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

• Published in: BMC bioinformatics Vol. 11; no. 1; p. 381
• Main Authors: Liang, Shide, Zheng, Dandan, Standley, Daron M, Yao, Bo, Zacharias, Martin, Zhang, Chi
• Format: Journal Article
• Language: English
• Published: London BioMed Central 16.07.2010; BioMed Central Ltd; BMC
• Subjects: Algorithms; Antigen-Antibody Complex - chemistry; Antigen-Antibody Complex - immunology; Antigenic determinants; Applications software; Bioinformatics; Biomedical and Life Sciences; Computational biology; Computational Biology - methods; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Epitope Mapping; Epitopes, B-Lymphocyte - chemistry; Epitopes, B-Lymphocyte - immunology; Humans; Life Sciences; Methodology; Methodology Article; Microarrays; Physiological aspects; Regression Analysis; ROC Curve; Structural analysis; United States; Germany; Support Vector Regression Model; Surface Patch; Surface Residue; Antigenic Epitope; Support Vector Regression
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-11-381

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 . 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).