Two-stage multi-class support vector machines to protein secondary structure prediction

Bioinformatics techniques to protein secondary structure (PSS) prediction are mostly single-stage approaches in the sense that they predict secondary structures of proteins by taking into account only the contextual information in amino acid sequences. In this paper, we propose two-stage Multi-class Support Vector Machine (MSVM) approach where a MSVM predictor is introduced to the output of the first stage MSVM to capture the sequential relationship among secondary structure elements for the prediction. By using position specific scoring matrices, generated by PSI-BLAST, the two-stage MSVM approach achieves Q(3) accuracies of 78.0% and 76.3% on the RS126 dataset of 126 nonhomologous globular proteins and the CB396 dataset of 396 nonhomologous proteins, respectively, which are better than the highest scores published on both datasets to date.