Designing of a multi-epitopes-based peptide vaccine against rift valley fever virus and its validation through integrated computational approaches

Abstract

Since its discovery, the Rift Valley Fever virus (RVFV) has been the source of numerous outbreaks in the Arab Peninsulas and Africa, wreaking havoc on humans and animals. The lack of therapeutics or licensed human vaccines limits the options for controlling RVFV outbreaks. Therefore, RVFV has been prioritized for rapid research and innovation of prevention strategies to control and prevent its outbreaks. The purpose of this study was to design a multi-epitope-based peptide vaccine (MEBPV) against RVFV. Bioinformatics approaches were used to design a potent MEBPV that can potentially activate both CD8⁺ and CD4⁺ T-cell immune responses, and several computational tools were employed to investigate its biological activities. Three antigenic proteins (Nucleocapsid (N), Glycoprotein C (GC), and Glycoprotein N (GN)) from the RVFV were chosen and potential immunogenic T- and B -cell epitopes were predicted from them. Based on in silico analysis, a MEBPV based on highly scored T and B-cell epitopes (6 CTL, 5 HTL, and 4 LBL) combined with linkers and adjuvants was developed. The finest predicted model was used for docking studies with Toll-like receptors (TLR3 and TLR8) and MHC molecules (MHC I and MHC II) after predicting and analyzing the tertiary structure of MEBPV. The designed MEBPV was then tested for stability with TLR3 and TLR8 receptors using molecular dynamics (MD) simulation and MMGBSA analysis. The MEBPV -TLR3, MEBPV -TLR8, MEBPV-MHC I and MEBPV -MHC II docked models were found stable during simulation time in MD and MMGBSA studies. In silico analysis revealed that the constructed vaccine could elicit both cell-mediated and humoral immune responses simultaneously. The proposed MEBPV could be a strong candidate against RVFV, but it will need to be tested in the laboratory to guarantee its safety and immunogenicity.

Keywords: Immunoinformatics; Molecular dynamics simulation; Multi-epitope-based peptide vaccine; Rift valley fever virus.