SVMTriP: A method to predict B-Cell linear antigenic epitopes

Bo Yao, Dandan Zheng, Shide Liang, Chi Zhang

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations

Abstract

Identifying protein antigenic epitopes recognizable by antibodies is the key step for new immuno-diagnostic reagent discovery and vaccine design. To facilitate this process and improve its efficiency, computational methods were developed to predict antigenic epitopes. For the linear B-cell epitope prediction, many methods were developed, including BepiPred, ABCPred, AAP, BCPred, BayesB, BEOracle/BROracle, BEST, and SVMTriP. Among these methods, SVMTriP, a frontrunner, utilized Support Vector Machine by combining the tri-peptide similarity and Propensity scores. Applied on non-redundant B-cell linear epitopes extracted from IEDB, SVMTriP achieved a sensitivity of 80.1% and a precision of 55.2% with a five-fold cross-validation. The AUC value was 0.702. The combination of similarity and propensity of tri-peptide subsequences can improve the prediction performance for linear B-cell epitopes. A webserver based on this method was constructed for public use. The server and all datasets used in the corresponding study are available at http://sysbio.unl.edu/SVMTriP. This chapter describes the webserver of SVMTriP.

Original languageEnglish (US)
Title of host publicationMethods in Molecular Biology
PublisherHumana Press Inc.
Pages299-307
Number of pages9
DOIs
StatePublished - 2020

Publication series

NameMethods in Molecular Biology
Volume2131
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029

Keywords

  • Linear B-cell epitope prediction
  • Support vector machine

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics

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