Immunoinformatic and dynamic simulation-based designing of a multi-epitope vaccine against emerging pathogen Elizabethkingia anophelis

Abstract

Elizabethkingia anophelis is an emerging human pathogen causing neonatal meningitis, catheter-associated infections and nosocomial outbreaks with high mortality rates. Besides, they are resistant to most antibiotics used in empirical therapy. In this study, therefore, we used immunoinformatic approaches to design an epitope-based vaccine against E. anophelis as an alternative preventive measure. Initially, T-cell (CTL and HTL) and B-cell (LBL) epitopes were predicted from the highest antigenic protein. The CTL and HTL epitopes together had a population coverage of 99.97% around the world. Eventually, 6 CTL, 7 HTL, and 2 LBL epitopes were selected and used to construct a multiepitope vaccine. The vaccine protein was found to be highly immunogenic, non-allergenic, and non-toxic. Codon adaptation and in silico cloning were performed to ensure better expression within E. coli K12 host system. The stability of the vaccine structure was also improved by disulphide bridging. In addition, molecular docking and dynamic simulation revealed good and stable binding affinity between the vaccine and receptor. The immune simulation showed higher levels of T-cell and B-cell activities which was in coherence with actual immune response. Repeated exposure simulation resulted in higher clonal selection and faster antigen clearance. Nevertheless, experimental validation is required to ensure the immunogenic potency and safety of this vaccine to control E. anophelis infection in the future.