333 lines
12 KiB
Python
Executable file
333 lines
12 KiB
Python
Executable file
#!/usr/bin/env python3
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import subprocess
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import os
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import numpy as np
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import pandas as pd
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from contextlib import suppress
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from pathlib import Path
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import re
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import csv
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import argparse
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#https://realpython.com/python-pathlib/
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# FIXME
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#strong dependency of file and path names
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#cannot pass file with path. Need to pass them separately
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#assumptions made for dir struc as standard
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#datadir + drug + input
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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/foldx/')
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os.getcwd()
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#=======================================================================
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#%% command line args
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arg_parser = argparse.ArgumentParser()
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arg_parser.add_argument('-d', '--drug', help = 'drug name', default = None)
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arg_parser.add_argument('-g', '--gene', help = 'gene name (case sensitive)', default = None)
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arg_parser.add_argument('--datadir', help = 'Data Directory. By default, it assmumes homedir + git/Data')
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arg_parser.add_argument('-i', '--input_dir', help = 'Input dir containing pdb files. By default, it assmumes homedir + <drug> + input')
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arg_parser.add_argument('-o', '--output_dir', help = 'Output dir for results. By default, it assmes homedir + <drug> + output')
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arg_parser.add_argument('-pdb', '--pdb_file', help = 'PDB File to process. By default, it assmumes a file called <gene>_complex.pdb in input_dir')
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arg_parser.add_argument('-m', '--mutation_file', help = 'Mutation list. By default, assumes a file called <gene>_snps.csv exists')
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arg_parser.add_argument('-c1', '--chain1', help = 'Chain1 ID', default = 'A') # case sensitive
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arg_parser.add_argument('-c2', '--chain2', help = 'Chain2 ID', default = 'B') # case sensitive
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args = arg_parser.parse_args()
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#=======================================================================
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#%% variable assignment: input and output
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#drug = 'pyrazinamide'
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#gene = 'pncA'
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#gene_match = gene + '_p.'
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#%%=====================================================================
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# Command line options
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drug = args.drug
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gene = args.gene
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datadir = args.datadir
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indir = args.input_dir
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outdir = args.output_dir
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mut_filename = args.mutation_file
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chainA = args.chain1
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chainB = args.chain2
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pdb_filename = args.pdb_file
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# os.path.splitext will fail interestingly with file.pdb.txt.zip
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#pdb_name = os.path.splitext(pdb_file)[0]
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# Just the filename, thanks
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#pdb_name = Path(in_filename_pdb).stem
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#==============
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# directories
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#==============
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if not datadir:
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datadir = homedir + '/' + 'git/Data'
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if not indir:
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indir = datadir + '/' + drug + '/input'
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if not outdir:
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outdir = datadir + '/' + drug + '/output'
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# FIXME: this is a temporary directory and should be correctly handled
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process_dir = datadir + '/' + drug +'/' + 'processing'
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#os.mkdir(process_dir)
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#=======
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# input
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#=======
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# FIXME
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if pdb_filename:
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pdb_name = Path(pdb_filename).stem
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else:
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pdb_filename = gene.lower() + '_complex.pdb'
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pdb_name = Path(pdb_filename).stem
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infile_pdb = indir + '/' + pdb_filename
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actual_pdb_filename = Path(infile_pdb).name
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if mut_filename:
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mutation_file = mut_filename
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else:
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mutation_file = gene.lower() + '_mcsm_snps.csv'
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infile_muts = outdir + '/' + mutation_file
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#=======
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# output
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#=======
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out_filename = gene.lower() + '_foldx.csv'
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outfile_foldx = outdir + '/' + out_filename
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print('Arguments being passed:'
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, '\nDrug:', args.drug
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, '\ngene:', args.gene
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, '\ninput dir:', indir
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, '\noutput dir:', outdir
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, '\npdb file:', infile_pdb
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, '\npdb name:', pdb_name
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, '\nactual pdb name:', actual_pdb_filename
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, '\nmutation file:', infile_muts
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, '\nchain1:', args.chain1
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, '\noutput file:', outfile_foldx
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, '\n=============================================================')
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#=======================================================================
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def getInteractionEnergy(filename):
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data = pd.read_csv(filename,sep = '\t')
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return data['Interaction Energy'].loc[0]
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def getInteractions(filename):
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data = pd.read_csv(filename, index_col = 0, header = 0, sep = '\t')
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contactList = getIndexes(data,1)
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number = len(contactList)
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return number
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def formatMuts(mut_file,pdbname):
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with open(mut_file) as csvfile:
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readCSV = csv.reader(csvfile)
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muts = []
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for row in readCSV:
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mut = row[0]
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muts.append(mut)
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mut_list = []
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outfile = process_dir + '/individual_list_' + pdbname + '.txt'
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with open(outfile, 'w') as output:
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for m in muts:
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print(m)
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mut = m[:1] + chainA+ m[1:]
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mut_list.append(mut)
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mut = mut + ';'
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print(mut)
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output.write(mut)
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output.write('\n')
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return mut_list
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def getIndexes(data, value):
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colnames = data.columns.values
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listOfPos = list()
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result = data.isin([value])
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result.columns = colnames
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seriesdata = result.any()
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columnNames = list(seriesdata[seriesdata==True].index)
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for col in columnNames:
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rows = list(result[col][result[col]==True].index)
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for row in rows:
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listOfPos.append((row,col))
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return listOfPos
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def loadFiles(df):
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# load a text file in to np matrix
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resultList = []
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f = open(df,'r')
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for line in f:
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line = line.rstrip('\n')
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aVals = line.split('\t')
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fVals = list(map(np.float32, sVals))
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resultList.append(fVals)
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f.close()
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return np.asarray(resultList, dtype=np.float32)
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#=======================================================================
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def main():
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pdbname = pdb_name
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comp = '' # for complex only
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mut_filename = infile_muts #pnca_mcsm_snps.csv
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mutlist = formatMuts(mut_filename, pdbname)
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print(mutlist)
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nmuts = len(mutlist)
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print(nmuts)
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print(mutlist)
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print('start')
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#subprocess.check_output(['bash','repairPDB.sh', pdbname, process_dir])
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subprocess.check_output(['bash','repairPDB.sh', indir, actual_pdb_filename, process_dir])
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print('end')
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output = subprocess.check_output(['bash', 'runfoldx.sh', pdbname, process_dir])
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for n in range(1,nmuts+1):
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print(n)
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with suppress(Exception):
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subprocess.check_output(['bash', 'runPrintNetworks.sh', pdbname, str(n), process_dir])
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for n in range(1,nmuts+1):
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print(n)
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with suppress(Exception):
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subprocess.check_output(['bash', 'mutrenamefiles.sh', pdbname, str(n), process_dir])
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out = subprocess.check_output(['bash','renamefiles.sh', pdbname, process_dir])
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if comp=='y':
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chain1=chainA
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chain2=chainB
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with suppress(Exception):
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subprocess.check_output(['bash','runcomplex.sh', pdbname, chain1, chain2, process_dir])
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for n in range(1,nmuts+1):
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with suppress(Exception):
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subprocess.check_output(['bash','mutruncomplex.sh', pdbname, chain1, chain2, str(n), process_dir])
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interactions = ['Distances','Electro_RR','Electro_MM','Electro_SM','Electro_SS','Disulfide_RR','Disulfide_MM','Disulfide_SM','Disulfide_SS',
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'Hbonds_RR','Hbonds_MM','Hbonds_SM','Hbonds_SS','Partcov_RR','Partcov_MM','Partcov_SM','Partcov_SS','VdWClashes_RR','VdWClashes_MM',
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'VdWClashes_SM','VdWClashes_SS','Volumetric_RR','Volumetric_MM','Volumetric_SM','Volumetric_SS']
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dGdatafile = process_dir + '/Dif_' + pdbname + '_Repair.txt'
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dGdata = pd.read_csv(dGdatafile, sep = '\t')
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ddG=[]
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print('ddG')
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print(len(dGdata))
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for i in range(0,len(dGdata)):
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ddG.append(dGdata['total energy'].loc[i])
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nint = len(interactions)
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wt_int = []
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for i in interactions:
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filename = process_dir + '/Matrix_' + i + '_'+ pdbname + '_Repair_PN.txt'
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wt_int.append(getInteractions(filename))
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print('wt')
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print(wt_int)
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ntotal = nint+1
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print(ntotal)
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print(nmuts)
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data = np.empty((ntotal,nmuts))
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data[0] = ddG
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print(data)
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for i in range(0,len(interactions)):
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d=[]
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p=0
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for n in range(1, nmuts+1):
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print(i)
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filename = process_dir + '/Matrix_' + interactions[i] + '_' + pdbname + '_Repair_' + str(n) + '_PN.txt'
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mut = getInteractions(filename)
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diff = wt_int[i] - mut
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print(diff)
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print(wt_int[i])
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print(mut)
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d.append(diff)
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print(d)
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data[i+1] = d
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interactions = ['ddG', 'Distances','Electro_RR','Electro_MM','Electro_SM','Electro_SS','Disulfide_RR','Disulfide_MM','Disulfide_SM','Disulfide_SS', 'Hbonds_RR','Hbonds_MM','Hbonds_SM','Hbonds_SS','Partcov_RR','Partcov_MM','Partcov_SM','Partcov_SS','VdWClashes_RR','VdWClashes_MM',
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'VdWClashes_SM','VdWClashes_SS','Volumetric_RR','Volumetric_MM','Volumetric_SM','Volumetric_SS']
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print(interactions)
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IE = []
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if comp=='y':
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wtfilename = process_dir + '/Summary_' + pdbname + '_Repair_AC.txt'
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wtE = getInteractionEnergy(wtfilename)
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print(wtE)
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for n in range(1,nmuts+1):
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print(n)
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filename = process_dir + '/Summary_' + pdbname + '_Repair_' + str(n) + '_AC.txt'
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mutE = getInteractionEnergy(filename)
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print(mutE)
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diff = wtE - mutE
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print(diff)
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IE.append(diff)
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print(IE)
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IEresults = pd.DataFrame(IE,columns = ['Interaction Energy'], index = mutlist)
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IEfilename = 'foldx_complexresults_'+pdbname+'.csv'
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IEresults.to_csv(IEfilename)
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print(len(IE))
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data = np.append(data,[IE], axis = 0)
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print(data)
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interactions = ['ddG','Distances','Electro_RR','Electro_MM','Electro_SM','Electro_SS','Disulfide_RR','Disulfide_MM','Disulfide_SM','Disulfide_SS', 'Hbonds_RR','Hbonds_MM','Hbonds_SM','Hbonds_SS','Partcov_RR','Partcov_MM','Partcov_SM','Partcov_SS','VdWClashes_RR','VdWClashes_MM',
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'VdWClashes_SM','VdWClashes_SS','Volumetric_RR','Volumetric_MM','Volumetric_SM','Volumetric_SS','Interaction Energy']
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mut_file = process_dir + '/individual_list_' + pdbname + '.txt'
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with open(mut_file) as csvfile:
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readCSV = csv.reader(csvfile)
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mutlist = []
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for row in readCSV:
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mut = row[0]
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mutlist.append(mut)
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print(mutlist)
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print(len(mutlist))
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print(data)
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results = pd.DataFrame(data, columns = mutlist, index = interactions)
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results.append(ddG)
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#print(results.head())
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# my style formatted results
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results2 = results.T # transpose df
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results2.index.name = 'mutationinformation' # assign name to index
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results2 = results2.reset_index() # turn it into a columns
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results2['mutationinformation'] = results2['mutationinformation'].replace({r'([A-Z]{1})[A-Z]{1}([0-9]+[A-Z]{1});' : r'\1 \2'}, regex = True) # capture mcsm style muts (i.e not the chain id)
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results2['mutationinformation'] = results2['mutationinformation'].str.replace(' ', '') # remove empty space
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results2.rename(columns = {'Distances': 'Contacts'}, inplace = True)
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# lower case columns
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results2.columns = results2.columns.str.lower()
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print('Writing file in the format below:\n'
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, results2.head()
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, '\nNo. of rows:', len(results2)
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, '\nNo. of cols:', len(results2.columns))
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outputfilename = outfile_foldx
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#outputfilename = 'foldx_results_' + pdbname + '.csv'
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#results.to_csv(outputfilename)
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results2.to_csv(outputfilename, index = False)
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if __name__ == '__main__':
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main()
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