SVMTriP: A Method to Predict B-Cell Linear Antigenic Epitopes

  • Bo Yao
  • Dandan Zheng
  • Shide Liang
  • Chi ZhangEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 2131)


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 This chapter describes the webserver of SVMTriP.

Key words

Linear B-cell epitope prediction Support vector machine 



The work is supported by funding under C.Z.’s startup funds from the University of Nebraska, Lincoln, NE. This work was completed utilizing the Holland Computing Center of the University of Nebraska.


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© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Quantitative Biomedical Research CenterUniversity of Texas Southwestern Medical CenterDallasUSA
  2. 2.Department of Radiation OncologyUniversity of Nebraska Medical CenterOmahaUSA
  3. 3.Department of R&DBio-Thera SolutionsGuangzhouChina
  4. 4.School of Biological Sciences, University of Nebraska – LincolnLincolnUSA

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