title: Support vector machine for predicting alpha-turn types.
abstract: Tight turns play an important role in globular proteins from both the structural and functional points of view. Of tight turns, beta-turns and gamma-turns have been extensively studied, but alpha-turns were little investigated. Recently, a systematic search for alpha-turns classified alpha-turns into nine different types according to their backbone trajectory features. In this paper, Support Vector Machines (SVMs), a new machine learning method, is proposed for predicting the alpha-turn types in proteins. The high rates of correct prediction imply that that the formation of different alpha-turn types is evidently correlated with the sequence of a pentapeptide, and hence can be approximately predicted based on the sequence information of the pentapeptide alone, although the incorporation of its interaction with the other part of a protein, the so-called "long distance interaction", will further improve the prediction quality.
authors.affiliation: Shanghai Research Centre of Biotechnology, Chinese Academy of Sciences, Shanghai, China. ycai@umist.ac.uk
authors.forename: Yu-Dong
authors.initials: YD
authors.lastname: Cai
authors.forename: Kai-Yan
authors.initials: KY
authors.lastname: Feng
authors.forename: Yi-Xue
authors.initials: YX
authors.lastname: Li
authors.forename: Kuo-Chen
authors.initials: KC
authors.lastname: Chou
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D005822:Genetic Vectors; D008969:Molecular Sequence Data; D010455:Peptides / Q000737:chemistry; D011487:Protein Conformation; D017433:Protein Structure, Secondary; D012984:Software
chemical_list: D010455:Peptides