In-silico Immunomodelling of SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a positive-sense single-strand RNA virus belonging to the Coronaviridae family, responsible for coronavirus infectious disease 2019 (COVID-19) with the rapid transmission. This study aimed to characterize and compare SARS-CoV-2 and SARS-CoV major viral proteins and predict antigen proteasomal cleavage patterns, MHC class I processing and presentation, and B T-cell and anti-inflammatory epitopes.

The amino acid sequences of spike surface (S) glycoprotein, membrane (M) glycoprotein, envelop (E) protein, and nucleocapsid (N) phosphoprotein was obtained from NCBI. The sequences were aligned by MEGA 7.0 and modeled by SWISS-MODEL. The proteasomal cleavage pattern, MHC class I processing, and T-cell epitopes were predicted via IEDB analysis and EPISOFT. The B-cell epitopes were predicted by BepiPred 2.0. Also, the prediction of anti-inflammatory epitopes was performed by AntiInflam.

Two major antigen proteins, S glycoprotein and M glycoprotein of SARS-CoV-2, respectively, showed 26.57% and 20.59% less efficiency in proteasomal cleavage and presentation to MHC class I, comparing SARS-CoV. There were fewer B-cell predicted epitopes in SARS-CoV-2, comparing SARS-CoV. The anti-inflammatory properties of SARS-CoV-2 S glycoprotein and N protein were higher than SARS-CoV.

It seems that the evolution of SARS-CoV-2 is on the way to reducing antigen-presenting to MHC class I and escaping cellular immunity. Moreover, the predicted hotspot epitopes potentially can be used to induce adaptive cellular immunity against SARS-CoV-2. Besides, SARS-CoV-2 appears to be less immunopathogenic than SARS-CoV due to its higher anti-inflammatory proteins.