Prediction of protein binding sites using physical and chemical descriptors and the support vector machine regression method

被引:0
|
作者
孙重华 [1 ,2 ]
江凡 [1 ]
机构
[1] Beijing National Laboratory for Condensed Matter Physics,Institute of Physics,Chinese Academy of Sciences
[2] Graduate School of the Chinese Academy of Sciences
基金
中国国家自然科学基金;
关键词
protein binding site; support vector machine regression; cross-validation; neighbour residue;
D O I
暂无
中图分类号
Q51 [蛋白质];
学科分类号
071010 ; 081704 ;
摘要
In this paper a new continuous variable called core-ratio is defined to describe the probability for a residue to be in a binding site,thereby replacing the previous binary description of the interface residue using 0 and 1.So we can use the support vector machine regression method to fit the core-ratio value and predict the protein binding sites.We also design a new group of physical and chemical descriptors to characterize the binding sites.The new descriptors are more effective,with an averaging procedure used.Our test shows that much better prediction results can be obtained by the support vector regression (SVR) method than by the support vector classification method.
引用
收藏
页码:5 / 10
页数:6
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