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.
Keywords: Linear B-cell epitope prediction; Support vector machine.