<p>The ongoing global health crisis caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the virus which leads to Coronavirus Disease 2019 (COVID-19) has impacted not only the health of people everywhere, but the economy in nations across the world. While vaccine candidates and therapeutics are currently undergoing clinical trials, there is yet to be a proven effective treatment or cure for COVID-19. In this study, we have presented a synergistic computational platform, including molecular dynamics simulations and immunoinformatics techniques, to rationally design a multi-epitope vaccine candidate for COVID-19. This platform combines epitopes across Linear B Lymphocytes (LBL), Cytotoxic T Lymphocytes (CTL) and Helper T Lymphocytes (HTL) derived from both mutant and wild-type spike glycoproteins from SARS-CoV-2 with diverse protein conformations. In addition, this vaccine construct also takes the considerable glycan shield of the spike glycoprotein into account, which protects it from immune response. We have identified a vaccine candidate (a 35.9 kDa protein), named COVCCF, which is composed of 5 LBL, 6 HTL, and 6 CTL epitopes from the spike glycoprotein of SARS-CoV-2. Using multi-dose immune simulations, COVCCF induces elevated levels of immunoglobulin activity (IgM, IgG1, IgG2), and induces strong responses from B lymphocytes, CD4 T-helper lymphocytes, and CD8 T-cytotoxic lymphocytes. COVCCF induces cytokines important to innate immunity, including IFN-γ, IL4, and IL10. Additionally, COVCCF has ideal pharmacokinetic properties and low immune-related toxicities. In summary, this study provides a powerful, computational vaccine design platform for rapid development of vaccine candidates (including COVCCF) for effective prevention of COVID-19.</p>
In-silico design of envelope based multi-epitope vaccine candidate against Kyasanur forest disease virus
