Designing a New Multiepitope-based Vaccine Against COVID-19

Abstract

Background: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has currently caused a significant pandemic among worldwide populations. The high transmission and mortality rates of the disease necessitate studies for rapid designing and effective vaccine production. This study aims to predict and design a novel multi-epitope vaccine against the SARS-CoV-2 virus using bioinformatics approaches. Materials and Methods: Coronavirus envelope proteins, Open Reading Frame 7b (ORF7b), Open Reading Frame 8 (ORF8), Open Reading Frame 10 (ORF10), and Nonstructural protein 9 (Nsp9) were selected as targets for epitope mapping using Immune Epitope Data Bank (IEDB) and BepiPred 2.0 servers. Also, molecular docking studies were performed to determine the candidate vaccine’s affinity to Toll-Like Receptor (TLR3, TLR4) and Major Histocompatibility Complex (MHC I and MHC II) molecules. Thirteen epitopes were selected to construct the multi-epitope vaccine. Results: We found that the constructed peptide has valuable antigenicity, stability, and appropriate half-life. The Ramachandran and ERRAT plots approved the quality of the predicted model after the refinement process. Molecular docking investigations revealed that antibody-mode in the ClusPro 2.0 server showed the lowest binding energy for MHC I, MHC II, TLR3, and TLR4. Conclusion: The designed vaccine has a good antigenicity and stability and could be a proper vaccine candidate against the Coronavirus Disease 2019 (COVID-19) infectious disease though, in vitro and in vivo experiments are necessary to complete and confirm our results.