BTXpred: prediction of bacterial toxins.

BTXpred: prediction of bacterial toxins. Research Abstract Details 

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BTXpred: prediction of bacterial toxins. Abstract Text:

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.

BTXpred: prediction of bacterial toxins. Publishing Authors By Initials

For similar investigative techniques: genetic techniques: sequence analysis: sequence analysis, protein research abstracts see: investigative techniques: genetic techniques: sequence analysis: sequence analysis, protein research

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BTXpred: prediction of bacterial toxins. Journal Published:

PUBLICATION TYPE: Validation Studies

Journal: In silico biology

VOLUME: 7

Page Numbers: 405-12

Journal Abbreviation: In Silico Biol. (Gedrukt)

ISSN: 1386-6338

DAY: 28

MONTH: 04

YEAR: 2007

BTXpred: prediction of bacterial toxins. Information

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LANGUAGE: eng

NlmUniqueID: 9815902

BTXpred: prediction of bacterial toxins. Keywords Mesh Terms:

KEYWORDS: Sequence Analysis, Protein

MESH TERMS: methods

Chemical & Substance for Abstract: BTXpred: prediction of bacterial toxins. Information

Substance Name: Bacterial Toxins

Registry Number: 0

Grant and Affiliation Information for BTXpred: prediction of bacterial toxins.

AFFILIATION: Institute of Microbial Technology Sector-39A, Chandigarh, India.

Country: Netherlands

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MEDLINETA: In Silico Biol

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