Support Vector Machine-based method for predicting subcellular localization of mycobacterial proteins using evolutionary information and motifsReport as inadecuate




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BMC Bioinformatics

, 8:337

First Online: 13 September 2007Received: 15 May 2007Accepted: 13 September 2007

Abstract

BackgroundIn past number of methods have been developed for predicting subcellular location of eukaryotic, prokaryotic Gram-negative and Gram-positive bacteria and human proteins but no method has been developed for mycobacterial proteins which may represent repertoire of potent immunogens of this dreaded pathogen. In this study, attempt has been made to develop method for predicting subcellular location of mycobacterial proteins.

ResultsThe models were trained and tested on 852 mycobacterial proteins and evaluated using five-fold cross-validation technique. First SVM Support Vector Machine model was developed using amino acid composition and overall accuracy of 82.51% was achieved with average accuracy mean of class-wise accuracy of 68.47%. In order to utilize evolutionary information, a SVM model was developed using PSSM Position-Specific Scoring Matrix profiles obtained from PSI-BLAST Position-Specific Iterated BLAST and overall accuracy achieved was of 86.62% with average accuracy of 73.71%. In addition, HMM Hidden Markov Model, MEME-MAST Multiple Em for Motif Elicitation-Motif Alignment and Search Tool and hybrid model that combined two or more models were also developed. We achieved maximum overall accuracy of 86.8% with average accuracy of 89.00% using combination of PSSM based SVM model and MEME-MAST. Performance of our method was compared with that of the existing methods developed for predicting subcellular locations of Gram-positive bacterial proteins.

ConclusionA highly accurate method has been developed for predicting subcellular location of mycobacterial proteins. This method also predicts very important class of proteins that is membrane-attached proteins. This method will be useful in annotating newly sequenced or hypothetical mycobacterial proteins. Based on above study, a freely accessible web server TBpred http:-www.imtech.res.in-raghava-tbpred- has been developed.

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2105-8-337 contains supplementary material, which is available to authorized users.

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Author: Mamoon Rashid - Sudipto Saha - Gajendra PS Raghava

Source: https://link.springer.com/



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