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GENESIS CGDYN: large-scale coarse-grained MD simulation with dynamic load balancing for heterogeneous biomolecular systems

Author

Listed:
  • Jaewoon Jung

    (RIKEN Center for Computational Science
    RIKEN Cluster for Pioneering Research)

  • Cheng Tan

    (RIKEN Center for Computational Science)

  • Yuji Sugita

    (RIKEN Center for Computational Science
    RIKEN Cluster for Pioneering Research
    RIKEN Center for Biosystems Dynamics Research)

Abstract

Residue-level coarse-grained (CG) molecular dynamics (MD) simulation is widely used to investigate slow biological processes that involve multiple proteins, nucleic acids, and their complexes. Biomolecules in a large simulation system are distributed non-uniformly, limiting computational efficiency with conventional methods. Here, we develop a hierarchical domain decomposition scheme with dynamic load balancing for heterogeneous biomolecular systems to keep computational efficiency even after drastic changes in particle distribution. These schemes are applied to the dynamics of intrinsically disordered protein (IDP) droplets. During the fusion of two droplets, we find that the changes in droplet shape correlate with the mixing of IDP chains. Additionally, we simulate large systems with multiple IDP droplets, achieving simulation sizes comparable to those observed in microscopy. In our MD simulations, we directly observe Ostwald ripening, a phenomenon where small droplets dissolve and their molecules redeposit into larger droplets. These methods have been implemented in CGDYN of the GENESIS software, offering a tool for investigating mesoscopic biological processes using the residue-level CG models.

Suggested Citation

  • Jaewoon Jung & Cheng Tan & Yuji Sugita, 2024. "GENESIS CGDYN: large-scale coarse-grained MD simulation with dynamic load balancing for heterogeneous biomolecular systems," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-47654-1
    DOI: 10.1038/s41467-024-47654-1
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    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.
    2. Taranpreet Kaur & Muralikrishna Raju & Ibraheem Alshareedah & Richoo B. Davis & Davit A. Potoyan & Priya R. Banerjee, 2021. "Sequence-encoded and composition-dependent protein-RNA interactions control multiphasic condensate morphologies," Nature Communications, Nature, vol. 12(1), pages 1-16, December.
    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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