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A general computational design strategy for stabilizing viral class I fusion proteins

Author

Listed:
  • Karen J. Gonzalez

    (University of Georgia)

  • Jiachen Huang

    (University of Georgia
    University of Georgia)

  • Miria F. Criado

    (University of Georgia
    Auburn University)

  • Avik Banerjee

    (University of Georgia
    University of Georgia)

  • Stephen M. Tompkins

    (University of Georgia
    University of Georgia)

  • Jarrod J. Mousa

    (University of Georgia
    University of Georgia
    University of Georgia)

  • Eva-Maria Strauch

    (University of Georgia
    University of Georgia
    Washington University)

Abstract

Many pathogenic viruses rely on class I fusion proteins to fuse their viral membrane with the host cell membrane. To drive the fusion process, class I fusion proteins undergo an irreversible conformational change from a metastable prefusion state to an energetically more stable postfusion state. Mounting evidence underscores that antibodies targeting the prefusion conformation are the most potent, making it a compelling vaccine candidate. Here, we establish a computational design protocol that stabilizes the prefusion state while destabilizing the postfusion conformation. With this protocol, we stabilize the fusion proteins of the RSV, hMPV, and SARS-CoV-2 viruses, testing fewer than a handful of designs. The solved structures of these designed proteins from all three viruses evidence the atomic accuracy of our approach. Furthermore, the humoral response of the redesigned RSV F protein compares to that of the recently approved vaccine in a mouse model. While the parallel design of two conformations allows the identification of energetically sub-optimal positions for one conformation, our protocol also reveals diverse molecular strategies for stabilization. Given the clinical significance of viruses using class I fusion proteins, our algorithm can substantially contribute to vaccine development by reducing the time and resources needed to optimize these immunogens.

Suggested Citation

  • Karen J. Gonzalez & Jiachen Huang & Miria F. Criado & Avik Banerjee & Stephen M. Tompkins & Jarrod J. Mousa & Eva-Maria Strauch, 2024. "A general computational design strategy for stabilizing viral class I fusion proteins," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45480-z
    DOI: 10.1038/s41467-024-45480-z
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