Measuring the sequence-affinity landscape of antibodies with massively parallel titration curves
Abstract
Despite the central role that antibodies play in the adaptive immune system and in biotechnology, much remains unknown about the quantitative relationship between an antibody's amino acid sequence and its antigen binding affinity. Here we describe a new experimental approach, called Tite-Seq, that is capable of measuring binding titration curves and corresponding affinities for thousands of variant antibodies in parallel. The measurement of titration curves eliminates the confounding effects of antibody expression and stability that arise in standard deep mutational scanning assays. We demonstrate Tite-Seq on the CDR1H and CDR3H regions of a well-studied scFv antibody. Our data shed light on the structural basis for antigen binding affinity and suggests a role for secondary CDR loops in establishing antibody stability. Tite-Seq fills a large gap in the ability to measure critical aspects of the adaptive immune system, and can be readily used for studying sequence-affinity landscapes in other protein systems.
Data availability
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Saccharomyces cerevisiae high-throughput titration curvesPublicly available at the NCBI BioProject database (accession no: PRJNA344711).
Article and author information
Author details
Funding
European Research Council (StG n. 306312)
- Rhys M Adams
- Thierry Mora
- Aleksandra M Walczak
Simons Center for Quantitative Biology
- Justin B Kinney
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Jesse D Bloom, Fred Hutchinson Cancer Research Center, United States
Version history
- Received: November 10, 2016
- Accepted: December 27, 2016
- Accepted Manuscript published: December 30, 2016 (version 1)
- Accepted Manuscript updated: January 3, 2017 (version 2)
- Version of Record published: January 26, 2017 (version 3)
Copyright
© 2016, Adams 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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