local-PSSM.md

Local PSSM:

Step 0: Download UniRef Protein Database:

We can download UniRef (e.g., UniRef50, UniRef90, UniRef100) protein database from the given website (https://www.uniprot.org/downloads). After successfully downloading, we have to uncompress the file.

 

Download Using wget (Copy Download Link from Website):

user@machine:~$ wget https://ftp.uniprot.org/pub/databases/uniprot/uniref/unirefX/unirefX.fasta.gz   ### (e.g., X = {50, 90, 100})

Uncompress (unirefX.fasta.gz):

user@machine:~$ gunzip unirefX.fasta.gz   ### (e.g., X = {50, 90, 100})

   

Step 1: Create Local Database:

user@machine:~$ /home/user/ncbi-blast-2.12.0+/bin/makeblastdb -in protein.fa -dbtype prot -out Pluto -parse_seqids

 

After that, we will get below files.

Pluto.pdb
Pluto.phr
Pluto.pin
Pluto.pog
Pluto.pos
Pluto.pot
Pluto.psq
Pluto.ptf
Pluto.pto

   

Step 2: Split Multile FASTA File into Single FASTA Files:

File = '/home/user/Bioinformatics/multiSequences.fa'

from Bio import SeqIO # Install (If you don't have it.): pip install biopython

C= 1
for record in SeqIO.parse(File, 'fasta'):
    openFile = open(str(C) + '.fasta', 'w')
    SeqIO.write(record, openFile, 'fasta')
    C += 1
#end-for

 

Notes:

1. I renamed the origial name of FASTA sequence as it is helpful for tracking the implementation.
2. I used sequential numerical order rather than the original sequence name.
3. Renaming the sequence is optional.
4. We will find the updated FASTA splitting procedure from given URL.
5. We can also use Colab for the splitting Multiple FASTA sequence into single sequences [Update Implementation].

   

Step 3: Implementation/Generate Local PSSMs:

import glob
import os

### Parameters:
iteration = 3       # If we increase the number iteration, then we will get the good quality of PSSM.
evalue    = 0.001   # E-value
###

C = 0
for file in sorted(glob.glob('*.fasta')):
    os.system('/home/user/ncbi-blast-2.12.0+/bin/psiblast -query {} -db Pluto -num_iterations={} -evalue={} -out psiblastout.txt -out_ascii_pssm {}.pssm'.format(file, iteration, evalue, file))
#end-for

 

Notes:

1. We will find the updated implementation from given URL.