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Title:
EPSVR and EPMeta: prediction of antigenic epitopes using support vector regression and multiple server results | BMC Bioinformatics | Full Text
Description:
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).
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Keywords {🔍}

prediction, residues, surface, epitope, epsvr, pubmed, article, antigenic, set, server, epitopes, residue, epitopic, number, predicted, google, scholar, epitopia, auc, structures, protein, epces, patch, central, cas, score, methods, discontinuous, test, complex, bmc, servers, training, proteins, bcell, structure, support, svr, single, accuracy, vector, pepito, seppa, antigenantibody, antigen, bioinformatics, epmeta, multiple, meta, method,

Topics {✒️}

springer nature support vector regression x-ray-derived accessible sites support vector classifier project home page linear b-cell epitopes author information authors predicting b-cell epitopes conclusions availability discontinuous b-cell epitopes b-cell antigenic epitopes continuous b-cell epitopes virus-specific synthetic peptide open access article pairwise t-student test multiple antibody-binding sites predict b-cell epitopes antigen-antibody complexes limits central surface residue authors scientific editing exposed protein-protein interfaces download andersen ph small c-terminal domain antigen-antibody complex structures privacy choices/manage cookies protein-protein binding interfaces protein-protein interface prediction authors’ original file epitope prediction methods trained svr model svr model trained epitope information derived cell epitope prediction article liang multiple server results residue epitope propensity discontinuous epitope prediction antigenic epitope consisted incorporating consensus results amino acid residue c-terminal domain prediction methods exist predicting effective epitopes massey cancer center conformational epitope database experimental antigenic epitope prediction tools evaluation residue interface propensity

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