Improved method for predicting ?-turn using support vector machine

Motivation: Numerous methods for predicting β-turns in proteins have been developed based on various computational schemes. Here, we introduce a new method of β-turn prediction that uses the support vector machine (SVM) algorithm together with predicted secondary structure information. Various parameters from the SVM have been adjusted to achieve optimal prediction performance. Results: The SVM method achieved excellent performance as measured by the Matthews correlation coefficient (MCC = 0.45) using a 7-fold cross validation on a database of 426 non-homologous protein chains. To our best knowledge, this MCC value is the highest achieved so far for predicting β-turn. The overall prediction accuracy Qtotal was 77.3%, which is the best among the existing prediction methods. Among its unique attractive features, the present SVM method avoids overtraining and compresses information and provides a predicted reliability index. Availability: The algorithm is available via a web server on: http://serine.umdnj.edu/~zhangq3/betaturn/ Contact: welshwj@umdnj.edu Supplementary information: http://serine.umdnj.edu/~zhangq3/betaturn

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