A Two-stage Classifier for Protein B-turn Prediction Using Support Vector Machines

β-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 β-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 demon- strate that it achieves substantial improvements over BetaTurn, the current best method. The prediction accuracy, Qtotal, and the Matthews correlation coefficient (MCC) of our approach are 79% and 0.47 respectively, compared to 77% and 0.45 respec- tively for BetaTurn.