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Pretrainable geometric graph neural network for antibody affinity maturation

Author

Listed:
  • Huiyu Cai

    (BioGeometry
    Mila-Québec AI Institute
    Université de Montréal)

  • Zuobai Zhang

    (Mila-Québec AI Institute
    Université de Montréal)

  • Mingkai Wang

    (Fudan University
    Fudan University)

  • Bozitao Zhong

    (Mila-Québec AI Institute
    Université de Montréal)

  • Quanxiao Li

    (Fudan University
    Fudan University)

  • Yuxuan Zhong

    (Fudan University
    Fudan University)

  • Yanling Wu

    (Fudan University
    Fudan University)

  • Tianlei Ying

    (Fudan University
    Fudan University)

  • Jian Tang

    (BioGeometry
    Mila-Québec AI Institute
    HEC Montréal)

Abstract

Increasing the binding affinity of an antibody to its target antigen is a crucial task in antibody therapeutics development. This paper presents a pretrainable geometric graph neural network, GearBind, and explores its potential in in silico affinity maturation. Leveraging multi-relational graph construction, multi-level geometric message passing and contrastive pretraining on mass-scale, unlabeled protein structural data, GearBind outperforms previous state-of-the-art approaches on SKEMPI and an independent test set. A powerful ensemble model based on GearBind is then derived and used to successfully enhance the binding of two antibodies with distinct formats and target antigens. ELISA EC50 values of the designed antibody mutants are decreased by up to 17 fold, and KD values by up to 6.1 fold. These promising results underscore the utility of geometric deep learning and effective pretraining in macromolecule interaction modeling tasks.

Suggested Citation

  • Huiyu Cai & Zuobai Zhang & Mingkai Wang & Bozitao Zhong & Quanxiao Li & Yuxuan Zhong & Yanling Wu & Tianlei Ying & Jian Tang, 2024. "Pretrainable geometric graph neural network for antibody affinity maturation," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51563-8
    DOI: 10.1038/s41467-024-51563-8
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    References listed on IDEAS

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    3. Josh Abramson & Jonas Adler & Jack Dunger & Richard Evans & Tim Green & Alexander Pritzel & Olaf Ronneberger & Lindsay Willmore & Andrew J. Ballard & Joshua Bambrick & Sebastian W. Bodenstein & David , 2024. "Accurate structure prediction of biomolecular interactions with AlphaFold 3," Nature, Nature, vol. 630(8016), pages 493-500, June.
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