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Learned features of antibody-antigen binding affinity.

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  • 1Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States.

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Identifying antibody-antigen binding affinity predictors is crucial for therapeutic antibody engineering. Simple features can predict binding affinity as effectively as complex ones, with combined features yielding the best results.

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affinityantibodyantigenclassificationfeatureslearningstructure

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Area of Science:

  • Biochemistry
  • Structural Biology
  • Immunology

Background:

  • Predicting antibody-antigen binding affinity is essential for developing effective therapeutic antibodies.
  • The conformational diversity of antibody complementarity determining regions and antibody-antigen interactions presents significant challenges.
  • Existing methods require identification of features that can accurately discriminate between high and low binding affinities.

Purpose of the Study:

  • To identify features that can discriminate high- and low-binding affinity antibodies using the structural antibody database (SAbDab).
  • To compare the predictive power of 'complex' (energetic, statistical, network-based, machine-learned) and 'simple' (contact counts) feature sets.
  • To establish a foundation for future studies aimed at enhancing antibody affinity through feature-guided engineering.

Main Methods:

  • Utilized the structural antibody database (SAbDab) for feature extraction.
  • Developed 'complex' feature sets based on protein-protein interaction representations.
  • Developed 'simple' feature sets based on antibody-antigen contact counts.
  • Investigated the predictive potential of 700 features across eight feature sets using classification models.

Main Results:

  • Simple feature sets performed comparably to complex feature sets in predicting binding affinity.
  • Combining features from all eight sets achieved the best classification performance (median cross-validation AUROC and F1-score of 0.72).
  • Classification performance improved when potential data leakage sources (e.g., homologous antibodies) were not removed, highlighting a common pitfall.
  • A performance plateau was observed across different featurization approaches, indicating a need for more affinity-labeled structural data.

Conclusions:

  • Simple features are effective predictors of antibody-antigen binding affinity.
  • Combining diverse feature sets maximizes predictive performance.
  • Careful consideration of data leakage and the need for more labeled data are crucial for advancing antibody affinity prediction.