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Performance efficient macromolecular mechanics via sub-nanometer shape based coarse graining

Author

Listed:
  • Alexander J. Bryer

    (University of Delaware)

  • Juan S. Rey

    (University of Delaware)

  • Juan R. Perilla

    (University of Delaware)

Abstract

Dimensionality reduction via coarse grain modeling is a valuable tool in biomolecular research. For large assemblies, ultra coarse models are often knowledge-based, relying on a priori information to parameterize models thus hindering general predictive capability. Here, we present substantial advances to the shape based coarse graining (SBCG) method, which we refer to as SBCG2. SBCG2 utilizes a revitalized formulation of the topology representing network which makes high-granularity modeling possible, preserving atomistic details that maintain assembly characteristics. Further, we present a method of granularity selection based on charge density Fourier Shell Correlation and have additionally developed a refinement method to optimize, adjust and validate high-granularity models. We demonstrate our approach with the conical HIV-1 capsid and heteromultimeric cofilin-2 bound actin filaments. Our approach is available in the Visual Molecular Dynamics (VMD) software suite, and employs a CHARMM-compatible Hamiltonian that enables high-performance simulation in the GPU-resident NAMD3 molecular dynamics engine.

Suggested Citation

  • Alexander J. Bryer & Juan S. Rey & Juan R. Perilla, 2023. "Performance efficient macromolecular mechanics via sub-nanometer shape based coarse graining," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37801-5
    DOI: 10.1038/s41467-023-37801-5
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    References listed on IDEAS

    as
    1. Juan R. Perilla & Klaus Schulten, 2017. "Physical properties of the HIV-1 capsid from all-atom molecular dynamics simulations," Nature Communications, Nature, vol. 8(1), pages 1-10, December.
    2. Jodi Kraus & Ryan W. Russell & Elena Kudryashova & Chaoyi Xu & Nidhi Katyal & Juan R. Perilla & Dmitri S. Kudryashov & Tatyana Polenova, 2022. "Magic angle spinning NMR structure of human cofilin-2 assembled on actin filaments reveals isoform-specific conformation and binding mode," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    3. Henry Chan & Mathew J. Cherukara & Badri Narayanan & Troy D. Loeffler & Chris Benmore & Stephen K. Gray & Subramanian K. R. S. Sankaranarayanan, 2019. "Machine learning coarse grained models for water," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
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