BTXpred: prediction of bacterial toxins.

Saha, Sudipto and Raghava, G.P.S. (2007) BTXpred: prediction of bacterial toxins. In silico biology, 7 (4-5). pp. 405-412. ISSN 1386-6338

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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.

Item Type:	Article
Additional Information:	Supplementary information is available at http://www.imtech.res.in/raghava/btxpred/supplementary.html.
Uncontrolled Keywords:	bacterial toxins, exotoxins, endotoxins, BTXpred, prediction server
Subjects:	Q Science > QH Natural history > QH301 Biology QH301 Biology
Depositing User:	Dr. K.P.S.Sengar
Date Deposited:	08 Dec 2011 19:41
Last Modified:	08 Dec 2011 19:41
URI:	http://crdd.osdd.net/open/id/eprint/606

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