Real value prediction of protein solvent accessibility using enhanced PSSM features

Darby Tien-Hao Chang,Chih-Peng Wu,Hsuan-Yu Huang,Yu-Tang Syu

doi:10.1186/1471-2105-9-s12-s12

Darby Tien-Hao Chang, Chih-Peng Wu + Show 2 more

Open Access

https://doi.org/10.1186/1471-2105-9-s12-s12

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Journal: BMC Bioinformatics	Publication Date: Dec 1, 2008
Citations: 60	License type: CC BY 2.0

Affiliation: National Cheng Kung University

Abstract

BackgroundPrediction 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).ResultsThis 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.ConclusionExperimental 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.

Highlights

Prediction of protein solvent accessibility, called accessible surface area (ASA) prediction, is an important step for tertiary structure prediction directly from one-dimensional sequences
Solvent accessibility is considered as a crucial factor in protein folding and prediction of protein solvent accessibility, called accessible surface area (ASA) prediction, is an important step in tertiary structure prediction [3]
The feature selection mechanism incorporated in this study can be applied to other regression problems using the position specific scoring matrix (PSSM)

Summary

Introduction

Prediction of protein solvent accessibility, called accessible surface area (ASA) prediction, is an important step for tertiary structure prediction directly from one-dimensional sequences. Various machine learning methods have been adopted, including neural networks [4,5,6,7,8,9,10,11], Bayesian statistics [12], logistic functions [13], information theory [14,15,16] and support vector machines (SVMs) [17,18,19] Among these machine learning methods, neural networks were the first technique used in predicting protein solvent accessibility and are still extensively adopted in recent works. Several features were used to train these machine learning methods, such as local residue composition [4,5], probability profiles [20] and position specific scoring matrix (PSSM) [21]

Methods

Results

Conclusion