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Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search

Author

Listed:
  • Patrick Bryant

    (Science for Life Laboratory
    Stockholm University)

  • Gabriele Pozzati

    (Science for Life Laboratory
    Stockholm University)

  • Wensi Zhu

    (Science for Life Laboratory
    Stockholm University)

  • Aditi Shenoy

    (Science for Life Laboratory
    Stockholm University)

  • Petras Kundrotas

    (Science for Life Laboratory
    The University of Kansas)

  • Arne Elofsson

    (Science for Life Laboratory
    Stockholm University)

Abstract

AlphaFold can predict the structure of single- and multiple-chain proteins with very high accuracy. However, the accuracy decreases with the number of chains, and the available GPU memory limits the size of protein complexes which can be predicted. Here we show that one can predict the structure of large complexes starting from predictions of subcomponents. We assemble 91 out of 175 complexes with 10–30 chains from predicted subcomponents using Monte Carlo tree search, with a median TM-score of 0.51. There are 30 highly accurate complexes (TM-score ≥0.8, 33% of complete assemblies). We create a scoring function, mpDockQ, that can distinguish if assemblies are complete and predict their accuracy. We find that complexes containing symmetry are accurately assembled, while asymmetrical complexes remain challenging. The method is freely available and accesible as a Colab notebook https://colab.research.google.com/github/patrickbryant1/MoLPC/blob/master/MoLPC.ipynb .

Suggested Citation

  • Patrick Bryant & Gabriele Pozzati & Wensi Zhu & Aditi Shenoy & Petras Kundrotas & Arne Elofsson, 2022. "Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33729-4
    DOI: 10.1038/s41467-022-33729-4
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    References listed on IDEAS

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    Cited by:

    1. Wenjing Yan & Yongwang Zhong & Xin Hu & Tuan Xu & Yinghua Zhang & Stephen Kales & Yanyan Qu & Daniel C. Talley & Bolormaa Baljinnyam & Christopher A. LeClair & Anton Simeonov & Brian M. Polster & Ruil, 2023. "Auranofin targets UBA1 and enhances UBA1 activity by facilitating ubiquitin trans-thioesterification to E2 ubiquitin-conjugating enzymes," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    2. Claudio Mirabello & Björn Wallner & Björn Nystedt & Stavros Azinas & Marta Carroni, 2024. "Unmasking AlphaFold to integrate experiments and predictions in multimeric complexes," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    3. Mads Jeppesen & Ingemar André, 2023. "Accurate prediction of protein assembly structure by combining AlphaFold and symmetrical docking," Nature Communications, Nature, vol. 14(1), pages 1-13, December.

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