Real value prediction of protein solvent accessibility using enhanced PSSM features.

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 propens

SUBMITTER: Chang DT 

PROVIDER: S-EPMC2638152 | biostudies-literature | 2008 Dec

REPOSITORIES: biostudies-literature