Engineering a multi-epitope-based vaccine candidate against anti-microbial-resistant Aeromonas caviae using an integrated immunoinformatics approach

Abstract

Aeromonas caviae is a surging and opportunistic bacterium that causes gastrointestinal infections and septicemia. Due to the overuse of antibiotics, A. caviae has evolved to be antimicrobial resistant. This emergence has become a significant concern, as the effectiveness of antibiotics is threatened, demanding the need for alternative therapeutic approaches. The research employs immunoinformatics-based computational techniques to develop a multi-epitope-based vaccine for treating infections triggered by A. caviae. The whole proteome of A. caviae (strain: GSH8M-1) was retrieved and analyzed for anti-microbial resistance and was sequestered. Cytoplasmic membrane and periplasmic membrane proteins were compiled for B-cell and T-cell epitope prediction. Among those, six promiscuous epitopes (EIKPKDYPK, PYKFAPDGF, TTLGDDAKR, LPARAARTM, SLLPARAAR, FELDDKASL) were then connected using GPGPG linkers. Human β-defensin adjuvant was connected by an EAAK linker. Docking of the vaccine construct was performed with Human toll-like receptor 2 (TLR-2), TLR-3, Major Histocompatibility Class I (MHC-I), and MHC-II, and TLR2 with the lowest docking score was selected. Finally, the presumed vaccine was subjected to codon optimization, In silco cloning and immune simulations. This evaluation ensures the feasibility of the vaccine to elicit the immune response against antimicrobial resistance A. caviae infections, laying the preparation for future experimental investigations and clinical applications.