Computational and experimental validation of B and T-cell epitopes of the in vivo immune response to a novel malarial antigen

Abstract

Vaccine development efforts will be guided by algorithms that predict immunogenic epitopes. Such prediction methods rely on classification-based algorithms that are trained against curated data sets of known B and T cell epitopes. It is unclear whether this empirical approach can be applied prospectively to predict epitopes associated with protective immunity for novel antigens. We present a comprehensive comparison of in silico B and T cell epitope predictions with in vivo validation using an previously uncharacterized malaria antigen, CelTOS. CelTOS has no known conserved structural elements with any known proteins, and thus is not represented in any epitope databases used to train prediction algorithms. This analysis represents a blind assessment of this approach in the context of a novel, immunologically relevant antigen. The limited accuracy of the tested algorithms to predict the in vivo immune responses emphasizes the need to improve their predictive capabilities for use as tools in vaccine design.