Learning antibody sequence constraints from allelic inclusion
Open accessPreprint — published as 10.1016/j.cels.2025.101368 · Paper page
Abstract
Antibodies and B-cell receptors (BCRs) are produced by B cells, and are built of a heavy chain and a light chain. Although each B cell could express two different heavy chains and four different light chains, usually only a unique pair of heavy chain and light chain is expressed—a phenomenon known as allelic exclusion . However, a small fraction of naive-B cells violate allelic exclusion by expressing two productive light chains, one of which has impaired function; this has been called allelic inclusion . We demonstrate that these B cells can be used to learn constraints on antibody sequence. Using large-scale single-cell sequencing data from humans, we find examples of light chain allelic inclusion in thousands of naive-B cells, which is an order of magnitude larger than existing datasets. We train machine learning models to identify the abnormal sequences in these cells. The resulting models correlate with antibody properties that they were not trained on, including polyreactivity, surface expression, and mutation usage in affinity maturation. These correlations are larger than what is achieved by existing antibody modeling approaches, indicating that allelic inclusion data contains useful new information. We also investigate the impact of similar selection forces on the heavy chain in mouse, and observe that pairing with the surrogate light chain significantly restricts heavy chain diversity.
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Biophysical trade-offs in antibody evolution are resolved by conformation-mediated epistasisPreprint
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Dell'uomo, D., Satz, A., & Averso, B. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.06.687005
- Authors
- Jagota, M., Hsu, C., Mazumder, T., Sung, K., DeWitt, W. S., Listgarten, J., Matsen, F. A., Ye, C. J., & Song, Y. S.
- Affiliations
- University of California, Berkeley (US)
- University of California, San Francisco (US)
- Fred Hutch Cancer Center (US)
- University of Washington (US)
- Innovative Genomics Institute (US)
- Corresponding author
- Song, Yun S.
- ORCID
- Venue
- Cell Systems
- Publisher
- openRxiv
- Published
- 2024-10-25
- Volume
- 16
- Issue
- 9
- Pages
- 101368-101368
- ISSN
- 2405-4712 (ISSN-L)
- PMC
- PMC11526943
- OpenAlex
- W4413138774
- Cited by
- 2
- References
- 65
- Citations by year
- 2026: 1 · 2025: 1
- FWCI
- 0.51
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cell Systems (acceptedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by-nc-nd)
- Funders
- National Institutes of Health
- Peder Sather Center for Advanced Study
- National Institute of General Medical Sciences (R35GM134922)
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (P30AR070155)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Keywords
- antibody light chain, B cells, autoreactivity, polyreactivity, cell surface expression, surrogate light chain, single-cell RNA sequencing, machine learning prediction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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