Computational systems immunology and multi-scale modeling for the design of a Multi-Epitope Vaccine (MEV) against emerging multidrug-resistant Klebsiella michiganensis

Klebsiella michiganensis , an emerging multidrug-resistant member of the Klebsiella oxytoca complex, has become an important opportunistic pathogen responsible for nosocomial infections, including bacteremia and ventilator-associated pneumonia. Owing to its rapidly evolving resistance to carbapenems, new preventive strategies are urgently needed. This study presents a computational systems immunology framework integrating subtractive proteomics, reverse vaccinology, and immunoinformatics to design a multi-epitope vaccine (MEV) against K. michiganensis strain ATCC 8724. From the complete proteome (5,483 proteins), four proteins associated with the bacterial envelope and LPS-related processes were prioritized as computational vaccine targets: the outer-membrane β-barrel proteins BamA and LptD, the periplasmic LPS transport component LptA, and the outer-membrane lipoprotein LolB. These were selected as non-homologous and antigenic candidates. Cytotoxic T-lymphocyte, helper T-lymphocyte, and B-cell epitopes derived from these proteins were evaluated for antigenicity, toxicity, allergenicity, and global HLA population coverage. The selected epitopes were assembled using AAY, GPGPG, and KK linkers with the Mycobacterium tuberculosis 50 S ribosomal protein L7/L12 as an adjuvant to construct a 386-amino-acid chimeric MEV. Structural modeling and refinement showed 95.6% residues in favored regions of the Ramachandran plot. Molecular docking with the TLR1 component (chain B) of the TLR1–TLR2 heterodimer predicted a favorable interaction (ΔG = − 14.8 kcal/mol), while a 100-ns molecular dynamics simulation suggested maintenance of the overall docked-complex architecture. C-ImmSim predicted increases in immunoglobulin and cytokine levels consistent with potential activation of humoral and cellular immunity. Codon optimization (CAI = 0.94, GC = 50%) supported predicted expression feasibility in E. coli . This computationally designed MEV represents a candidate for future experimental evaluation against K. michiganensis , requiring further experimental validation.