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Structure prediction of alternative protein conformations

Author

Listed:
  • Patrick Bryant

    (Freie Universität Berlin
    Stockholm University
    Science for Life Laboratory)

  • Frank Noé

    (Freie Universität Berlin
    Microsoft Research AI4Science)

Abstract

Proteins are dynamic molecules whose movements result in different conformations with different functions. Neural networks such as AlphaFold2 can predict the structure of single-chain proteins with conformations most likely to exist in the PDB. However, almost all protein structures with multiple conformations represented in the PDB have been used while training these models. Therefore, it is unclear whether alternative protein conformations can be genuinely predicted using these networks, or if they are simply reproduced from memory. Here, we train a structure prediction network, Cfold, on a conformational split of the PDB to generate alternative conformations. Cfold enables efficient exploration of the conformational landscape of monomeric protein structures. Over 50% of experimentally known nonredundant alternative protein conformations evaluated here are predicted with high accuracy (TM-score > 0.8).

Suggested Citation

  • Patrick Bryant & Frank Noé, 2024. "Structure prediction of alternative protein conformations," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51507-2
    DOI: 10.1038/s41467-024-51507-2
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    References listed on IDEAS

    as
    1. Patrick Bryant & Gabriele Pozzati & Arne Elofsson, 2022. "Author Correction: Improved prediction of protein-protein interactions using AlphaFold2," Nature Communications, Nature, vol. 13(1), pages 1-1, December.
    2. Patrick Bryant & Gabriele Pozzati & Arne Elofsson, 2022. "Improved prediction of protein-protein interactions using AlphaFold2," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    3. Martin Steinegger & Johannes Söding, 2018. "Clustering huge protein sequence sets in linear time," Nature Communications, Nature, vol. 9(1), pages 1-8, December.
    4. John Jumper & Richard Evans & Alexander Pritzel & Tim Green & Michael Figurnov & Olaf Ronneberger & Kathryn Tunyasuvunakool & Russ Bates & Augustin Žídek & Anna Potapenko & Alex Bridgland & Clemens Me, 2021. "Highly accurate protein structure prediction with AlphaFold," Nature, Nature, vol. 596(7873), pages 583-589, August.
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