Support Vector Machine for predicting α-turn types

被引:70
作者
Cai, YD
Feng, KY
Li, YX
Chou, KC
机构
[1] Chinese Acad Sci, Shanghai Res Ctr Biotechnol, Shanghai 200233, Peoples R China
[2] Univ Manchester, Sch Med, Manchester M13 9PT, Lancs, England
[3] Chinese Acad Sci, Bioinformat Ctr, Shanghai Inst Biol Sci, Shanghai 200030, Peoples R China
[4] Gordon Life Sci Inst, Kalamazoo, MI 49009 USA
关键词
long distance interaction; Support Vector Machine; tight turns;
D O I
10.1016/S0196-9781(03)00100-1
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Tight turns play an important role in globular proteins from both the structural and functional points of view. Of tight turns, P-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. (C) 2003 Elsevier Science Inc. All rights reserved.
引用
收藏
页码:629 / 630
页数:2
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