94 lines
2.7 KiB
Python
94 lines
2.7 KiB
Python
#!/usr/bin/python
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#%%
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# Task: Hydrophobicity (Kd) values for amino acid sequence using the Kyt&-Doolittle
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# Same output as using the expasy server https://web.expasy.org/protscale/
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# useful links
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# https://biopython.org/DIST/docs/api/Bio.SeqUtils.ProtParamData-pysrc.html
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# https://jbloomlab.github.io/dms_tools2/dms_tools2.dssp.html
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#%%
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# load packages
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from pylab import *
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from Bio.SeqUtils import ProtParamData
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from Bio.SeqUtils.ProtParam import ProteinAnalysis
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from Bio import SeqIO
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#from Bio.Alphabet.IUPAC import IUPACProtein
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#import pprint as pp
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import pandas as pd
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import numpy as np
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import sys, os
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#%%
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# specify input and output variables
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homedir = os.path.expanduser('~')
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#=======
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# input
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#=======
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indir = 'git/Data/pyrazinamide/input/original'
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in_filename = "3pl1.fasta.txt"
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infile = homedir + '/' + indir + '/' + in_filename
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print(infile)
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#=======
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# output
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#=======
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outdir = 'git/Data/pyrazinamide/output'
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out_filename = "kd.csv"
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outfile = homedir + '/' + outdir + '/' + out_filename
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print(outfile)
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#%%
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# specify window size for hydropathy profile computation
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# https://web.expasy.org/protscale/pscale/protscale_help.html
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my_window = 3
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offset = round((my_window/2)-0.5)
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fh = open(infile)
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for record in SeqIO.parse(fh, "fasta"):
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id = record.id
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seq = record.seq
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num_residues = len(seq)
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fh.close()
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sequence = str(seq)
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X = ProteinAnalysis(sequence)
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kd_values = (X.protein_scale(ProtParamData.kd , window = my_window)) # edge weight is set to default (100%)
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# sanity checks
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print('Sequence Length:', num_residues)
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print('kd_values Length:',len(kd_values))
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print('Window Length:',my_window)
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print('Window Offset:',offset)
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# make a df each for; aa sequence and kd_values. Then reset index for each df which will allow easy merging of the two
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# df1: df of aa seq with index reset to start from 1 (reflective of the actual aa position in a sequence)
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dfSeq = pd.DataFrame({'aa_wt':list(sequence)})
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dfSeq.index = np.arange(1, len(dfSeq) + 1) #python is not inclusive
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# df2: df of kd_values with index reset to start from offset + 1 and subsequent matched length of the kd_values
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dfVals = pd.DataFrame({'kd_values':kd_values})
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dfVals.index = np.arange(offset + 1, len(dfVals) + 1 + offset)
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# sanity checks
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max(dfVals['kd_values'])
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min(dfVals['kd_values'])
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# Merge the two on index (as these are now reflective of the aa position numbers): df1 and df2
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df = pd.concat([dfSeq, dfVals], axis = 1)
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# rename index to position
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df = df.rename_axis('position')
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print(df)
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#%%
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# write file
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df.to_csv(outfile, header = True, index = True)
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#%% Plot
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# http://www.dalkescientific.com/writings/NBN/plotting.html
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plot(kd_values, linewidth = 1.0)
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#axis(xmin = 1, xmax = num_residues)
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xlabel("Residue Number")
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ylabel("Hydrophobicity")
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title("K&D Hydrophobicity for " + id)
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show()
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#%% end of script
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