Science Translational MedicineImmunologyPeer-reviewed journal article
Computationally designed mRNA-launched protein nanoparticle vaccines
Grace G. HendricksiD, Lilit GrigoryaniD, Mary Jane NavarroiD, Nicholas CatanzaroiD, Miranda L. HubbardiD, John M. PowersiD, Melissa D. MattocksiD, Catherine TreicheliD, Alexandra C. WallsiD, Jimin LeeiD, Daniel EllisiD, Jing Yang WangiD, Suna Cheng, Marcos C. MirandaiD, Adian S. ValdeziD, Cara W. ChaoiD, Sidney ChaniD, Christine MeniD, Max R. JohnsoniD, Samantha K. ZepedaiD, Sebastian OlsiD, Harold Sai-yin HuiiD, Sheng-Yang Wu, Victor LujaniD, Hiromi MuramatsuiD, , Neil P. KingiD
Abstract
Both protein nanoparticle and mRNA vaccines were clinically de-risked during the COVID-19 pandemic1-6. These vaccine modalities have complementary strengths: antigen display on protein nanoparticles can enhance the magnitude, quality, and durability of antibody responses7-10, while mRNA vaccines can be rapidly manufactured11 and elicit antigen-specific CD4 and CD8 T cells12,13. Here we leverage a computationally designed icosahedral protein nanoparticle that was redesigned for optimal secretion from eukaryotic cells14 to develop an mRNA-launched nanoparticle vaccine for SARS-CoV-2. The nanoparticle, which displays 60 copies of a stabilized variant of the Wuhan-Hu-1 Spike receptor binding domain (RBD)15, formed monodisperse, antigenically intact assemblies upon secretion from transfected cells. An mRNA vaccine encoding the secreted RBD nanoparticle elicited 5- to 28-fold higher levels of neutralizing antibodies than an mRNA vaccine encoding membrane-anchored Spike, induced higher levels of CD8 T cells than the same immunogen when delivered as an adjuvanted protein nanoparticle, and protected mice from vaccine-matched and -mismatched SARS-CoV-2 challenge. Our data establish that delivering protein nanoparticle immunogens via mRNA vaccines can combine the benefits of each modality and, more broadly, highlight the utility of computational protein design in genetic immunization strategies.
- organism: mouse
- assay: multiome
- assay: proteomics
- research focus: drug response / drug discovery
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: experimental assay
- approach: deep learning
- approach: statistical model
- Citations
- 24
- FWCI
- 15.32
- Field percentile
- top 1%
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