Design of a Multi-epitope Vaccine Against Acinetobacter baumannii Using Immunoinformatics Approach.

Abstract

Acinetobacter baumannii is one of the most successful pathogens causing nosocomial infections and has significantly multidrug-resistant. So far, there are no certain treatments to protect against infection with A. baumannii, therefore an effective A. baumannii vaccine needed. The purpose of this study was to predict antigenic epitopes of CarO protein for designing the A. baumannii vaccine using immunoinformatics analysis. CarO protein is one of the most important factors in the resistance against the antibiotic Carbapenem. In this study, T and B-cell epitopes of CarO protein were predicted and screened based on the antigenicity, toxicity, allergenicity features. The epitopes were linked by suitable linkers. Four different adjuvants were attached to the vaccine constructs which among them, vaccine construct 3 was chosen to predict the secondary and the 3D structure of the vaccine. The refinement process was performed to improve the quality of the 3D model structure; the validation process is performed using the Ramachandran plot and ProSA z-score. The designed vaccine's binding affinity to six various HLA molecules and TLR 2 and TLR4 were evaluated by molecular docking. Finally, in silico gene cloning was performed in the pET28a (+) vector. The findings suggest that the vaccine may be a promising vaccine to prevent A. baumannii infection.