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

Karen J. Gonzalez, Jiachen Huang, Miria F. Criado, Avik Banerjee, Stephen M. Tompkins, Jarrod J. Mousa and Eva-Maria Strauch ()
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Karen J. Gonzalez: University of Georgia
Jiachen Huang: University of Georgia
Miria F. Criado: University of Georgia
Avik Banerjee: University of Georgia
Stephen M. Tompkins: University of Georgia
Jarrod J. Mousa: University of Georgia
Eva-Maria Strauch: University of Georgia

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-45480-z

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