https://github.com/raghavagps/toxinpred3
An improved method for predicting toxicity of the peptides and designing of non-toxic peptides
https://github.com/raghavagps/toxinpred3
bioinformatics ensemble-machine-learning machine-learning-algorithms motif-prediction peptide-therapeutics toxicity-prediction
Last synced: 8 months ago
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An improved method for predicting toxicity of the peptides and designing of non-toxic peptides
- Host: GitHub
- URL: https://github.com/raghavagps/toxinpred3
- Owner: raghavagps
- License: gpl-3.0
- Created: 2023-06-13T11:10:37.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2025-05-29T11:47:56.000Z (about 1 year ago)
- Last Synced: 2025-09-15T01:58:41.391Z (9 months ago)
- Topics: bioinformatics, ensemble-machine-learning, machine-learning-algorithms, motif-prediction, peptide-therapeutics, toxicity-prediction
- Language: Python
- Homepage: http://webs.iiitd.edu.in/raghava/toxinpred3
- Size: 9.65 MB
- Stars: 15
- Watchers: 1
- Forks: 3
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ToxinPred3.0
A method for predicting toxicity of the peptides
# Introduction
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 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 toxinpred3
```
To know about the available option for the pip package, type the following command:
```
toxinpred3 -h
```
# Standalone
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
# Important Note
- 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.
ToxinPred3.0 Package Files
=======================
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
## Installation via PIP
User can install ToxinPred3 via PIP also
```
pip install toxinpred3
```
## Reference:
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