Prediction of relative solvent accessibility using pace regression
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Date
2010-02-09T14:07:18Z
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Abstract
In this paper, a new approach for prediction of protein solvent accessibility is presented. The prediction of relative solvent accessibility gives helpful information for the prediction of native
structure of a protein. Recent years several RSA prediction methods including those that
generate real values and those that predict discrete states (buried vs. exposed) have been developed. We propose a novel method for real value prediction that aims at minimizing the prediction error when compared with existing methods. The proposed method is based on Pace Regression (PR) predictor. The improved prediction quality is a result of features of PSIBLAST
profile and the PR method because pace regression is optimal when the number of
coefficients tends to infinity. The experiment results on Manesh dataset show that the proposed method is an improvement in average prediction accuracy and training time.
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Keywords
pace regression, PSI-BLAST, relative solvent accessibility