dzjxzyd / r-peptidecutter

Hi there 👋

🔭 I’m currently working on bioactive peptide discovery by employing machine learning & NLP & molecular docking & dynamics simulation

🌱 I’m currently learning NLP, especially focusing on large language model

📫 How to reach me: zhenjiao.at.ksu.edu

😄 Pronouns: He/Him

r-peptidecutter's Issues

PeptideCutter_one_enzyme_combination_auto_read_fasta.py

author Zhenjiao Du [email protected]

Grain science & Industry, Kansas State University

primary intention of the simulated tools

1. collect all the unknown enzyme cleavage cites and create a one-stop tools

2. no web tools take the hydrolyzing order into consideration

a total of 51 enzymes and chemicals

cleavage posiiton rule

p4 p3 p2 p1 p1' p2'

the number in the cleavage was the position number of the last amino acid position (p1 residue position) of the peptide

the following enzyme cleavage cite are available at

https://web.expasy.org/peptide_cutter/peptidecutter_enzymes.html#exceptions

for the last 16 enzymes, they are available another paper published at JAFC

the related tool are available at http://hazralab.iitr.ac.in/new-documentation-01.html

input the enzymes and chemicals functions

Arg-C proteinase

the R amino acid was retained by the N terminal amino acid

def Arg_C(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i]=='R':
cleavage.append(i)
return cleavage

ASP-D

the D amino acid was retanied by the C terminal peptides

def Asp_D(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i+1]=='D':
cleavage.append(i)
return cleavage

Asp-N Endopeptidase + N-terminal Glu:

the D and E amino acid was retanied by the C terminal peptides

Asp(D) or Glu(E) in position P1'

def Asp_N(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i+1]=='D' or seq[i+1] == 'E':
cleavage.append(i)
return cleavage

BNPs endopeptidase

def BNPs(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i]=='W':
cleavage.append(i)
return cleavage

#Caspase 1
def Caspase1(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-4:
if seq[i]=='F'or seq[i] == 'W'or seq[i] == 'Y'or seq[i] == 'L':
if seq[i+2]=='H'or seq[i+2] == 'A'or seq[i+2] == 'T':
if seq[i+3]=='D':
if seq[i+4]!='P'or seq[i+4]!='E'or seq[i+4]!='D'or seq[i+4]!='Q'or seq[i+4]!='K'or seq[i+4]!='R':
cleavage.append(i+3)
return cleavage

#Caspase 2
def Caspase2(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-4:
if seq[i]=='D':
if seq[i+1]=='V':
if seq[i+2]=='A':
if seq[i+3]=='D':
if seq[i+4]!='P'or seq[i+4]!='E'or seq[i+4]!='D'or seq[i+4]!='Q'or seq[i+4]!='K'or seq[i+4]!='R':
cleavage.append(i+3)
return cleavage
#Caspase 3
def Caspase3(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-4:
if seq[i]=='D':
if seq[i+1]=='M':
if seq[i+2]=='Q':
if seq[i+3]=='D':
if seq[i+4]!='P'or seq[i+4]!='E'or seq[i+4]!='D'or seq[i+4]!='Q'or seq[i+4]!='K'or seq[i+4]!='R':
cleavage.append(i+3)
return cleavage
#Caspase 4
def Caspase4(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-4:
if seq[i]=='L' :
if seq[i+1]=='E':
if seq[i+2]=='V':
if seq[i+3]=='D':
if seq[i+4]!='P'or seq[i+4]!='E'or seq[i+4]!='D'or seq[i+4]!='Q'or seq[i+4]!='K'or seq[i+4]!='R':
cleavage.append(i+3)
return cleavage
#Caspase 5
def Caspase5(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-3:
if seq[i]=='L' or seq[i]== 'W' :
if seq[i+1]=='E':
if seq[i+2]=='H':
if seq[i+3]=='D':
cleavage.append(i+3)
return cleavage
#Caspase 6
def Caspase6(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-4:
if seq[i]=='V' :
if seq[i+1]=='E':
if seq[i+2]=='H'or seq[i]== 'I' :
if seq[i+3]=='D':
if seq[i+4]!='P'or seq[i+4]!='E'or seq[i+4]!='D'or seq[i+4]!='Q'or seq[i+4]!='K'or seq[i+4]!='R':
cleavage.append(i+3)
return cleavage
#Caspase 7
def Caspase7(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-4:
if seq[i]=='D' :
if seq[i+1]=='E':
if seq[i+2]=='V' :
if seq[i+3]=='D':
if seq[i+4]!='P'or seq[i+4]!='E'or seq[i+4]!='D'or seq[i+4]!='Q'or seq[i+4]!='K'or seq[i+4]!='R':
cleavage.append(i+3)
return cleavage
#Caspase 8
def Caspase8(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-4:
if seq[i]=='I' or seq[i]=='L':
if seq[i+1]=='E':
if seq[i+2]=='T':
if seq[i+3]=='D':
if seq[i+4]!='P'or seq[i+4]!='E'or seq[i+4]!='D'or seq[i+4]!='Q'or seq[i+4]!='K'or seq[i+4]!='R':
cleavage.append(i+3)
return cleavage
#Caspase 9
def Caspase9(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-3:
if seq[i]=='L' :
if seq[i+1]=='E':
if seq[i+2]=='H':
if seq[i+3]=='D':
cleavage.append(i+3)
return cleavage
#Caspase 10
def Caspase10(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-3:
if seq[i]=='I' :
if seq[i+1]=='E':
if seq[i+2]=='A':
if seq[i+3]=='D':
cleavage.append(i+3)
return cleavage

#Chymotrypsin-high specificity(C-term to [FYW], not before P)
def chymo_high(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i]=='F' or seq[i] == 'Y':
if seq[i+1] != 'P':
cleavage.append(i)
if seq[i]=='W' :
if seq[i+1] != 'P' and seq[i] != 'M':
cleavage.append(i)
return cleavage

#Chymotrypsin-low specificity (C-term to [FYWML], not before P)
def chymo_low(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i]=='F' or seq[i] == 'L' or seq[i] == 'Y':
if seq[i+1] != 'P':
cleavage.append(i)
if seq[i]=='W' :
if seq[i+1] != 'P' and seq[i] != 'M':
cleavage.append(i)
if seq[i]=='M' :
if seq[i+1] != 'P' and seq[i] != 'Y':
cleavage.append(i)
if seq[i]=='H' :
if seq[i+1] != 'D' and seq[i] != 'M' and seq[i] != 'P' and seq[i] != 'W':
cleavage.append(i)
return cleavage

#Clostripain (Clostridiopeptidase B)
def Clostripain(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i]=='R':
cleavage.append(i)
return cleavage
#CNBr
def CNBr(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i]=='M':
cleavage.append(i)
return cleavage
#Enterokinase
def Enterokinase(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-3:
if seq[i]=='D' or seq[i]== 'E':
if seq[i+1]=='D' or seq[i+1]== 'E':
if seq[i+2]=='D' or seq[i+2]== 'E':
if seq[i+3]=='K':
cleavage.append(i+3)
return cleavage

#Factor_xa
#A,F,G,I,L,T,V or M
def Factor_xa(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-3:
if seq[i]=='A' or seq[i]== 'F'or seq[i]== 'G'or seq[i]== 'I'or seq[i]== 'L'or seq[i]== 'T'or seq[i]== 'V'or seq[i]== 'M':
if seq[i+1]=='D' or seq[i+1]== 'E':
if seq[i+2]=='G':
if seq[i+3]=='R':
cleavage.append(i+3)
return cleavage
#Formic_acid
def Formic_acid(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i]=='D':
cleavage.append(i)
return cleavage
#Glutamyl_endopeptidase
def Glutamyl_endopeptidase(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i]=='E':
cleavage.append(i)
return cleavage
#GranzymeB
def GranzymeB(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-3:
if seq[i]=='I':
if seq[i+1]== 'E':
if seq[i+2]=='P':
if seq[i+3]=='D':
cleavage.append(i+3)
return cleavage
#Hydroxylamine
def Hydroxylamine(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i]=='N' and seq[i+1] == 'G':
cleavage.append(i)
return cleavage
#Iodosobenzoic_acid
def Iodosobenzoic_acid(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i]=='W':
cleavage.append(i)
return cleavage
#LysC
def LysC(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i]=='K':
cleavage.append(i)
return cleavage
#Neutrophil_elastase
def Neutrophil_elastase(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i]=='A' or seq[i]== 'V':
cleavage.append(i)
return cleavage
#NTCB (2-nitro-5-thiocyanobenzoic acid)
def NTCB(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i+1]=='C':
cleavage.append(i)
return cleavage
#Pepsin (pH1.3)
def Pepsin_1(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i+1]== 'L' or seq[i+1]== 'F':
cleavage.append(i)
if i>=2 and i<seq_len-1:
if seq[i-1]=='P'or seq[i-2] == 'H' or seq[i-2] == 'K' or seq[i-2] == 'R' or seq[i-2]=='P':
cleavage.remove(i)
if i ==1:
if seq[i-1]=='P':
cleavage.remove(i)
if i==seq_len-1:
if seq[i-1]=='R':
cleavage.remove(i)

        if seq[i]== 'L' or seq[i]== 'F':
            if i not in cleavage: # when meet ASLLAL the double L will cause the repeat postion extraction
                cleavage.append(i)
            if i>=2 and i<seq_len-1:
                if seq[i-1]=='P'or seq[i-2] == 'H' or  seq[i-2] == 'K' or  seq[i-2] == 'R' or seq[i-2]=='P':
                    cleavage.remove(i)
            if i ==1:
                if seq[i-1]=='P':
                    cleavage.remove(i)
            if i==seq_len-1:
                if seq[i-1]=='R':
                    cleavage.remove(i)
            if i ==seq_len:
                if seq[i]== 'L' or seq[i]== 'F':
                    cleavage.remove(i)

return cleavage

#Pepsin (pH>2)
def Pepsin_2(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i+1]== 'L' or seq[i+1]== 'F' or seq[i+1]== 'Y' or seq[i+1]== 'W':
cleavage.append(i)
if i>=2 and i<seq_len-1:
if seq[i-1]=='P'or seq[i-2] == 'H' or seq[i-2] == 'K' or seq[i-2] == 'R' or seq[i-2]=='P':
cleavage.remove(i)
if i ==1:
if seq[i-1]=='P':
cleavage.remove(i)
if i==seq_len-1:
if seq[i-1]=='R':
cleavage.remove(i)
if seq[i]== 'L' or seq[i]== 'F'or seq[i]== 'Y' or seq[i]== 'W':
if i not in cleavage: # when meet ASLLAL the double L will cause the repeat postion extraction
cleavage.append(i)
if i>=2 and i<seq_len-1:
if seq[i-1]=='P'or seq[i-2] == 'H' or seq[i-2] == 'K' or seq[i-2] == 'R' or seq[i-2]=='P':
cleavage.remove(i)
if i ==1:
if seq[i-1]=='P':
cleavage.remove(i)
if i==seq_len-1:
if seq[i-1]=='R':
cleavage.remove(i)
if i ==seq_len:
if seq[i]== 'L' or seq[i]== 'F'or seq[i]== 'Y' or seq[i]== 'W':
cleavage.append(i)

return cleavage

#Proline_endopeptidase
def Proline_endopeptidase(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-2:
if seq[i]=='H' and seq[i]== 'K'and seq[i]== 'R':
if seq[i+1]=='P' :
if seq[i+2]!='P':
cleavage.append(i+1)
return cleavage
#Proteinase_K
#A,E,F,I,L,T,V,W or Y
def Proteinase_K(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i]=='A' or seq[i]== 'E' or seq[i]== 'F'or seq[i]== 'I'or seq[i]== 'L'or seq[i]== 'T'or seq[i]== 'V'or seq[i]== 'W'or seq[i]== 'Y':
cleavage.append(i)
return cleavage
#Staphylococcal_peptidase_I
def Staphylococcal_peptidase_I(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if i==0:
if seq[i]== 'E':
cleavage.append(i)
if seq[i]!='E':
if seq[i+1]== 'E':
cleavage.append(i+1)
return cleavage

#Thermolysin
def Thermolysin(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i]!='E'and seq[i]!= 'D':
if seq[i+1]== 'A' or seq[i+1] == 'F'or seq[i+1] == 'I'or seq[i+1] == 'L'or seq[i+1] == 'M'or seq[i+1] == 'V':
cleavage.append(i)
return cleavage
#Thrombin
def Thrombin(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-2:
if seq[i]=='G':
if seq[i+1] == 'R':
if seq[i+2] =='G':
cleavage.append(i+1)
if i < seq_len-4:
if seq[i]== 'A' or seq[i] == 'F'or seq[i] == 'G'or seq[i] == 'I'or seq[i] == 'L'or seq[i] == 'T'or seq[i] == 'V' or seq[i] == 'M':
if seq[i+1]== 'A' or seq[i+1] == 'F'or seq[i+1] == 'G' or seq[i+1] == 'I'or seq[i+1] == 'L'or seq[i+1] == 'T'or seq[i+1] == 'V' or seq[i+1] == 'W':
if seq[i+2]=='P':
if seq[i+3]=='R':
if seq[i+4]!='D' and seq[i+4] != 'E':
cleavage.append(i+3)
if i+5 == seq_len-1:# position index is from 0 instead of 1
if seq[i+5]=='D' and seq[i+5] == 'E':
cleavage.remove(i+3)
return cleavage
#Trypsin (please note the exceptions!)
def Trypsin(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i]=='K' or seq[i] =='R':
if seq[i+1] != 'P':
cleavage.append(i)
if i < seq_len-2:
if seq[i] == 'W':
if seq[i+1] == 'K':
if seq[i+2] =='P':
cleavage.append(i+1)
if i < seq_len-2:
if seq[i] =='M':
if seq[i+1] == 'R':
if seq[i+2] =='P':
cleavage.append(i+1)
if i < seq_len-2:
if seq[i] =='E':
if seq[i+1] == 'R':
if seq[i+2] =='P':
cleavage.append(i+1)
#situation will block the cleavage
if i < seq_len-2:
if seq[i]=='D' or seq[i]=='C':
if seq[i+1] == 'K':
if seq[i+2] == 'D':
if i+1 in cleavage:
cleavage.remove(i+1)
if i < seq_len-2:
if seq[i]=='C':
if seq[i+1] == 'K':
if seq[i+2] == 'H' or seq[i+2] == 'Y':
if i+1 in cleavage:
cleavage.remove(i+1)
if i < seq_len-2:
if seq[i]=='C':
if seq[i+1] == 'R':
if seq[i+2] == 'K':
if i+1 in cleavage:
cleavage.remove(i+1)
if i < seq_len-2:
if seq[i]=='R':
if seq[i+1] == 'R':
if seq[i+2] == 'H' or seq[i+2] =='R':
if i+1 in cleavage:
cleavage.remove(i+1)
return cleavage

only pepsin and trypsin are following the cleavage cites data from PeptideCutter

obtanined from http://hazralab.iitr.ac.in/new-documentation-01.html

Elastase_1

def Elastase_1(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i]=='A':
cleavage.append(i)
return cleavage

Elastase_2 (Neutrophil_elastase)

def Elastase_2(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i]=='A' or seq[i]=='V':
cleavage.append(i)
return cleavage

chymotrypsinogen_B1

def chymotrypsinogen_B1(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i]=='F' or seq[i]=='L'or seq[i]=='W'or seq[i]=='Y':
cleavage.append(i)
return cleavage

chymotrypsinogen_C

def chymotrypsinogen_C(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i]=='F' or seq[i]=='M' or seq[i]=='L'or seq[i]=='W'or seq[i]=='Y' or seq[i]=='N'or seq[i]=='Q':
cleavage.append(i)
return cleavage

pancreatic_enteropeptidase_E

def pancreatic_enteropeptidase_E(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i]=='A':
cleavage.append(i)
return cleavage

Enteropeptidase ///this protein need to take the p1' into consideration

def Enteropeptidase(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i]=='K' and seq[i+1] =='I':
cleavage.append(i)
return cleavage

prostasin ///highly specificity

def prostasin(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-7:
if seq[i]=='R':
if seq[i+1]=='K':
if seq[i+2]=='R':
if seq[i+3]=='K':
if seq[i+4]=='I':
if seq[i+5]=='S':
if seq[i+6]=='G':
if seq[i+7]=='K':
cleavage.append(i+3)
return cleavage

#Gastricsin
def Gastricsin(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i]=='Y':
cleavage.append(i)
return cleavage

Fruit_Bromelain

def Fruit_Bromelain(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-2:
if seq[i]=='F':
if seq[i+1]=='V':
if seq[i+2]=='R':
cleavage.append(i+2)
return cleavage
#Stem_Bromelain
def Stem_Bromelain(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i]=='R':
if seq[i+1]=='R':
cleavage.append(i+1)
return cleavage

Ananain

def Ananain(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-2:
if seq[i]=='F':
if seq[i+1]=='V':
if seq[i+2]=='R':
cleavage.append(i+2)
return cleavage

Papaya_proteinase

def Papaya_proteinase(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i]=='G':
cleavage.append(i)
return cleavage

Chymopapain

def Chymopapain(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i]== 'A' or seq[i] == 'V'or seq[i] == 'L'or seq[i] == 'I'or seq[i] == 'F'or seq[i] == 'W':
if seq[i+1] == 'R' or seq[i+1] == 'L':
cleavage.append(i+1)
return cleavage

Chymosin

def Chymosin(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i]=='S' or seq[i]=='F':
if seq[i+1]=='M'or seq[i]=='A':
cleavage.append(i)
return cleavage

#Caricain
#same to the chymopapain
def Caricain(seq,seq_len):
cleavage=[]
for i in range(seq_len):
if i < seq_len-1:
if seq[i]== 'A' or seq[i] == 'V'or seq[i] == 'L'or seq[i] == 'I'or seq[i] == 'F'or seq[i] == 'Y' or seq[i] == 'W':
if seq[i+1] == 'R' or seq[i+1] == 'L':
cleavage.append(i+1)
return cleavage

auto_input the protien sequence

import pandas as pd
import numpy as np
import collections

import os
os.getcwd()
os.chdir('/Users/zhenjiaodu/Desktop/QSAR dipeptide submittsion file/final_revision_15_AUG/Table S2')
os.getcwd()
import glob

read and storage all the fasta file name in my folder

fasta_name = glob.glob("*.fasta")
sum_accession_number=[]
sum_seq=[]
fasta_name
for file_name in fasta_name:
#print(file_name)
fasta = open(file_name, mode='r')
temp =[] # create a list for the fasta content temporaty storage

for line in fasta:
    temp.append(line.strip()) # delete all the /n in the original fasta file

temp_seq=str() # collect the seq info
temp_name=str() #collect the seq name

for i in range(len(temp)):
    if i == 0:
        temp_name=temp[0].split('|')[1] # get the first line and extract the second values (accession_number)
    if i >= 1:
        temp_seq= temp_seq+temp[i]

sum_accession_number.append(temp_name)
sum_seq.append(temp_seq)

running the 51 enzymes and chemicals below

then running the options

options contanined the 47 functions

options= [Arg_C,Asp_D,Asp_N,BNPs,Caspase1,Caspase2,Caspase3,Caspase4,Caspase5,Caspase6,Caspase7,Caspase8,Caspase9,Caspase10,chymo_high,chymo_low,Clostripain,CNBr,Enterokinase,Factor_xa,Formic_acid,Glutamyl_endopeptidase,GranzymeB,Hydroxylamine,Iodosobenzoic_acid,LysC,Neutrophil_elastase,NTCB,Pepsin_1,Pepsin_2,Proline_endopeptidase,Proteinase_K,Staphylococcal_peptidase_I,Thermolysin,Thrombin,Trypsin,Elastase_1,Elastase_2, chymotrypsinogen_B1, chymotrypsinogen_C, pancreatic_enteropeptidase_E, Enteropeptidase,prostasin,Gastricsin,Fruit_Bromelain,Stem_Bromelain,Ananain,Papaya_proteinase,Chymopapain,Chymosin, Caricain]

assign the function name to option_names for further file generating

options_names= ['Arg_C','Asp_D','Asp_N','BNPs','Caspase1','Caspase2','Caspase3','Caspase4','Caspase5','Caspase6','Caspase7','Caspase8','Caspase9','Caspase10','chymo_high','chymo_low','Clostripain','CNBr','Enterokinase','Factor_xa','Formic_acid','Glutamyl_endopeptidase','GranzymeB','Hydroxylamine','Iodosobenzoic_acid','LysC','Neutrophil_elastase','NTCB','Pepsin_1','Pepsin_2','Proline_endopeptidase','Proteinase_K','Staphylococcal_peptidase_I','Thermolysin','Thrombin','Trypsin','Elastase_1','Elastase_2', 'chymotrypsinogen_B1', 'chymotrypsinogen_C', 'pancreatic_enteropeptidase_E', 'Enteropeptidase','prostasin','Gastricsin','Fruit_Bromelain','Stem_Bromelain','Ananain','Papaya_proteinase','Chymopapain','Chymosin', 'Caricain']

#desired peptide length
len_peptide = 2

Create a Pandas Excel writer using XlsxWriter as the engine. for the data collection at different sheet_name

writer = pd.ExcelWriter('one enzyme results.xlsx', engine='xlsxwriter')

for name in range(len(sum_seq)):
seq = sum_seq[name] #sequence info
seq_len = len(seq) #sequence len
accession_number = sum_accession_number[name]
summary = np.empty([50,51*4],dtype=object)
times = 0

# one enzyme simulation cutter
# the following loop generate all the possibility of a protein sequence from one single proteins
for j in range(len(options)):
    one_cleavage = []
    one_pep_seq=[]
    one_cleavage = options[j](seq,seq_len)
    one_cleavage.insert(0,0)
    one_cleavage.insert(len(one_cleavage),seq_len-1)# only to make it easy when extrac peptides below (we need to add 1 extract from the P1' instead of P1)
    #gain all hydrolyzed peptide sequences by the one enzymes and chemicals processed
    for i in range(len(one_cleavage)-1):
        # extract the sequence from the original protein sequences
        # the cleavage position was at the P1, so we should extract from P1
        if i ==0:
            pep_seq_temp = seq[one_cleavage[i]:one_cleavage[i+1]+1]
        else:
            pep_seq_temp = seq[one_cleavage[i]+1:one_cleavage[i+1]+1]
        if len(pep_seq_temp) == len_peptide: #check the peptide length
            one_pep_seq.append(pep_seq_temp)
    #one_pep_seq
    #delete the duplicated and count the number
    temp_list=[]
    temp_count=[]
    for i in one_pep_seq:
        if i not in temp_list:
            temp_list.append(i) # collect the unduplicate peptide info
            temp_count.append(1) #count as 1 for the occurance of a peptide
        else:
            index_repeat=temp_list.index(i) # find the position of the repeated peptide sequences
            temp_count[index_repeat]=temp_count[index_repeat]+1

    if len(temp_list) != 0:
        #temp_list
        # match the theoretical peptide sequence info and their number generated from the proteins under the enzymatic hydrolysis
        # match
        #match the orac and abst activity
        # import database
        abts_databased = pd.read_excel('ABTS_potential activity results.xlsx',header=0,index_col=0,na_filter = False)
        orac_databased = pd.read_excel('ORAC_potential activity results.xlsx',header=0,index_col=0,na_filter = False)
        # match
        results = collections.defaultdict(list)
        for i in range(len(temp_list)):
            ABTS = abts_databased['ABTS activity'][temp_list[i]]
            ORAC = orac_databased['ORAC activity'][temp_list[i]]
            results[i].append(temp_list[i])
            results[i].append(temp_count[i])
            results[i].append(str(ABTS))
            results[i].append(str(ORAC))

        #results export
        df = pd.DataFrame(results)
        df = df.T
        df = df.sort_values(by = df.columns[1],ascending=None) # column[1] asend based on the count number
        ky = df.to_numpy()

        #assign the well arrange data into the summary numpy
        for ky_row in range(ky.shape[0]+1):
            for ky_column in range(4):
                if ky_row==0: # assign the number of the column
                    if ky_column==0:
                        summary[ky_row,times*4+ky_column]=options_names[j]
                    if ky_column==1:
                        summary[ky_row,times*4+ky_column]='theoretical peptide amounts'
                    if ky_column==2:
                        summary[ky_row,times*4+ky_column]='ABTS activity, umol TE/umol peptide'
                    if ky_column==3:
                        summary[ky_row,times*4+ky_column]='ORAC activity, umol TE/umol peptide'
                if ky_row>0:
                    summary[ky_row,times*4+ky_column]=ky[ky_row-1,ky_column]  # column-1, so index back to the original data
        times = times +1

summary= pd.DataFrame(summary)
#summary_filename= accession_number+'_'+'one_enzyme'+'.csv'
summary_filename= accession_number
summary.to_excel(writer, sheet_name=summary_filename)

writer.save()