Designing a Multi-epitope Peptide Vaccine Against COVID-19 Variants Utilizing In-silico Tools

SARS-CoV-2 is the causative agent of Coronavirus 2019 or COVID-19 in the world. Novel coronavirus disease is a respiratory disease. To date, there have been challenges in the treatment for COVID-19 and emerged new variants like UK B1.1.7. Accordingly, an effective prevention regime is needed for this infection, which covers most variants. The purpose of this research was to predict the conserved epitopes of Spike and Nucleocapsid proteins from SARS-CoV-2 for the design of a novel coronavirus 2019 multi-epitope vaccine using in silico tools.

Computational analysis and immunoinformatics approaches include identification of potential conserve epitopes and selection of epitopes based on allergenicity, toxicity, antigenicity, and molecular docking were used for epitope prediction and screening. In the next step, selected segments of the epitopes were attached by the suitable linkers. Finally, Maltese-bound protein (MBP) as an adjuvant was added to the novel vaccine structure. The secondary and third structures of the designed multi-epitope vaccine were predicted via immunoinformatics algorithms. Predicted structure refined and validated for attaining best stability. In the end, immunoinformatics evaluation, molecular docking, and molecular dynamics were performed to confirm vaccine efficiency. Codon optimization and in silico cloning were done to ensure the expression yield of the novel multi-epitope vaccine in the target host.

 This study showed that our data support the suggestion that the designed vaccine could induce immune responses against SARS-CoV-2 variants.

 The structure designed had acceptable quality with software reviews. Further in vitro and in vivo experiments are needed to confirm the safety and immunogenicity of the candidate vaccine.