  Traditional vaccines against viruses are designed to target their surface
proteins, i.e., antigens, which can trigger the immune system to produce
specific antibodies to capture and neutralize the viruses. However, viruses
often evolve quickly, and their antigens are prone to mutations to avoid
recognition by the antibodies (antigenic drift). This limitation of the
antibody-mediated immunity could be addressed by the T-cell mediated immunity,
which is able to recognize conserved viral HLA peptides presented on
virus-infected cells. Thus, by targeting conserved regions on the genome of a
virus, T-cell epitope-based vaccines are less subjected to mutations and may
work effectively on different strains of the virus. Here we propose a
personalized workflow to identify an optimal set of T-cell epitopes based on
the HLA alleles and the immunopeptidome of an individual person. Specifically,
our workflow trains a machine learning model on the immunopeptidome and then
predicts HLA peptides from conserved regions of a virus that are most likely to
trigger responses from the person T cells. We applied the workflow to identify
T-cell epitopes for the SARS-COV-2 virus, which has caused the recent COVID-19
pandemic in more than 100 countries across the globe.
