title: Prediction of protein relative solvent accessibility with a two-stage SVM approach.
abstract: Information on relative solvent accessibility (RSA) of amino acid residues in proteins provides valuable clues to the prediction of protein structure and function. A two-stage approach with support vector machines (SVMs) is proposed, where an SVM predictor is introduced to the output of the single-stage SVM approach to take into account the contextual relationships among solvent accessibilities for the prediction. By using the position-specific scoring matrices (PSSMs) generated by PSI-BLAST, the two-stage SVM approach achieves accuracies up to 90.4% and 90.2% on the Manesh data set of 215 protein structures and the RS126 data set of 126 nonhomologous globular proteins, respectively, which are better than the highest published scores on both data sets to date. A Web server for protein RSA prediction using a two-stage SVM method has been developed and is available (http://birc.ntu.edu.sg/~pas0186457/rsa.html).
authors.affiliation: BioInformatics Research Centre, School of Computer Engineering, Nanyang Technological University, Singapore.
authors.forename: Minh N
authors.initials: MN
authors.lastname: Nguyen
authors.forename: Jagath C
authors.initials: JC
authors.lastname: Rajapakse
mesh_terms: D000596:Amino Acids; D001499:Bayes Theorem; D030562:Databases, Protein; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results; D012995:Solubility; D012997:Solvents
chemical_list: D000596:Amino Acids; D011506:Proteins; D012997:Solvents