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Direct generation of protein conformational ensembles via machine learning

Author

Listed:
  • Giacomo Janson

    (Michigan State University)

  • Gilberto Valdes-Garcia

    (Michigan State University)

  • Lim Heo

    (Michigan State University)

  • Michael Feig

    (Michigan State University)

Abstract

Dynamics and conformational sampling are essential for linking protein structure to biological function. While challenging to probe experimentally, computer simulations are widely used to describe protein dynamics, but at significant computational costs that continue to limit the systems that can be studied. Here, we demonstrate that machine learning can be trained with simulation data to directly generate physically realistic conformational ensembles of proteins without the need for any sampling and at negligible computational cost. As a proof-of-principle we train a generative adversarial network based on a transformer architecture with self-attention on coarse-grained simulations of intrinsically disordered peptides. The resulting model, idpGAN, can predict sequence-dependent coarse-grained ensembles for sequences that are not present in the training set demonstrating that transferability can be achieved beyond the limited training data. We also retrain idpGAN on atomistic simulation data to show that the approach can be extended in principle to higher-resolution conformational ensemble generation.

Suggested Citation

  • Giacomo Janson & Gilberto Valdes-Garcia & Lim Heo & Michael Feig, 2023. "Direct generation of protein conformational ensembles via machine learning," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36443-x
    DOI: 10.1038/s41467-023-36443-x
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    References listed on IDEAS

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    1. Andreas Mardt & Luca Pasquali & Hao Wu & Frank Noé, 2018. "VAMPnets for deep learning of molecular kinetics," Nature Communications, Nature, vol. 9(1), pages 1-11, December.
    2. Peter Eastman & Jason Swails & John D Chodera & Robert T McGibbon & Yutong Zhao & Kyle A Beauchamp & Lee-Ping Wang & Andrew C Simmonett & Matthew P Harrigan & Chaya D Stern & Rafal P Wiewiora & Bernar, 2017. "OpenMM 7: Rapid development of high performance algorithms for molecular dynamics," PLOS Computational Biology, Public Library of Science, vol. 13(7), pages 1-17, July.
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    4. Andreas Mardt & Luca Pasquali & Hao Wu & Frank Noé, 2018. "Author Correction: VAMPnets for deep learning of molecular kinetics," Nature Communications, Nature, vol. 9(1), pages 1-1, December.
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