Computational design and characterization of a multiepitope vaccine against carbapenemase-producingKlebsiella pneumoniaestrains, derived from antigens identified through reverse vaccinology

Klebsiella pneumoniae is a Gram-negative pathogen of clinical relevance, which can provoke serious urinary and blood infections and pneumonia. This bacterium is a major public health threat due to its resistance to several antibiotic classes. Using a reverse vaccinology approach, 7 potential antigens were identified, of which 4 were present in most of the sequences of Italian carbapenem-resistant K. pneumoniae clinical isolates. Bioinformatics tools demonstrated the antigenic potential of these bacterial proteins and allowed for the identification of T and B cell epitopes. This led to a rational design and in silico characterization of a multiepitope vaccine against carbapenem-resistant K. pneumoniae strains. As adjuvant, the mycobacterial heparin-binding hemagglutinin adhesin (HBHA), which is a Toll-like receptor 4 (TLR-4) agonist, was included, to increase the immunogenicity of the construct. The multiepitope vaccine candidate was analyzed by bioinformatics tools to assess its antigenicity, solubility, allergenicity, toxicity, physical and chemical parameters, and secondary and tertiary structures. Molecular docking binding energies to TLR-2 and TLR-4, two important innate immunity receptors involved in the immune response against K. pneumoniae infections, and molecular dynamics simulations of such complexes supported active interactions. A codon optimized multiepitope sequence cloning strategy is proposed, for production of recombinant vaccine in classical bacterial vectors. Finally, a 3 dose-immunization simulation with the multiepitope construct induced both cellular and humoral immune responses. These results suggest that this multiepitope construct has potential as a vaccination strategy against carbapenem-resistant K. pneumoniae and deserves further validation.(c) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).