svmtrip a method to predict antigenic epitopes using support vector machine to integrate tri-peptide similarity and propensitysvmtrip方法预测抗原决定使用支持向量机集成tri-peptide相似性和倾向.pdf

SVMTriP: A Method to Predict Antigenic Epitopes Using Support Vector Machine to Integrate Tri-Peptide Similarity and Propensity 1 2 3 1 Bo Yao , Lin Zhang , Shide Liang *, Chi Zhang * 1 School of Biological Sciences, Center for Plant Science and Innovation, University of Nebraska, Lincoln, Nebraska, United States of America, 2 Department of Statistics, University of Nebraska, Lincoln, Nebraska, United States of America, 3 Systems Immunology Lab, Immunology Frontier Research Center, Osaka University, Suita, Osaka, Japan 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 construct