Novel structure-driven features for accurate prediction of protein structural class

Abstract

Prediction of protein structural class plays an important role in inferring tertiary structure and function of a protein. Extracting good representation from protein sequence is fundamental for this prediction task. In this paper, a novel computational method is proposed to predict protein structural class solely from the predicted secondary structure information. A total of 27 features rationally divided into 3 different groups are extracted to characterize general contents and spatial arrangements of the predicted secondary structural elements. Then, a multi-class nonlinear support vector machine classifier is used to implement prediction. Various prediction accuracies evaluated by the jackknife cross-validation test are reported on four widely-used low-homology benchmark datasets. Comparing with the state-of-the-art in protein structural class prediction, the proposed method achieves the highest overall accuracies on all the four datasets. The experimental results confirm that the proposed structure-driven features are very useful for accurate prediction of protein structural class.