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Scalable computation of anisotropic vibrations for large macromolecular assemblies

Author

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  • Jordy Homing Lam

    (University of Southern California
    University of Southern California
    University of Southern California)

  • Aiichiro Nakano

    (University of Southern California
    University of Southern California
    University of Southern California)

  • Vsevolod Katritch

    (University of Southern California
    University of Southern California
    University of Southern California
    University of Southern California)

Abstract

The Normal Mode Analysis (NMA) is a standard approach to elucidate the anisotropic vibrations of macromolecules at their folded states, where low-frequency collective motions can reveal rearrangements of domains and changes in the exposed surface of macromolecules. Recent advances in structural biology have enabled the resolution of megascale macromolecules with millions of atoms. However, the calculation of their vibrational modes remains elusive due to the prohibitive cost associated with constructing and diagonalizing the underlying eigenproblem and the current approaches to NMA are not readily adaptable for efficient parallel computing on graphic processing unit (GPU). Here, we present eigenproblem construction and diagonalization approach that implements level-structure bandwidth-reducing algorithms to transform the sparse computation in NMA to a globally-sparse-yet-locally-dense computation, allowing batched tensor products to be most efficiently executed on GPU. We map, optimize, and compare several low-complexity Krylov-subspace eigensolvers, supplemented by techniques such as Chebyshev filtering, sum decomposition, external explicit deflation and shift-and-inverse, to allow fast GPU-resident calculations. The method allows accurate calculation of the first 1000 vibrational modes of some largest structures in PDB ( > 2.4 million atoms) at least 250 times faster than existing methods.

Suggested Citation

  • Jordy Homing Lam & Aiichiro Nakano & Vsevolod Katritch, 2024. "Scalable computation of anisotropic vibrations for large macromolecular assemblies," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-47685-8
    DOI: 10.1038/s41467-024-47685-8
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    References listed on IDEAS

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    1. S. Schilbach & M. Hantsche & D. Tegunov & C. Dienemann & C. Wigge & H. Urlaub & P. Cramer, 2017. "Structures of transcription pre-initiation complex with TFIIH and Mediator," Nature, Nature, vol. 551(7679), pages 204-209, November.
    2. Gongpu Zhao & Juan R. Perilla & Ernest L. Yufenyuy & Xin Meng & Bo Chen & Jiying Ning & Jinwoo Ahn & Angela M. Gronenborn & Klaus Schulten & Christopher Aiken & Peijun Zhang, 2013. "Mature HIV-1 capsid structure by cryo-electron microscopy and all-atom molecular dynamics," Nature, Nature, vol. 497(7451), pages 643-646, May.
    3. Owen Pornillos & Barbie K. Ganser-Pornillos & Mark Yeager, 2011. "Atomic-level modelling of the HIV capsid," Nature, Nature, vol. 469(7330), pages 424-427, January.
    4. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
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