%0 Journal Article
%T Support Vector Machine-based method for predicting subcellular localization of mycobacterial proteins using evolutionary information and motifs
%A Mamoon Rashid
%A Sudipto Saha
%A Gajendra PS Raghava
%J BMC Bioinformatics
%D 2007
%I BioMed Central
%R 10.1186/1471-2105-8-337
%X The 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.A 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.According to the GOLD (Genomes OnLine Database) database [1] as on 12th Dec, 2006 genomes of nine mycobacterial species have been sequenced and published creating a heap of about 45055 kb of genomic data. The coming years will see a lot more as genome-sequencing projects are holding about 19 mycobacterial species in pipeline. Moreover, functions of 48% of the predicted 3995 proteins of Mycobacterium tuberculosis H37Rv are yet to be assigned [2]. Therefore a robust and reliable computer algorithm for functional annotati
%U http://www.biomedcentral.com/1471-2105/8/337