Prediction of beta-hairpins in proteins using physicochemical properties and structure information.
In this study, we propose a new method to predict hairpins in proteins and its evaluation based on the support vector machine. Different from previous methods, new feature representation scheme based on auto covariance is adopted. We also investigate two structure properties of proteins (protein secondary structure and residue conformation propensity), and examine their effects on prediction. Moreover, we employ an ensemble classifier approach based on the majority voting to improve prediction accuracy on hairpins. Experimental results on a dataset of 1926 protein chains show that our approach outperforms those previously published in the literature, which demonstrates the effectiveness of the proposed method.
Xia
Jun-Feng
JF
Intelligent Computing Laboratory, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, 230031, China.
Wu
Min
M
You
Zhu-Hong
ZH
Zhao
Xing-Ming
XM
Li
Xue-Ling
XL
D000596:Amino Acids / Q000737:chemistry; D019295:Computational Biology / Q000379:methods*; D057927:Hydrophobic and Hydrophilic Interactions; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*
D000596:Amino Acids; D011506:Proteins