Computational and rational design of a Crimean–Congo hemorrhagic fever virus vaccine candidate

• Applicant: Dr. Antonia Sophia Peter
• Subject Area: Clinical Infectiology and Tropical Medicine
• Term: since 2026
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 573773705

The Crimean-Congo Hemorrhagic Fever Virus (CCHFV) is a negative-strand RNA virus belonging to the Nairoviridae family. It is transmitted via Hyalomma ticks or through contact with bodily fluids of infected humans or animals. CCHFV is endemic in Africa, Asia, and parts of Europe. Climate-related changes may further expand its geographic distribution. Despite a case fatality rate of approximately 30 %, no approved vaccines or specific therapies are currently available. This is largely due to insufficient understanding of the virus, particularly its structure, host interactions, and genetic diversity. The virus is enveloped and carries a class II fusion protein on its surface, composed of the glycoproteins Gn and Gc. This complex undergoes conformational changes during cell entry, transitioning from a metastable prefusion to a more stable postfusion state. Gn is believed to stabilize Gc in its prefusion conformation. Unlike other Bunyavirales, however, CCHFV Gn does not contain the receptor-binding domain. Instead, this structure is found in the glycoprotein GP38. Recent studies suggest that GP38 interacts with the Gn-Gc complex, although its precise role remains to be fully elucidated. Stabilizing the prefusion conformation is considered a promising strategy for vaccine development. To this end, computational methods based on artificial intelligence such as AlphaFold, ProteinMPNN, and ESM, as well as biophysical platforms like Rosetta, will be employed. These approaches have already proven successful in stabilizing class I glycoproteins of other viruses, such as HIV and SARS-CoV-2. The applicant has previously developed a design pipeline for stabilizing class I fusion proteins of Arenaviruses. This platform combines artificial intelligence with biophysical algorithms and has yielded vaccine candidates with minimal mutations, which were tested in animal models. The same platform will now be adapted and modified for the class II fusion protein of CCHFV. The aim of this project is to use computational methods to predict mutations that can stabilize the surface proteins of CCHFV, enabling the generation of vaccine candidates that closely resemble the wild-type structure while achieving broad efficacy. These candidates will subsequently be characterized in terms of their biophysical properties and their binding to established antibodies.

• DFG Programme: Research Grants