Distinguishing Enzymes and Non-enzymes Based on Structural Information with an Alignment Free Approach

Abstract

Background: Knowledge of protein functions is very crucial for the understanding of biological processes. Experimental methods for protein function prediction are powerless to treat the growing amount of protein sequence and structure data. Objective: To develop some computational techniques for the protein function prediction. Method: Based on the residue interaction network features and the motion mode information, an SVM model was constructed and used as the predictor. The role of these features was analyzed and some interesting results were obtained. Results: An alignment-free method for the classification of enzyme and non-enzyme is developed in this work. There is not any single feature that occupies a dominant position in the prediction process. The topological and the information-theoretic residue interaction network features have a better performance. The combination of the fast mode and the slow mode can get a better explanation for the classification result. Conclusion: The method proposed in this paper can act as a classifier for the enzymes and nonenzymes.