Construction of an immunoinformatics-based multi-epitope vaccine candidate targeting Kyasanur forest disease virus

Kyasanur forest disease (KFD) is one of the neglected tick-borne viral zoonoses. KFD virus (KFDV) was initially considered endemic to the Western Ghats region of Karnataka state in India. Over the years, there have been reports of its spread to newer areas within and outside Karnataka. The absence of an effective treatment for KFD mandates the need for further research and development of novel vaccines. The present study was designed to develop a multi-epitope vaccine candidate against KFDV using immunoinformatics approaches. A total of 74 complete KFDV genome sequences were analysed for genetic recombination followed by phylogeny. Computational prediction of B- and T-cell epitopes belonging to envelope protein was performed and epitopes were prioritised based on IFN-Gamma, IL-4, IL-10 stimulation and checked for allergenicity and toxicity. The eight short-listed epitopes (three MHC-Class 1, three MHC-Class 2 and two B-cell) were then combined using various linkers to construct the vaccine candidate. Molecular docking followed by molecular simulations revealed stable interactions of the vaccine candidate with immune receptor complex namely Toll-like receptors (TLR2-TLR6). Codon optimization followed by in-silico cloning of the designed multi-epitope vaccine construct into the pET30b (+) expression vector was carried out. Immunoinformatics analysis of the multi-epitope vaccine candidate in the current study has potential to significantly accelerate the initial stages of vaccine development. Experimental validation of the potential multi-epitope vaccine candidate remains crucial to confirm effectiveness and safety in real-world conditions.