ArticleNucleic acids research2020
Learning the heterogeneous hypermutation landscape of immunoglobulins from high-throughput repertoire data.
Article in Nucleic acids research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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27 citing papers in PubMed, 48 citations in OpenAlex.
- Entrenchment of germline amino-acid differences in antibody affinity maturation.bioRxiv : the preprint server for biology · 2026Article
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- Comprehensive characterization of V(D)J recombination from long-read transcriptomic data with VDJcraft.bioRxiv : the preprint server for biology · 2026Article
- Dynamics of memory B cells and plasmablasts in healthy individuals.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Somatic hypermutation patterns are shaped by both motif position and sequence grammar.The EMBO journal · 2026Article
- The current landscape of adaptive immune receptor genomic and repertoire data: OGRDB and VDJbase.Nucleic acids research · 2026Article
- Nucleotide context models outperform protein language models for predicting antibody affinity maturation.PLoS computational biology · 2025Article
- Nucleotide context models outperform protein language models for predicting antibody affinity maturation.bioRxiv : the preprint server for biology · 2025Article
- Separating selection from mutation in antibody language models.bioRxiv : the preprint server for biology · 2025Article
- Collateral mutagenesis funnels multiple sources of DNA damage into a ubiquitous mutational signature.bioRxiv : the preprint server for biology · 2025Article
- Article
- A Sitewise Model of Natural Selection on Individual Antibodies via a Transformer-Encoder.Molecular biology and evolution · 2025Article
- Enhancing sequence alignment of adaptive immune receptors through multi-task deep learning.Nucleic acids research · 2025Article
- Protein language model pseudolikelihoods capture features of in vivo B cell selection and evolution.Briefings in bioinformatics · 2025Article
- Leveraging DAGs to improve context-sensitive and abundance-aware tree estimation.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Article
- Combining mutation and recombination statistics to infer clonal families in antibody repertoires.eLife · 2024Article
- Interpretable deep learning reveals the role of an E-box motif in suppressing somatic hypermutation of AGCT motifs within human immunoglobulin variable regions.Frontiers in immunology · 2024Article
- Antibody Dynamics Simulation-A Mathematical Exploration of Clonal Deletion and Somatic Hypermutation.Biomedicines · 2023Article
- Article
- Genotype-phenotype landscapes for immune-pathogen coevolution.Trends in immunology · 2023Review
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Somatic hypermutations of immunoglobulin (Ig) genes occurring during affinity maturation drive B-cell receptors' ability to evolve strong binding to their antigenic targets. The landscape of these mutations is highly heterogeneous, with certain regions of the Ig gene being preferentially targeted. However, a rigorous quantification of this bias has been difficult because of phylogenetic correlations between sequences and the interference of selective forces. Here, we present an approach that corrects for these issues, and use it to learn a model of hypermutation preferences from a recently published large IgH repertoire dataset. The obtained model predicts mutation profiles accurately and in a reproducible way, including in the previously uncharacterized Complementarity Determining Region 3, revealing that both the sequence context of the mutation and its absolute position along the gene are important. In addition, we show that hypermutations occurring concomittantly along B-cell lineages tend to co-localize, suggesting a possible mechanism for accelerating affinity maturation.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.