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Prediction of mitochondrial proteins using support vector machine and hidden Markov model.

DIR@IMTECH: CSIR-Institute of Microbial Technology

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Title Prediction of mitochondrial proteins using support vector machine and hidden Markov model.
 
Creator Kumar, Manish
Verma, Ruchi
Raghava, G.P.S.
 
Subject QR Microbiology
 
Description Mitochondria are considered as one of the core organelles of eukaryotic cells hence prediction of mitochondrial proteins is one of the major challenges in the field of genome annotation. This study describes a method, MitPred, developed for predicting mitochondrial proteins with high accuracy. The data set used in this study was obtained from Guda, C., Fahy, E. & Subramaniam, S. (2004) Bioinformatics 20, 1785-1794. First support vector machine-based modules/methods were developed using amino acid and dipeptide composition of proteins and achieved accuracy of 78.37 and 79.38%, respectively. The accuracy of prediction further improved to 83.74% when split amino acid composition (25 N-terminal, 25 C-terminal, and remaining residues) of proteins was used. Then BLAST search and support vector machine-based method were combined to get 88.22% accuracy. Finally we developed a hybrid approach that combined hidden Markov model profiles of domains (exclusively found in mitochondrial proteins) and the support vector machine-based method. We were able to predict mitochondrial protein with 100% specificity at a 56.36% sensitivity rate and with 80.50% specificity at 98.95% sensitivity. The method estimated 9.01, 6.35, 4.84, 3.95, and 4.25% of proteins as mitochondrial in Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, mouse, and human proteomes, respectively. MitPred was developed on the above hybrid approach.
 
Publisher ASBMB
 
Date 2006-03-03
 
Type Article
PeerReviewed
 
Format application/pdf
 
Identifier http://crdd.osdd.net/open/160/1/raghava2006.pdf
Kumar, Manish and Verma, Ruchi and Raghava, G.P.S. (2006) Prediction of mitochondrial proteins using support vector machine and hidden Markov model. The Journal of biological chemistry, 281 (9). pp. 5357-63. ISSN 0021-9258
 
Relation http://www.jbc.org/content/281/9/5357.long
http://crdd.osdd.net/open/160/