Journal
PEPTIDES
Volume 24, Issue 4, Pages 629-630Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/S0196-9781(03)00100-1
Keywords
long distance interaction; Support Vector Machine; tight turns
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available