Immunoinformatic Aided Design and Evaluation of a Potential Multi-epitope Vaccine against Multidrug Resistant Acinetobacter baumannii

Abstract

Acinetobacter baumannii is an opportunistic, gram-negative pathogen which is particularly notorious for its extensive antimicrobial resistance profile. It causes a number of nosocomial infections, including bacteremia, ventilator-associated pneumonia, and meningitis. The clinical treatment of Acinetobacter baumannii infection has become increasingly difficult; a few pandrug resistant strains are thought to be unsusceptible to any current antibiotics. Infections caused by drug resistant strains have significantly higher mortality rates. Novel therapeutic agents, particularly prophylactic ones, are urgently required to save the lives of vulnerable individuals admitted in critical care. The aim of this study is to develop a novel potential multivalent vaccine against A. baumannii. In this study, pan-genomic and reverse vaccinology approaches have been applied for the identification of putative vaccine candidates for A. baumannii, using 208 publicly available complete genomes. Through a series of screening and analysis steps, to identify highly immunogenic and antigenic candidate proteins and the harboring epitopes, a total of 10 CTL epitopes and 4 HTL epitopes were screened in 8 prioritized vaccine candidates. These epitopes were then rationally linked together, and an adjuvant was added to form a multiepitope peptide vaccine. The designed polyvalent vaccine was modelled, refined, and simulated in the cellular environment to check its stability and flexibility. The vaccine construct showed encouraging results in interaction analysis with toll like receptors and in immune simulation experiments. In the future, we aim to express the multicomponent vaccine and evaluate it in vitro, followed by validation in the animal model. This will help determine the true immunological potential of the vaccine and further preclinical trials can be pursued