Sequence based prediction of relative solvent accessibility using two-stage support vector regression with confidence values

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http://www.scirp.org/journal/PaperInformation.aspx?PaperID=7#.VNMsPyzQrzE

Author(s)  

Ke Chen, Michal Kurgan, Lukasz Kurgan

Affiliation(s)

Department of Electrical and Computer Engineering, University of Alberta, T6G 2V4, Edmonton, CANADA..
Department of Electrical and Computer Engineering, University of Alberta, T6G 2V4, Edmonton, CANADA..
Department of Electrical and Computer Engineering, University of Alberta, T6G 2V4, Edmonton, CANADA..

ABSTRACT

Predicted relative solvent accessibility (RSA) provides useful information for prediction of binding sites and reconstruction of the 3D-structure based on a protein sequence. Recent years observed development of several RSA prediction methods including those that generate real values and those that predict discrete states (buried vs. exposed). We propose a novel method for real value prediction that aims at minimizing the prediction error when compared with six existing methods. The proposed method is based on a two-stage Support Vector Regression (SVR) predictor. The improved prediction quality is a result of the developed composite sequence representation, which includes a custom-selected subset of features from the PSI-BLAST profile, secondary structure predicted with PSI-PRED, and binary code that indicates position of a given residue with respect to sequence termini. Cross validation tests on a benchmark dataset show that our method achieves 14.3 mean absolute error and 0.68 correlation. We also propose a confidence value that is associated with each predicted RSA values. The confidence is computed based on the difference in predictions from the two-stage SVR and a second two-stage Linear Regression (LR) predictor. The confidence values can be used to indicate the quality of the output RSA predictions.

 

KEYWORDS

relative solvent accessibility, support vector regression, PSI-BLAST, PSI-PRED, secondary protein structure

Cite this paper

Chen, K. , Kurgan, M. and Kurgan, L. (2008) Sequence based prediction of relative solvent accessibility using two-stage support vector regression with confidence values. Journal of Biomedical Science and Engineering, 1, 1-9. doi: 10.4236/jbise.2008.11001.

 

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