Hierarchical sequence-affinity landscapes shape the evolution of breadth in an anti-influenza receptor binding site antibody

Abstract
Data availability
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Abstract

Broadly neutralizing antibodies (bnAbs) that neutralize diverse variants of a particular virus are of considerable therapeutic interest¹. Recent advances have enabled us to isolate and engineer these antibodies as therapeutics, but eliciting them through vaccination remains challenging, in part due to our limited understanding of how antibodies evolve breadth². Here, we analyze the landscape by which an anti-influenza receptor binding site (RBS) bnAb, CH65, evolved broad affinity to diverse H1 influenza strains^3,4. We do this by generating an antibody library of all possible evolutionary intermediates between the unmutated common ancestor (UCA) and the affinity-matured CH65 antibody and measure the affinity of each intermediate to three distinct H1 antigens. We find that affinity to each antigen requires a specific set of mutations - distributed across the variable light and heavy chains - that interact non-additively (i.e., epistatically). These sets of mutations form a hierarchical pattern across the antigens, with increasingly divergent antigens requiring additional epistatic mutations beyond those required to bind less divergent antigens. We investigate the underlying biochemical and structural basis for these hierarchical sets of epistatic mutations and find that epistasis between heavy chain mutations and a mutation in the light chain at the V_H-V_L interface is essential for binding a divergent H1. Collectively, this work is the first to comprehensively characterize epistasis between heavy and light chain mutations and shows that such interactions are both strong and widespread. Together with our previous study analyzing a different class of anti-influenza antibodies⁵, our results implicate epistasis as a general feature of antibody sequence-affinity landscapes that can potentiate and constrain the evolution of breadth.

Data availability

Data and code used for this study are available at https://github.com/amphilli/CH65-comblib. Antibody affinity and expression data are also available in an interactive data browser at https://ch65-ma90-browser.netlify.app/. FASTQ files from high-throughput sequencing are deposited in the NCBI BioProject database under PRJNA886089. X-ray crystal structures of the Fabs reported here are available at the Protein Data Bank (8EK6 and 8EKH).

The following data sets were generated

1. Phillips AM
2. Maurer DP
3. Brooks C
4. Dupic T
5. Schmidt AG
6. Desai MM
(2022) Binding affinity landscape of CH65 to divergent influenza H1 strains
NCBI BioProject, PRJNA886089.

https://www.ncbi.nlm.nih.gov/bioproject/PRJNA886089/
1. Phillips AM
2. Maurer DP
3. Brooks C
4. Dupic T
5. Schmidt AG
6. Desai MM
(2022) CH65 Tite-Seq KD Measurements
CH65 online data browser.

https://ch65-ma90-browser.netlify.app

Article and author information

Author details

Angela M Phillips

1Department of Organismic and Evolutionary Biology, University of California, San Francisco, San Francisco, United States

For correspondence
angela.phillips@ucsf.edu

Competing interests
Angela M Phillips, has recently consulted for Leyden Labs..

"This ORCID iD identifies the author of this article:" 0000-0002-9806-7574
Daniel P Maurer

Department of Microbiology, Harvard Medical School, Cambridge, United States

Competing interests
No competing interests declared.
Caelan Brooks

Department of Physics, Harvard University, Cambridge, MA, United States

Competing interests
No competing interests declared.
Thomas Dupic

Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States

Competing interests
No competing interests declared.
Aaron G Schmidt

Department of Microbiology, Harvard Medical School, Cambridge, United States

Competing interests
No competing interests declared.
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, recently consulted for Leyden Labs..

"This ORCID iD identifies the author of this article:" 0000-0002-9581-1150

Funding

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

Angela M Phillips

Human Frontier Science Program (Postdoctoral Fellowship)

Thomas Dupic

National Institutes of Health (R01AI146779)

Aaron G Schmidt

National Institutes of Health (P01AI89618-A1)

Aaron G Schmidt

National Science Foundation (DMS-1764269)

Michael M Desai

National Science Foundation (DMS-1655960)

Michael M Desai

National Institutes of Health (GM104239)

Michael M Desai

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

Copyright

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.