algpred3 1.3

Prediction of allergenic and non-allergenic peptides

AlgPred3

AlgPred3 is a computational framework for the prediction, analysis, and design of allergenic peptides using machine learning, sequence-derived features, and motif-based approaches.

Introduction

AlgPred3 is specifically designed for peptide-level allergen prediction using curated allergenic and non-allergenic peptide datasets. The final prediction model is based on an Extra Trees (ET) classifier trained using an optimized feature subset selected through SVC-L1 feature selection.

Key Features

• Prediction of allergenic and non-allergenic peptides
• Protein scanning for allergenic regions
• Peptide design and mutant generation
• Motif-based allergen detection
• SHAP-based model interpretability
• Web server, standalone package, GitHub repository, and PyPI package

Web Server

https://webs.iiitd.edu.in/raghava/algpred3/

Installation

pip install algpred3

GitHub Installation

git clone https://github.com/raghavagps/algpred3.git
cd algpred3
python algpred3.py -h

Usage

Prediction

python algpred3.py -i example.fasta -o prediction.csv -j pred

Protein Scan

python algpred3.py -i protein.fasta -o scan.csv -j scan

Design

python algpred3.py -i peptide.fasta -o design.csv -j des

Workflow

1. Sequence validation
2. Feature extraction
3. Feature selection (SVC-L1)
4. Extra Trees prediction
5. SHAP interpretation
6. Output generation

Citation

Kumar N, Mehta NK, Kumar P, Bajiya N, Rathore AS, Raghava GPS.

AlgPred3: Prediction of Allergenic Peptides and Identification of Allergen-Specific Sequence Motifs.

(Journal information to be added after publication)

Disclaimer

AlgPred3 is intended for research purposes only. Predictions should be considered computational assessments and hypothesis-generating results. Experimental validation is recommended.