Background: The world is currently facing the coronavirus disease-2019 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Researchers from different parts of the world have employed diverse approaches to create a safe and effective vaccine as it saves millions of lives. Vaccines are created from the viral particle to train the body for a natural defense against invading pathogens. It is important to understand the concept of the vaccine design, especially the multi-epitope T-cells subunit vaccine. Methods: In this regard, we employed bioinformatics and immunoinformatic tools to illustrate the concept of the computer-based vaccine design. The computational methods consist of evaluation and selection of SARS-CoV-2 structural proteins, prediction of cytotoxic T-lymphocyte (CTL) epitopes, prediction of helper T-cell (HTL) epitope, multi-epitope vaccine candidate construct, antigenicity and allergenicity prediction of the designed candidate vaccine, physiochemical properties and solubility evaluation, secondary/tertiary structure prediction, refinement and validation of model vaccine tertiary structure, molecular docking of fusion proteins and Toll-like receptor 9 protein, and in silico cloning of the vaccine. Results: A total of 454 amino acid sequences were generated from CTL and HTL epitopes. The query solubility value (QuerySol) of the vaccine construct was 0.419, including the human beta-defensin-2 adjuvant and peptide linkers. A circular clone of vaccine and pEX-C-His plasmid was achieved after in silico ligation using the annealed primer. Conclusion: Here, we provide essential information on computer-assisted multi-epitopes T-cell subunit vaccine design.
Computer-Assisted Multi-Epitopes T-Cell Subunit Covid-19 Vaccine Design