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Updated: Jan 9, 2026

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Nucleotide context models outperform protein language models for predicting antibody affinity maturation.

Mackenzie M Johnson1, Kevin Sung1, Hugh K Haddox1

  • 1Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America.

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|December 1, 2025
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Accurately modeling somatic hypermutation (SHM) using nucleotide context significantly improves predictions of antibody affinity maturation. Nucleotide-based models outperform advanced protein language models in predicting B cell receptor evolution.

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Area of Science:

  • Immunology
  • Computational Biology
  • Bioinformatics

Background:

  • Antibodies are key to adaptive immunity, developing from B cell receptors (BCRs) on B cells.
  • BCRs undergo affinity maturation, a process involving somatic hypermutation (SHM) and selection, to enhance antigen binding.
  • Computational models for affinity maturation have emerged from molecular evolution and language modeling perspectives.

Purpose of the Study:

  • To compare the predictive power of molecular evolution and language modeling approaches for antibody affinity maturation.
  • To evaluate models of SHM, SHM with selection, and protein language models using BCR sequence data.
  • To introduce EPAM, a framework for benchmarking antibody evolution prediction models.

Main Methods:

  • Compared nucleotide-based SHM models with protein language models on human BCR repertoire data and a mouse experiment.
  • Utilized phylogenetic trees of BCR sequences to assess model performance.
  • Incorporated selection estimates from deep mutational scanning experiments.

Main Results:

  • Precise modeling of SHM, incorporating nucleotide context, substantially improved predictions of affinity maturation.
  • A nucleotide-based convolutional neural network modeling SHM outperformed state-of-the-art protein language models.
  • Including selection estimates provided only a modest improvement in predictive power.

Conclusions:

  • Nucleotide context is critical for accurately modeling somatic hypermutation and predicting antibody affinity maturation.
  • Nucleotide-based models offer a powerful alternative to protein language models for understanding B cell receptor evolution.
  • The EPAM framework facilitates further research into antibody evolution and predictive modeling.