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

doi:10.1088/1674-1056/19/11/110502

中国物理B ›› 2010, Vol. 19 ›› Issue (11): 110502-110201.doi: 10.1088/1674-1056/19/11/110502

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

江凡¹, 孙重华²

(1)Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China; (2)Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China; Graduate School of the Chinese Academy of Sciences, Beijing 100049, China

收稿日期:2010-06-25 修回日期:2010-07-07 出版日期:2010-11-15 发布日期:2010-11-15
基金资助:
Project supported by the National Natural Science Foundation of China (Grant Nos. 10674172 and 10874229).

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

Sun Zhong-Hua(孙重华)^a)b) and Jiang Fan(江凡)^a)†

^a Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China; ^b Graduate School of the Chinese Academy of Sciences, Beijing 100049, China

Received:2010-06-25 Revised:2010-07-07 Online:2010-11-15 Published:2010-11-15
Supported by:
Project supported by the National Natural Science Foundation of China (Grant Nos. 10674172 and 10874229).

摘要/Abstract

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.

Abstract: 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.

Key words: protein binding site, support vector machine regression, cross-validation, neighbour residue

中图分类号: (Proteins)

87.14.E-

87.15.A- (Theory, modeling, and computer simulation) 87.15.K- (Molecular interactions; membrane-protein interactions) 87.15.N- (Properties of solutions of macromolecules)

孙重华, 江凡. Prediction of protein binding sites using physical and chemical descriptors and the support vector machine regression method[J]. 中国物理B, 2010, 19(11): 110502-110201.

Sun Zhong-Hua(孙重华) and Jiang Fan(江凡). Prediction of protein binding sites using physical and chemical descriptors and the support vector machine regression method[J]. Chin. Phys. B, 2010, 19(11): 110502-110201.

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

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

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