Endogenous adjuvants used in the study.

The resurgence of the Nipah virus (NiV) in 2023 has raised concerns for another potentially severe pandemic, given its history of high mortality from previous outbreaks. Unfortunately, no therapeutics and vaccines have been available for the virus. This study used immunoinformatics and molecular modeling to design and evaluate a multi-epitope subunit vaccine targeting NiV. The designed vaccine construct aims to stimulate immune responses in humans and two other intermediate animal hosts of the virus—swine and equine. Using several epitope prediction tools, ten peptides that induced B-lymphocyte responses, 17 peptides that induced cytotoxic T-lymphocyte (CTL) responses, and 12 peptides that induced helper T-lymphocyte (HTL) responses were mapped from nine NiV protein sequences. However, the CTL and HTL-inducing peptides were reduced to ten and eight, respectively, following molecular docking and dynamics. These screened peptides exhibited stability with 30 common major histocompatibility complex (MHC) receptors found in humans, swine, and equine. All peptides were linked using peptide linkers to form the multi-epitope construct and various adjuvants were tested to enhance its immunogenicity. The vaccine construct with resuscitation-promoting factor E (RpfE) adjuvant was selected as the final design based on its favorable physicochemical properties and superior immune response profile. Molecular docking was used to visualize the interaction of the vaccine to toll-like receptor 4 (TLR4), while molecular dynamics confirmed the structural stability of this interaction. Physicochemical property evaluation and computational simulations showed that the designed vaccine construct exhibited favorable properties and elicited higher antibody titers than the six multi-epitope NiV vaccine designs available in the literature. Further in vivo and in vitro experiments are necessary to validate the immunogenicity conferred by the designed vaccine construct and its epitope components. This study demonstrates the capability of computational methodologies in rational vaccine design and highlights the potential of cross-species vaccination strategies for mitigating potential NiV threats.