Porphyromonas gingivalis is a Gram-negative anaerobic bacterium, mainly present in the oral cavity and causes periodontal infections. Currently, no licensed vaccine is available against P. gingivalis and other oral bacterial pathogens. To develop a vaccine against P. gingivalis, herein, we applied a bacterial pan-genome analysis (BPGA) on the bacterial genomes that retrieved a total number of 4908 core proteins, which were further utilized for the identification of good vaccine candidates. After several vaccine candidacy analyses, three proteins, namely lytic transglycosylase domain-containing protein, FKBP-type peptidyl-propyl cis-trans isomerase and superoxide dismutase, were shortlisted for epitopes prediction. In the epitopes prediction phase, different types of B and T-cell epitopes were predicted and only those with an antigenic, immunogenic, non-allergenic, and non-toxic profile were selected. Moreover, all the predicted epitopes were joined with each other to make a multi-epitopes vaccine construct, which was linked further to the cholera toxin B-subunit to enhance the antigenicity of the vaccine. For downward analysis, a three dimensional structure of the designed vaccine was modeled. The modeled structure was checked for binding potency with major histocompatibility complex I (MHC-I), major histocompatibility complex II (MHC-II), and Toll-like receptor 4 (TLR-4) immune cell receptors which revealed that the designed vaccine performed proper binding with respect to immune cell receptors. Additionally, the binding efficacy of the vaccine was validated through a molecular dynamic simulation that interpreted strong intermolecular vaccine-receptor binding and confirmed the exposed situation of vaccine epitopes to the host immune system. In conclusion, the study suggested that the model vaccine construct has the potency to generate protective host immune responses and that it might be a good vaccine candidate for experimental in vivo and in vitro studies.
Keywords: Porphyromonas gingivalis; epitope vaccine; immunoinformatics; molecular dynamics simulations; pan-genomics.