BTXpred: prediction of bacterial toxins

Welcome to the official repository for BTXpred, a computational method developed for the prediction and classification of bacterial toxins using protein sequence information and machine learning techniques. Web Server: https://webs.iiitd.edu.in/raghava/btxpred/index.html Citation Saha, S., & Raghava, G. P. S. (2007). BTXpred: prediction of bacterial toxins. In Silico Biology, 7(4-5), 405–412. PMID: 18391233 About the Tool BTXpred is a bioinformatics-based prediction system designed to identify bacterial toxins directly from amino acid sequences. The method employs multiple computational approaches including Support Vector Machines (SVM), Hidden Markov Models (HMM), and PSI-BLAST for accurate toxin classification. The system was trained and validated on a non-redundant dataset containing both exotoxins and endotoxins. Key Features Bacterial Toxin Prediction Predicts whether a protein sequence is a bacterial toxin Sequence-based computational analysis Toxin Classification Differentiates: Exotoxins Endotoxins Exotoxin Family Identification Classifies exotoxins into categories such as: Neurotoxins Adenylate cyclase activating toxins Guanylate cyclase activating toxins