dict_taylor)
df['mut_aap_taylor'] = df['mutant_type'].map(lookup_dict_taylor)
df['wt_aap_kd'] = df['wild_type'].map(lookup_dict_kd)
df['mut_aap_kd'] = df['mutant_type'].map(lookup_dict_kd)
df['wt_aap_polarity'] = df['wild_type'].map(lookup_dict_polarity)
df['mut_aap_polarity'] = df['mutant_type'].map(lookup_dict_polarity)
df['wt_aa_calcprop'] = df['wild_type'].map(lookup_dict_calcprop)
df['mut_aa_calcprop'] = df['mutant_type'].map(lookup_dict_calcprop)
return df
#========================================