Epitope-driven vaccine design against Listeria monocytogenes: an in-silico approach.

Abstract

This study presents an in-silico approach to design a vaccine targeting Listeriolysin O (LLO) exotoxin in Listeria monocytogenes, a significant foodborne pathogen. Utilizing bioinformatics tools, we identified and prioritized B-cell and T-cell epitopes from the LLO sequence, ensuring non-toxicity, immunogenicity, non-allergenicity, and water solubility. The final vaccine construct, comprising 315 amino acids, was developed by combining selected epitopes with appropriate linkers and an adjuvant. Physicochemical characterization revealed favorable properties, including stability and solubility. Immune simulation using the C-ImmSim server predicted robust cellular and humoral responses, with significant increases in antibody levels and cytokine production within five days of administration in an in-silico study. Structural analysis of the vaccine construct yielded a refined 3D model with about 95% of residues in most favored regions of the Ramachandran plot. Protein-protein docking analysis using the ClusPro server predicted significant binding affinity between the vaccine construct and MHC-II receptors, with multiple hydrogen bonds and salt bridges contributing to stable interactions, as confirmed by PDBsum interaction analysis. Codon optimization for expression in Escherichia coli resulted in a high Codon Adaptation Index (0.977) and suitable GC content (53.59%). The optimized sequence was successfully integrated into a pET28a (+) vector in-silico. While these computational results are promising, experimental validation is necessary to confirm the vaccine's immunogenicity, safety, and efficacy against L. monocytogenes infection. This study demonstrates the potential of in-silico approaches in accelerating vaccine development against challenging pathogens and provides a foundation for further research into listeriosis prevention.