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 B...

• Type: Conference
• Year: 2008
• Where: BIBM
• Authors: Yasser El-Manzalawy, Drena Dobbs, Vasant Honavar
• Keywords: B-cell Epitopes, BIBM 2008, Bioinformatics, Linear B-cell Epitopes, Naive Bayes Classifiers