title: Support vector machines for predicting membrane protein types by using functional domain composition.
abstract: 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.
authors.affiliation: Shanghai Research Centre of Biotechnology, Chinese Academy of Sciences, Shanghai 200233, China. y.cai@umist.ac.uk
authors.forename: Yu-Dong
authors.initials: YD
authors.lastname: Cai
authors.forename: Guo-Ping
authors.initials: GP
authors.lastname: Zhou
authors.forename: Kuo-Chen
authors.initials: KC
authors.lastname: Chou
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D003205:Computing Methodologies; D016002:Discriminant Analysis; D008565:Membrane Proteins / Q000737:chemistry* / Q000145:classification*; D008969:Molecular Sequence Data; D011487:Protein Conformation; D017434:Protein Structure, Tertiary; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D008565:Membrane Proteins