Designing a new multi epitope-based vaccine against COVID-19 disease: an immunoinformatic study based on reverse vaccinology approach
Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has currently caused a significant pandemic among worldwide populations. The transmission speed and the high rate of mortality caused by the disease necessitate studies for the rapid designing and effective vaccine production. The purpose of this study is to predict and design a novel multi-epitope vaccine against the SARS-CoV-2 virus using bioinformatics approaches. Coronavirus envelope proteins, ORF7b, ORF8, ORF10, and NSP9 were selected as targets for epitope mapping using IEDB and BepiPred 2.0 Servers. Also, molecular docking studies were performed to determine the candidate vaccine's affinity to TLR3, TLR4, MHC I, and MHC II molecules. Thirteen epitopes were selected to construct the multi-epitope vaccine. We found that the constructed peptide has valuable antigenicity, stability, and appropriate half-life. The Ramachandran plot 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 MHCI, MHCII, TLR3, and TLR4. This study confirmed that the designed vaccine has a good antigenicity and stability and could be a proper vaccine candidate against the COVID-19 infectious disease though, in vitro and in vivo experiments are necessary to complete and confirm our results.