Abstract
OBJECTIVE: There is no licensed vaccine available to prevent the severe tick-borne disease Crimean-Congo hemorrhagic fever (CCHF), caused by the CCHF virus (CCHFV). This study sought to show that a combination of computational methods and data from published literature can inform the design of a multi-epitope antigen for CCHFV that has the potential to be immunogenic. METHODS: Cytotoxic and helper T-cell epitopes were evaluated on the CCHFV GPC using bioinformatic servers, and this data was combined with work from previous studies to identify potentially immunodominant regions of the GPC. Regions of the GPC were selected for generation of a model multi-epitope antigen in silico, and the percent residue identity and similarity of each region was compared across sequences representing the widespread geographical and ecological distribution of CCHFV. RESULTS: Eleven multi-epitope regions were joined together with flexible linkers in silico to generate a model multi-epitope antigen, termed EPIC, which included 812 (75.7%) of all predicted epitopes. EPIC was predicted to be antigenic by two independent bioinformatic servers, suggesting that multi-epitope antigens should be explored further for CCHFV vaccine development. CONCLUSION: The results presented within this manuscript provide information for potential targets within the CCHFV GPC for guiding future vaccine development.