Flaviviruses transmitted by arthropods, including the Murray Valley Encephalitis Virus (MVEV), are RNA viruses capable of causing severe encephalitis in various hosts. The spread of these viruses is closely linked to climatic conditions and the habitats of host and vector species, leading to outbreaks in new geographic regions. Notable encephalitis-causing flaviviruses include Japanese encephalitis virus (JEV), West Nile virus (WNV), and Kunjin virus (KUNV). MVEV, primarily spread by the mosquito Culex annulirostris and amplified by water birds such as egrets and Nankeen night herons, has caused significant outbreaks in Australia, including severe epidemics in 1951, 1956, and 1974. Despite its severity, no rapid diagnostic techniques or effective antiviral treatments are available, and current interventions are limited to supportive care and mosquito management. Given the absence of a licensed vaccine, this study aimed to develop a multi-epitope hybrid vaccine targeting MVEV using in silico approaches. The study focused on identifying B-cell and T-cell epitopes from the MVEV Envelope (E) protein, constructing a vaccine candidate, and computationally validating its immunogenic potential. The designed vaccine underwent rigorous analysis of its antigenic properties, allergenicity, and toxicity. Disulfide engineering and assessment of physicochemical properties ensured the structural integrity of the vaccine, supported by Ramachandran plot and ProSA web analyses. Molecular docking studies assessed the vaccine's binding affinities with TLR-3, and MHC-I. Population coverage analysis of MHC-I and MHC-II epitopes evaluated global efficacy. Additionally, molecular dynamics simulations explored the stability of docked complexes, and PDBsum analysis elucidated interaction details. Immunological simulations were conducted to predict immune response outcomes, providing comprehensive validation of the vaccine's antigenicity. The findings highlight the potential of a multi-epitope vaccine as a viable strategy for MVEV prevention.

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http://dx.doi.org/10.1016/j.micpath.2024.107171DOI Listing

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