Prediction of Discontinuous B-Cell Epitopes Using Logistic Regression and Structural Information
Rong Liu and Jianjun Hu*
Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208
- *Corresponding Author:
- Dr. Jianjun Hu
Department of Computer Science and Engineering
University of South Carolina, Columbia, SC 29208
E-mail: [email protected], [email protected]
Received Date: December 02, 2010; Accepted Date: January 02, 2011; Published Date: January 04, 2011
Citation: Liu R, Hu J (2011) Prediction of Discontinuous B-Cell Epitopes Using Logistic Regression and Structural Information. J Proteomics Bioinform 4: 010-015. doi: 10.4172/jpb.1000161
Copyright: © 2011 Liu R, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Computational prediction of discontinuous B-cell epitopes remains challenging, but it is an important task in vaccine design. In this study, we developed a novel computational method to predict discontinuous epitope residues by combining the logistic regression model with two important structural features, B-factor and relative accessible surface area (RASA). We conducted five-fold cross-validation on a representative dataset composed of antigen structures bound with antibodies and independent testing on Epitome database, respectively. Experimental results indicate that besides the well-known RASA feature, B-factor can also be used to identify discontinuous epitopes. Furthermore, these two features are complementary and their combination can remarkably improve the prediction performance. Comparison with existing approaches shows that our method can achieve better performance in terms of average AUC value and sensitivity for predicting discontinuous B-cell epitopes.