Bioinformatics Vol. 19 no. 13 2003
Pages 1656-1663
© 2003 Oxford University Press
Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan
Received on November 6, 2002 ; revised on January 24, 2003 and March 17, 2003
Motivation: The subcellular location of a protein is closely correlated to its function. Thus, computational prediction of subcellular locations from the amino acid sequence information would help annotation and functional prediction of protein coding genes in complete genomes. We have developed a method based on support vector machines (SVMs).
Results: We considered 12 subcellular locations in eukaryotic cells: chloroplast, cytoplasm, cytoskeleton, endoplasmic reticulum, extracellular medium, Golgi apparatus, lysosome, mitochondrion, nucleus, peroxisome, plasma membrane, and vacuole. We constructed a data set of proteins with known locations from the SWISS-PROT database. A set of SVMs was trained to predict the subcellular location of a given protein based on its amino acid, amino acid pair, and gapped amino acid pair compositions. The predictors based on these different compositions were then combined using a voting scheme. Results obtained through 5-fold cross-validation tests showed an improvement in prediction accuracy over the algorithm based on the amino acid composition only. This prediction method is available via the Internet.
Availability: http://www.genome.ad.jp/SIT/ploc.html
Supplementary information: http://web.kuicr.kyoto-u.ac.jp/~park/Seqdata/
Contact: kanehisa{at}kuicr.kyoto-u.ac.jp
* To whom correspondence should be addressed.
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