title: SVMTriP: a method to predict antigenic epitopes using support vector machine to integrate tri-peptide similarity and propensity.
abstract: Identifying protein surface regions preferentially recognizable by antibodies (antigenic epitopes) is at the heart of new immuno-diagnostic reagent discovery and vaccine design, and computational methods for antigenic epitope prediction provide crucial means to serve this purpose. Many linear B-cell epitope prediction methods were developed, such as BepiPred, ABCPred, AAP, BCPred, BayesB, BEOracle/BROracle, and BEST, towards this goal. However, effective immunological research demands more robust performance of the prediction method than what the current algorithms could provide. In this work, a new method to predict linear antigenic epitopes is developed; Support Vector Machine has been utilized by combining the Tri-peptide similarity and Propensity scores (SVMTriP). Applied to non-redundant B-cell linear epitopes extracted from IEDB, SVMTriP achieves a sensitivity of 80.1% and a precision of 55.2% with a five-fold cross-validation. The AUC value is 0.702. The combination of similarity and propensity of tri-peptide subsequences can improve the prediction performance for linear B-cell epitopes. Moreover, SVMTriP is capable of recognizing viral peptides from a human protein sequence background. A web server based on our method is constructed for public use. The server and all datasets used in the current study are available at http://sysbio.unl.edu/SVMTriP.
authors.affiliation: School of Biological Sciences, Center for Plant Science and Innovation, University of Nebraska, Lincoln, Nebraska, United States of America.
authors.forename: Bo
authors.initials: B
authors.lastname: Yao
authors.forename: Lin
authors.initials: L
authors.lastname: Zhang
authors.forename: Shide
authors.initials: S
authors.lastname: Liang
authors.forename: Chi
authors.initials: C
authors.lastname: Zhang
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D000941:Antigens / Q000276:immunology; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D018604:Epitope Mapping / Q000379:methods; D018985:Epitopes, B-Lymphocyte / Q000276:immunology*; D006801:Humans; D020407:Internet; D008969:Molecular Sequence Data; D009842:Oligopeptides / Q000276:immunology*; D015203:Reproducibility of Results; D060388:Support Vector Machine; D014764:Viral Proteins / Q000276:immunology
chemical_list: D000941:Antigens; D018985:Epitopes, B-Lymphocyte; D009842:Oligopeptides; D014764:Viral Proteins