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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) kills thousands of people worldwide every day, thus necessitating rapid development of countermeasures. Immunoinformatics analyses carried out here in search of immunodominant regions in recently identified SARS-CoV-2 unannotated open reading frames (uORFs) have identified eight linear B-cell, one conformational B-cell, 10 CD4+ T-cell, and 12 CD8+ T-cell promising epitopes. Among them, ORF9b B-cell and T-cell epitopes are the most promising followed by M.ext and ORF3c epitopes. ORF9b 40-48  (CD8+ T-cell epitope) is found to be highly immunogenic and antigenic with the highest allele coverage. Furthermore, it has overlap with four potent CD4+ T-cell epitopes. Structure-based B-cell epitope prediction has identified ORF9b 61-68  to be immunodominant, which partially overlaps with one of the linear B-cell epitopes (ORF9b 65-69 ). ORF3c CD4+ T-cell epitopes (ORF3c 2-16 , ORF3c 3-17 , and ORF3c 4-18 ) and linear B-cell epitope (ORF3c 14-22 ) have also been identified as the candidate epitopes. Similarly, M.ext and 7a.iORF1 (overlap with M and ORF7a) proteins have promising immunogenic regions. By considering the level of antigen expression, four ORF9b and five M.ext epitopes are finally shortlisted as potent epitopes. Mutation analysis has further revealed that the shortlisted potent uORF epitopes are resistant to recurrent mutations. Additionally, four N-protein (expressed by canonical ORF) epitopes are found to be potent. Thus, SARS-CoV-2 uORF B-cell and T-cell epitopes identified here along with canonical ORF epitopes may aid in the design of a promising epitope-based polyvalent vaccine (when connected through appropriate linkers) against SARS-CoV-2. Such a vaccine can act as a bulwark against SARS-CoV-2, especially in the scenario of emergence of variants with recurring mutations in the spike protein.

frontiers in immunology

Revelation of Potent Epitopes Present in Unannotated ORF Antigens of SARS-CoV-2 for Epitope-Based Polyvalent Vaccine Design Using Immunoinformatics Approach