Design of a multi-epitope vaccine using HA and M1 proteins from influenza and S, E, and M proteins from SARS-CoV-2 by in silico tools

Abstract

Among various infections, acute respiratory tract infections (ARTIs) have the highest incidence and mortality in the world. These infections are caused by various microorganisms such as the influenza and SARS-CoV-2 viruses. The potential effect of influenza alongside COVID-19 on mortality, morbidity, and health-service capacity is a main concern, although, recently, little is understood about the interaction between these two pathogenic respiratory viruses. Thus, the creation and development of effective vaccines are urgently needed to stop the spread of these diseases. In this study, we created a novel multi-epitope vaccine to protect against SARS-CoV-2 and influenza infections. For usage in vaccine construction, the HA and M1 proteins from the influenza virus and the S, E, and M proteins from the SARS-CoV-2 virus were examined. Using various bioinformatics techniques, the discovery of B and T cell epitopes, energy minimization, prediction of Interferon-gamma response, allergenicity, and toxicity were carried out. The most effective peptides were chosen via molecular docking. To create a multi-epitope vaccine, the chosen epitopes were mixed with the appropriate linkers and adjuvant. The I-TASSER server created the 3D model of the vaccine candidate. Optimization and evaluation of this model were investigated by Ramachandran maps and the Swiss-pdb viewer server. The obtained results showed that the designed vaccine is non-allergenic, and non-toxic with high stability and immunogenicity. The remarkable stability of the final free vaccines and in complex with TLR3, TLR4, TLR5, and TLR8 was confirmed by experiments using normal mode analysis. High amounts of immunoglobulins, T-cell responses, and INF-γ secretions were strikingly visible in the immune simulation. Overall, the new multi-epitope vaccine is expected to be a suitable vaccine against influenza and SARS-CoV-2 infections.