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DynamicBind: predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model

Author

Listed:
  • Wei Lu

    (Galixir Technologies)

  • Jixian Zhang

    (Galixir Technologies)

  • Weifeng Huang

    (Sun Yat-sen University)

  • Ziqiao Zhang

    (Galixir Technologies)

  • Xiangyu Jia

    (Galixir Technologies)

  • Zhenyu Wang

    (Galixir Technologies)

  • Leilei Shi

    (Galixir Technologies)

  • Chengtao Li

    (Galixir Technologies)

  • Peter G. Wolynes

    (Rice University)

  • Shuangjia Zheng

    (Shanghai Jiao Tong University)

Abstract

While significant advances have been made in predicting static protein structures, the inherent dynamics of proteins, modulated by ligands, are crucial for understanding protein function and facilitating drug discovery. Traditional docking methods, frequently used in studying protein-ligand interactions, typically treat proteins as rigid. While molecular dynamics simulations can propose appropriate protein conformations, they’re computationally demanding due to rare transitions between biologically relevant equilibrium states. In this study, we present DynamicBind, a deep learning method that employs equivariant geometric diffusion networks to construct a smooth energy landscape, promoting efficient transitions between different equilibrium states. DynamicBind accurately recovers ligand-specific conformations from unbound protein structures without the need for holo-structures or extensive sampling. Remarkably, it demonstrates state-of-the-art performance in docking and virtual screening benchmarks. Our experiments reveal that DynamicBind can accommodate a wide range of large protein conformational changes and identify cryptic pockets in unseen protein targets. As a result, DynamicBind shows potential in accelerating the development of small molecules for previously undruggable targets and expanding the horizons of computational drug discovery.

Suggested Citation

  • Wei Lu & Jixian Zhang & Weifeng Huang & Ziqiao Zhang & Xiangyu Jia & Zhenyu Wang & Leilei Shi & Chengtao Li & Peter G. Wolynes & Shuangjia Zheng, 2024. "DynamicBind: predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45461-2
    DOI: 10.1038/s41467-024-45461-2
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