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BioMed Research International
Volume 2016 (2016), Article ID 4354901, 9 pages
http://dx.doi.org/10.1155/2016/4354901
Research Article

Identification of Hot Spots in Protein Structures Using Gaussian Network Model and Gaussian Naive Bayes

1School of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou, Zhejiang 310018, China
2School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, Zhejiang 310018, China
3School of Community Health Sciences, University of Nevada Las Vegas, Las Vegas, NV 89154, USA

Received 21 August 2016; Revised 2 October 2016; Accepted 11 October 2016

Academic Editor: Guang Hu

Copyright © 2016 Hua Zhang 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.

Abstract

Residue fluctuations in protein structures have been shown to be highly associated with various protein functions. Gaussian network model (GNM), a simple representative coarse-grained model, was widely adopted to reveal function-related protein dynamics. We directly utilized the high frequency modes generated by GNM and further performed Gaussian Naive Bayes (GNB) to identify hot spot residues. Two coding schemes about the feature vectors were implemented with varying distance cutoffs for GNM and sliding window sizes for GNB based on tenfold cross validations: one by using only a single high mode and the other by combining multiple modes with the highest frequency. Our proposed methods outperformed the previous work that did not directly utilize the high frequency modes generated by GNM, with regard to overall performance evaluated using measure. Moreover, we found that inclusion of more high frequency modes for a GNB classifier can significantly improve the sensitivity. The present study provided additional valuable insights into the relation between the hot spots and the residue fluctuations.