A high-accuracy protein structural class prediction algorithm using predicted secondary structural information

One major problem with the existing algorithm for the prediction of protein structural classes is low accuracies for proteins from alpha/beta and alpha+beta classes. In this study, three novel features were rationally designed to model the differences between proteins from these two classes. In combination with other rational designed features, an 11-dimensional vector prediction method was proposed. By means of this method, the overall prediction accuracy based on 25PDB dataset was 1.5% higher than the previous best-performing method, MODAS. Furthermore, the prediction accuracy for proteins from alpha+beta class based on 25PDB dataset was 5% higher than the previous best-performing method, SCPRED. The prediction accuracies obtained with the D675 and FC699 datasets were also improved. (C) 2010 Elsevier Ltd. All rights reserved.