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BTXpred: prediction of bacterial toxins.

DIR@IMTECH: CSIR-Institute of Microbial Technology

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Title BTXpred: prediction of bacterial toxins.
 
Creator Saha, Sudipto
Raghava, G.P.S.
 
Subject QH301 Biology
 
Description 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.
 
Publisher Bioinformatiob System eV.
 
Date 2007
 
Type Article
PeerReviewed
 
Format text/html
 
Identifier http://crdd.osdd.net/open/606/1/raghavasilico.mht
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
 
Relation http://www.bioinfo.de/isb/2007070028/
http://crdd.osdd.net/open/606/