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

•Secondary structure-based approach of feature extraction is used.•Novel features are quite relevant to protein secondary structure.•Distance-related features can improve prediction performance for the mixed classes.•Experimental results show that our method is an effective computational tool.

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.