Improved method for predicting phi-turns in proteins using a two-stage classifier.
Phi-turns are irregular secondary structure elements consisting of short backbone fragments (six-amino-acid residues) where the backbone reverses its overall direction. They play an important role in proteins from both the structural and functional points of view. Recently, some methods have been proposed to predict phi-turns. In this study, a new method of phi-turn prediction that uses a two-stage classification scheme is proposed based on support vector machine. In addition, different from previous methods, new coding schemes based on the physicochemical properties and the structural properties of proteins are adopted. Seven-fold cross validation based on a dataset of 640 non-homologue protein chains is used to evaluate the performance of our method. The experiment results show our method can yield a promising performance, which confirms the effectiveness of the proposed approach.
Xia
Jun-Feng
JF
Intelligent Computing Laboratory, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, 230031, China. jfxia@mail.ustc.edu.cn
You
Zhu-Hong
ZH
Wu
Min
M
Wang
Shu-Lin
SL
Zhao
Xing-Ming
XM
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*
D011506:Proteins