Computer-Based Immunoinformatic Analysis to Predict Candidate T-Cell Epitopes for SARS-CoV-2 Vaccine Design - Mei, Xueyin; Gu, Pan; Shen, Chuanlai; Lin, Xue; Li, Jian (Frontiers in Immunology, 2022)
Since first outbreak of COVID-19, caused by SARS-CoV-2 virus in 2019, its high infectivity led to its prevalence around the world in an exceptionally short time. Efforts have been made to control the ongoing outbreak and among them, vaccine developments are going on high priority. New clinical trials add to growing evidence that vaccines from many countries were highly effective at preventing SARS-CoV-2 virus infection. One of them is B-cell based vaccines, which were common during pandemic. However, neutralizing antibody therapy becomes less effective when viruses mutate. In order to tackle the problem, we focused on T-cell immune mechanism. In this study, the mutate strains of the virus were selected globally from India (B.1.617.1 & B.1.617.2), United Kingdom (B.1.1.7), South Africa (B.1.351) and Brazil (P.1), and the overlapping peptides were collected based on mutation sites of S-protein. After that, residue scanning was used to predict affinity between overlapping peptide and HLA-A*11:01, the most frequent HLA allele among Chinese population. Then the binding free energy was evaluated with molecular docking to further verify the affinity changes after the mutations happen in the virus genomes. The affinity test results of three epitopes on spike protein from experimental validation were consistent with our predicted results, thereby supporting the inclusion of the epitopes 374 FSTFKCYGL382 in future vaccine design and providing a useful reference route to improve vaccine development.