Bioinformatic analysis of epitope-based vaccine design against the novel SARS-COV-2

Background: An outbreak of infection caused by SARS-COV-2 recently has brought great challenge to public health. Rapid identification of immune epitopes would be an efficient way to screen the candidates for vaccine development at the time of an impending pandemic. This study aimed to predict the protective epitopes with bioinformatic methods and resources for vaccine development. Methods: ABCpred and BepiPred servers were utilized for sequential B-cell epitope analysis. Discontinuous B-cell epitopes were predicted via DISCTOPE server. IEDB server was utilized for HLA-1 and HLA-2 binding peptides computation. Surface accessibility, antigenicity, and other important features of forcasted epitopes were characterized for immunogen potential evaluation. Results: A total of 63 sequential B-cell epitopes on spike protein were predicted and 3 peptides (333-338, 648-663, 1064-1079) exhibited high antigenicity score and good surface accessibility in our modeling. 8 residues within spike protein (Gly 184 , Gly 502 , Lys 558 , Pro 561 , Pro 809 , Ser 810 , Lys 811 , Pro 812 ) are forcasted as components of discontinuous B-cell epitopes. The bioinformatic analysis of HLA binding peptides within nucleocapsid protein produced 81 and 64 peptides being able to bind class-I and class-II molecule respectively. The peptide (104-112) has a high immunogenicity score and was predicted to bind a wide spectrum of both HLA-1 and HLA-2 molecules. Conclusions: Linear B-cell epitopes (333-338, 648-663, 1064-1079) and discontinuous B-cell epitopes (Gly 184 , Gly 502 , Lys 558 , Pro 561 , Pro 809 , Ser 810 , Lys 811 , Pro 812 ) on spike protein, T-cell epitope (104-112) within nucleocapsid protein were identified and recommended for developing vaccine against SARS-COV-2.