Vaccine Design from the Ensemble of Surface Glycoprotein Epitopes of SARS-CoV-2: An Immunoinformatics Approach
Noor Rahman, Fawad Ali, Zarrin Basharat, Muhammad Shehroz, Muhammad Kazim Khan, Philippe Jeandet, Eugenie Nepovimová, Kamil Kuča, Haroon Khan
Abstract
The present study aimed to work out a peptide-based multi-epitope vaccine against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We predicted different B-cell and T-cell epitopes by using the Immune Epitopes Database (IEDB). Homology modeling of the construct was done using SWISS-MODEL and then docked with different toll-like-receptors (TLR4, TLR7, and TLR8) using PatchDock, HADDOCK, and FireDock, respectively. From the overlapped epitopes, we designed five vaccine constructs C1-C5. Based on antigenicity, allergenicity, solubility, different physiochemical properties, and molecular docking scores, we selected the vaccine construct 1 (C1) for further processing. Docking of C1 with TLR4, TLR7, and TLR8 showed striking interactions with global binding energy of -43.48, -65.88, and -60.24 Kcal/mol, respectively. The docked complex was further simulated, which revealed that both molecules remain stable with minimum RMSF. Activation of TLRs induces downstream pathways to produce pro-inflammatory cytokines against viruses and immune system simulation shows enhanced antibody production after the booster dose. In conclusion, C1 was the best vaccine candidate among all designed constructs to elicit an immune response SARS-CoV-2 and combat the coronavirus disease (COVID-19).