Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2013, Article ID 870372, 9 pages
Research Article

Predicting β-Turns in Protein Using Kernel Logistic Regression

1School of Information Science and Engineering, Central South University, Changsha 410083, China
2Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, Canada S7N 5A9

Received 15 September 2012; Accepted 22 December 2012

Academic Editor: Zhirong Sun

Copyright © 2013 Murtada Khalafallah Elbashir et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A β-turn is a secondary protein structure type that plays a significant role in protein configuration and function. On average 25% of amino acids in protein structures are located in β-turns. It is very important to develope an accurate and efficient method for β-turns prediction. Most of the current successful β-turns prediction methods use support vector machines (SVMs) or neural networks (NNs). The kernel logistic regression (KLR) is a powerful classification technique that has been applied successfully in many classification problems. However, it is often not found in β-turns classification, mainly because it is computationally expensive. In this paper, we used KLR to obtain sparse β-turns prediction in short evolution time. Secondary structure information and position-specific scoring matrices (PSSMs) are utilized as input features. We achieved of 80.7% and MCC of 50% on BT426 dataset. These results show that KLR method with the right algorithm can yield performance equivalent to or even better than NNs and SVMs in β-turns prediction. In addition, KLR yields probabilistic outcome and has a well-defined extension to multiclass case.