In silico design of a multi-epitope mRNA vaccine candidate against rift valley fever virus using integrative immunoinformatics approaches
Abstract Rift Valley fever virus (RVFV) is a major zoonotic pathogen causing significant outbreaks in humans and livestock, with no licensed effective vaccines currently available. This study employed reverse vaccinology and immunoinformatics approaches to design three multi-epitope mRNA vaccine candidates against RVFV. Conserved antigenic regions from key viral proteins were used to identify cytotoxic T-lymphocyte (CTL), helper T-lymphocyte (HTL), and linear B-cell (LBL) epitopes. The selected epitopes were assembled into three vaccine constructs incorporating appropriate linkers and the Human Beta Defensin 1 adjuvant. All three constructs were predicted to be highly antigenic (scores 0.5075–0.5467), non-allergenic, and non-toxic. They exhibited favorable physicochemical properties, high solubility, and excellent structural stability, with over 93% of residues in favored regions of the Ramachandran plot. Molecular docking demonstrated strong binding affinities with Toll-like receptors TLR-2 and TLR-3, while molecular dynamics simulations confirmed the stability of the vaccine-receptor complexes. Immune simulations predicted robust activation of both innate and adaptive immune responses, including significant B-cell and T-cell proliferation and cytokine production. Codon optimization yielded high Codon Adaptation Index (CAI) values (0.9–1.0) and optimal GC content (50.9–55.2%), indicating strong potential for efficient expression. These findings suggest that the designed multi-epitope mRNA vaccine constructs possess favourable predicted antigenic, physicochemical, structural and receptor-binding properties. However, all results are derived exclusively from computational analyses and should be interpreted as preliminary. The constructs require rigorous in vitro and in vivo validation to confirm antigen expression, immune activation, safety, immunogenicity and protective efficacy against RVFV before they can be considered viable vaccine candidates.
Authors
- Elijah Kolawole Oladipo (ORCID: https://orcid.org/0000-0002-9646-5122)
- Julius Kola Oloke (ORCID: https://orcid.org/0000-0003-3046-5856)
- Olusola Nathaniel Majolagbe
- Simeon Kayowa Olatunde (ORCID: https://orcid.org/0000-0002-3733-9046)
- James Akinwumi Ogunniran (ORCID: https://orcid.org/0009-0001-6931-5946)
- Onile Olugbenga Samson
- Biobele Winner Sokari
- Abiodun Oyeyemi Akosile
- Jesulayomi Mercy Babarinde
- Hikimat Oyindamola Gbadegesin
Institutions
- Canterbury Christ Church University (GB)
- Federal University of Technology (NG)
- Adeleke University (NG)
- East Kent Hospitals University NHS Foundation Trust (GB)
- Elizade University (NG)
- University of Calabar (NG)
- Institut de Recherche Vaccinale (FR)
- Genomix Biotech (India) (IN)
- Artificial Intelligence in Medicine (Canada) (CA)
- Ladoke Akintola University of Technology (NG)
- Federal University of Technology Minna (NG)
- University of Birmingham (GB)
Publication Details
- Journal
- Discover Immunity.
- Published
- 2026-09-15
- DOI
- https://doi.org/10.1007/s44368-026-00035-w
- Primary Topic
- vaccines and immunoinformatics approaches
- Type
- article
- Field-Weighted Citation Impact
- 0.00