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DeepRank: a deep learning framework for data mining 3D protein-protein interfaces

Author

Listed:
  • Nicolas Renaud

    (Netherlands eScience Center)

  • Cunliang Geng

    (Netherlands eScience Center
    Utrecht University)

  • Sonja Georgievska

    (Netherlands eScience Center)

  • Francesco Ambrosetti

    (Utrecht University)

  • Lars Ridder

    (Netherlands eScience Center)

  • Dario F. Marzella

    (Center for Molecular and Biomolecular Informatics, Radboudumc)

  • Manon F. Réau

    (Utrecht University)

  • Alexandre M. J. J. Bonvin

    (Utrecht University)

  • Li C. Xue

    (Utrecht University
    Center for Molecular and Biomolecular Informatics, Radboudumc)

Abstract

Three-dimensional (3D) structures of protein complexes provide fundamental information to decipher biological processes at the molecular scale. The vast amount of experimentally and computationally resolved protein-protein interfaces (PPIs) offers the possibility of training deep learning models to aid the predictions of their biological relevance. We present here DeepRank, a general, configurable deep learning framework for data mining PPIs using 3D convolutional neural networks (CNNs). DeepRank maps features of PPIs onto 3D grids and trains a user-specified CNN on these 3D grids. DeepRank allows for efficient training of 3D CNNs with data sets containing millions of PPIs and supports both classification and regression. We demonstrate the performance of DeepRank on two distinct challenges: The classification of biological versus crystallographic PPIs, and the ranking of docking models. For both problems DeepRank is competitive with, or outperforms, state-of-the-art methods, demonstrating the versatility of the framework for research in structural biology.

Suggested Citation

  • Nicolas Renaud & Cunliang Geng & Sonja Georgievska & Francesco Ambrosetti & Lars Ridder & Dario F. Marzella & Manon F. Réau & Alexandre M. J. J. Bonvin & Li C. Xue, 2021. "DeepRank: a deep learning framework for data mining 3D protein-protein interfaces," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-27396-0
    DOI: 10.1038/s41467-021-27396-0
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    References listed on IDEAS

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    1. Kathryn Tunyasuvunakool & Jonas Adler & Zachary Wu & Tim Green & Michal Zielinski & Augustin Žídek & Alex Bridgland & Andrew Cowie & Clemens Meyer & Agata Laydon & Sameer Velankar & Gerard J. Kleywegt, 2021. "Highly accurate protein structure prediction for the human proteome," Nature, Nature, vol. 596(7873), pages 590-596, August.
    2. Andrew W. Senior & Richard Evans & John Jumper & James Kirkpatrick & Laurent Sifre & Tim Green & Chongli Qin & Augustin Žídek & Alexander W. R. Nelson & Alex Bridgland & Hugo Penedones & Stig Petersen, 2020. "Improved protein structure prediction using potentials from deep learning," Nature, Nature, vol. 577(7792), pages 706-710, January.
    3. 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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    Cited by:

    1. Ziqi Gao & Chenran Jiang & Jiawen Zhang & Xiaosen Jiang & Lanqing Li & Peilin Zhao & Huanming Yang & Yong Huang & Jia Li, 2023. "Hierarchical graph learning for protein–protein interaction," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    2. Simon L. Dürr & Andrea Levy & Ursula Rothlisberger, 2023. "Metal3D: a general deep learning framework for accurate metal ion location prediction in proteins," Nature Communications, Nature, vol. 14(1), pages 1-14, December.

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