Real value prediction of protein solvent accessibility using enhanced PSSM features

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 ...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Darby Tien-Hao Chang, Hsuan-Yu Huang, Yu-Tang Syu, Chih-Peng Wu
• Keywords: ASA Prediction, BMCBI 2008, Prediction, Solvent Accessibility