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This article is part of the supplement: Seventh International Conference on Bioinformatics (InCoB2008)

Open Access Research

Real value prediction of protein solvent accessibility using enhanced PSSM features

Darby Tien-Hao Chang1*, Hsuan-Yu Huang1, Yu-Tang Syu1 and Chih-Peng Wu2

Author Affiliations

1 Department of Electrical Engineering, National Cheng Kung University, Tainan, 70101, Taiwan, R.O.C

2 Department of Computer Science and Information Engineering, National Taiwan University, Taipei, 10617, Taiwan, R.O.C

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BMC Bioinformatics 2008, 9(Suppl 12):S12  doi:10.1186/1471-2105-9-S12-S12

Published: 12 December 2008

Abstract

Background

Prediction of protein solvent accessibility, also called accessible surface area (ASA) prediction, is an important step for tertiary structure prediction directly from one-dimensional sequences. Traditionally, predicting solvent accessibility is regarded as either a two- (exposed or buried) or three-state (exposed, intermediate or buried) classification problem. However, the states of solvent accessibility are not well-defined in real protein structures. Thus, a number of methods have been developed to directly predict the real value ASA based on evolutionary information such as position specific scoring matrix (PSSM).

Results

This study enhances the PSSM-based features for real value ASA prediction by considering the physicochemical properties and solvent propensities of amino acid types. We propose a systematic method for identifying residue groups with respect to protein solvent accessibility. The amino acid columns in the PSSM profile that belong to a certain residue group are merged to generate novel features. Finally, support vector regression (SVR) is adopted to construct a real value ASA predictor. Experimental results demonstrate that the features produced by the proposed selection process are informative for ASA prediction.

Conclusion

Experimental results based on a widely used benchmark reveal that the proposed method performs best among several of existing packages for performing ASA prediction. Furthermore, the feature selection mechanism incorporated in this study can be applied to other regression problems using the PSSM. The program and data are available from the authors upon request.