A novel coronavirus (SARS-CoV-2) emerged from China in late 2019 and rapidly spread across the globe, infecting millions of people and generating societal disruption on a level not seen since the 1918 influenza pandemic. A safe and effective vaccine is desperately needed to prevent the continued spread of SARS-CoV-2; yet, rational vaccine design efforts are currently hampered by the lack of knowledge regarding viral epitopes targeted during an immune response, and the need for more in-depth knowledge on betacoronavirus immunology. To that end, we developed a computational workflow using a series of open-source algorithms and webtools to analyze the proteome of SARS-CoV-2 and identify putative T cell and B cell epitopes. Utilizing a set of stringent selection criteria to filter peptide epitopes, we identified 41 T cell epitopes (5 HLA class I, 36 HLA class II) and 6 B cell epitopes that could serve as promising targets for peptide-based vaccine development against this emerging global pathogen. To our knowledge, this is the first study to comprehensively analyze all 10 (structural, non-structural and accessory) proteins from SARS-CoV-2 using predictive algorithms to identify potential targets for vaccine development.
【저자키워드】 immunology, Vaccines, Peptide vaccines, Immunogenetics, 【초록키워드】 SARS-CoV-2, Vaccine, Vaccine development, immune response, pandemic, knowledge, Vaccine design, Betacoronavirus, Novel coronavirus, Spread, China, Protein, T cell, pathogen, Algorithm, target, proteome, epitope, B cell epitopes, HLA class I, HLA class II, B cell epitope, Safe, criteria, Predictive, effort, peptide-based vaccine, infecting, peptide epitopes, Prevent, effective, Cell, identify, lack, globe, 1918 influenza, 【제목키워드】 B cell, proteome, T cell epitope, the SARS-CoV-2,