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prediction.py
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prediction.py
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from tkinter import E
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
import time
import os
import pandas as pd
import requests
import json
import csv
import re
from collections import defaultdict
import itertools
import matplotlib.pyplot as plt
def get_pdb_from_swissprot(list_of_swissprot_ids):
"""This function gets a PDB ID for each of the Swissprot IDs by looking for EM and X-Ray data on the Swissprot page of the ID and gets only
PDB IDs that start with 1 or with 10 to 19. Ex: 1-2000 or 13-2000. An ID with 21-2000 is not accepted. The function outputs a list of the PDB
IDs."""
options = Options()
options.headless = True
pdb_ids = []
for id in list_of_swissprot_ids:
try:
uniprot_url = 'https://www.uniprot.org/uniprot/' + id
uniprot = webdriver.Firefox(options=options, executable_path = '../ScrapyEpitope/geckodriver')
uniprot.get(uniprot_url)
wait_uniprot = WebDriverWait(uniprot, 60)
wait_uniprot.until(ec.visibility_of_element_located((By.XPATH, '//section[@id="structure"]/div/div[2]/protvista-uniprot-structure/div/div[2]/protvista-datatable/table/tbody')))
uniprot_pdb_ids = uniprot.find_element(By.XPATH, '//section[@id="structure"]/div/div[2]/protvista-uniprot-structure/div/div[2]/protvista-datatable/table/tbody').text.splitlines()
wait_uniprot.until(ec.visibility_of_element_located((By.XPATH, '//section[@id="sequence-container"]/ul/li[1]/div/div[2]')))
for row in uniprot_pdb_ids:
newrow = row.split()
if (newrow[2] == 'EM' or newrow[2] == 'X-ray') and (newrow[6].startswith('1-') == True):
pdb_ids.append(newrow[1])
print("The following PDB ID was found for " + id + ": " + newrow[1])
break
else:
if (newrow[2] == 'EM' or newrow[2] == 'X-ray') and re.search("^1.-", newrow[6]):
print("The following PDB ID was found for " + id + ": " + newrow[1])
pdb_ids.append(newrow[1])
uniprot.close()
except:
print("No PDB format for: " + id)
uniprot.close()
return pdb_ids
def getStartEndPositions(pattern, seq):
all_instances = [m.start() for m in re.finditer(pattern, seq)]
if len(all_instances) > 1:
raise Exception("Too many found")
elif len(all_instances) == 0:
return None
return (all_instances[0], all_instances[0] + len(pattern))
def swissprotIDSequenceLength(list_of_swissprot_ids):
"""This function outputs a dictionary with the sequences and length of the sequence (values) of Swissprot ID (key)"""
protein_dict = {}
for id in list_of_swissprot_ids:
baseUrl="http://www.uniprot.org/uniprot/"
currentUrl=baseUrl+id+".fasta"
response = requests.post(currentUrl)
response_lines = response.text.split("\n")[1:]
cData=''.join(response_lines)
protein_dict[id] = (cData, len(cData))
return protein_dict
def mhci(conserved_sequences_dict, list_of_alleles, list_of_lengths):
"""This function uses the REST API from IEDB to access to MHC I Binding tool and predict the MHC Class I epitopes of the given conserved sequences.
It also needs a list of alleles and their respective lengths to run. It employs different methods to predict MHC Class I epitopes, including a
consensus approach which combines ANN, SMM, Comblib, NetMHCpan and Consensus. The results are returned as a list of lists"""
#alleles = ",".join(list_of_alleles)
converted_list = [str(element) for element in list_of_lengths]
lengths = ",".join(converted_list)
mhci_results = []
columns = ["protein_id"] + ["conserved_sequence"] + ["allele"] + ["seq_num"] + ["start"] + ["end"] + ["length"] + \
["peptide"] + ["core"] + ["icore"] + ["score"] + ["percentile_rank"]
mhci_results.append(columns)
#print(conserved_sequences_dict)
for key, value in conserved_sequences_dict.items():
print("Predicting linear epitopes of protein " + key + " with MHCI")
for conserved_sequence in value:
if len(conserved_sequence) <= 4000:
for allele in list_of_alleles:
time.sleep(10)
print(key, allele)
input_allele = []
for i in range(len(list_of_lengths)):
input_allele.append(allele)
alleles = ",".join(input_allele)
if len(conserved_sequence) >= max(list_of_lengths):
try:
headers = {
'Content-Type': 'application/x-www-form-urlencoded',
}
data = 'method=recommended&sequence_text=' + conserved_sequence + '&allele=' + alleles + '&length=' + lengths
response = requests.post('http://tools-cluster-interface.iedb.org/tools_api/mhci/', headers=headers, data=data)
response_data_split_by_line = response.content.decode('utf-8').splitlines()
response_body = []
for line in response_data_split_by_line:
split_line = line.split("\t")
response_body.append(split_line)
df = pd.DataFrame(response_body[1:], columns=response_body[0])
df["percentile_rank"] = pd.to_numeric(df["percentile_rank"], errors='coerce')
df = df.loc[df['percentile_rank'] <= 1]
if df.empty == True:
#print("No epitopes predicted for protein " + key + " with sequence length " + str(len(conserved_sequence)) + " and allele " + allele)
continue
else:
rows = [[i for i in row[1:]] for row in df.itertuples()]
for i in rows:
i = [key] + [conserved_sequence] + i
mhci_results.append(i)
except:
#print("Retrying prediction of linear epitopes of protein " + key + " and allele " + allele + " with MHCI")
try:
headers = {
'Content-Type': 'application/x-www-form-urlencoded',
}
data = 'method=recommended&sequence_text=' + conserved_sequence + '&allele=' + alleles + '&length=' + lengths
response = requests.post('http://tools-cluster-interface.iedb.org/tools_api/mhci/', headers=headers, data=data)
response_data_split_by_line = response.content.decode('utf-8').splitlines()
response_body = []
for line in response_data_split_by_line:
split_line = line.split("\t")
response_body.append(split_line)
df = pd.DataFrame(response_body[1:], columns=response_body[0])
df["percentile_rank"] = pd.to_numeric(df["percentile_rank"], errors='coerce')
df = df.loc[df['percentile_rank'] <= 1]
if df.empty == True:
#print("No epitopes predicted for protein " + key + " with sequence length " + str(len(conserved_sequence)) + " and allele " + allele)
continue
else:
rows = [[i for i in row[1:]] for row in df.itertuples()]
for i in rows:
i = [key] + [conserved_sequence] + i
mhci_results.append(i)
except:
print("Epitope prediction for protein " + key + " with sequence length " + str(len(conserved_sequence)) + " and allele " + allele + " failed")
continue
else:
print("Protein too long.")
continue
with open('epitope_prediction_results/mhci_epitopes.csv', 'w', newline="") as f:
writer = csv.writer(f)
writer.writerows(mhci_results)
print("MHCI prediction done\n")
def mhci_proc(conserved_sequences_dict, list_of_alleles, list_of_lengths):
"""This function uses the REST API from IEDB to access to MHC I Processing tool and predict the MHC Class I epitopes of the given conserved sequences.
It also needs a list of alleles and their respective lengths to run. It combines predictors of proteasomal processing, TAP transport, and MHC binding
to produce an overall score for each peptide's intrinsic potential of being a T cell epitope. The results are returned as a list of lists"""
#alleles = ",".join(list_of_alleles)
converted_list = [str(element) for element in list_of_lengths]
lengths = ",".join(converted_list)
mhci_proc_results = []
columns = ["protein_id"] + ["conserved_sequence"] + ["allele"] + ['#'] + ["start"] + ["end"] + ['peptide_length'] + \
["peptide"] + ["proteasome_score"] + ["tap_score"] + ["mhci_score"] + ["processing_score"] + ["total_score"] + ["mhci_ic50"]
mhci_proc_results.append(columns)
for key, value in conserved_sequences_dict.items():
print("Predicting linear epitopes of protein " + key + " with MHCI Processing")
for conserved_sequence in value:
if len(conserved_sequence) <= 4000:
for allele in list_of_alleles:
time.sleep(10)
print(key, allele)
input_allele = []
for i in range(len(list_of_lengths)):
input_allele.append(allele)
alleles = ",".join(input_allele)
if len(conserved_sequence) >= max(list_of_lengths):
try:
headers = {
'Content-Type': 'application/x-www-form-urlencoded',
}
data = 'method=recommended&sequence_text=' + conserved_sequence + '&allele=' + alleles + '&length=' + lengths
response = requests.post('http://tools-cluster-interface.iedb.org/tools_api/processing/', headers=headers, data=data)
response_data_split_by_line = response.content.decode('utf-8').splitlines()
response_body = []
for line in response_data_split_by_line:
split_line = line.split("\t")
response_body.append(split_line)
df = pd.DataFrame(response_body[1:], columns=response_body[0])
df["total_score"] = pd.to_numeric(df["total_score"])
df = df.loc[df['total_score'] > 0]
if df.empty == True:
#print("No epitopes predicted for protein " + key + " with sequence length " + str(len(conserved_sequence)) + " and allele " + allele)
continue
else:
rows = [[i for i in row[1:]] for row in df.itertuples()]
for i in rows:
i = [key] + [conserved_sequence] + i
mhci_proc_results.append(i)
except:
#print("Retrying prediction of linear epitopes of protein " + key + " and allele " + allele + " with MHCI")
try:
headers = {
'Content-Type': 'application/x-www-form-urlencoded',
}
data = 'method=recommended&sequence_text=' + conserved_sequence + '&allele=' + alleles + '&length=' + lengths
response = requests.post('http://tools-cluster-interface.iedb.org/tools_api/processing/', headers=headers, data=data)
response_data_split_by_line = response.content.decode('utf-8').splitlines()
response_body = []
for line in response_data_split_by_line:
split_line = line.split("\t")
response_body.append(split_line)
df = pd.DataFrame(response_body[1:], columns=response_body[0])
df["total_score"] = pd.to_numeric(df["total_score"])
df = df.loc[df['total_score'] > 0]
if df.empty == True:
#print("No epitopes predicted for protein " + key + " with sequence length " + str(len(conserved_sequence)) + " and allele " + allele)
continue
else:
rows = [[i for i in row[1:]] for row in df.itertuples()]
for i in rows:
i = [key] + [conserved_sequence] + i
mhci_proc_results.append(i)
except:
print("Epitope prediction for protein " + key + " with sequence length " + str(len(conserved_sequence)) + " and allele " + allele + " failed")
continue
else:
print("Protein too long.")
continue
with open('epitope_prediction_results/mhci_proc_epitopes.csv', 'w', newline="") as f:
writer = csv.writer(f)
writer.writerows(mhci_proc_results)
print("MHCI Processing prediction done\n")
def mhcii(conserved_sequences_dict, list_of_alleles, list_of_lengths):
"""This function uses the REST API from IEDB to access to MHC II Binding tool and predict the MHC Class II epitopes of the given conserved sequences.
It also needs a list of alleles and their respective lengths to run. It employs different methods to predict MHC Class II epitopes, including a consensus
approach which combines NN-align, SMM-align and Combinatorial library methods. The results are returned as a list of lists"""
alleles = ",".join(list_of_alleles)
converted_list = [str(element) for element in list_of_lengths]
lengths = ",".join(converted_list)
mhcii_results = []
columns = ["protein_id"] + ["conserved_sequence"] + ["allele"] + ["seq_num"] + ["start"] + ["end"] + ["length"] + \
["method"] + ["peptide"] + ["percentile_rank"] + ["adjusted_rank"] + \
["comblib_core"] + ['comblib_score'] + ['comblib_rank'] + ["comblib_adjusted_rank"] + \
["smm_align_core"] + ["smm_align_ic50"] + ["smm_align_rank"] + ["smm_align_adjusted_rank"] + \
['nn_align_core'] + ['nn_align_ic50'] + ["nn_align_rank"] + ["nn_align_adjusted_rank"] + \
["netmhciipan_core"] + ["netmhciipan_ic50"] + ["netmhciipan_rank"] + ['netmhciipan_adjusted_rank'] + \
['sturniolo_core'] + ["sturniolo_score"] + ["sturniolo_rank"] + ["sturniolo_adjusted_rank"]
mhcii_results.append(columns)
for key, value in conserved_sequences_dict.items():
print("Predicting linear epitopes of protein " + key + " with MHCII")
time.sleep(10)
for conserved_sequence in value:
if len(conserved_sequence) >= max(list_of_lengths):
try:
headers = {
'Content-Type': 'application/x-www-form-urlencoded',
}
data = 'method=recommended&sequence_text=' + conserved_sequence + '&allele=' + alleles + '&length=' + lengths
response = requests.post('http://tools-cluster-interface.iedb.org/tools_api/mhcii/', headers=headers, data=data)
response_data_split_by_line = response.content.decode('utf-8').splitlines()
response_body = []
for line in response_data_split_by_line:
split_line = line.split("\t")
response_body.append(split_line)
df = pd.DataFrame(response_body[1:], columns=response_body[0])
df["adjusted_rank"] = pd.to_numeric(df["adjusted_rank"])
df = df.loc[df['adjusted_rank'] <= 1]
if df.empty == True:
print("No epitopes predicted for protein " + key + " with sequence length " + str(len(conserved_sequence)))
continue
else:
rows = [[i for i in row[1:]] for row in df.itertuples()]
for i in rows:
i = [key] + [conserved_sequence] + i
mhcii_results.append(i)
except:
print("Retrying prediction of linear epitopes of protein " + key + " with MHCII")
try:
headers = {
'Content-Type': 'application/x-www-form-urlencoded',
}
data = 'method=recommended&sequence_text=' + conserved_sequence + '&allele=' + alleles + '&length=' + lengths
response = requests.post('http://tools-cluster-interface.iedb.org/tools_api/mhcii/', headers=headers, data=data)
response_data_split_by_line = response.content.decode('utf-8').splitlines()
response_body = []
for line in response_data_split_by_line:
split_line = line.split("\t")
response_body.append(split_line)
df = pd.DataFrame(response_body[1:], columns=response_body[0])
df["adjusted_rank"] = pd.to_numeric(df["adjusted_rank"])
df = df.loc[df['adjusted_rank'] <= 1]
if df.empty == True:
print("No epitopes predicted for protein " + key + " with sequence length " + str(len(conserved_sequence)))
continue
else:
rows = [[i for i in row[1:]] for row in df.itertuples()]
for i in rows:
i = [key] + [conserved_sequence] + i
mhcii_results.append(i)
except:
print("Epitope prediction for protein " + key + " and sequence length " + str(len(conserved_sequence)) + " failed")
continue
with open('epitope_prediction_results/mhcii_epitopes.csv', 'w', newline="") as f:
writer = csv.writer(f)
writer.writerows(mhcii_results)
print("MHCII prediction done\n")
def bepipred2(conserved_sequences_dict):
"""This function uses the REST API from IEDB to access to Linear Antigen Prediction method Bepipred 2.0 and predict the linear epitopes of the given conserved
sequences using a Random Forest algorithm trained on epitopes and non-epitope amino acids determined from crystal structures. The results are returned as a
list of lists"""
bepipred2_results = []
columns = ["protein_id"] + ["conserved_sequence"] + ["predicted_epitope"] + ["start_position"] + ["end_position"]
bepipred2_results.append(columns)
for key, sequences in conserved_sequences_dict.items():
print("Predicting linear epitopes of protein " + key + " with Bepipred 2.0")
time.sleep(10)
for conserved_sequence in sequences:
try:
headers = {
'Content-Type': 'application/x-www-form-urlencoded',
}
data = {
'method': 'Bepipred-2.0',
'sequence_text': conserved_sequence,
'window_size': '7',
}
response = requests.post('http://tools-cluster-interface.iedb.org/tools_api/bcell/', headers=headers, data=data)
response_data_split_by_line = response.content.decode('utf-8').splitlines()
response_body = []
for line in response_data_split_by_line:
split_line = line.split("\t")
response_body.append(split_line)
df = pd.DataFrame(response_body[1:], columns=response_body[0])
df["Score"] = pd.to_numeric(df["Score"])
df = df.loc[df['Score'] >= 0.5]
if df.empty == True:
print("No epitopes predicted for protein " + key + " with sequence length " + str(len(conserved_sequence)))
continue
else:
positions = df.iloc[:, 0].to_list()
residues = df.iloc[:, 1].to_list()
predicted = []
epitope = ''
for index, value in enumerate(positions):
if (index != len(positions) - 1 and int(value) + 1 == int(positions[index + 1])):
epitope = epitope + residues[index]
elif (index != len(positions) - 1 and int(value) + 1 != int(positions[index + 1])):
epitope = epitope + residues[index]
if len(epitope) >= 7:
predicted.append(epitope)
predicted.append(int(value) - len(epitope) + 1)
predicted.append(int(value))
predicted = [key] + [conserved_sequence] + predicted
bepipred2_results.append(predicted)
epitope = ''
predicted = []
elif index == len(positions) - 1:
epitope = epitope + residues[index]
if len(epitope) >= 7:
predicted.append(epitope)
predicted.append(int(value) - len(epitope) + 1)
predicted.append(int(value))
predicted = [key] + [conserved_sequence] + predicted
bepipred2_results.append(predicted)
epitope = ''
predicted = []
except:
print("Retrying prediction of linear epitopes of protein " + key + " with Bepipred 2.0")
try:
headers = {
'Content-Type': 'application/x-www-form-urlencoded',
}
data = {
'method': 'Bepipred-2.0',
'sequence_text': conserved_sequence,
'window_size': '7',
}
response = requests.post('http://tools-cluster-interface.iedb.org/tools_api/bcell/', headers=headers, data=data)
response_data_split_by_line = response.content.decode('utf-8').splitlines()
response_body = []
for line in response_data_split_by_line:
split_line = line.split("\t")
response_body.append(split_line)
df = pd.DataFrame(response_body[1:], columns=response_body[0])
df["Score"] = pd.to_numeric(df["Score"])
df = df.loc[df['Score'] >= 0.5]
if df.empty == True:
print("No epitopes predicted for protein " + key + " with sequence length " + str(len(conserved_sequence)))
continue
else:
positions = df.iloc[:, 0].to_list()
residues = df.iloc[:, 1].to_list()
predicted = []
epitope = ''
for index, value in enumerate(positions):
if (index != len(positions) - 1 and int(value) + 1 == int(positions[index + 1])):
epitope = epitope + residues[index]
elif (index != len(positions) - 1 and int(value) + 1 != int(positions[index + 1])):
epitope = epitope + residues[index]
if len(epitope) >= 7:
predicted.append(epitope)
predicted.append(int(value) - len(epitope) + 1)
predicted.append(int(value))
predicted = [key] + [conserved_sequence] + predicted
bepipred2_results.append(predicted)
epitope = ''
predicted = []
elif index == len(positions) - 1:
epitope = epitope + residues[index]
if len(epitope) >= 7:
predicted.append(epitope)
predicted.append(int(value) - len(epitope) + 1)
predicted.append(int(value))
predicted = [key] + [conserved_sequence] + predicted
bepipred2_results.append(predicted)
epitope = ''
predicted = []
except:
print("Epitope prediction for protein " + key + " and sequence length " + str(len(conserved_sequence)) + " failed")
continue
with open('epitope_prediction_results/bepipred2.0_epitopes.csv', 'w', newline="") as f:
writer = csv.writer(f)
writer.writerows(bepipred2_results)
print("Bepipred 2.0 prediction done\n")
def bepipred(conserved_sequences_dict):
"""This function uses the REST API from IEDB to access to Linear Antigen Prediction method Bepipred and predict the linear epitopes of the given conserved
sequences using a combination of a hidden Markov model and a propensity scale method. The results are returned as a list of lists"""
bepipred_results = []
columns = ["protein_id"] + ["conserved_sequence"] + ["predicted_epitope"] + ["start_position"] + ["end_position"]
bepipred_results.append(columns)
for key, sequences in conserved_sequences_dict.items():
print("Predicting linear epitopes of protein " + key + " with Bepipred 1.0")
for conserved_sequence in sequences:
try:
time.sleep(1)
headers = {
'Content-Type': 'application/x-www-form-urlencoded',
}
data = {
'method': 'Bepipred',
'sequence_text': conserved_sequence,
'window_size': '7',
}
response = requests.post('http://tools-cluster-interface.iedb.org/tools_api/bcell/', headers=headers, data=data)
response_data_split_by_line = response.content.decode('utf-8').splitlines()
response_body = []
for line in response_data_split_by_line:
split_line = line.split("\t")
response_body.append(split_line)
df = pd.DataFrame(response_body[1:], columns=response_body[0])
df["Score"] = pd.to_numeric(df["Score"])
df = df.loc[df['Score'] >= 0.35]
if df.empty == True:
print("No epitopes predicted for protein " + key + " with sequence length " + str(len(conserved_sequence)))
continue
else:
positions = df.iloc[:, 0].to_list()
residues = df.iloc[:, 1].to_list()
predicted = []
epitope = ''
for index, value in enumerate(positions):
if (index != len(positions) - 1 and int(value) + 1 == int(positions[index + 1])):
epitope = epitope + residues[index]
elif (index != len(positions) - 1 and int(value) + 1 != int(positions[index + 1])):
epitope = epitope + residues[index]
if len(epitope) >= 7:
predicted.append(epitope)
predicted.append(int(value) - len(epitope) + 1)
predicted.append(int(value))
predicted = [key] + [conserved_sequence] + predicted
bepipred_results.append(predicted)
epitope = ''
predicted = []
elif index == len(positions) - 1:
epitope = epitope + residues[index]
if len(epitope) >= 7:
predicted.append(epitope)
predicted.append(int(value) - len(epitope) + 1)
predicted.append(int(value))
predicted = [key] + [conserved_sequence] + predicted
bepipred_results.append(predicted)
epitope = ''
predicted = []
except:
print("Retrying prediction of linear epitopes of protein " + key + " with Bepipred 1.0")
try:
time.sleep(1)
headers = {
'Content-Type': 'application/x-www-form-urlencoded',
}
data = {
'method': 'Bepipred',
'sequence_text': conserved_sequence,
'window_size': '7',
}
response = requests.post('http://tools-cluster-interface.iedb.org/tools_api/bcell/', headers=headers, data=data)
response_data_split_by_line = response.content.decode('utf-8').splitlines()
response_body = []
for line in response_data_split_by_line:
split_line = line.split("\t")
response_body.append(split_line)
df = pd.DataFrame(response_body[1:], columns=response_body[0])
df["Score"] = pd.to_numeric(df["Score"])
df = df.loc[df['Score'] >= 0.35]
if df.empty == True:
print("No epitopes predicted for protein " + key + " with sequence length " + str(len(conserved_sequence)))
continue
else:
positions = df.iloc[:, 0].to_list()
residues = df.iloc[:, 1].to_list()
predicted = []
epitope = ''
for index, value in enumerate(positions):
if (index != len(positions) - 1 and int(value) + 1 == int(positions[index + 1])):
epitope = epitope + residues[index]
elif (index != len(positions) - 1 and int(value) + 1 != int(positions[index + 1])):
epitope = epitope + residues[index]
if len(epitope) >= 7:
predicted.append(epitope)
predicted.append(int(value) - len(epitope) + 1)
predicted.append(int(value))
predicted = [key] + [conserved_sequence] + predicted
bepipred_results.append(predicted)
epitope = ''
predicted = []
elif index == len(positions) - 1:
epitope = epitope + residues[index]
if len(epitope) >= 7:
predicted.append(epitope)
predicted.append(int(value) - len(epitope) + 1)
predicted.append(int(value))
predicted = [key] + [conserved_sequence] + predicted
bepipred_results.append(predicted)
epitope = ''
predicted = []
except:
print("Epitope prediction for protein " + key + " and sequence length " + str(len(conserved_sequence)) + " failed")
continue
with open('epitope_prediction_results/bepipred1.0_epitopes.csv', 'w', newline="") as f:
writer = csv.writer(f)
writer.writerows(bepipred_results)
print("Bepipred 1.0 prediction done\n")
def emini(conserved_sequences_dict):
"""This function uses the REST API from IEDB to access the Linear Antigen Prediction method Emini and predict the linear epitopes of the given conserved
sequences. The calculation is based on surface accessibility scale on a product. The results are returned as a list of lists"""
emini_results = []
columns = ["protein_id"] + ["conserved_sequence"] + ["predicted_epitope"] + ["start_position"] + ["end_position"]
emini_results.append(columns)
for key, sequences in conserved_sequences_dict.items():
print("Predicting linear epitopes of protein " + key + " with Emini")
for conserved_sequence in sequences:
try:
time.sleep(1)
headers = {
'Content-Type': 'application/x-www-form-urlencoded',
}
data = {
'method': 'Emini',
'sequence_text': conserved_sequence,
'window_size': '6',
}
response = requests.post('http://tools-cluster-interface.iedb.org/tools_api/bcell/', headers=headers, data=data)
response_data_split_by_line = response.content.decode('utf-8').splitlines()
response_body = []
for line in response_data_split_by_line:
split_line = line.split("\t")
response_body.append(split_line)
df = pd.DataFrame(response_body[1:], columns=response_body[0])
df["Score"] = pd.to_numeric(df["Score"])
df = df.loc[df['Score'] >= 1]
if df.empty == True:
print("No epitopes predicted for protein " + key + " with sequence length " + str(len(conserved_sequence)))
continue
else:
positions = df.iloc[:, 0].to_list()
residues = df.iloc[:, 1].to_list()
predicted = []
epitope = ''
for index, value in enumerate(positions):
if (index != len(positions) - 1 and int(value) + 1 == int(positions[index + 1])):
epitope = epitope + residues[index]
elif (index != len(positions) - 1 and int(value) + 1 != int(positions[index + 1])):
epitope = epitope + residues[index]
if len(epitope) >= 7:
predicted.append(epitope)
predicted.append(int(value) - len(epitope) + 1)
predicted.append(int(value))
predicted = [key] + [conserved_sequence] + predicted
emini_results.append(predicted)
epitope = ''
predicted = []
elif index == len(positions) - 1:
epitope = epitope + residues[index]
if len(epitope) >= 7:
predicted.append(epitope)
predicted.append(int(value) - len(epitope) + 1)
predicted.append(int(value))
predicted = [key] + [conserved_sequence] + predicted
emini_results.append(predicted)
epitope = ''
predicted = []
except:
print("Retrying prediction of linear epitopes of protein " + key + " with Emini")
try:
time.sleep(1)
headers = {
'Content-Type': 'application/x-www-form-urlencoded',
}
data = {
'method': 'Emini',
'sequence_text': conserved_sequence,
'window_size': '6',
}
response = requests.post('http://tools-cluster-interface.iedb.org/tools_api/bcell/', headers=headers, data=data)
response_data_split_by_line = response.content.decode('utf-8').splitlines()
response_body = []
for line in response_data_split_by_line:
split_line = line.split("\t")
response_body.append(split_line)
df = pd.DataFrame(response_body[1:], columns=response_body[0])
df["Score"] = pd.to_numeric(df["Score"])
df = df.loc[df['Score'] >= 1]
if df.empty == True:
print("No epitopes predicted for protein " + key + " with sequence length " + str(len(conserved_sequence)))
continue
else:
positions = df.iloc[:, 0].to_list()
residues = df.iloc[:, 1].to_list()
predicted = []
epitope = ''
for index, value in enumerate(positions):
if (index != len(positions) - 1 and int(value) + 1 == int(positions[index + 1])):
epitope = epitope + residues[index]
elif (index != len(positions) - 1 and int(value) + 1 != int(positions[index + 1])):
epitope = epitope + residues[index]
if len(epitope) >= 7:
predicted.append(epitope)
predicted.append(int(value) - len(epitope) + 1)
predicted.append(int(value))
predicted = [key] + [conserved_sequence] + predicted
emini_results.append(predicted)
epitope = ''
predicted = []
elif index == len(positions) - 1:
epitope = epitope + residues[index]
if len(epitope) >= 7:
predicted.append(epitope)
predicted.append(int(value) - len(epitope) + 1)
predicted.append(int(value))
predicted = [key] + [conserved_sequence] + predicted
emini_results.append(predicted)
epitope = ''
predicted = []
except:
print("Epitope prediction for protein " + key + " and sequence length " + str(len(conserved_sequence)) + " failed")
continue
with open('epitope_prediction_results/emini_epitopes.csv', 'w', newline="") as f:
writer = csv.writer(f)
writer.writerows(emini_results)
print("Emini prediction done\n")
def choufasman(conserved_sequences_dict):
"""This function uses the REST API from IEDB to access the Linear Antigen Prediction method Chou-Fasman and predict the linear epitopes of the given
conserved sequences. It uses the Chou and Fasman scale which is commonly used to predict beta turns. The results are returned as a list of lists"""
choufasman_results = []
columns = ["protein_id"] + ["conserved_sequence"] + ["predicted_epitope"] + ["start_position"] + ["end_position"]
choufasman_results.append(columns)
for key, sequences in conserved_sequences_dict.items():
print("Predicting linear epitopes of protein " + key + " with Chou-Fasman")
for conserved_sequence in sequences:
try:
time.sleep(1)
headers = {
'Content-Type': 'application/x-www-form-urlencoded',
}
data = {
'method': 'Chou-Fasman',
'sequence_text': conserved_sequence,
'window_size': '7',
}
response = requests.post('http://tools-cluster-interface.iedb.org/tools_api/bcell/', headers=headers, data=data)
response_data_split_by_line = response.content.decode('utf-8').splitlines()
response_body = []
for line in response_data_split_by_line:
split_line = line.split("\t")
response_body.append(split_line)
df = pd.DataFrame(response_body[1:], columns=response_body[0])
df["Score"] = pd.to_numeric(df["Score"])
average_of_scores = df["Score"].mean()
df = df.loc[df['Score'] >= average_of_scores]
if df.empty == True:
print("No epitopes predicted for protein " + key + " with sequence length " + str(len(conserved_sequence)))
continue
else:
positions = df.iloc[:, 0].to_list()
residues = df.iloc[:, 1].to_list()
predicted = []
epitope = ''
for index, value in enumerate(positions):
if (index != len(positions) - 1 and int(value) + 1 == int(positions[index + 1])):
epitope = epitope + residues[index]
elif (index != len(positions) - 1 and int(value) + 1 != int(positions[index + 1])):
epitope = epitope + residues[index]
if len(epitope) >= 7:
predicted.append(epitope)
predicted.append(int(value) - len(epitope) + 1)
predicted.append(int(value))
predicted = [key] + [conserved_sequence] + predicted
choufasman_results.append(predicted)
epitope = ''
predicted = []
elif index == len(positions) - 1:
epitope = epitope + residues[index]
if len(epitope) >= 7:
predicted.append(epitope)
predicted.append(int(value) - len(epitope) + 1)
predicted.append(int(value))
predicted = [key] + [conserved_sequence] + predicted
choufasman_results.append(predicted)
epitope = ''
predicted = []
except:
print("Retrying prediction of linear epitopes of protein " + key + " with Chou-Fasman")
try:
time.sleep(1)
headers = {
'Content-Type': 'application/x-www-form-urlencoded',
}
data = {
'method': 'Chou-Fasman',
'sequence_text': conserved_sequence,
'window_size': '7',
}
response = requests.post('http://tools-cluster-interface.iedb.org/tools_api/bcell/', headers=headers, data=data)
response_data_split_by_line = response.content.decode('utf-8').splitlines()
response_body = []
for line in response_data_split_by_line:
split_line = line.split("\t")
response_body.append(split_line)
df = pd.DataFrame(response_body[1:], columns=response_body[0])
df["Score"] = pd.to_numeric(df["Score"])
average_of_scores = df["Score"].mean()
df = df.loc[df['Score'] >= average_of_scores]
if df.empty == True:
print("No epitopes predicted for protein " + key + " with sequence length " + str(len(conserved_sequence)))
continue
else:
positions = df.iloc[:, 0].to_list()
residues = df.iloc[:, 1].to_list()
predicted = []
epitope = ''
for index, value in enumerate(positions):
if (index != len(positions) - 1 and int(value) + 1 == int(positions[index + 1])):
epitope = epitope + residues[index]
elif (index != len(positions) - 1 and int(value) + 1 != int(positions[index + 1])):
epitope = epitope + residues[index]
if len(epitope) >= 7:
predicted.append(epitope)
predicted.append(int(value) - len(epitope) + 1)
predicted.append(int(value))
predicted = [key] + [conserved_sequence] + predicted
choufasman_results.append(predicted)
epitope = ''
predicted = []
elif index == len(positions) - 1:
epitope = epitope + residues[index]
if len(epitope) >= 7:
predicted.append(epitope)
predicted.append(int(value) - len(epitope) + 1)
predicted.append(int(value))
predicted = [key] + [conserved_sequence] + predicted
choufasman_results.append(predicted)
epitope = ''
predicted = []
except:
print("Epitope prediction for protein " + key + " and sequence length " + str(len(conserved_sequence)) + " failed")
continue
with open('epitope_prediction_results/choufasman_epitopes.csv', 'w', newline="") as f:
writer = csv.writer(f)
writer.writerows(choufasman_results)
print("Chou-Fasman prediction done\n")
def karplusschulz(conserved_sequences_dict):
"""This function uses the REST API from IEDB to access the Linear Antigen Prediction method Karplus-Schulz and predict the linear epitopes of the given
conserved sequences. In this method, flexibility scale based on mobility of protein segments on the basis of the known temperature B factors of the
a-carbons of 31 proteins of known structure was constructed. The results are returned as a list of lists"""
karplusschulz_results = []
columns = ["protein_id"] + ["conserved_sequence"] + ["predicted_epitope"] + ["start_position"] + ["end_position"]
karplusschulz_results.append(columns)
for key, sequences in conserved_sequences_dict.items():
print("Predicting linear epitopes of protein " + key + " with Karplus-Schulz")
for conserved_sequence in sequences:
try:
time.sleep(1)
headers = {
'User-agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/47.0.2526.80 Safari/537.36',
'Content-Type': 'application/x-www-form-urlencoded'
}
data = {
'method': 'Karplus-Schulz',
'sequence_text': conserved_sequence,
'window_size': '7',
}
response = requests.post('http://tools-cluster-interface.iedb.org/tools_api/bcell/', headers=headers, data=data)
response_data_split_by_line = response.content.decode('utf-8').splitlines()
response_body = []
for line in response_data_split_by_line:
split_line = line.split("\t")
response_body.append(split_line)
df = pd.DataFrame(response_body[1:], columns=response_body[0])
df["Score"] = pd.to_numeric(df["Score"])
average_of_scores = df["Score"].mean()
df = df.loc[df['Score'] >= average_of_scores]
if df.empty == True:
print("No epitopes predicted for protein " + key + " with sequence length " + str(len(conserved_sequence)))
continue
else:
positions = df.iloc[:, 0].to_list()
residues = df.iloc[:, 1].to_list()
predicted = []
epitope = ''
for index, value in enumerate(positions):
if (index != len(positions) - 1 and int(value) + 1 == int(positions[index + 1])):
epitope = epitope + residues[index]
elif (index != len(positions) - 1 and int(value) + 1 != int(positions[index + 1])):
epitope = epitope + residues[index]
if len(epitope) >= 7:
predicted.append(epitope)
predicted.append(int(value) - len(epitope) + 1)