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

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.

Keywords: protein binding site support vector machine regression cross-validation neighbour residue

Received: 25 June 2010
Revised: 07 July 2010
Accepted manuscript online:

PACS:	87.14.E-	(Proteins)
	87.15.A-	(Theory, modeling, and computer simulation)
	87.15.K-	(Molecular interactions; membrane-protein interactions)
	87.15.N-	(Properties of solutions of macromolecules)

Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 10674172 and 10874229).

Cite this article:

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 2010 Chin. Phys. B 19 110502

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

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