Immunoinformatics‐Based Design of a Multi‐Epitope Vaccine Targeting a Conserved Copper–Associated Protein in <i>Neisseria gonorrhoeae</i>

Neisseria gonorrhoeae poses an urgent public health challenge due to rapidly increasing antimicrobial resistance and the absence of an effective vaccine. Targeting conserved bacterial pathways involved in essential physiological processes may provide new opportunities for vaccine antigen discovery. In this study, we applied an immunoinformatics-based pipeline to explore a conserved copper-associated protein as a potential antigenic source for multi-epitope vaccine design. Analysis of 396 clinical genomes confirmed high assembly quality and identified a highly conserved hypothetical protein (AKOBGLPP_01618) through proteome-wide screening for metal-binding signatures. The candidate exhibited strong predicted antigenicity, high prevalence across isolates (99.5%), and no detectable homology with human proteins. In silico analyses suggested potential copper-binding features based on predicted metal-coordinating residues and conserved genomic context. Epitope mapping identified B-cell epitope-rich regions that were refined into conserved, surface-accessible peptides. Predicted cytotoxic and helper T-cell epitopes were filtered for immunogenicity, safety, and strain conservancy prior to inclusion in a multi-epitope construct containing a TLR4-targeting adjuvant and processing-optimized linkers. The resulting 155-amino-acid construct demonstrated favorable physicochemical characteristics, predicted solubility, and an estimated global population coverage of 74.84%. Docking analyses suggested a consistent interaction propensity with the TLR4/MD-2 complex across multiple predicted conformations. Although these findings provide preliminary computational support for the proposed construct, experimental validation will be required to confirm antigen function, immunogenicity, and receptor engagement. Collectively, this study identifies a conserved copper-associated protein as a potential antigen source and presents a computational framework for exploring vaccine candidates against N. gonorrhoeae.