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

BMC Bioinformatics

Methodology Article

Dandan Zheng¹  Bo Yao²  Chi Zhang²  Martin Zacharias³  Shide Liang⁴  Daron M Standley⁵ 

[1] Department of Radiation Oncology, Massey Cancer Center, Virginia Commonwealth University, 23298, Richmond, VA, USA;School of Biological Sciences, University of Nebraska, 68588, Lincoln, NE, USA;School of Engineering and Science, Jacobs University Bremen, Campus Ring 1, D-28759, Bremen, Germany;Physics Department, Technical University Munich, James Franck Str, D-85747, Garching, Germany;School of Engineering and Science, Jacobs University Bremen, Campus Ring 1, D-28759, Bremen, Germany;Systems Immunology Lab, Immunology Frontier Research Center, Osaka University, Suita, 565-0871, Osaka, Japan;Systems Immunology Lab, Immunology Frontier Research Center, Osaka University, Suita, 565-0871, Osaka, Japan;

关键词: Support Vector Regression;    Surface Patch;    Antigenic Epitope;    Surface Residue;    Support Vector Regression Model;   

DOI  :  10.1186/1471-2105-11-381

 received in 2010-03-21, accepted in 2010-07-16,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundAccurate 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.ResultsIn 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.ConclusionThe 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).

【 授权许可】

Unknown   
© Liang et al; licensee BioMed Central Ltd. 2010. This article is published under license to 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.