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Related Experiment Video

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In Vivo Modeling of the Morbid Human Genome using Danio rerio
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Taking the biology seriously makes models better.

Antonio Matas-Gil1,2, Andreas Tiffeau-Mayer1,2

  • 1Division of Infection and Immunity, University College London, London, United Kingdom.

Elife
|April 20, 2026
PubMed
Summary

A novel training method enhances protein language models to accurately predict antibody affinity maturation. This biologically-informed approach improves predictions for antibody development and engineering.

Keywords:
affinity maturationantibody engineeringantibody language modelevolutionary biologyfunctional predictionhumanimmunologyinflammationmutation-selection model,somatic hypermutation

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Area of Science:

  • * Computational biology
  • * Protein engineering
  • * Immunology

Background:

  • * Antibody affinity maturation is crucial for developing effective therapeutics.
  • * Traditional methods for predicting maturation trajectories are often slow and resource-intensive.
  • * Protein language models (PLMs) have shown promise in understanding protein sequences but require specialized training for specific biological tasks.

Purpose of the Study:

  • * To develop a new training paradigm for protein language models.
  • * To enable PLMs to predict antibody affinity maturation trajectories.
  • * To improve the efficiency and accuracy of antibody engineering and design.

Main Methods:

  • * Implementation of a biologically-informed training paradigm for PLMs.
  • * Training PLMs on datasets relevant to antibody sequences and affinity.
  • * Evaluating the predictive performance of the trained models on antibody maturation.

Main Results:

  • * The new training paradigm significantly improved the ability of PLMs to predict affinity maturation.
  • * Biologically-informed training enhanced model interpretability regarding antibody sequence-function relationships.
  • * The models demonstrated high accuracy in forecasting antibody evolution under selection pressure.

Conclusions:

  • * Biologically-informed training is a powerful strategy for adapting PLMs to specific biological prediction tasks.
  • * This approach can accelerate antibody discovery and optimization processes.
  • * The developed method offers a new tool for computational antibody engineering.