ArticleiScience2022
Deep learning model of somatic hypermutation reveals importance of sequence context beyond hotspot targeting.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 29 citations in OpenAlex.
- Article
- Entrenchment of germline amino-acid differences in antibody affinity maturation.bioRxiv : the preprint server for biology · 2026Article
- Article
- AI-Driven BCR Modeling for Precision Immunology.International journal of molecular sciences · 2026Review
- 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
- 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
- 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
- Article
- The landscape of somatic mutations in lymphoblastoid cell lines.Cell genomics · 2023Article
- Antibody repertoire sequencing analysis.Acta biochimica et biophysica Sinica · 2022Review
- Article
Corrections and comments
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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
B cells undergo somatic hypermutation (SHM) of the Immunoglobulin (Ig) variable region to generate high-affinity antibodies. SHM relies on the activity of activation-induced deaminase (AID), which mutates C>U preferentially targeting WRC (W=A/T, R=A/G) hotspots. Downstream mutations at WA Polymerase η hotspots contribute further mutations. Computational models of SHM can describe the probability of mutations essential for vaccine responses. Previous studies using short subsequences (
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Registered trials
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