Abstract
Nipah virus (NiV) remains a critical zoonotic threat in South and Southeast Asia due to its high fatality rate and the absence of a licensed human vaccine. In this study, an immunoinformatics-driven strategy was employed to design a population-specific multi-epitope vaccine targeting the fusion (F) and glycoprotein (G) of NiV. A total of four B-cell epitopes, ten cytotoxic T lymphocyte (CTL) epitopes, and eight helper T lymphocyte (HTL) epitopes were selected based on strong HLA binding affinity, high antigenicity, and favorable safety profiles. All selected epitopes were predicted to be non-allergenic and non-toxic, with VaxiJen antigenicity. Population coverage analysis revealed extensive coverage across endemic regions, exceeding 97.98% in South Asia and 99.41% in Southeast Asia. The final multi-epitope construct demonstrated favorable physicochemical properties, including structural stability and hydrophilic characteristics. Structural modeling and validation confirmed a reliable tertiary structure, with 92.2% of residues located in favored regions of the Ramachandran plot and an ERRAT score of 90.5. Molecular docking analysis showed strong binding affinities between the vaccine construct and Toll-like receptors, particularly TLR3 (-17.0 ΔG kcal/mol, 8 hydrogen bonds, 7 salt bridges), followed by TLR4 (-15.8 ΔG kcal/mol, 14 hydrogen bonds, 3 salt bridges) and TLR2 (-15.1 ΔG kcal/mol, 10 hydrogen bonds, 3 salt bridges), suggesting a potential for innate immune receptor engagement that warrants further experimental validation. Structure-based flexibility analyses suggested limited conformational fluctuations at the predicted vaccine-receptor interaction interfaces. Immune simulation predicted robust humoral and cellular immune responses, characterized by elevated IgG titers, cytokine production, and the generation of memory B and T cells. Codon optimization and in silico cloning into the pET-28a(+) vector indicated high expression feasibility in Escherichia coli K12. Overall, this study presents a rational computational framework for developing a safe, immunogenic, and region-specific NiV vaccine candidate, warranting further experimental validation.