The landscape of antibody binding affinity in SARS-CoV-2 Omicron BA.1 evolution
- Harvard University, United States
- 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. -
Article and author information
Author details
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.
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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