期刊
PEPTIDES
卷 24, 期 4, 页码 629-630出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/S0196-9781(03)00100-1
关键词
long distance interaction; Support Vector Machine; tight turns
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.
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