The landscape of antibody binding affinity in SARS-CoV-2 Omicron BA.1 evolution

  1. Alief Moulana
  2. Thomas Dupic
  3. Angela M Phillips  Is a corresponding author
  4. Jeffrey Chang
  5. Anne A Roffler
  6. Allison J Greaney
  7. Tyler Starr
  8. Jesse D Bloom
  9. Michael M Desai  Is a corresponding author
  1. Harvard University, United States
  2. Fred Hutchinson Cancer Research Center, United States

Abstract

The Omicron BA.1 variant of SARS-CoV-2 escapes convalescent sera and monoclonal antibodies that are effective against earlier strains of the virus. This immune evasion is largely a consequence of mutations in the BA.1 receptor binding domain (RBD), the major antigenic target of SARS-CoV-2. Previous studies have identified several key RBD mutations leading to escape from most antibodies. However, little is known about how these escape mutations interact with each other and with other mutations in the RBD. Here, we systematically map these interactions by measuring the binding affinity of all possible combinations of these 15 RBD mutations (215 = 32,768 genotypes) to four monoclonal antibodies (LY-CoV016, LY-CoV555, REGN10987, and S309) with distinct epitopes. We find that BA.1 can lose affinity to diverse antibodies by acquiring a few large-effect mutations and can reduce affinity to others through several small-effect mutations. However, our results also reveal alternative pathways to antibody escape that do not include every large-effect mutation. Moreover, epistatic interactions are shown to constrain affinity decline in S309 but only modestly shape the affinity landscapes of other antibodies. Together with previous work on the ACE2 affinity landscape, our results suggest that escape of each antibody is mediated by distinct groups of mutations, whose deleterious effects on ACE2 affinity are compensated by another distinct group of mutations (most notably Q498R and N501Y).

Data availability

Raw sequencing reads have been deposited in the NCBI BioProject database under accession number PRJNA877045.All associated metadata are available at https://github.com/desai-lab/omicron_ab_landscape. -

The following data sets were generated

Article and author information

Author details

  1. Alief Moulana

    Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0389-7082
  2. Thomas Dupic

    Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States
    Competing interests
    No competing interests declared.
  3. Angela M Phillips

    Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States
    For correspondence
    angela.phillips@ucsf.edu
    Competing interests
    Angela M Phillips, has or has recently consulted for Leyden Labs..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9806-7574
  4. Jeffrey Chang

    Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States
    Competing interests
    No competing interests declared.
  5. Anne A Roffler

    Department of Physics, Harvard University, Cambridge, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8412-0322
  6. Allison J Greaney

    Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, United States
    Competing interests
    Allison J Greaney, is an inventor on Fred Hutch licensed patents related to viral deep mutational scanning.(patentnumbers WO2022146484, WO2020006494 and application number US20210147832)..
  7. Tyler Starr

    Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, United States
    Competing interests
    Tyler Starr, is an inventor on Fred Hutch licensed patents related to viral deep mutational scanning.(patentnumbers WO2022146484, WO2020006494 and application number US20210147832)..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6713-6904
  8. Jesse D Bloom

    Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, United States
    Competing interests
    Jesse D Bloom, is an inventor on Fred Hutch licensed patents related to viral deep mutational scanning. (patentnumbers WO2022146484, WO2020006494 and application number US20210147832). J.D.B. has or has recently consulted for Apriori Bio, Oncorus, Moderna, and Merck..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1267-3408
  9. Michael M Desai

    Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States
    For correspondence
    mdesai@oeb.harvard.edu
    Competing interests
    Michael M Desai, has or has recently consulted for Leyden Labs..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9581-1150

Funding

Human Frontier Science Program (Postdoctoral Fellowship)

  • Thomas Dupic

Howard Hughes Medical Institute (Hanna H. Gray Postdoctoral Fellowship)

  • Angela M Phillips

National Science Foundation (Graduate Research Fellowship Program)

  • Jeffrey Chang

National Science Foundation (Simons Center DMS-1764269)

  • Michael M Desai

National Science Foundation (Harvard Quantitative Biology Initiative DEB-1655960)

  • Michael M Desai

National Institutes of Health (Harvard Quantitative Biology InitiativeGM104239)

  • Michael M Desai

National Institutes of Health (NIH/NIAID R01AI141707)

  • Jesse D Bloom

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Jos W van der Meer, Radboud University Medical Centre, Netherlands

Version history

  1. Received: September 13, 2022
  2. Preprint posted: September 14, 2022 (view preprint)
  3. Accepted: February 6, 2023
  4. Accepted Manuscript published: February 21, 2023 (version 1)
  5. Version of Record published: February 23, 2023 (version 2)

Copyright

© 2023, Moulana et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Alief Moulana
  2. Thomas Dupic
  3. Angela M Phillips
  4. Jeffrey Chang
  5. Anne A Roffler
  6. Allison J Greaney
  7. Tyler Starr
  8. Jesse D Bloom
  9. Michael M Desai
(2023)
The landscape of antibody binding affinity in SARS-CoV-2 Omicron BA.1 evolution
eLife 12:e83442.
https://doi.org/10.7554/eLife.83442

Share this article

https://doi.org/10.7554/eLife.83442

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