Combining Sequence Information and Predicted Secondary Structural Feature to Predict Protein Structural Classes

Abstract

Structural class of protein is important in understanding of folding patterns. Effective and reliable computational methods are needed for prediction of protein structural class. In this paper, a novel method for prediction of protein structural class was proposed, which combined protein sequence information and predicted secondary structural feature, and used support vector machine classifier to classify attributes of protein. Jackknife cross-validation was taken to evaluate the the performance of proposed method, using three benchmark datasets. Results demonstrate that the proposed method combining the predicted secondary structural feature with sequence information is more efficient than the existing methods, which indicates the necessity to extract more information to improve protein structural class prediction.