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Background:  Rift Valley fever virus (RVFV) is an emerging arbovirus infecting both animals and humans. Any form of direct contact with body fluids, blood or tissue of infected animals is the mode of transmission of this pathogen. Despite being an emerging virus, no proper vaccinations are yet available for the public. Our objective is to compose a multiepitope vaccine utilizing immuno-bioinformatics as a strategy against RVFV.    Methods:  To identify immunodominant epitopes and design a potent vaccine candidate, we applied a series of immunoinformatic approaches with molecular dynamics and immune response simulation frameworks.    Results:  A glycoprotein with the highest antigenicity was selected and employed for determining promising epitopes. We selected T cell epitopes based on their immunological potencies and cytokine inducing properties, while B cell epitopes were selected based on their antigenic features. Finally, we selected four cytotoxic T-lymphocyte, two helper T-lymphocyte, and three linear B-lymphocyte epitopes that were arranged into a vaccine construct with appropriate adjuvants and linkers. The chimera protein was modeled, refined, and validated prior to docking against toll-like receptor 4. Docking studies suggest strong binding interactions while dynamics simulation revealed the stable nature of the docked complex. Furthermore, the immune simulation showed robust and prolonged immune responses with rapid antigen clearance. Finally, codon optimization and cloning conducted with  Escherichia coli  K12 suggests high translation efficiency within the host system.    Conclusion:  We believe that our designed multiepitope vaccine is a promising prophylactic candidate against RVFV pathogenesis.

Discovery of potential immune epitopes and peptide vaccine design - a prophylactic strategy against Rift Valley fever virus