Evaluation of peptide selection approaches for epitope-based vaccine design.

A major challenge in epitope-based vaccine (EV) design stems from the vast genomic variation of pathogens and the diversity of the host cellular immune system. Several computational approaches have been published to assist the selection of potential T cell epitopes for EV design. So far, no thorough comparison between the current methods has been realized. Using human immunodeficiency virus as test case, different EV selection algorithms were evaluated with respect to their ability to select small peptides sets with broad coverage of allelic and pathogenic diversity. The methods were compared in terms of in silico measurements simulating important vaccine properties like the ability of inducing protection against a multivariant pathogen in a population; the predicted immunogenicity; pathogen, allele, and population coverage; as well as the conservation of selected epitopes. Additionally, we evaluate the use of human leukocyte antigen (HLA) supertypes with regards to their applicability for population-spanning vaccine design. The results showed that in terms of induced protection methods that simultaneously aim to optimize pathogen and HLA coverage significantly outperform methods focusing on pathogen coverage alone. Moreover, supertype-based approaches for coverage of HLA diversity were showed to yield only satisfying results in populations in which the supertype representatives are prevalent.