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 for complete information including algorithm behind the approach.

Installation

To install the package, type the following command:

pip install toxinpred3

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.