IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-34603-z.html
   My bibliography  Save this article

Deep learning to decompose macromolecules into independent Markovian domains

Author

Listed:
  • Andreas Mardt

    (Freie Universität Berlin, Department of Mathematics and Computer Science)

  • Tim Hempel

    (Freie Universität Berlin, Department of Mathematics and Computer Science
    Freie Universität Berlin, Department of Physics)

  • Cecilia Clementi

    (Freie Universität Berlin, Department of Physics
    Rice University, Department of Chemistry
    Rice University, Center for Theoretical Biological Physics)

  • Frank Noé

    (Freie Universität Berlin, Department of Mathematics and Computer Science
    Freie Universität Berlin, Department of Physics
    Rice University, Department of Chemistry
    Microsoft Research AI4Science)

Abstract

The increasing interest in modeling the dynamics of ever larger proteins has revealed a fundamental problem with models that describe the molecular system as being in a global configuration state. This notion limits our ability to gather sufficient statistics of state probabilities or state-to-state transitions because for large molecular systems the number of metastable states grows exponentially with size. In this manuscript, we approach this challenge by introducing a method that combines our recent progress on independent Markov decomposition (IMD) with VAMPnets, a deep learning approach to Markov modeling. We establish a training objective that quantifies how well a given decomposition of the molecular system into independent subdomains with Markovian dynamics approximates the overall dynamics. By constructing an end-to-end learning framework, the decomposition into such subdomains and their individual Markov state models are simultaneously learned, providing a data-efficient and easily interpretable summary of the complex system dynamics. While learning the dynamical coupling between Markovian subdomains is still an open issue, the present results are a significant step towards learning Ising models of large molecular complexes from simulation data.

Suggested Citation

  • Andreas Mardt & Tim Hempel & Cecilia Clementi & Frank Noé, 2022. "Deep learning to decompose macromolecules into independent Markovian domains," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34603-z
    DOI: 10.1038/s41467-022-34603-z
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-34603-z
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-022-34603-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Christian Hentrich & Mateusz Putyrski & Hanh Hanuschka & Waldemar Preis & Sarah-Jane Kellmann & Melissa Wich & Manuel Cavada & Sarah Hanselka & Victor S. Lelyveld & Francisco Ylera, 2024. "Engineered reversible inhibition of SpyCatcher reactivity enables rapid generation of bispecific antibodies," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    2. Cheng Shen & Yuqing Zhang & Wenwen Cui & Yimeng Zhao & Danqi Sheng & Xinyu Teng & Miaoqing Shao & Muneyoshi Ichikawa & Jin Wang & Motoyuki Hattori, 2023. "Structural insights into the allosteric inhibition of P2X4 receptors," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    3. F. P. Panei & P. Gkeka & M. Bonomi, 2024. "Identifying small-molecules binding sites in RNA conformational ensembles with SHAMAN," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    4. Shana Bergman & Rosemary J. Cater & Ambrose Plante & Filippo Mancia & George Khelashvili, 2023. "Substrate binding-induced conformational transitions in the omega-3 fatty acid transporter MFSD2A," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    5. Kuang-Ting Ko & Frank Lennartz & David Mekhaiel & Bora Guloglu & Arianna Marini & Danielle J. Deuker & Carole A. Long & Matthijs M. Jore & Kazutoyo Miura & Sumi Biswas & Matthew K. Higgins, 2022. "Structure of the malaria vaccine candidate Pfs48/45 and its recognition by transmission blocking antibodies," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    6. Hongjun Bai & Eric Lewitus & Yifan Li & Paul V. Thomas & Michelle Zemil & Mélanie Merbah & Caroline E. Peterson & Thujitha Thuraisamy & Phyllis A. Rees & Agnes Hajduczki & Vincent Dussupt & Bonnie Sli, 2024. "Contemporary HIV-1 consensus Env with AI-assisted redesigned hypervariable loops promote antibody binding," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    7. Do Hoon Kwon & Feng Zhang & Brett A. McCray & Shasha Feng & Meha Kumar & Jeremy M. Sullivan & Wonpil Im & Charlotte J. Sumner & Seok-Yong Lee, 2023. "TRPV4-Rho GTPase complex structures reveal mechanisms of gating and disease," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    8. Ritaban Halder & Daniel A. Nissley & Ian Sitarik & Yang Jiang & Yiyun Rao & Quyen V. Vu & Mai Suan Li & Justin Pritchard & Edward P. O’Brien, 2023. "How soluble misfolded proteins bypass chaperones at the molecular level," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    9. 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.
    10. Jeffrey A. Ruffolo & Lee-Shin Chu & Sai Pooja Mahajan & Jeffrey J. Gray, 2023. "Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    11. Jin H. Yang & Hugo B. Brandão & Anders S. Hansen, 2023. "DNA double-strand break end synapsis by DNA loop extrusion," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    12. Rodrigo G. Fernandez Lahore & Niccolò P. Pampaloni & Enrico Schiewer & M.-Marcel Heim & Linda Tillert & Johannes Vierock & Johannes Oppermann & Jakob Walther & Dietmar Schmitz & David Owald & Andrew J, 2022. "Calcium-permeable channelrhodopsins for the photocontrol of calcium signalling," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    13. Yi-Tzu Kuo & Amanda Souza Câmara & Veit Schubert & Pavel Neumann & Jiří Macas & Michael Melzer & Jianyong Chen & Jörg Fuchs & Simone Abel & Evelyn Klocke & Bruno Huettel & Axel Himmelbach & Dmitri Dem, 2023. "Holocentromeres can consist of merely a few megabase-sized satellite arrays," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    14. Po-Yin Chen & Yung-Chih Chen & Po-Pang Chen & Kuan-Ting Lin & Karen Sargsyan & Chao-Ping Hsu & Wei-Le Wang & Kuo-Chiang Hsia & See-Yeun Ting, 2024. "A whole-cell platform for discovering synthetic cell adhesion molecules in bacteria," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    15. S. M. Ayala Mariscal & M. L. Pigazzini & Y. Richter & M. Özel & I. L. Grothaus & J. Protze & K. Ziege & M. Kulke & M. ElBediwi & J. V. Vermaas & L. Colombi Ciacchi & S. Köppen & F. Liu & J. Kirstein, 2022. "Identification of a HTT-specific binding motif in DNAJB1 essential for suppression and disaggregation of HTT," Nature Communications, Nature, vol. 13(1), pages 1-25, December.
    16. Nicolas Papadopoulos & Audrey Nédélec & Allison Derenne & Teodor Asvadur Şulea & Christian Pecquet & Ilyas Chachoua & Gaëlle Vertenoeil & Thomas Tilmant & Andrei-Jose Petrescu & Gabriel Mazzucchelli &, 2023. "Oncogenic CALR mutant C-terminus mediates dual binding to the thrombopoietin receptor triggering complex dimerization and activation," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    17. Janni Harju & Muriel C. F. Teeseling & Chase P. Broedersz, 2024. "Loop-extruders alter bacterial chromosome topology to direct entropic forces for segregation," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    18. Tom Dixon & Derek MacPherson & Barmak Mostofian & Taras Dauzhenka & Samuel Lotz & Dwight McGee & Sharon Shechter & Utsab R. Shrestha & Rafal Wiewiora & Zachary A. McDargh & Fen Pei & Rajat Pal & João , 2022. "Predicting the structural basis of targeted protein degradation by integrating molecular dynamics simulations with structural mass spectrometry," Nature Communications, Nature, vol. 13(1), pages 1-24, December.
    19. Joseph G. Beton & Thomas Mulvaney & Tristan Cragnolini & Maya Topf, 2024. "Cryo-EM structure and B-factor refinement with ensemble representation," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    20. Re’em Moskovitz & Tossapol Pholcharee & Sophia M. DonVito & Bora Guloglu & Edward Lowe & Franziska Mohring & Robert W. Moon & Matthew K. Higgins, 2023. "Structural basis for DARC binding in reticulocyte invasion by Plasmodium vivax," Nature Communications, Nature, vol. 14(1), pages 1-9, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34603-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.