hlancpred

Introduction

HLAncPred is developed for predicting and scanning the peptides with the ability to bind non-classical class-I HLA alleles such as HLA-G*01:01,HLA-G*01:03,HLA-G*01:04, HLA-E*01:01, HLA-E*01:03 . More information on HLAncPred is available from its web-server https://webs.iiitd.edu.in/raghava/hlancpred/stand.html. This page provides information about stnadalone version of HLAncPred. Please read/cite the content about the HLAncPred for complete information including algorithm behind HLAncPred.

Reference

Dhall A, Patiyal S, Raghava GPS (2022) HLAncPred: a method for predicting promiscuous non-classical HLA binding sites. Brief Bioinform. doi: 10.1093/bib/bbac192

Pip installation

The pip version of HLAncpred is also available for easy installation and usage of the tool. The following command is required to install the package

pip install hlancpred

To know about the available option for the pip package, type the following command:

hlancpred -h

Models

In this program, five best models devoted to each allele, have been implemented for predicting the non-classical class-I HLA binder peptides. The models were trained on experimentally verified binders and randomly generated non-binders using Swiss-Prot database.

Modules/Jobs

This program implements two modules (job types); i) Predict: for prediction of non-classical class-I HLA allele binder peptides, ii) Scan: for creating all possible overlapping peptides of given length (window) and computing binding potential (score) of the overlapping peptides.

Minimum USAGE

Minimum usage is "python3 hlancpred.py -i peptide.fa -a G0101," where peptide.fa is a input fasta file, and 'G0101' is the name for HLA-G*01:01 against which the binders will be predicted. This will predict the binding ability of sequence in fasta format against HLA-G*01:01. It will use other parameters by default. It will save output in "outfile.csv" in CSV (comma seperated variables).

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Full Usage

Following is complete list of all options, you may get these options by "python hlancpred.py -h"

Usage

hlancpred.py [-h] -i INPUT -a {G0101,G0103,G0104,E0101,E0103} [-o OUTPUT] [-j {1,2}] [-w {8,9,10,11,12,13,14,15}] [-d {1,2}]

Please provide following arguments

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## Optional arguments:

 -h, --help            
      show this help message and exit
  
  -i INPUT, --input INPUT
      Input: protein or peptide sequence in FASTA format or single sequence per line in single letter code
                        
  -a {G0101,G0103,G0104,E0101,E0103}, --allele {G0101,G0103,G0104,E0101,E0103}
      Please provide the name of allele for the prediction of binder peptides
                        
  -o OUTPUT, --output OUTPUT
      Output: File for saving results by default outfile.csv
      
  -j {1,2}, --job {1,2}
      Job Type: 1:predict, and 2:scan, by default 1
      
  -w {8,9,10,11,12,13,14,15}, --winleng {8,9,10,11,12,13,14,15}
      Window Length: 8 to 35 (scan mode only), by default 9
      
  -d {1,2}, --display {1,2}
      Display: 1:Only binders, 2: All peptides, by default 
  

Input File:

It allow users to provide input in two format; i) FASTA format (standard) and ii) Simple Format. In case of simple format, file should have one peptide sequence in a single line in single letter code (eg. peptide.seq).

Note:

1: In case of predict and design module (job), the length of peptide should be upto 15 amino acids. If a sequence with length more than 15 will be provided, the program will take first 15 residues, and ignore the rest. In case of scan module, minimum length of protein/peptide sequence should be equal to window length (pattern), see peptide.fa. 2: Program will ignore peptides having length less than 8 residues (e.g., protein.fa).

Output File:

Program will save the results in the CSV format, in case user do not provide output file name, it will be stored in "outfile.csv".

Allele:

The program needs the name of the allele as shown in the usage, it could be G0101, G0103, G0104, E0101, and E0103.

## HLAncPred Package Files
It contantain following files, brief descript of these files given below

- INSTALLATION  			: Installations instructions

- LICENSE       			: License information

- README.md     			: This file provide information about this package

- Models           		: This folder comprises for five models devoted to five alleles

- hlancpred.py 			: Main python program 

- peptide.fa			: Example file contain peptide sequenaces in FASTA format

- peptide.seq			: Example file contain peptide sequenaces in simple format

- protein.fa			: Example file contain protein sequenaces in FASTA format 

- example_predict_output.csv	: Example output file for predict module

- example_scan_output.csv		: Example output file for scan module

Address for contact

Prof. G. P. S. Raghava, Head Department of Computational Biology,

Indraprastha Institute of Information Technology (IIIT),

Okhla Phase III, New Delhi 110020 ; Phone:+91-11-26907444;

Email: raghava@iiitd.ac.in Web: http://webs.iiitd.edu.in/raghava/

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