Abstract: This paper describes a method developed for predicting bacterial
toxins from their amino acid sequences. All the modules, developed in this
study, were trained and tested on a non-redundant dataset of 150 bacterial
toxins that included 77 exotoxins and 73 endotoxins. Firstly, support vector
machines (SVM) based modules were developed for predicting the bacterial toxins
using amino acids and dipeptides composition and achieved an accuracy of
96.07% and 92.50%, respectively. Secondly, SVM based modules were
developed for discriminating entotoxins and exotoxins, using amino acids and
dipeptides composition and achieved an accuracy of 95.71% and 92.86%,
respectively. In addition, modules have been developed for classifying the
exotoxins (e.g. activate adenylate cyclase, activate guanylate cyclase,
neurotoxins) using hidden Markov models (HMM), PSI-BLAST and a combination of
the two and achieved overall accuracy of 95.75%, 97.87% and 100%,
respectively. Based on the above study, a web server called 'BTXpred' has been
developed, which is available at http://www.imtech.res.in/raghava/btxpred/. Supplementary information is available at
http://www.imtech.res.in/raghava/btxpred/supplementary.html.
Keywords: Bacterial toxins, exotoxins, endotoxins, BTXpred, prediction server
