Prediction of the beta-hairpins in proteins using support vector machine.

By using of the composite vector with increment of diversity and scoring function to express the information of sequence, a support vector machine (SVM) algorithm for predicting beta-hairpin motifs is proposed. The prediction is done on a dataset of 3,088 non homologous proteins containing 6,027 beta-hairpins. The overall accuracy of prediction and Matthew's correlation coefficient are 79.9% and 0.59 for the independent testing dataset. In addition, a higher accuracy of 83.3% and Matthew's correlation coefficient of 0.67 in the independent testing dataset are obtained on a dataset previously used by Kumar et al. (Nuclic Acid Res 33:154-159). The performance of the method is also evaluated by predicting the beta-hairpins of in the CASP6 proteins, and the better results are obtained. Moreover, this method is used to predict four kinds of supersecondary structures. The overall accuracy of prediction is 64.5% for the independent testing dataset.