Computational Design of a Vaccine for Porphyromonas gingivalis

Abstract

One of the major contributors to the world's morbidity and mortality rate is infectious diseases. Manufacture of a vaccine against the causative pathogen serves as a miracle in such desperate times slowing down the misuse and overuse of antibiotics. The current project aimed at developing multiepitope vaccine candidates against a gram-negative bacterium, Porphyromonas gingivalis, which is a risk factor of Alzheimer's disease. Roundabout 50 million people around the globe are living with Alzheimer's disease or other kinds of dementia. In the US, it is the sixth highest cause of death as stated by the Centers for Disease Control and Prevention (CDC). A combination of drugs may help decrease the symptoms but there is no absolute cure for the disease which justifies such a high mOliality rate. Hence, potential vaccine candidates can be designed against the bacterial species that is considered to be the major risk factor of Alzheimer's disease and dementia using reverse vaccinology approach to prompt ilmate and adaptive immunity in the host. Using subtractive proteomics on the reference proteome of P. gingivalis, four target proteins were shortlisted: inner membrane ferrous iron transport protein B, inner membrane sulphatase domain-containing protein, plasma membrane putative iron compound ABC transpOlier permease protein, and inner membrane putative glycosyltransferases that satisfied the standards of vaccine candidacy. Promising epitopes from these prioritized proteins are then joined together via GPGPG linkers and with Cholera toxin B subunit as an adjuvant via EAAAK linker to formulate a multi-epitope vaccine construct. A 3D model of this construct is then constructed, refined, enhanced for stability, and later optimized. The findings from the current study can be used by the vaccinologists in producing a highly effective vaccine against P. gingivalis hopefully reducing the cases of Alzheimer's disease around the globe.