Prediction β-hairpin motifs in enzyme protein using three methods

Abstract

The authors use three methods, including matrix scoring algorithm, increment of diversity algorithm and Random Forest algorithm. They are used to predict β-hairpin motifs in the ArchDB-EC and ArchDB40 dataset. In the ArchDB-EC dataset, we obtain the accuracy of 68.5%, 79.8% and 84.3%, respectively. Matthew's correlation coefficient are 0.17, 0.61 and 0.63, respectively. Using same three methods in the ArchDB40 dataset, we obtain the accuracy and Matthew's correlation coefficient of 67.9% and 0.39, 75.2% and 0.51, 83.5% and 0.60, respectively. Experiments show that Random Forest algorithm for predicting β-hairpin motifs is best and the predictive results in ArchDB40 dataset are better than previous results.