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Asynchronous Reciprocal Coupling of Martini 2.2 Coarse-Grained and CHARMM36 All-Atom Simulations in an Automated Multiscale Framework
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    Biomolecular Systems

    Asynchronous Reciprocal Coupling of Martini 2.2 Coarse-Grained and CHARMM36 All-Atom Simulations in an Automated Multiscale Framework
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    • Cesar A. López
      Cesar A. López
      Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
    • Xiaohua Zhang
      Xiaohua Zhang
      Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
    • Fikret Aydin
      Fikret Aydin
      Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
      More by Fikret Aydin
    • Rebika Shrestha
      Rebika Shrestha
      NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
    • Que N. Van
      Que N. Van
      NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
      More by Que N. Van
    • Christopher B. Stanley
      Christopher B. Stanley
      Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
    • Timothy S. Carpenter
      Timothy S. Carpenter
      Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
    • Kien Nguyen
      Kien Nguyen
      Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
      More by Kien Nguyen
    • Lara A. Patel
      Lara A. Patel
      Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
      Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
    • De Chen
      De Chen
      NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
      More by De Chen
    • Violetta Burns
      Violetta Burns
      Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
    • Nicolas W. Hengartner
      Nicolas W. Hengartner
      Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
    • Tyler J. E. Reddy
      Tyler J. E. Reddy
      Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
    • Harsh Bhatia
      Harsh Bhatia
      Computing Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
      More by Harsh Bhatia
    • Francesco Di Natale
      Francesco Di Natale
      Computing Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
    • Timothy H. Tran
      Timothy H. Tran
      NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
    • Albert H. Chan
      Albert H. Chan
      NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
    • Dhirendra K. Simanshu
      Dhirendra K. Simanshu
      NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
    • Dwight V. Nissley
      Dwight V. Nissley
      NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
    • Frederick H. Streitz
      Frederick H. Streitz
      Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
    • Andrew G. Stephen
      Andrew G. Stephen
      NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
    • Thomas J. Turbyville
      Thomas J. Turbyville
      NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
    • Felice C. Lightstone
      Felice C. Lightstone
      Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
    • Sandrasegaram Gnanakaran
      Sandrasegaram Gnanakaran
      Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
    • Helgi I. Ingólfsson
      Helgi I. Ingólfsson
      Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
    • Chris Neale*
      Chris Neale
      Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
      *Email: [email protected]. Tel: 1-505-667-8715.
      More by Chris Neale
    Other Access OptionsSupporting Information (2)

    Journal of Chemical Theory and Computation

    Cite this: J. Chem. Theory Comput. 2022, 18, 8, 5025–5045
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    https://doi.org/10.1021/acs.jctc.2c00168
    Published July 22, 2022
    Copyright © 2022 American Chemical Society

    Abstract

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    Abstract Image

    The appeal of multiscale modeling approaches is predicated on the promise of combinatorial synergy. However, this promise can only be realized when distinct scales are combined with reciprocal consistency. Here, we consider multiscale molecular dynamics (MD) simulations that combine the accuracy and macromolecular flexibility accessible to fixed-charge all-atom (AA) representations with the sampling speed accessible to reductive, coarse-grained (CG) representations. AA-to-CG conversions are relatively straightforward because deterministic routines with unique outcomes are achievable. Conversely, CG-to-AA conversions have many solutions due to a surge in the number of degrees of freedom. While automated tools for biomolecular CG-to-AA transformation exist, we find that one popular option, called Backward, is prone to stochastic failure and the AA models that it does generate frequently have compromised protein structure and incorrect stereochemistry. Although these shortcomings can likely be circumvented by human intervention in isolated instances, automated multiscale coupling requires reliable and robust scale conversion. Here, we detail an extension to Multiscale Machine-learned Modeling Infrastructure (MuMMI), including an improved CG-to-AA conversion tool called sinceCG. This tool is reliable (∼98% weakly correlated repeat success rate), automatable (no unrecoverable hangs), and yields AA models that generally preserve protein secondary structure and maintain correct stereochemistry. We describe how the MuMMI framework identifies CG system configurations of interest, converts them to AA representations, and simulates them at the AA scale while on-the-fly analyses provide feedback to update CG parameters. Application to systems containing the peripheral membrane protein RAS and proximal components of RAF kinase on complex eight-component lipid bilayers with ∼1.5 million atoms is discussed in the context of MuMMI.

    Copyright © 2022 American Chemical Society

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    Supporting Information

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jctc.2c00168.

    • Tabulated lipid compositions for CG simulations with one RAS-RBDCRD; figures depicting the number of stereochemical errors after CG-to-AA conversion of two-lipid systems via Backward or sinceCG (immediately after, and in subsequent simulations); location of the C-terminal residue of RAS helix 5 in AA simulations; and unit cell dimensions over time for MuMMI simulations of monomeric RAS-RBDCRD (PDF)

    • Correction of side-chain orientation in RAS β-strand 2 during sinceCG-based conversion of a snapshot from a CG simulation lacking scFix (Movie S1) (MPG)

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    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

    Cited By

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    This article is cited by 9 publications.

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    Journal of Chemical Theory and Computation

    Cite this: J. Chem. Theory Comput. 2022, 18, 8, 5025–5045
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.jctc.2c00168
    Published July 22, 2022
    Copyright © 2022 American Chemical Society

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