ArticleeLife2025
Thrifty wide-context models of B cell receptor somatic hypermutation.
Article in eLife, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Article
- Entrenchment of germline amino-acid differences in antibody affinity maturation.bioRxiv : the preprint server for biology · 2026Article
- Article
- 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
- A Sitewise Model of Natural Selection on Individual Antibodies via a Transformer-Encoder.Molecular biology and evolution · 2025Article
- The mutation rate of SARS-CoV-2 is highly variable between sites and is influenced by sequence context, genomic region, and RNA structure.Nucleic acids research · 2025Article
- The mutation rate of SARS-CoV-2 is highly variable between sites and is influenced by sequence context, genomic region, and RNA structure.bioRxiv : the preprint server for biology · 2025Article
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7 authors.
Funding
Abstract
Somatic hypermutation (SHM) is the diversity-generating process in antibody affinity maturation. Probabilistic models of SHM are needed for analyzing rare mutations, understanding the selective forces guiding affinity maturation, and understanding the underlying biochemical process. High-throughput data offers the potential to develop and fit models of SHM on relevant data sets. In this article, we model SHM using modern frameworks. We are motivated by recent work suggesting the importance of a wider context for SHM; however, assigning an independent rate to each k-mer leads to an exponential proliferation of parameters. Thus, using convolutions on 3-mer embeddings, we develop 'thrifty' models of SHM of various sizes; these can have fewer free parameters than a 5-mer model and yet have a significantly wider context. These offer a slight performance improvement over a 5-mer model, and other modern model elaborations worsen performance. We also find that a per-site effect is not necessary to explain SHM patterns given nucleotide context. Also, the two current methods for fitting an SHM model-on out-of-frame sequence data and on synonymous mutations-produce significantly different results, and augmenting out-of-frame data with synonymous mutations does not aid out-of-sample performance.
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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.