bioRxivMicrobiology
Stabilization of the SARS-CoV-2 Spike receptor-binding domain using deep mutational scanning and structure-based design
D. Ellis, N. Brunette, K. H. D. Crawford, A. C. Walls, M. N. Pham, C. Chen, K.-L. Herpoldt, B. Fiala, M. Murphy, D. Pettie, J. C. Kraft, K. D. Malone, M. J. Navarro, C. Ogohara, E. Kepl, R. Ravichandran, C. Sydeman, M. Ahlrichs, M. Johnson, A. Blackstone, L. Carter, T. N. Starr, A. J. Greaney, K. K. Lee, D. Veesler, , N. P. King
Abstract
The unprecedented global demand for SARS-CoV-2 vaccines has demonstrated the need for highly effective vaccine candidates that are thermostable and amenable to large-scale manufacturing. Nanoparticle immunogens presenting the receptor-binding domain (RBD) of the SARS-CoV-2 Spike protein (S) in repetitive arrays are being advanced as second-generation vaccine candidates, as they feature robust manufacturing characteristics and have shown promising immunogenicity in preclinical models. Here, we used previously reported deep mutational scanning (DMS) data to guide the design of stabilized variants of the RBD. The selected mutations fill a cavity in the RBD that has been identified as a linoleic acid binding pocket. Screening of several designs led to the selection of two lead candidates that expressed at higher yields than the wild-type RBD. These stabilized RBDs possess enhanced thermal stability and resistance to aggregation, particularly when incorporated into an icosahedral nanoparticle immunogen that maintained its integrity and antigenicity for 28 days at 35-40{degrees}C, while corresponding immunogens displaying the wild-type RBD experienced aggregation and loss of antigenicity. The stabilized immunogens preserved the potent immunogenicity of the original nanoparticle immunogen, which is currently being evaluated in a Phase I/II clinical trial. Our findings may improve the scalability and stability of RBD-based coronavirus vaccines in any format and more generally highlight the utility of comprehensive DMS data in guiding vaccine design.
- organism: virus
- assay: proteomics
- research focus: drug response / drug discovery
- approach: deep learning
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