Computational and deep learning-assisted multi-epitope vaccine design targeting the RNA-dependent RNA polymerase of Nipah virus

Nipah virus (NiV) remains a highly fatal zoonotic pathogen with no licensed vaccine. This study developed a multi-epitope vaccine targeting the highly conserved RNA-dependent RNA polymerase (RdRp) using an integrated approach combining immunoinformatics and deep learning. Conserved regions of RdRp yielded 11 CTL , 8 HTL, and 5 B-cell epitopes, which were incorporated into a final construct fused to β-defensin adjuvant. Docking of the vaccine with human TLR4 produced a HADDOCK score of -92.8, with an estimated binding free energy of -13.3 kcal/mol. A 400-ns molecular dynamics simulation demonstrated structural stability, with a mean RMSD of 3.4 Å, and stable compactness, as reflected by a radius of gyration (Rg) of 26.2 Å. MM-GBSA calculations revealed a favourable ΔG bind of -49.6 kcal/mol, supporting stable receptor engagement. Codon optimisation for E. coli K-12 achieved a CAI of 1.0 and a GC content of 42.7%, confirming its suitability for high-level expression. These computational results identify a structurally stable, immunogenic, and expression-ready vaccine candidate that extends current NiV vaccine research beyond the commonly targeted surface glycoproteins by focusing on a conserved replication protein with reduced mutational pressure.