Immunoinformatics Approaches for Developing Multi-Epitope Subunit Vaccines to Combat Acinetobacter Baumannii

Abstract Acinetobacter baumannii , a Gram-negative opportunistic pathogen that widely exists in the environment, causes numerous nosocomial infection cases in the ICU. As the agent of pneumonia, septicemia, and meningitis, A. baumannii is usually multidrug-resistant and has a high mortality rate. In this study, we aimed to develop a vaccine for Acinetobacter baumannii through in silico prediction. With the help of immunoinformatic methods, which have proven to be effective and convenient, our team managed to develop multiepitope subunit vaccines for A. baumannii . Three proteins closely associated with the immunologic process were selected and computed. Finally, 9 cytotoxic T-lymphocyte (CTL) epitopes, 10 helper T-lymphocyte (HTL) epitopes, and 11 linear B cell (LBL) epitopes were selected to construct 2 vaccines. LT-IIb was selected as an adjuvant and attached to the vaccine N-terminus. All subunits are linked by suitable linkers. According to examinations, both MEV is antigenic and stable without poisonousness or allergenicity. Molecular docking revealed steady and solid binding ability between MEV and Toll-like receptors (TLRs) and the major histocompatibility complex (MHC). Finally, MEV codons were optimized into a plasmid for in silico cloning, which is pet-28a+ from E. coli K-12 in this research. Although this vaccine performed well in immune stimulation, further experiments are expected to guarantee its safety and immunogenic profile.