Predicting protective linear B-cell epitopes using evolutionary information

Mapping B-cell epitopes plays an important role in vaccine design, immunodiagnostic tests, and antibody production. Because the experimental determination of B-cell epitopes is time-consuming and expensive, there is an urgent need for computational methods for reliable identification of putative B-cell epitopes from antigenic sequences. In this study, we explore the utility of evolutionary profiles derived from antigenic sequences in improving the performance of machine learning methods for protective linear B-cell epitope prediction. Specifically, we compare propensity scale based methods with a Naive Bayes classifier using three different representations of the classifier input: amino acid identities, position specific scoring matrix (PSSM) profiles, and dipeptide composition. We find that in predicting protective linear B-cell epitopes, a Naive Bayes classifier trained using PSSM profiles significantly outperforms the propensity scale based methods as well as the Naive Bayes classifiers trained using the amino acid identity or dipeptide composition representations of input data.