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. 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 AUCs of EPSVR and EPMeta are 0.597 and 0.638,
respectively, which are higher than that of any other existing single server.
Chi Zhang obtained his Ph.D. in 2002, from University of Kentucky and had been postdoctoral fellow in the State University of New York at Buffalo
from 2002 to 2007. Now, he is an Assistant Professor in School of Biological Sciences, University of Nebraska, Lincoln, NE. He has published more
than 30 papers in peer-reviewed journals.
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