Personalized workflow to identify optimal T-cell epitopes for peptide-based vaccines against COVID-19

Qiao, Rui; Tran, Ngoc Hieu; Shan, Baozhen; Ghodsi, Ali; Li, Ming

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.