Methodological approach to identify immunogenic epitopes candidates for vaccines against emerging pathogens tailored to defined HLA populations

Vaccines stimulate cells of the adaptive immune system, generating a protective and lasting memory, and are the main public health strategy to protect the world population from emerging pathogens such as the SARS-CoV-2 virus, responsible for millions of deaths in the recent COVID-19 pandemic. Several in-silico algorithms have facilitated the selection of antigens as vaccine candidates; however, their predictive capacity remains limited and it is necessary to continue training them, using information obtained in immunological assays. In this work, the SARS-CoV-2 proteome was sampled using a series of concatenated algorithms that allowed us to define a series of candidate viral peptides for a vaccine against SARS-CoV-2 in individuals from Colombian, whose haplotypes for HLA-I and II were incorporated as part of the algorithm. The immunogenicity of the peptides predicted with three tools or with the combination of them was evaluated and found that short peptides predicted and selected as highly immunogenic peptides were capable of expanding memory CD8 T lymphocytes with an activation phenotype. Altogether, our results outline a pipeline that combines a bioinformatic and immunological approach useful to select immunogenic epitopes from emerging pathogens as vaccine candidates tailored to the population's HLA-Haplotypes.