Real value solvent accessibility prediction using adaptive support vector regression

Abstract

Knowledge of the secondary structure and solvent accessibility of a protein plays a vital role in prediction of fold, and eventually the tertiary structure of the protein. This paper deals with prediction of relative solvent accessibility, given only the amino-acid sequence. In this paper, we use an improved support vector regression (SVR) and new kernels for real valued prediction of solvent accessibility. In this regard, two main issues are addressed. First we address the problem of c selection, which we found to be somewhat problematic in our earlier work (c is a parameter with significant influence on noise insensitivity and generalization of SVRs). In particular, rather than employ the ..