3-State Protein Secondary Structure Prediction based on SCOPe Classes

HIGHLIGHTS

DSPRED method based on machine learning algorithms to predict 3-state secondary structure elements.

Comparative results for secondary structure prediction.

High accuracy of 82.36% based on SCOPe (Structural Classification of Proteins - extended) structural classes.

Abstract

Improving the accuracy of protein secondary structure prediction has been an important task in bioinformatics since it is not only the starting point in obtaining tertiary structure in hierarchical modeling but also enhances sequence analysis and sequence-structure threading to help determine structure and function. Herein we present a model based on DSPRED classifier, a hybrid method composed of dynamic Bayesian networks and a support vector machine to predict 3-state secondary structure information of proteins. We used the SCOPe (Structural Classification of Proteins-extended) database to train and test the model. The results show that DSPRED reached a Q₃ accuracy rate of 82.36% when trained and tested using proteins from all SCOPe classes. We compared our method with the popular PSIPRED on the SCOPe test datasets and found that our method outperformed PSIPRED.

Keywords:
Protein secondary structure prediction; SCOPe; Support Vector Machine; Dynamic Bayesian Network