Mapping mutations to the SARS-CoV-2 RBD that escape binding by different classes of antibodies

Creators: Greaney, Allison J.; Starr, Tyler N.; Barnes, Christopher O.; Weisblum, Yiska; Schmidt, Fabian; Caskey, Marina; Gaebler, Christian; Cho, Alice; Agudelo, Marianna; Finkin, Shlomo; Wang, Zijun; Poston, Daniel; Muecksch, Frauke; Hatziioannou, Theodora; Bieniasz, Paul D.; Robbiani, Davide F.; Nussenzweig, Michel C.; Bjorkman, Pamela J.; Bloom, Jesse D.

Abstract

Monoclonal antibodies targeting a variety of epitopes have been isolated from individuals previously infected with SARS-CoV-2, but the relative contributions of these different antibody classes to the polyclonal response remains unclear. Here we use a yeast-display system to map all mutations to the viral spike receptor-binding domain (RBD) that escape binding by representatives of three potently neutralizing classes of anti-RBD antibodies with high-resolution structures. We compare the antibody-escape maps to similar maps for convalescent polyclonal plasmas, including plasmas from individuals from whom some of the antibodies were isolated. While the binding of polyclonal plasma antibodies are affected by mutations across multiple RBD epitopes, the plasma-escape maps most resemble those of a single class of antibodies that target an epitope on the RBD that includes site E484. Therefore, although the human immune system can produce antibodies that target diverse RBD epitopes, in practice the polyclonal response to infection is skewed towards a single class of antibodies targeting an epitope that is already undergoing rapid evolution.

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© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. We thank Andrea Loes for experimental assistance and Cathy Lin for administrative support; Dolores Covarrubias, Andy Marty, and the Genomics and Flow Cytometry core facilities at the Fred Hutchinson Cancer Research Center for experimental support; J. Vielmetter, P. Hoffman, and the Protein Expression Center in the Beckman Institute at Caltech for expression assistance. This work was supported by the NIAID/NIH (R01AI141707 and R01AI127893 to J.D.B., T32AI083203 to A.J.G., P01 AI138398-S1 to M.C.N. and P.J.B.) and the Gates Foundation (INV-004949). Support was also provided by the Caltech Merkin Institute for Translational Research (P.J.B.), a George Mason University Fast Grant (P.J.B.), and ATAC consortium EC 101003650 (D.F.R.); NIH grants U19 AI111825 and NIH U01 AI151698 for the United World Antiviral Research Network, UWARN (M.C.N. and D.F.R.). The Scientific Computing Infrastructure at Fred Hutch is funded by ORIP grant S10OD028685. T.N.S. is a Washington Research Foundation Innovation Fellow at the University of Washington Institute for Protein Design and a Howard Hughes Medical Institute Fellow of the Damon Runyon Cancer Research Foundation (DRG-2381-19). C.O.B. was supported by the Hanna Gray Fellowship Program from the Howard Hughes Medical Institute and the Postdoctoral Enrichment Program from the Burroughs Wellcome Fund. J.D.B., P.D.B., and M.C.N. are Investigators of the Howard Hughes Medical Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the US government or the other sponsors. Code availability: The complete custom code computational pipeline for escape-mapping data analysis is available at https://github.com/jbloomlab/SARS-CoV-2-RBD_MAP_Rockefeller and archived in the Zenodo repository (https://doi.org/10.5281/zenodo.4901733). Markdown summaries of the escape-mapping data analysis steps are at https://github.com/jbloomlab/SARS-CoV-2-RBD_MAP_Rockefeller/blob/main/results/summary/summary.md. Author Contributions: Conceptualization: A.J.G., C.O.B., M.C.N., P.J.B. and J.D.B.; methodology: A.J.G., T.N.S. and J.D.B.; investigation: A.J.G.; software: A.J.G., T.N.S. and J.D.B.; formal analysis: A.J.G. and J.D.B.; VSV escape data: Y.W., F.S. and D.P.; resources: M.C.N., D.F.R., M.C., C.G., A.C., M.A., S.F., Z.W. and F.M.; writing—original draft: A.J.G. and J.D.B.; writing—review and editing: all authors; supervision: T.H., P.D.B., M.C.N., P.J.B. and J.D.B. Competing interests: Subsequent to completion and submission of the initial version of this study, J.D.B. began consulting for Moderna on viral evolution and epidemiology. J.D.B. has the potential to receive a share of IP revenue as an inventor on a Fred Hutch optioned technology/patent (application WO2020006494) related to deep mutational scanning of viral proteins. The Rockefeller University has filed a provisional patent application related to SARS-CoV-2 monoclonal antibodies on which D.F.R. and M.C.N. are inventors. The Rockefeller University has applied for a patent relating to the replication-competent VSV/SARS-CoV-2 chimeric virus on which Y.W, F.S., T.H. and P.B. are inventors (US patent 63/036,124). The remaining authors declare no competing interests. Peer review information: Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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Mapping mutations to the SARS-CoV-2 RBD that escape binding by different classes of antibodies

Abstract

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