Computational mining of MHC class II epitopes for the development of universal immunogenic proteins.

Abstract

The human leukocyte antigen (HLA) gene complex, one of the most diverse gene complexes found in the human genome, largely dictates how our immune systems recognize pathogens. Specifically, HLA genetic variability has been linked to vaccine effectiveness in humans and it has likely played some role in the shortcomings of the numerous human vaccines that have failed clinical trials. This variability is largely impossible to evaluate in animal models, however, as their immune systems generally 1) lack the diversity of the HLA complex and/or 2) express major histocompatibility complex (MHC) receptors that differ in specificity when compared to human MHC. In order to effectively engage the majority of human MHC receptors during vaccine design, here, we describe the use of HLA population frequency data from the USA and MHC epitope prediction software to facilitate the in silico mining of universal helper T cell epitopes and the subsequent design of a universal human immunogen using these predictions. This research highlights a novel approach to using in silico prediction software and data processing to direct vaccine development efforts.