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Biophysical Journal 84:3257-3263 (2003)
© 2003 The Biophysical Society

Support Vector Machines for Predicting Membrane Protein Types by Using Functional Domain Composition

Yu-Dong Cai *, Guo-Ping Zhou {dagger} and Kuo-Chen Chou {ddagger}

* Shanghai Research Centre of Biotechnology, Chinese Academy of Sciences, Shanghai 200233, China; {dagger} Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115; and {ddagger} Upjohn Laboratories, Pharmacia, Kalamazoo, Michigan 49007

Correspondence: Address reprint requests to Yu-Dong Cai, Biomolecular Sciences Dept., UMIST, P.O. Box 88, Manchester M60 1QD, UK. E-mail: y.cai{at}umist.ac.uk.

Membrane proteins are generally classified into the following five types: 1), type I membrane protein; 2), type II membrane protein; 3), multipass transmembrane proteins; 4), lipid chain-anchored membrane proteins; and 5), GPI-anchored membrane proteins. In this article, based on the concept of using the functional domain composition to define a protein, the Support Vector Machine algorithm is developed for predicting the membrane protein type. High success rates are obtained by both the self-consistency and jackknife tests. The current approach, complemented with the powerful covariant discriminant algorithm based on the pseudo-amino acid composition that has incorporated quasi-sequence-order effect as recently proposed by K. C. Chou (2001), may become a very useful high-throughput tool in the area of bioinformatics and proteomics.




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