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Sequence-only prediction of binding affinity changes: a robust and interpretable model for antibody engineering.

Chen Liu1, Mingchen Li1, Yang Tan1

  • 1School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.

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|August 12, 2025
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Summary

ProtAttBA, a deep learning model, predicts antibody-antigen binding affinity changes using only sequence data. This offers a rapid, cost-effective alternative to traditional methods for antibody engineering.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Immunology

Background:

  • Enhancing antibody-antigen binding affinity is crucial for antibody engineering.
  • Traditional experimental methods for assessing binding affinity are costly and time-consuming.
  • Current deep learning models often require complex structural data, which is not always available.

Purpose of the Study:

  • To develop a deep learning model, ProtAttBA, for predicting antibody-antigen binding affinity changes.
  • To create a model that relies solely on sequence information, overcoming limitations of structure-dependent methods.
  • To provide a rapid and cost-effective computational tool for antibody engineering.

Main Methods:

  • ProtAttBA utilizes a pre-training phase for learning protein sequence patterns.
  • A supervised training phase employs labeled antibody-antigen complex data.
  • A cross-attention-based regressor is trained to predict binding affinity changes.

Main Results:

  • ProtAttBA achieves competitive performance compared to sequence- and structure-based methods on open benchmarks.
  • The model demonstrates robustness, particularly with uncertain complex structures.
  • The attention mechanism provides interpretability, identifying critical residues impacting binding affinity.

Conclusions:

  • ProtAttBA offers a rapid, cost-effective computational tool for antibody engineering.
  • The model can accelerate the development of novel therapeutic antibodies.
  • Sequence-based prediction of binding affinity is a viable and robust alternative to structure-based methods.