In silico Design of a Crimean-Congo Hemorrhagic Fever Virus Glycoprotein Multi-Epitope Antigen for Vaccine Development

Megan C. Mears, Dennis A. Bente

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Objective: No licensed vaccine is 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 with immunogenic potential. Methods: Cytotoxic and helper T-cell epitopes on the CCHFV glycoprotein precursor (GPC) were evaluated with bioinformatic servers. These data were 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 percentage residue identity and similarity of each region were compared across sequences representing the widespread geographical and ecological distribution of CCHFV. Results: Eleven multi-epitope regions were joined 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, thus suggesting that multi-epitope antigens should be explored further for CCHFV vaccine development. Conclusion: The results presented herein provide information on potential targets within the CCHFV GPC for guiding future vaccine development.

Original languageEnglish (US)
Article number34
JournalZoonoses (Ireland)
Volume2
Issue number1
DOIs
StatePublished - 2022

Keywords

  • Bioinformatics
  • CCHFV glycoprotein precursor
  • Crimean-Congo hemorrhagic fever virus
  • epitope prediction
  • multi-epitope antigen

ASJC Scopus subject areas

  • Infectious Diseases
  • veterinary (miscalleneous)

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