High-throughput identification of prefusion-stabilizing mutations in SARS-CoV-2 spike

Timothy J.C. Tan(University of Illinois Urbana-Champaign), Zongjun Mou(University School), Ruipeng Lei(University of Illinois Urbana-Champaign), Wenhao O. Ouyang(University of Illinois Urbana-Champaign), Meng Yuan(Scripps Research Institute), Ge Song(Scripps Research Institute), Raiees Andrabi(Scripps Research Institute), Ian A. Wilson(Scripps Research Institute), Collin Kieffer(University of Illinois Urbana-Champaign), Xinghong Dai(University School), Kenneth A. Matreyek(University School), Nicholas C. Wu(University of Illinois Urbana-Champaign)
Nature Communications
April 10, 2023
Cited by 41Open Access
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Abstract

Designing prefusion-stabilized SARS-CoV-2 spike is critical for the effectiveness of COVID-19 vaccines. All COVID-19 vaccines in the US encode spike with K986P/V987P mutations to stabilize its prefusion conformation. However, contemporary methods on engineering prefusion-stabilized spike immunogens involve tedious experimental work and heavily rely on structural information. Here, we establish a systematic and unbiased method of identifying mutations that concomitantly improve expression and stabilize the prefusion conformation of the SARS-CoV-2 spike. Our method integrates a fluorescence-based fusion assay, mammalian cell display technology, and deep mutational scanning. As a proof-of-concept, we apply this method to a region in the S2 domain that includes the first heptad repeat and central helix. Our results reveal that besides K986P and V987P, several mutations simultaneously improve expression and significantly lower the fusogenicity of the spike. As prefusion stabilization is a common challenge for viral immunogen design, this work will help accelerate vaccine development against different viruses.


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