il2pred 1.0.2

il2pred: A tool for predicting IL2 inducing or non-IL2 inducing peptides

il2Pred

A computational method to predict il2 inducing or il2 non-inducing peptides based on amino acid composition or motifs

Introduction

il2Pred is a tool developed by Raghva-Lab in 2024. It is designed to predict whether a peptide is il2 inducer or not. It utilizes amino-acid compositions and motifs as features to make predictions using Random Forest. il2Pred is also available as web-server at https://webs.iiitd.edu.in/raghava/il2Pred. Please read/cite the content about the il2Pred for complete information including algorithm behind the approach.

PIP Installation

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

pip install il2Pred

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

il2Pred -h

Standalone

Standalone version of il2Pred is written in python3 and the following libraries are necessary for a successful run:

• scikit-learn = 1.5.2
• Pandas
• Numpy
• python - 3.12.2

Minimum USAGE

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

python il2pred.py -h

To run the example, type the following command:

python il2Pred.py -i peptide.fa

This will predict the probability whether a submitted sequence will il2 inducer or not. It will use other parameters by default. It will save the output in "outfile.csv" in CSV (comma separated variables).

Full Usage

usage: il2Pred.py [-h] -i INPUT [-o OUTPUT] [-t THRESHOLD] [-j {1,2,3}] [-m {1,2}] [-d {1,2}]
                    [-wd WORKING]
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To run the example, type the following command:

il2Pred.py -i peptide.fa

Please provide following arguments.
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Following is complete list of all options, you may get these options
usage: il2Pred.py [-h] 
                     [-i INPUT]
                     [-o OUTPUT]
		     [-j {1,2,3}] 
                     [-m {1,2}] 

Please provide following arguments

optional arguments:

options:
  -h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        Input: protein or peptide sequence in FASTA format
  -o OUTPUT, --output OUTPUT
                        Output: File for saving results by default outfile.csv
  -t THRESHOLD, --threshold THRESHOLD
                        Threshold: Value between 0 to 1 by default 0.48
  -j {1,2,3}, --job {1,2,3}
			Job: 1: il2 vs non il2, 2: il2 vs mhc non binder il2, 3: il2 vs mixed 
  -m {1,2}, --model {1,2}
                        Model: 1: ET (feature_selection_model) , 2: hybrid (MERCI + ET)
                        feature based on ET, by default 1
  -d {1,2}, --display {1,2}
                        Display: 1: il2 inducing peptides, 2: All proteins, by default 2
  -wd WORKING, --working WORKING
                        Working Directory: Temporary directory to write files

Input File: It allow users to provide input in the FASTA format.

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

Threshold: User should provide threshold between 0 and 1, by default its 0.5.

Display type: This option allow users to display only il2 inducing peptides or all the input peptides.

Working Directory: Directory where intermediate files as well as final results will be saved

il2Pred Package Files

It contains the following files, brief description of these files given below

LICENSE : License information

README.md : This file provide information about this package

model1 : First dataset model

model2 : Second dataset model

model3 : Third dataset model

merci : This folder contains merci locator file

motifs1 : This folder contain motifs of main dataset

motifs2 : This folder contain motifs of second dataset

motifs3 : This folder contain motifs of third dataset

il2Pred.py : Main python program

peptide.fa : Example file containing peptide sequences in FASTA format

output.csv : Example output file for the program