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Structure prediction of protein-ligand complexes from sequence information with Umol

Author

Listed:
  • Patrick Bryant

    (Freie Universität Berlin
    Stockholm University
    Science for Life Laboratory)

  • Atharva Kelkar

    (Freie Universität Berlin)

  • Andrea Guljas

    (Freie Universität Berlin)

  • Cecilia Clementi

    (Freie Universität Berlin)

  • Frank Noé

    (Freie Universität Berlin
    Freie Universität Berlin
    Microsoft Research AI4Science)

Abstract

Protein-ligand docking is an established tool in drug discovery and development to narrow down potential therapeutics for experimental testing. However, a high-quality protein structure is required and often the protein is treated as fully or partially rigid. Here we develop an AI system that can predict the fully flexible all-atom structure of protein-ligand complexes directly from sequence information. We find that classical docking methods are still superior, but depend upon having crystal structures of the target protein. In addition to predicting flexible all-atom structures, predicted confidence metrics (plDDT) can be used to select accurate predictions as well as to distinguish between strong and weak binders. The advances presented here suggest that the goal of AI-based drug discovery is one step closer, but there is still a way to go to grasp the complexity of protein-ligand interactions fully. Umol is available at: https://github.com/patrickbryant1/Umol .

Suggested Citation

  • Patrick Bryant & Atharva Kelkar & Andrea Guljas & Cecilia Clementi & Frank Noé, 2024. "Structure prediction of protein-ligand complexes from sequence information with Umol," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48837-6
    DOI: 10.1038/s41467-024-48837-6
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    References listed on IDEAS

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    3. Anastasiia V. Sadybekov & Vsevolod Katritch, 2023. "Computational approaches streamlining drug discovery," Nature, Nature, vol. 616(7958), pages 673-685, April.
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