Open Access Methodology article

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

Shide Liang13, Dandan Zheng2, Daron M Standley3, Bo Yao5, Martin Zacharias14* and Chi Zhang5*

Author Affiliations

1 School of Engineering and Science, Jacobs University Bremen, Campus Ring 1, D-28759 Bremen, Germany

2 Department of Radiation Oncology, Massey Cancer Center, Virginia Commonwealth University, Richmond, VA, 23298, USA

3 Systems Immunology Lab, Immunology Frontier Research Center, Osaka University, Suita, Osaka, 565-0871, Japan

4 Physics Department, Technical University Munich, James Franck Str., D-85747 Garching, Germany

5 School of Biological Sciences, University of Nebraska, Lincoln, NE, 68588, USA

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BMC Bioinformatics 2010, 11:381  doi:10.1186/1471-2105-11-381

Published: 16 July 2010



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.


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 webcite.


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).