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pred_mhcii_analyse.py
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pred_mhcii_analyse.py
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from ast import Continue
from multiprocessing.sharedctypes import Value
from queue import Empty
import re
import os
from xmlrpc.client import ProtocolError
import requests
import time
import random
import csv
from csv import reader
import pandas as pd
import matplotlib.pyplot as plt
from Bio.SeqUtils.ProtParam import ProteinAnalysis
from urllib import request
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as ec
from selenium.webdriver.firefox.options import Options
from selenium.webdriver.common.keys import Keys
def read_prediction_results():
"""This function will read all the results from the csv files created by the prediction methods in prediction.py, remove the duplicate peptides,
then return them in a tuple of lists"""
df_sarscov2_mhci = pd.read_csv('iedb_epitopes/sarscov2_mhci.csv')
df_sarscov2_mhci.drop_duplicates(subset=['Description'], keep=False, inplace=True)
df_sarscov2_mhci = df_sarscov2_mhci.loc[(df_sarscov2_mhci['Parent_Protein'] == 'Spike glycoprotein') | (df_sarscov2_mhci['Parent_Protein'] == 'Nucleoprotein')
| (df_sarscov2_mhci['Parent_Protein'] == 'Replicase polyprotein 1ab') | (df_sarscov2_mhci['Parent_Protein'] == 'Membrane protein')
| (df_sarscov2_mhci['Parent_Protein'] == 'ORF3a protein')]
df_sarscov_mhci = pd.read_csv('iedb_epitopes/sarscov_mhci.csv')
df_sarscov_mhci.drop_duplicates(subset=['Description'], keep=False, inplace=True)
df_sarscov_mhci = df_sarscov_mhci.loc[(df_sarscov_mhci['Parent_Protein'] == 'Spike glycoprotein') | (df_sarscov_mhci['Parent_Protein'] == 'Nucleoprotein')
| (df_sarscov_mhci['Parent_Protein'] == 'Replicase polyprotein 1ab') | (df_sarscov_mhci['Parent_Protein'] == 'Replicase polyprotein 1a')
| (df_sarscov_mhci['Parent_Protein'] == 'ORF3a protein')]
df_mers_mhci = pd.read_csv('iedb_epitopes/mers_mhci.csv')
df_mers_mhci.drop_duplicates(subset=['Description'], keep=False, inplace=True)
df_mers_mhci = df_mers_mhci.loc[(df_mers_mhci['Parent_Protein'] == 'Spike glycoprotein') | (df_mers_mhci['Parent_Protein'] == 'Nucleoprotein')
| (df_mers_mhci['Parent_Protein'] == 'Replicase polyprotein 1ab')]
df_hcovoc43_mhci = pd.read_csv('iedb_epitopes/hcovoc43_mhci.csv')
df_hcovoc43_mhci.drop_duplicates(subset=['Description'], keep=False, inplace=True)
df_hcovoc43_mhci = df_hcovoc43_mhci.loc[(df_hcovoc43_mhci['Parent_Protein'] == 'Spike glycoprotein') | (df_hcovoc43_mhci['Parent_Protein'] == 'Nucleoprotein')
| (df_hcovoc43_mhci['Parent_Protein'] == 'Replicase polyprotein 1ab')]
df_hku1_mhci = pd.read_csv('iedb_epitopes/hku1_mhci.csv')
df_hku1_mhci.drop_duplicates(subset=['Description'], keep=False, inplace=True)
df_hku1_mhci = df_hku1_mhci.loc[(df_hku1_mhci['Parent_Protein'] == 'Spike glycoprotein') | (df_hku1_mhci['Parent_Protein'] == 'Nucleoprotein')
| (df_hku1_mhci['Parent_Protein'] == 'Replicase polyprotein 1ab')]
df_nl63_mhci = pd.read_csv('iedb_epitopes/nl63_mhci.csv')
df_nl63_mhci.drop_duplicates(subset=['Description'], keep=False, inplace=True)
df_nl63_mhci = df_nl63_mhci.loc[(df_nl63_mhci['Parent_Protein'] == 'Spike glycoprotein') | (df_nl63_mhci['Parent_Protein'] == 'Nucleoprotein')
| (df_nl63_mhci['Parent_Protein'] == 'Replicase polyprotein 1ab')]
df_229e_mhci = pd.read_csv('iedb_epitopes/229e_mhci.csv')
df_229e_mhci.drop_duplicates(subset=['Description'], keep=False, inplace=True)
df_229e_mhci = df_229e_mhci.loc[(df_229e_mhci['Parent_Protein'] == 'Spike glycoprotein') | (df_229e_mhci['Parent_Protein'] == 'Nucleoprotein')
| (df_229e_mhci['Parent_Protein'] == 'Replicase polyprotein 1ab')]
mhci_frames = [df_sarscov2_mhci, df_sarscov_mhci, df_mers_mhci, df_hcovoc43_mhci, df_hku1_mhci, df_nl63_mhci, df_229e_mhci]
mhci_df = pd.concat(mhci_frames)
iedb_mhci_epitopes = mhci_df['Description'].tolist()
my_mhci_epitopes = list(filter (lambda epitope: len (epitope) > 6, iedb_mhci_epitopes))
mhci_epitopes = [ x for x in my_mhci_epitopes if "X" not in x ]
#print(mhci_epitopes)
df_sarscov2_mhcii = pd.read_csv('iedb_epitopes/sarscov2_mhcii.csv')
df_sarscov2_mhcii.drop_duplicates(subset=['Description'], keep=False, inplace=True)
df_sarscov2_mhcii = df_sarscov2_mhcii.loc[(df_sarscov2_mhcii['Parent_Protein'] == 'Spike glycoprotein') | (df_sarscov2_mhcii['Parent_Protein'] == 'Nucleoprotein')
| (df_sarscov2_mhcii['Parent_Protein'] == 'Replicase polyprotein 1ab') | (df_sarscov2_mhcii['Parent_Protein'] == 'Membrane protein')
| (df_sarscov2_mhcii['Parent_Protein'] == 'ORF3a protein')]
df_sarscov_mhcii = pd.read_csv('iedb_epitopes/sarscov_mhcii.csv')
df_sarscov_mhcii.drop_duplicates(subset=['Description'], keep=False, inplace=True)
df_sarscov_mhcii = df_sarscov_mhcii.loc[(df_sarscov_mhcii['Parent_Protein'] == 'Spike glycoprotein') | (df_sarscov_mhcii['Parent_Protein'] == 'Nucleoprotein')
| (df_sarscov_mhcii['Parent_Protein'] == 'Replicase polyprotein 1ab') | (df_sarscov_mhcii['Parent_Protein'] == 'Replicase polyprotein 1a')
| (df_sarscov_mhcii['Parent_Protein'] == 'ORF3a protein')]
df_mers_mhcii = pd.read_csv('iedb_epitopes/mers_mhcii.csv')
df_mers_mhcii.drop_duplicates(subset=['Description'], keep=False, inplace=True)
df_mers_mhcii = df_mers_mhcii.loc[(df_mers_mhcii['Parent_Protein'] == 'Spike glycoprotein') | (df_mers_mhcii['Parent_Protein'] == 'Nucleoprotein')
| (df_mers_mhcii['Parent_Protein'] == 'Replicase polyprotein 1ab')]
df_hcovoc43_mhcii = pd.read_csv('iedb_epitopes/hcovoc43_mhcii.csv')
df_hcovoc43_mhcii.drop_duplicates(subset=['Description'], keep=False, inplace=True)
df_hcovoc43_mhcii = df_hcovoc43_mhcii.loc[(df_hcovoc43_mhcii['Parent_Protein'] == 'Spike glycoprotein') | (df_hcovoc43_mhcii['Parent_Protein'] == 'Nucleoprotein')
| (df_hcovoc43_mhcii['Parent_Protein'] == 'Replicase polyprotein 1ab')]
df_hku1_mhcii = pd.read_csv('iedb_epitopes/hku1_mhcii.csv')
df_hku1_mhcii.drop_duplicates(subset=['Description'], keep=False, inplace=True)
df_hku1_mhcii = df_hku1_mhcii.loc[(df_hku1_mhcii['Parent_Protein'] == 'Spike glycoprotein') | (df_hku1_mhcii['Parent_Protein'] == 'Nucleoprotein')
| (df_hku1_mhcii['Parent_Protein'] == 'Replicase polyprotein 1ab')]
df_nl63_mhcii = pd.read_csv('iedb_epitopes/nl63_mhcii.csv')
df_nl63_mhcii.drop_duplicates(subset=['Description'], keep=False, inplace=True)
df_nl63_mhcii = df_nl63_mhcii.loc[(df_nl63_mhcii['Parent_Protein'] == 'Spike glycoprotein') | (df_nl63_mhcii['Parent_Protein'] == 'Nucleoprotein')
| (df_nl63_mhcii['Parent_Protein'] == 'Replicase polyprotein 1ab')]
df_229e_mhcii = pd.read_csv('iedb_epitopes/229e_mhcii.csv')
df_229e_mhcii.drop_duplicates(subset=['Description'], keep=False, inplace=True)
df_229e_mhcii = df_229e_mhcii.loc[(df_229e_mhcii['Parent_Protein'] == 'Spike glycoprotein') | (df_229e_mhcii['Parent_Protein'] == 'Nucleoprotein')
| (df_229e_mhcii['Parent_Protein'] == 'Replicase polyprotein 1ab')]
mhcii_frames = [df_sarscov2_mhcii, df_sarscov_mhcii, df_mers_mhcii, df_hcovoc43_mhcii, df_hku1_mhcii, df_nl63_mhcii, df_229e_mhcii]
mhcii_df = pd.concat(mhcii_frames)
iedb_mhcii_epitopes = mhcii_df['Description'].tolist()
my_mhcii_epitopes = list(filter (lambda epitope: len (epitope) > 6, iedb_mhcii_epitopes))
mhcii_epitopes = [ x for x in my_mhcii_epitopes if "X" not in x ]
#print(len(mhcii_epitopes))
df_sarscov2_linear = pd.read_csv('iedb_epitopes/sarscov2_linear.csv')
df_sarscov2_linear.drop_duplicates(subset=['Description'], keep=False, inplace=True)
df_sarscov2_linear = df_sarscov2_linear.loc[(df_sarscov2_linear['Parent_Protein'] == 'Spike glycoprotein') | (df_sarscov2_linear['Parent_Protein'] == 'Nucleoprotein')
| (df_sarscov2_linear['Parent_Protein'] == 'Replicase polyprotein 1ab') | (df_sarscov2_linear['Parent_Protein'] == 'Membrane protein')
| (df_sarscov2_linear['Parent_Protein'] == 'ORF3a protein')]
df_sarscov_linear = pd.read_csv('iedb_epitopes/sarscov_linear.csv')
df_sarscov_linear.drop_duplicates(subset=['Description'], keep=False, inplace=True)
df_sarscov_linear = df_sarscov_linear.loc[(df_sarscov_linear['Parent_Protein'] == 'Spike glycoprotein') | (df_sarscov_linear['Parent_Protein'] == 'Nucleoprotein')
| (df_sarscov_linear['Parent_Protein'] == 'Replicase polyprotein 1ab') | (df_sarscov_linear['Parent_Protein'] == 'Replicase polyprotein 1a')
| (df_sarscov_linear['Parent_Protein'] == 'ORF3a protein')]
df_mers_linear = pd.read_csv('iedb_epitopes/mers_linear.csv')
df_mers_linear.drop_duplicates(subset=['Description'], keep=False, inplace=True)
df_mers_linear = df_mers_linear.loc[(df_mers_linear['Parent_Protein'] == 'Spike glycoprotein') | (df_mers_linear['Parent_Protein'] == 'Nucleoprotein')
| (df_mers_linear['Parent_Protein'] == 'Replicase polyprotein 1ab')]
df_hcovoc43_linear = pd.read_csv('iedb_epitopes/hcovoc43_linear.csv')
df_hcovoc43_linear.drop_duplicates(subset=['Description'], keep=False, inplace=True)
df_hcovoc43_linear = df_hcovoc43_linear.loc[(df_hcovoc43_linear['Parent_Protein'] == 'Spike glycoprotein') | (df_hcovoc43_linear['Parent_Protein'] == 'Nucleoprotein')
| (df_hcovoc43_linear['Parent_Protein'] == 'Replicase polyprotein 1ab')]
df_hku1_linear = pd.read_csv('iedb_epitopes/hku1_linear.csv')
df_hku1_linear.drop_duplicates(subset=['Description'], keep=False, inplace=True)
df_hku1_linear = df_hku1_linear.loc[(df_hku1_linear['Parent_Protein'] == 'Spike glycoprotein') | (df_hku1_linear['Parent_Protein'] == 'Nucleoprotein')
| (df_hku1_linear['Parent_Protein'] == 'Replicase polyprotein 1ab')]
df_nl63_linear = pd.read_csv('iedb_epitopes/nl63_linear.csv')
df_nl63_linear.drop_duplicates(subset=['Description'], keep=False, inplace=True)
df_nl63_linear = df_nl63_linear.loc[(df_nl63_linear['Parent_Protein'] == 'Spike glycoprotein') | (df_nl63_linear['Parent_Protein'] == 'Nucleoprotein')
| (df_nl63_linear['Parent_Protein'] == 'Replicase polyprotein 1ab')]
df_229e_linear = pd.read_csv('iedb_epitopes/229e_linear.csv')
df_229e_linear.drop_duplicates(subset=['Description'], keep=False, inplace=True)
df_229e_linear = df_229e_linear.loc[(df_229e_linear['Parent_Protein'] == 'Spike glycoprotein') | (df_229e_linear['Parent_Protein'] == 'Nucleoprotein')
| (df_229e_linear['Parent_Protein'] == 'Replicase polyprotein 1ab')]
linear_frames = [df_sarscov2_linear, df_sarscov_linear, df_mers_linear, df_hcovoc43_linear, df_hku1_linear, df_nl63_linear, df_229e_linear]
linear_df = pd.concat(linear_frames)
iedb_linear_epitopes = linear_df['Description'].tolist()
my_linear_epitopes = list(filter (lambda epitope: len (epitope) > 6, iedb_linear_epitopes))
linear_epitopes = [ x for x in my_linear_epitopes if "X" not in x ]
# df = df.loc[(df['Parent_Protein'] == 'Spike glycoprotein')]
# epitopes = df_sarscov2_mhci['Description'].tolist()
# print(epitopes)
# print(df['Antigen_Name'])
# df.drop_duplicates(subset=['peptide'], keep=False, inplace=True)
# mhci_prediction_list = [df.columns.tolist()] + df.values.tolist()
# #protein_ids = df['protein_id'].tolist()
# #print(protein_ids)
# df = pd.read_csv('epitope_prediction_results/mhci_proc_epitopes.csv')
# df.drop_duplicates(subset=['peptide'], keep=False, inplace=True)
# mhci_proc_prediction_list = [df.columns.tolist()] + df.values.tolist()
# df = pd.read_csv('epitope_prediction_results/mhcii_epitopes.csv')
# df.drop_duplicates(subset=['peptide'], keep=False, inplace=True)
# mhcii_prediction_list = [df.columns.tolist()] + df.values.tolist()
# df = pd.read_csv('epitope_prediction_results/bepipred2.0_epitopes.csv')
# df.drop_duplicates(subset=['predicted_epitope'], keep=False, inplace=True)
# bepipred2_prediction_list = [df.columns.tolist()] + df.values.tolist()
# df = pd.read_csv('epitope_prediction_results/bepipred1.0_epitopes.csv')
# df.drop_duplicates(subset=['predicted_epitope'], keep=False, inplace=True)
# bepipred_prediction_list = [df.columns.tolist()] + df.values.tolist()
# df = pd.read_csv('epitope_prediction_results/emini_epitopes.csv')
# df.drop_duplicates(subset=['predicted_epitope'], keep=False, inplace=True)
# emini_prediction_list = [df.columns.tolist()] + df.values.tolist()
# df = pd.read_csv('epitope_prediction_results/choufasman_epitopes.csv')
# df.drop_duplicates(subset=['predicted_epitope'], keep=False, inplace=True)
# choufasman_prediction_list = [df.columns.tolist()] + df.values.tolist()
# df = pd.read_csv('epitope_prediction_results/karplusschulz_epitopes.csv')
# df.drop_duplicates(subset=['predicted_epitope'], keep=False, inplace=True)
# karplusschulz_prediction_list = [df.columns.tolist()] + df.values.tolist()
# df = pd.read_csv('epitope_prediction_results/kolaskartongaonkar_epitopes.csv')
# df.drop_duplicates(subset=['predicted_epitope'], keep=False, inplace=True)
# kolaskartongaonkar_prediction_list = [df.columns.tolist()] + df.values.tolist()
# df = pd.read_csv('epitope_prediction_results/parker_epitopes.csv')
# df.drop_duplicates(subset=['predicted_epitope'], keep=False, inplace=True)
# parker_prediction_list = [df.columns.tolist()] + df.values.tolist()
# df = pd.read_csv('epitope_prediction_results/ellipro_linear_epitopes.csv')
# df.drop_duplicates(subset=['peptide'], keep=False, inplace=True)
# ellipro_linear_prediction_list = [df.columns.tolist()] + df.values.tolist()
# df = pd.read_csv('epitope_prediction_results/ellipro_discontinous_epitopes.csv')
# df.drop_duplicates(subset=['peptide'], keep=False, inplace=True)
# ellipro_discontinous_prediction_list = [df.columns.tolist()] + df.values.tolist()
# #print(ellipro_discontinous_prediction_list)
# df = pd.read_csv('epitope_prediction_results/discotope_epitopes.csv')
# df.drop_duplicates(subset=['peptide'], keep=False, inplace=True)
# discotope_prediction_list = [df.columns.tolist()] + df.values.tolist()
# #print(discotope_prediction_list)
# return mhci_prediction_list, mhci_proc_prediction_list, mhcii_prediction_list, bepipred2_prediction_list, \
# bepipred_prediction_list, emini_prediction_list, choufasman_prediction_list, karplusschulz_prediction_list, \
# kolaskartongaonkar_prediction_list, parker_prediction_list, ellipro_linear_prediction_list, ellipro_discontinous_prediction_list, \
# discotope_prediction_list
return mhci_epitopes, mhcii_epitopes, linear_epitopes
def make_inputs_for_analysis():
"""This function will get the prediction results and swissprot ids and make the inputs needed for all of the analysis tools and
return them"""
# pop_cov_input = ''
# for i in range(len(results_from_prediction)):
# if i == 0 or i == 1:
# for x in results_from_prediction[i]:
# peptide = x[7]
# allele = x[2]
# if peptide != 'peptide' or allele != 'allele':
# pop_cov_input = pop_cov_input + peptide + '\t' + allele + '\n'
# x.remove(x[7])
# x.insert(0, peptide)
# elif i == 2:
# for x in results_from_prediction[i]:
# peptide = x[8]
# allele = x[2]
# if peptide != 'peptide' or allele != 'allele':
# pop_cov_input = pop_cov_input + peptide + '\t' + allele + '\n'
# x.remove(x[8])
# x.insert(0, peptide)
# elif i == 3 or i == 4 or i == 5 or i == 6 or i == 7 or i == 8 or i == 9:
# for x in results_from_prediction[i]:
# peptide = x[2]
# x.remove(x[2])
# x.insert(0, peptide)
# elif i == 10:
# for x in results_from_prediction[i]:
# peptide = x[4]
# x.remove(x[4])
# x.insert(0, peptide)
# elif i == 11 or i == 12:
# for x in results_from_prediction[i]:
# peptide = x[2]
# x.remove(x[2])
# x.insert(0, peptide)
# list_of_all_linear_epitopes = []
# immunogenicity_input = ''
# immunogenicity_indexes = len(results_from_prediction[0][1:]) + len(results_from_prediction[1][1:])
# for i in range(len(results_from_prediction)-2):
# for x in results_from_prediction[i][1:]:
# list_of_all_linear_epitopes.append(x[0])
# if i == 0 or i == 1:
# for x in results_from_prediction[i][1:]:
# immunogenicity_input = immunogenicity_input + x[0] + '\n'
# list_of_all_nonlinear_epitopes = []
# for index, value in enumerate(results_from_prediction[-2]):
# if index > 0:
# list_of_aa_with_pos = value[0].split(',')
# list_of_aa_without_pos = []
# for i in list_of_aa_with_pos:
# new_aa = i[0]
# list_of_aa_without_pos.append(new_aa)
# nonlinear_epitope = ''.join(list_of_aa_without_pos)
# list_of_all_nonlinear_epitopes.append(nonlinear_epitope)
# for index, value in enumerate(results_from_prediction[-1]):
# if index > 0:
# list_of_aa_with_pos = value[0].split(',')
# list_of_aa_without_pos = []
# for i in list_of_aa_with_pos:
# new_aa = i[0]
# list_of_aa_without_pos.append(new_aa)
# nonlinear_epitope = ''.join(list_of_aa_without_pos)
# list_of_all_nonlinear_epitopes.append(nonlinear_epitope)
# print(list_of_all_nonlinear_epitopes)
# big_list_of_all_epitopes = prediction_results[0] + prediction_results[1] + prediction_results[2]
# list_of_all_epitopes = random.sample(big_list_of_all_epitopes, 1000)
#print(all(isinstance(item, str) for item in list_of_all_epitopes))
with open('epitopes.csv', newline='') as f:
reader = csv.reader(f)
data = list(reader)
list_of_all_epitopes = data[8]
#print(list_of_all_epitopes)
input_string = ''
for i in range(len(list_of_all_epitopes)):
input_string = input_string + '>seq' + str(i+1) + '\n' + list_of_all_epitopes[i] + '\n'
toxinpred_chunks = []
toxinpred_epitopes = []
toxinpred_excluded_indexes = []
for i in range(len(list_of_all_epitopes)):
if len(list_of_all_epitopes[i]) <= 50:
toxinpred_epitopes.append([list_of_all_epitopes[i]])
else:
toxinpred_excluded_indexes.append(i)
for i in range(0, len(toxinpred_epitopes), 400):
toxinpred_400e_chunk = toxinpred_epitopes[i:i + 400]
toxinpred_input = ''
for n in range(len(toxinpred_400e_chunk)):
toxinpred_input = toxinpred_input + '>seq' + str(n + 1) + '\n' + toxinpred_400e_chunk[n][0] + '\n'
if toxinpred_input != '':
toxinpred_chunks.append(toxinpred_input)
def has_numbers(inputString):
return any(char.isdigit() for char in inputString)
algpred_chunks = []
for i in range(0, len(list_of_all_epitopes), 400):
algpred_400e_chunk = list_of_all_epitopes[i:i + 400]
algpred_input = ''
for n in range(len(algpred_400e_chunk)):
#print(algpred_400e_chunk[n])
algpred_input = algpred_input + '>seq' + str(n + 1) + '\n' + algpred_400e_chunk[n] + '\n'
#algpred_input = algpred_input + algpred_400e_chunk[n] + '\n'
if has_numbers(algpred_400e_chunk[n]) == True:
print(algpred_400e_chunk[n])
if algpred_input is not Empty:
algpred_chunks.append(algpred_input)
# all_protein_fasta = ''
# for id in list_of_swissprot_ids:
# baseUrl="http://www.uniprot.org/uniprot/"
# currentUrl=baseUrl+id+".fasta"
# response = requests.post(currentUrl)
# cData=''.join(response.text)
# all_protein_fasta = all_protein_fasta + cData
seq_file = open('pred_mhcii_seq_file.txt', 'a')
seq_file.write(input_string[:-1])
seq_file.close()
# pop_cov_file = open('pop_cov_file.txt', 'a')
# pop_cov_file.write(pop_cov_input[:-1])
# pop_cov_file.close()
# immunogenicity_file = open('immunogenicity_file.txt', 'a')
# immunogenicity_file.write(immunogenicity_input)
# immunogenicity_file.close()
# cluster_file = open('cluster_file.txt', 'a')
# cluster_file.write(input_string)
# cluster_file.close()
# conservancy_file = open('conservancy_seq_file.txt', 'a')
# conservancy_file.write(input_string)
# conservancy_file.close()
# conservancy_file = open('conservancy_protein_file.txt', 'a')
# conservancy_file.write(all_protein_fasta)
# conservancy_file.close()
current_directory = os.getcwd()
final_directory = os.path.join(current_directory, r'pred_mhcii_analysis_results')
if not os.path.exists(final_directory):
os.makedirs(final_directory)
print("Inputs finished")
return list_of_all_epitopes, toxinpred_chunks, toxinpred_excluded_indexes, algpred_chunks
def protparam(list_of_linear_epitopes):
# protparam_file = open('iedb_analysis_results/protparam_analysis.csv', 'a')
# protparam_file.write(input_string[:-1])
# protparam_file.close()
columns_to_add = ['peptide', 'mol_weight', 'isoelectric_point', 'aromaticity','instability_index','helix_2_struc', 'turn_2_struc', 'sheet_2_struc', 'reduces_cys', 'disulfide_bridge',
'hydropathicity', 'charge_at_pH7']
with open('pred_mhcii_analysis_results/protparam_analysis.csv', 'a') as f:
writer = csv.writer(f)
writer.writerow(columns_to_add)
output_list = []
for index, epitope in enumerate(list_of_linear_epitopes):
#print(index, epitope)
row = []
x = ProteinAnalysis(epitope)
mol_weight = x.molecular_weight()
mol_weight = round(mol_weight, 3)
isoel_point = x.isoelectric_point()
isoel_point = round(isoel_point, 3)
aromaticity = x.aromaticity()
aromaticity = round(aromaticity, 3)
insta_index = x.instability_index()
insta_index = round(insta_index, 3)
sec_struc = x.secondary_structure_fraction()
sec_struc = [round(num, 3) for num in sec_struc]
helix_2_struc = sec_struc[0]
turn_2_struc = sec_struc[1]
sheet_2_struc = sec_struc[2]
epsilon_prot = x.molar_extinction_coefficient()
reduCys = epsilon_prot[0]
disulfBridge = epsilon_prot[1]
hydropathicity = x.gravy()
hydropathicity = round(hydropathicity, 3)
#flex_list = [str(round(num, 2)) for num in x.flexibility()]
#flexibility = ': '.join(flex_list)
chpH = x.charge_at_pH(7)
chpH = round(chpH, 3)
#row.extend((mol_weight, isoel_point, aromaticity, insta_index, helix_2_struc, turn_2_struc, sheet_2_struc, reduCys, disulfBridge, hydropathicity, flexibility, chpH))
row.extend((mol_weight, isoel_point, aromaticity, insta_index, helix_2_struc, turn_2_struc, sheet_2_struc, reduCys, disulfBridge, hydropathicity, chpH))
output_list.insert(index, row)
row.insert(0,epitope)
with open('pred_mhcii_analysis_results/protparam_analysis.csv', 'a') as f:
writer = csv.writer(f)
writer.writerow(row)
print("Protparam analysis done")
return output_list
def immunogenicity():
options = Options()
options.headless = True
try:
immunogenicity_mhci_url = 'http://tools.iedb.org/immunogenicity/'
immunogenicity_mhci = webdriver.Firefox(options=options, executable_path = '../ScrapyEpitope/geckodriver')
immunogenicity_mhci.get(immunogenicity_mhci_url)
immunogenicity_mhci.find_element(By.NAME, "sequence_file").send_keys(os.getcwd()+"/immunogenicity_file.txt")
immunogenicity_mhci.find_element(By.NAME, "submit").click()
wait_immunogenicity_mhci = WebDriverWait(immunogenicity_mhci, 1200)
wait_immunogenicity_mhci.until(ec.visibility_of_element_located((By.XPATH, "/html/body/div[3]/table")))
immunogenicity_mhci_results_table = immunogenicity_mhci.find_element(By.XPATH, '/html/body/div[3]/table/tbody').text.splitlines()
immunogenicity_mhci.close()
immunogenicity_mhci_results_table = list(dict.fromkeys(immunogenicity_mhci_results_table))
print("Immunogenicity analysis done")
return immunogenicity_mhci_results_table
except:
print("Immunogenicity analysis failed")
def vaxijen():
columns_to_add = ['antigenicity_score', 'antigen_prediction']
with open('pred_mhcii_analysis_results/vaxijen_analysis.csv', 'a') as f:
writer = csv.writer(f)
writer.writerow(columns_to_add)
options = Options()
options.headless = True
try:
vaxijen_url = 'http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html'
# vaxijen = webdriver.Firefox(executable_path = '../ScrapyEpitope/geckodriver')
vaxijen = webdriver.Firefox(options=options, executable_path = '../ScrapyEpitope/geckodriver')
vaxijen.get(vaxijen_url)
time.sleep(5)
vaxijen.find_element(By.XPATH, "/html/body/div/table/tbody/tr[4]/td[3]/form/table/tbody/tr[1]/td[2]/p/input").send_keys(os.getcwd()+"/pred_mhcii_seq_file.txt")
vaxijen.find_element(By.XPATH, "/html/body/div/table/tbody/tr[4]/td[3]/form/table/tbody/tr[2]/td[2]/p/select/option[2]").click()
vaxijen.find_element(By.XPATH, "/html/body/div/table/tbody/tr[4]/td[3]/form/table/tbody/tr[2]/td[3]/input").send_keys('0.5')
vaxijen.find_element(By.XPATH, "/html/body/div/table/tbody/tr[4]/td[3]/form/table/tbody/tr[3]/td[2]/input[1]").click()
wait_vaxijen = WebDriverWait(vaxijen, 1200)
wait_vaxijen.until(ec.visibility_of_element_located((By.XPATH, "/html/body/div/table/tbody/tr[3]/td[3]/font/b")))
vaxijen_results_body = vaxijen.find_element(By.XPATH, '/html/body/div/table/tbody/tr[4]/td[3]/table/tbody').text.splitlines()
vaxijen.close()
vaxijen_results = []
for value in vaxijen_results_body:
if value.startswith('Overall') == True:
result = []
antigenicity_val = float(re.search(r'[-+]?\d*\.*\d+', value).group())
result.append(antigenicity_val)
if antigenicity_val >= 0.5:
result.append('Probable Antigen')
else:
result.append('Probable Non-Antigen')
vaxijen_results.append(result)
with open('pred_mhcii_analysis_results/vaxijen_analysis.csv', 'a') as f:
writer = csv.writer(f)
writer.writerows(vaxijen_results)
print("Vaxijen antigenicity analysis done")
return vaxijen_results
except:
print("Vaxijen antigenicity analysis failed")
def cluster(list_of_linear_epitopes):
options = Options()
options.headless = True
cluster_analysis_url = 'http://tools.iedb.org/cluster/'
try:
if len(list_of_linear_epitopes) < 3000:
cluster = webdriver.Firefox(options=options, executable_path = '../ScrapyEpitope/geckodriver')
cluster.maximize_window()
cluster.get(cluster_analysis_url)
cluster.find_element(By.NAME, "sequence_file").send_keys(os.getcwd()+"/cluster_file.txt")
cluster.find_element(By.NAME, "submit").click()
wait_cluster = WebDriverWait(cluster, 1200)
wait_cluster.until(ec.visibility_of_element_located((By.XPATH, "/html/body/div[3]/form/table/tbody/tr[2]/th")))
cluster.find_element(By.NAME, "submit").click()
wait_cluster.until(ec.visibility_of_element_located((By.XPATH, "/html/body/div[3]/form/table/tbody")))
cluster.find_element(By.NAME, "submit").click()
wait_cluster.until(ec.visibility_of_element_located((By.XPATH, "/html/body/div[3]/div/div[3]/div[2]")))
cluster_results_table = cluster.find_element(By.XPATH, '/html/body/div[3]/div/div[3]/div[2]/div[1]/table/tbody').text.splitlines()
cluster.close()
cluster_list_of_rows = []
cluster_columns = ['Cluster.Sub-Cluster Number','Peptide Number','Alignment','Position','Description','Peptide']
cluster_list_of_rows.append(cluster_columns)
for i in range(len(cluster_results_table)):
if i == 0:
continue
else:
cluster_row = cluster_results_table[i].split(' ')
if len(cluster_row) == 6:
cluster_list_of_rows.append(cluster_row)
else:
indexes_to_remove = len(cluster_row) - 6
seq = cluster_row.pop(4)
for i in range(indexes_to_remove):
removed_duplicate = cluster_row.pop(4)
seq = seq + removed_duplicate
cluster_row.insert(4, seq)
cluster_list_of_rows.append(cluster_row)
with open('analysis_results/cluster_analysis.csv', 'w') as f:
writer = csv.writer(f)
writer.writerows(cluster_list_of_rows)
print("Cluster analysis done")
else:
print("Too many epitopes for cluster analysis (>3000)")
except:
print("Cluster analysis failed")
def conservancy():
options = Options()
options.headless = True
try:
conservancy_analysis_url = 'http://tools.iedb.org/conservancy/'
conservancy = webdriver.Firefox(options=options, executable_path = '../ScrapyEpitope/geckodriver')
conservancy.maximize_window()
conservancy.get(conservancy_analysis_url)
conservancy.find_element(By.NAME, "epitope_file").send_keys(os.getcwd()+"/conservancy_seq_file.txt")
conservancy.find_element(By.NAME, "protein_file").send_keys(os.getcwd()+"/conservancy_protein_file.txt")
conservancy.find_element(By.NAME, "submit").click()
wait_conservancy = WebDriverWait(conservancy, 6000)
wait_conservancy.until(ec.visibility_of_element_located((By.ID, "result_table")))
conservancy_results_columns = conservancy.find_element(By.XPATH, '/html/body/div[3]/table/thead/tr').text.splitlines()
conservancy_results_table = conservancy.find_element(By.XPATH, '/html/body/div[3]/table/tbody').text.splitlines()
conservancy.close()
conservancy_list_of_rows = []
conservancy_list_of_rows.append(conservancy_results_columns)
for row in conservancy_results_table:
conservancy_row = row.split(' ')
conservancy_list_of_rows.append(conservancy_row)
with open('analysis_results/conservancy_analysis.csv', 'w') as f:
writer = csv.writer(f)
writer.writerows(conservancy_list_of_rows)
print("Conservancy analysis done")
except:
print("Conservancy analysis failed")
def try_population_coverage(counter=10):
"""The following function tries 10 times to make population coverage work, since it can run into errors. It uploads the file, then
selects 'World' option, select both MHC classes and clicks submit. It then waits for the results, saves the graph in a .png file and
returns the results table"""
population_coverage_url = 'http://tools.iedb.org/population/'
if counter == 0:
print("Population coverage analysis failed after 10 tries")
return
try:
population_coverage = webdriver.Firefox(executable_path = '../ScrapyEpitope/geckodriver')
population_coverage.maximize_window()
population_coverage.get(population_coverage_url)
population_coverage.find_element(By.ID, "id_epitope_allele_file").send_keys(os.getcwd()+"/pop_cov_file.txt")
population_coverage.find_element(By.XPATH, "/html/body/div[3]/form/table[2]/tbody/tr[2]/td[1]/select/option[1]").click()
population_coverage.find_element(By.XPATH, "/html/body/div[3]/form/table[2]/tbody/tr[4]/td/input").click()
time.sleep(3)
population_coverage.find_element(By.NAME, "submit").click()
wait_population_coverage = WebDriverWait(population_coverage, 1200)
wait_population_coverage.until(ec.visibility_of_element_located((By.CLASS_NAME, "popcov")))
population_coverage_results_table = population_coverage.find_element(By.XPATH, '/html/body/div[3]/table[1]/tbody').text.splitlines()
if population_coverage_results_table is None:
try_population_coverage(counter-1)
time.sleep(3)
with open('analysis_results/population_coverage_graph.png', 'wb') as file:
file.write(population_coverage.find_element(By.XPATH, '/html/body/div[3]/table[2]/tbody/tr[3]/td/img').screenshot_as_png)
time.sleep(10)
population_coverage.close()
result_in_text = ''
for index, row in enumerate(population_coverage_results_table):
new_row = row.split(' ')
if index == 0:
result_in_text = new_row[1] + ': ' + new_row[2] + '\n' + new_row[0] + '\t'
elif index == 1:
result_in_text = result_in_text + new_row[0][:-1] + '\t' + new_row[1][:-1] + '\t' + new_row[2][:-1] + '\n'
elif index == 4:
std_dev = new_row[0] + '_' + new_row[1]
result_in_text = result_in_text + std_dev + '\t' + new_row[2] + '\t' + new_row[3] + '\t' + new_row[4]
else:
result_in_text = result_in_text + new_row[0] + '\t' + new_row[1] + '\t' + new_row[2] + '\t' + new_row[3] + '\n'
pop_cov_results = open('analysis_results/pop_cov_results.txt', 'a')
pop_cov_results.write(result_in_text)
pop_cov_results.close()
print("Population coverage analysis done")
except:
try:
population_coverage.close()
except:
pass
print("Retrying population coverage ...")
try_population_coverage(counter-1)
return
def algpred(algpred_chunks):
columns_to_add = ['peptide', 'allergen_prediction']
with open('analysis_iedb_results/algpred_analysis.csv', 'a') as f:
writer = csv.writer(f)
writer.writerow(columns_to_add)
def algpred_try_until_it_works(chunk_of_400, counter = 5):
"""The following function tries 10 times to make algpred2 work, since it can run into errors. It uploads the sequences in chunks of 400
and clicks submit. It then waits for the results and saves them in their respective order. After all sequences are analysed it returns a
list of lists with all the results"""
if counter == 0:
print("Algpred failed after 10 tries")
return
algpred_seq_file = open('algpred_iedb_seq_file.txt', 'a')
print(chunk_of_400)
algpred_seq_file.write(chunk_of_400)
algpred_seq_file.close()
options = Options()
options.headless = True
algpred2_url = 'https://webs.iiitd.edu.in/raghava/algpred2/batch.html'
try:
algpred2 = webdriver.Firefox(options=options, executable_path = '../ScrapyEpitope/geckodriver')
algpred2.get(algpred2_url)
algpred2.find_element(By.XPATH, "/html/body/header/div[3]/section/form/table/tbody/tr/td/font/p/font[2]/input").send_keys(os.getcwd()+"/algpred_iedb_seq_file.txt")
algpred2.find_element(By.XPATH, "/html/body/header/div[3]/section/form/table/tbody/tr/td/font/font/p[3]/font/font[2]/input[2]").click()
wait_algpred2 = WebDriverWait(algpred2, 1200)
wait_algpred2.until(ec.visibility_of_element_located((By.CLASS_NAME, "scrollable")))
algpred2_results_table = algpred2.find_element(By.XPATH, '/html/body/header/div[3]/main/div/table[2]/tbody').text.splitlines()
algpred2.close()
os.remove(os.getcwd()+"/algpred_iedb_seq_file.txt")
input_for_results = []
for index, row in enumerate(algpred2_results_table):
algpred2_result_row = row.split(' ')
seq_nr = int(algpred2_result_row[0][3:])
if index != len(algpred2_results_table) - 1:
algpred2_result_next_row = algpred2_results_table[index+1].split(' ')
seq_nr_next = int(algpred2_result_next_row[0][3:])
if seq_nr != seq_nr_next:
input_for_results.append(algpred2_result_row[-1])
else:
continue
else:
input_for_results.append(algpred2_result_row[-1])
return input_for_results
except:
try:
algpred2.close()
except:
pass
os.remove(os.getcwd()+"/algpred_iedb_seq_file.txt")
print("Retrying algpred ...")
algpred_try_until_it_works(chunk_of_400, counter-1)
return
algpred_all_results = []
for seq_of_400_epitopes in algpred_chunks:
algpred_results = algpred_try_until_it_works(seq_of_400_epitopes)
if algpred_results is not None:
for parameter in algpred_results:
algpred_all_results.append(parameter)
with open('analysis_iedb_results/algpred_analysis.csv', 'a') as f:
writer = csv.writer(f)
writer.writerows(parameter)
elif algpred_results is None:
for empty_index in range(400):
algpred_all_results.append(None)
algpred_write_to_file = []
#algpred_write_to_file.append(seq_of_400_epitopes[empty_index])
algpred_write_to_file.append(None)
with open('analysis_iedb_results/algpred_analysis.csv', 'a') as f:
writer = csv.writer(f)
writer.writerow(algpred_write_to_file)
print("Algpred2 allergenicity analysis done")
return algpred_all_results
def toxinpred(toxinpred_chunks, toxinpred_excluded_indexes):
columns_to_add = ['peptide', 'toxinpred_svm_score', 'toxicity_prediction', 'hydrophobicity', 'steric_hinderance', 'sidebulk', 'amphipathicity', 'hydrophilicity', 'net_hydrogen']
with open('analysis_iedb_results/toxinpred_analysis.csv', 'a') as f:
writer = csv.writer(f)
writer.writerow(columns_to_add)
def toxinpred_try_until_it_works(chunk_of_400, counter=10):
"""The following function tries 10 times to make toxinpred work, since it can run into errors. It uploads the sequences in chunks of 400
and clicks submit. It then waits for the results and saves them in their respective order. After all sequences are analysed it returns a
list of lists with all the results"""
if counter == 0:
print("Toxinpred failed after 10 tries")
return
def check_400(other_counter=10):
if other_counter == 0:
return
try:
toxinpred.find_element(By.XPATH, "/html/body/div[2]/table/tfoot/tr/td/select").click()
toxinpred.find_element(By.XPATH, "/html/body/div[2]/table/tfoot/tr/td/select/option[8]").click()
except:
toxinpred.refresh()
check_400(other_counter-1)
return
else:
toxinpred_results_table = toxinpred.find_element(By.XPATH, '/html/body/div[2]/table/tbody').text.splitlines()
toxinpred.close()
return toxinpred_results_table
options = Options()
options.headless = True
toxinpred_url = 'https://webs.iiitd.edu.in/raghava/toxinpred/multi_submit.php'
try:
toxinpred = webdriver.Firefox(executable_path = '../ScrapyEpitope/geckodriver')
wait_toxinpred = WebDriverWait(toxinpred, 1200)
toxinpred.get(toxinpred_url)
toxinpred.find_element(By.XPATH, "/html/body/table[2]/tbody/tr/td/form/fieldset/table[1]/tbody/tr[2]/td/textarea").send_keys(chunk_of_400)
time.sleep(5)
toxinpred.find_element(By.NAME, "checkAll").click()
toxinpred.find_element(By.XPATH, "/html/body/table[2]/tbody/tr/td/form/fieldset/table[2]/tbody/tr[3]/td/input[2]").click()
wait_toxinpred.until(ec.visibility_of_element_located((By.ID, "tableTwo")))
time.sleep(5)
table_of_epitopes = check_400()
return table_of_epitopes
except:
try:
toxinpred.close()
except:
pass
print("Retrying toxinpred...")
toxinpred_try_until_it_works(chunk_of_400, counter-1)
return
# Gets toxinpred results and stores them in analysis_results:
toxinpred_results = []
for index, seq_of_400_epitopes in enumerate(toxinpred_chunks):
toxinpred_results_table = toxinpred_try_until_it_works(seq_of_400_epitopes)
if toxinpred_results_table is not None:
for results_index, row in enumerate(toxinpred_results_table):
toxinpred_result_row = row.split(' ')
toxinpred_row_included_indexes = [2,3,4,5,6,8,9,10]
toxinpred_new_result_row = [toxinpred_result_row[x] for x in toxinpred_row_included_indexes]
toxinpred_results.append(toxinpred_new_result_row)
toxinpred_write_to_file = []
for value in range(len(toxinpred_result_row)):
toxinpred_write_to_file.append(value)
with open('analysis_iedb_results/toxinpred_analysis.csv', 'a') as f:
writer = csv.writer(f)
writer.writerow(toxinpred_write_to_file)
# for parameter in toxinpred_new_result_row:
# toxinpred_results[400*index + results_index].append(parameter)
elif toxinpred_results_table is None:
empty_list = [None] * 8
for i in range(400):
#toxinpred_results[400*index + empty_index].append(empty_list)
toxinpred_results.append(empty_list)
toxinpred_write_to_file = []
for value in range(len(empty_list)):
toxinpred_write_to_file.append(value)
with open('analysis_iedb_results/toxinpred_analysis.csv', 'a') as f:
writer = csv.writer(f)
writer.writerow(toxinpred_write_to_file)
print("Toxinpred toxicity analysis done")
none_list = [None] * 8
for index in toxinpred_excluded_indexes:
toxinpred_results.insert(index, none_list)
return toxinpred_results
def expasy_and_solubility(list_of_epitopes, linear = 'Yes'):
expasy_results = []
protein_sol_results = []
columns_to_add = ['peptide', '(-)_charged_residues (asp+glu)','(+)_charged_residues (arg+lys)', 'half_life_hours (mammalian reticulocytes, in vitro)',
'aliphatic_index']
with open('pred_mhcii_analysis_results/expasy_analysis.csv', 'a') as f:
writer = csv.writer(f)
writer.writerow(columns_to_add)
aa_composition_results = [['peptide', 'Ala (A)', 'Arg (R)', 'Asn (N)', 'Asp (D)', 'Cys (C)', 'Gln (Q)', 'Glu (E)', 'Gly (G)', 'His (H)', 'Ile (I)', 'Leu (L)'
, 'Lys (K)', 'Met (M)', 'Phe (F)', 'Pro (P)', 'Ser (S)', 'Thr (T)', 'Trp (W)', 'Tyr (Y)', 'Val (V)', 'Pyl (O)', 'Sec (U)']]
atomic_composition_results = [['peptide', 'Carbon (C)', 'Hydrogen (H)', 'Nitrogen (N)', 'Oxygen (O)', 'Sulfur (S)']]
with open('pred_mhcii_analysis_results/iter_aa_composition_analysis.csv', 'a') as f:
writer = csv.writer(f)
writer.writerow(aa_composition_results[0])
with open('pred_mhcii_analysis_results/iter_atomic_composition_analysis.csv', 'a') as f:
writer = csv.writer(f)
writer.writerow(atomic_composition_results[0])
options = Options()
options.headless = True
expasy_url = 'https://web.expasy.org/protparam/'
# protein_sol_url = 'https://protein-sol.manchester.ac.uk/'
for index, seq in enumerate(list_of_epitopes):
try:
print("Analysing sequence with expasy: ", seq)
expasy = webdriver.Firefox(options=options, executable_path = '../ScrapyEpitope/geckodriver')
wait_expasy = WebDriverWait(expasy, 60)
expasy.get(expasy_url)
expasy.find_element(By.XPATH, "/html/body/div[2]/div[2]/form/textarea").send_keys(seq)
expasy.find_element(By.XPATH, "/html/body/div[2]/div[2]/form/p[1]/input[2]").click()
# if len(seq) >= 21:
# protein_sol = webdriver.Firefox(options=options, executable_path = '../ScrapyEpitope/geckodriver')
# protein_sol.get(protein_sol_url)
# protein_sol.find_element(By.NAME, "sequence-input").send_keys(seq)
# protein_sol.find_element(By.NAME, "singleprediction").click()
# time.sleep(3)
# protein_sol_result = protein_sol.find_element(By.XPATH, '/html/body/div[3]/div/div[1]/p[2]').text
# protein_sol_results.append(protein_sol_result)
# protein_sol.close()
# else:
# protein_sol_results.append(None)
wait_expasy.until(ec.visibility_of_element_located((By.XPATH, "/html/body/div[2]/div[2]/pre[2]/form/input[1]")))
expasy_text = expasy.find_element(By.XPATH, '/html/body/div[2]/div[2]/pre[2]').text.splitlines()
expasy.close()
aa_composition = expasy_text[6:28]
neg_residues = expasy_text[32]
pos_residues = expasy_text[33]
atomic_composition = expasy_text[37:42]
half_life = expasy_text[56:64]
aliphatic_index = expasy_text[-3]
if len(expasy_text) == 75:
half_life = expasy_text[56:64]
elif len(expasy_text) == 71:
half_life = expasy_text[52:60]
elif len(expasy_text) == 72:
half_life = expasy_text[53:61]
elif len(expasy_text) == 79:
half_life = expasy_text[60:68]
elif len(expasy_text) == 76:
half_life = expasy_text[57:65]
aa_row = [seq]
for i in range(len(aa_composition)):
cell = aa_composition[i].split()
aa_row.append(cell[2])
aa_composition_results.append(aa_row)
with open('pred_mhcii_analysis_results/iter_aa_composition_analysis.csv', 'a') as f:
writer = csv.writer(f)
writer.writerow(aa_row)
atomic_row = [seq]
for i in range(len(atomic_composition)):
cell = atomic_composition[i].split()
atomic_row.append(cell[2])
atomic_composition_results.append(atomic_row)
with open('pred_mhcii_analysis_results/iter_atomic_composition_analysis.csv', 'a') as f:
writer = csv.writer(f)
writer.writerow(atomic_row)
expasy_row = []
expasy_row.append(neg_residues[-1])
expasy_row.append(pos_residues[-1])
expasy_row.append(half_life[4].split()[4])
expasy_row.append(float(aliphatic_index.split()[2]))
expasy_results.append(expasy_row)
expasy_row.insert(0, seq)
with open('pred_mhcii_analysis_results/expasy_analysis.csv', 'a') as f:
writer = csv.writer(f)
writer.writerow(expasy_row)
except:
print(seq + " expasy failed")
none_list = [None] * 4
expasy_results.append(none_list)
none_atomic_row = [seq]
for i in range(6):
none_atomic_row.append(None)
atomic_composition_results.append(none_atomic_row)
none_aa_row = [seq]
for i in range(23):
none_aa_row.append(None)
aa_composition_results.append(none_aa_row)
continue
# print("Expasy analysis done")
# print("Protein solubility analysis done")
if linear == 'Yes':
print("Expasy analysis on linear epitopes done")
with open('pred_mhcii_analysis_results/aa_composition.csv', 'w') as f:
writer = csv.writer(f)
writer.writerows(aa_composition_results)
with open('pred_mhcii_analysis_results/atomic_composition.csv', 'w') as f:
writer = csv.writer(f)
writer.writerows(atomic_composition_results)
elif linear == 'No':
print("Expasy analysis on nonlinear epitopes done")
with open('pred_mhcii_analysis_results/nonlinear_aa_composition.csv', 'w') as f:
writer = csv.writer(f)
writer.writerows(aa_composition_results)
with open('pred_mhcii_analysis_results/nonlinear_atomic_composition.csv', 'w') as f:
writer = csv.writer(f)
writer.writerows(atomic_composition_results)
return expasy_results, protein_sol_results
def pepstats(list_of_sequences):
columns_to_add = ['peptide', 'tiny_aa_percentage', 'small_aa_percentage', 'aliphatic_aa_percentage', 'aromatic_aa_percentage', 'non_polar_aa_percentage', 'polar_aa_percentage',
'charged_aa_percentage', 'basic_aa_percentage', 'acidic_aa_percentage']
with open('pred_mhcii_analysis_results/pepstats_analysis.csv', 'a') as f:
writer = csv.writer(f)
writer.writerow(columns_to_add)
pepstats_results = []