A method for predicting toxicity of the peptides
ToxinPred3.0 is developed for predicting, mapping and scanning toxic/non-toxic peptides. It uses only composition based features for predicting toxic/non-toxic peptides. The final model also deploys a motif-based module which has been implemented using MERCI. More information on ToxinPred3.0 is available from its web server http://webs.iiitd.edu.in/raghava/toxinpred3. Please read/cite the content about toxinpred3.0 for complete information including algorithm behind the approach.
PIP version is also available for easy installation and usage of this tool. The following command is required to install the package
pip install toxinpred3
To know about the available option for the pip package, type the following command:
toxinpred3 -h
Standalone version of ToxinPred3.0 is written in python3 and the following libraries are necessary for a successful run:
- scikit-learn
!pip install scikit-learn==1.0.2
- Pandas
- Numpy
- Due to large size of the model file, we have compressed model.
- It is crucial to unzip the file before attempting to use the code or model. The compressed file must be extracted to its original form for the code to function properly.
Minimum USAGE
To know about the available option for the standalone, type the following command:
toxinpred3.py -h
To run the example, type the following command:
toxinpred3.py -i peptide.fa
Full Usage:
Following is complete list of all options, you may get these options
usage: toxinpred3.py [-h]
[-i INPUT]
[-o OUTPUT]
[-t THRESHOLD]
[-m {1,2}]
[-d {1,2}]
Please provide following arguments
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
-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 0.38
-m {1,2}, -- model Model
Model: 1: ML model, 2: Hybrid model, by default 2
-d {1,2}, --display {1,2}
Display: 1:Toxin peptide, 2: All peptides, by
default 1
Input File: It allow users to provide input in two format; i) FASTA format (standard) (e.g. peptide.fa) 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).
Output File: Program will save result in CSV format, in case user do not provide output file name, it will be stored in outfile.csv.
Threshold: User should provide threshold between 0 and 1, please note score is proportional to toxic potential of peptide.
Models: In this program, two models have been incorporated; i) Model1 for predicting given input peptide sequence as toxic and non-toxic peptide using Extra tree based on amino-acid composition (AAC) and di peptide composition (DPC) of the peptide;
ii) Model2 for predicting given input peptide sequence as toxic and non-toxic peptide using Hybrid approach, which is the ensemble of Extra tree + MERCI. It combines the scores generated from machine learning (ET), and MERCI as Hybrid Score, and the prediction is based on Hybrid Score.
It contain following files, brief description of these files given below
INSTALLATION : Installation instructions
LICENSE : License information
merci : This folder contains the program to run MERCI
README.md : This file provide information about this package
toxinpred3.py : Main python program
peptide.fa : Example file contain peptide sequences in FASTA format
peptide.seq : Example file contain peptide sequences in simple format
User can install ToxinPred3 via PIP also
pip install toxinpred3
Rathore AS, Arora A, Choudhury S, Tijare P, Raghava GPS (2024) ToxinPred3.0:An improved method for predicting the toxicity of peptides. Comput Biol Med. 179:108926 . https://doi.org/10.1016/j.compbiomed.2024.108926
Rathore AS, Arora A, Choudhury S, Tijare P, Raghava GPS. ToxinPred3.0:An improved method for predicting the toxicity of peptides. bioRxiv 2023.08.11.552911; doi: https://doi.org/10.1101/2023.08.11.552911
