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Protein language model pseudolikelihoods capture features of in vivo B cell selection and evolution
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Abstract
B cell selection and evolution play crucial roles in dictating successful immune responses. Recent advancements in sequencing technologies and deep-learning strategies have paved the way for generating and exploiting an ever-growing wealth of antibody repertoire data. The self-supervised nature of protein language models (PLMs) has demonstrated the ability to learn complex representations of antibody sequences and has been leveraged for a wide range of applications including diagnostics, structural modeling, and antigen-specificity predictions. PLM-derived likelihoods have been used to improve antibody affinities in vitro, raising the question of whether PLMs can capture and predict features of B cell selection in vivo. Here, we explore how general and antibody-specific PLM-generated sequence pseudolikelihoods (SPs) relate to features of in vivo B cell selection such as expansion, isotype usage, and somatic hypermutation (SHM) at single-cell resolution. Our results demonstrate that the type of PLM and the region of the antibody input sequence significantly affect the generated SP. Contrary to previous in vitro reports, we observe a negative correlation between SPs and binding affinity, whereas repertoire features such as SHM and isotype usage were strongly correlated with SPs. By constructing evolutionary lineage trees of B cell clones from human and mouse repertoires, we observe that SHMs are routinely among the most likely mutations suggested by PLMs and that mutating residues have lower absolute likelihoods than conserved residues. Our findings highlight the potential of PLMs to predict features of antibody selection and further suggest their potential to assist in antibody discovery and engineering.
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“We began by employing the three general PLMs ESM-C [29], ESM-1b [12] and ProtBERT [13] as well as the three antibody-specific PLMs Ablang2 [20], Ablang1 [18] and Sapiens [22] on the heavy chain sequences from all input sources for all samples.”
A B-cell subset uniquely responsive to innate stimuli accumulates in aged mice
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Metadata
- Authors
- van Ginneken, D., Samant, A., Daga-Krumins, K., Glänzer, W., Agrafiotis, A., Kladis, E., Reddy, S. T., & Yermanos, A.
- Affiliations
- University Medical Center Utrecht (NL)
- ETH Zurich (CH)
- University of Geneva (CH)
- Corresponding author
- Yermanos, Alexander
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2025-07
- Volume
- 26
- Issue
- 4
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- PMC
- PMC12360699
- OpenAlex
- W4405385474
- Cited by
- 6
- References
- 5
- Citations by year
- 2026: 3 · 2025: 3
- FWCI
- 1.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Briefings in Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- ETH Zürich Research Collection (cc-by)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Life Sciences › Immunology and Microbiology › Immunology › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
- Keywords
- protein language models, B cell selection, somatic hypermutation, antibody repertoire, antibody affinity, CDR3, antibody evolution, antibody engineering, antigen specificity
- MeSH terms
- Animals, B-Lymphocytes, Humans, Evolution, Molecular, Somatic Hypermutation, Immunoglobulin, Mice
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