Biochemical patterns of antibody polyreactivity revealed through a bioinformatics-based analysis of CDR loops
Abstract
Antibodies are critical components of adaptive immunity, binding with high affinity to pathogenic epitopes. Antibodies undergo rigorous selection to achieve this high affinity, yet some maintain an additional basal level of low affinity, broad reactivity to diverse epitopes, a phenomenon termed 'polyreactivity'. While polyreactivity has been observed in antibodies isolated from various immunological niches, the biophysical properties that allow for promiscuity in a protein selected for high affinity binding to a single target remain unclear. Using a database of over 1,000 polyreactive and non-polyreactive antibody sequences, we created a bioinformatic pipeline to isolate key determinants of polyreactivity. These determinants, which include an increase in inter-loop crosstalk and a propensity for a neutral binding surface, are sufficient to generate a classifier able to identify polyreactive antibodies with over 75% accuracy. The framework from which this classifier was built is generalizable, and represents a powerful, automated pipeline for future immune repertoire analysis.
Data availability
All data generated and all code used for analysis in this study has been published on GitHub at github.com/ctboughter/AIMS.
Article and author information
Author details
Funding
National Institute of Biomedical Imaging and Bioengineering (EB009412)
- Christopher T Boughter
National Institute of Allergy and Infectious Diseases (AI147954)
- Christopher T Boughter
- Marta T Borowska
- Erin J Adams
National Institute of Allergy and Infectious Diseases (AI115471)
- Christopher T Boughter
- Marta T Borowska
- Erin J Adams
National Science Foundation (MCB-1517221)
- Christopher T Boughter
- Benoit Roux
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2020, Boughter 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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