A two-stage classifier for protein β-turn prediction using support vector machines

beta-turns play an important role in protein structures not only because of their sheer abundance, which is estimated to be approximately 25% of all protein residues, but also because of their significance in high-order structures of proteins. In this study, we introduce a new method of beta-turn prediction that uses a two-stage classification scheme and an integrated framework for input features. Ten-fold cross validation based on a benchmark dataset of 426 non-homologue protein chains is used to evaluate our method's performance. The experimental results demonstrate that it achieves substantial improvements over BetaTurn, the current best method. The prediction accuracy, Q(total), and the Matthews correlation coefficient (MCC) of our approach are 79% and 0.47 respectively, compared to 77% and 0.45 respectively for BetaTurn.