Advanced in silico design of an optimized multi-epitope peptide vaccine employing immunoinformatics and reverse vaccinology strategies on the model of Listeria monocytogenes

Abstract

Listeria monocytogenes is a food-borne pathogen responsible for causing listeriosis with severe consequences in expectant women and immunodeficient individuals due to age, viral infections, or transplants. Despite its alarming mortality rate of 21–50%, there is currently no appropriate medication or protective measure available to prevent the infection in humans. In this context, our current research intends to devise a proficient anti-listeriosis vaccine through thoughtful exploration of reverse vaccinology tools. We examined 368 protein sequences of L. monocytogenes strain CLIP80459 and culled 29 of them as the most potent immunogens. We then followed a stringent subtractive selection strategy to identify 11 cytotoxic T-cell, 9 helper T-cell, and 8 linear B-cell epitopes from the preselected antigens, based on multiple relevant structural, chemical, and immunological features and population coverage. We merged these epitopes using appropriate linkers and included an adjuvant to create the fused peptide vaccine. The physico-chemical and immunological properties of the chimeric peptide were modelled and analyzed, revealing it to be stable, non-toxic, non-allergenic, and highly soluble. Additional investigations involving molecular docking studies followed by molecular dynamics simulation and immune simulation revealed that the designed vaccine is adequately immunogenic and capable of stable, extensive interactions with HLA and TLR2, leading to activation of humoral and cell-mediated immunity. The peptide’s suitability for recombinant expression and simple purification using an E. coli host was demonstrated through in silico cloning studies. Thus, our study led to the development of a preventive yet safe vaccine against listeriosis that awaits wet-lab validation.