Immunoinformatics-based Multi-epitope Vaccine Design against Cryptococcus Neoformans Causing Cryptococcal Meningitis

Abstract

Aims: To develop a multi-epitope vaccine against Cryptococcus neoformans using computational immunoinformatics approaches for the prevention of cryptococcal meningitis. Study Design:  Computational in silico vaccine design study. Place and Duration of Study: The study was performed using publicly available protein sequence databases and bioinformatics tools during the project period. Methodology: Four immunogenic proteins—Mp88, Hsp70, Chitin Deacetylase 2, and a GPI-anchored cell wall protein—were selected as vaccine targets. Cytotoxic T-cell, Helper T-cell, and B-cell epitopes were predicted and screened for antigenicity, immunogenicity, allergenicity, and toxicity. Selected epitopes were linked with suitable linkers and adjuvants to design a multi-epitope vaccine construct. Physicochemical properties, structural modelling, molecular docking with immune receptors, molecular dynamics simulations, codon optimization, and in silico cloning were performed. Results: The final vaccine construct showed favorable physicochemical properties, structural stability, and strong interaction with immune receptor targets. Molecular dynamics simulations confirmed stability of the vaccine–receptor complex. Codon optimization and in silico cloning indicated efficient expression potential in a suitable host system. Conclusion: The proposed multi-epitope vaccine construct demonstrated favorable physicochemical properties, structural stability, and strong interaction with immune receptors in computational analyses. These findings highlight the potential of immunoinformatics-driven approaches for antifungal vaccine discovery and provide a promising candidate for future experimental validation against cryptococcal meningitis.