EPSVR and EPMeta: prediction of antigenic epitopes using support vector regression and multiple server results

Authors

Shide Liang, Jacobs University Bremen
Dandan Zheng, Virginia Commonwealth UniversityFollow
Daron M. Standley, Osaka University
Bo Yao, University of Nebraska
Martin Zacharias, Jacobs University Bremen
Chi Zhang, University of Nebraska

Document Type

Article

Original Publication Date

2010

Journal/Book/Conference Title

BMC Bioinformatics

DOI of Original Publication

10.1186/1471-2105-11-381

Comments

Originally published at http://dx.doi.org/10.1186/1471-2105-11-381

Date of Submission

August 2014

Abstract

Background Accurate prediction of antigenic epitopes is important for immunologic research and medical applications, but it is still an open problem in bioinformatics. The case for discontinuous epitopes is even worse - currently there are only a few discontinuous epitope prediction servers available, though discontinuous peptides constitute the majority of all B-cell antigenic epitopes. The small number of structures for antigen-antibody complexes limits the development of reliable discontinuous epitope prediction methods and an unbiased benchmark to evaluate developed methods.

Results In this work, we present two novel server applications for discontinuous epitope prediction: EPSVR and EPMeta, where EPMeta is a meta server. EPSVR, EPMeta, and datasets are available at http://sysbio.unl.edu/services webcite.

Conclusion The server application for discontinuous epitope prediction, EPSVR, uses a Support Vector Regression (SVR) method to integrate six scoring terms. Furthermore, we combined EPSVR with five existing epitope prediction servers to construct EPMeta. All methods were benchmarked by our curated independent test set, in which all antigens had no complex structures with the antibody, and their epitopes were identified by various biochemical experiments. The area under the receiver operating characteristic curve (AUC) of EPSVR was 0.597, higher than that of any other existing single server, and EPMeta had a better performance than any single server - with an AUC of 0.638, significantly higher than PEPITO and Disctope (p-value < 0.05).

Rights

© 2010 Liang et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Is Part Of

VCU Radiation Oncology Publications