Abstract
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.
Original language | English |
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Pages (from-to) | 5025-5045 |
Number of pages | 21 |
Journal | Journal of Chemical Theory and Computation |
Volume | 18 |
Issue number | 8 |
DOIs | |
State | Published - Aug 9 2022 |
Funding
The authors thank David van der Spoel for providing a solution to the nonlinear slowdown in processing large numbers of bonded interactions in GROMACS 2019.6. This work was supported in part by the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) program established by the U.S. Department of Energy (DOE) and the National Cancer Institute (NCI) of the National Institutes of Health. This work was performed under the auspices of the U.S. DOE by Lawrence Livermore National Laboratory (LLNL) under contract DE-AC52-07NA27344, Los Alamos National Laboratory (LANL) under contract DE-AC5206NA25396, Oak Ridge National Laboratory under contract DE-AC05-00OR22725, and under the auspices of the NCI by Frederick National Laboratory for Cancer Research under contract HHSN261200800001E. LLNL release number LLNL-JRNL-830356. Computations used resources provided by the LANL Institutional Computing Program, which is supported by the U.S. DOE National Nuclear Security Administration under contract DE-AC52-06NA25396, the Livermore Institutional Grand Challenge, and the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. DOE under Contract No. DE-AC05-00OR22725.
Funders | Funder number |
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National Institutes of Health | |
U.S. Department of Energy | |
National Cancer Institute | |
Office of Science | |
National Nuclear Security Administration | DE-AC52-06NA25396 |
National Nuclear Security Administration | |
Lawrence Livermore National Laboratory | DE-AC52-07NA27344 |
Lawrence Livermore National Laboratory | |
Oak Ridge National Laboratory | DE-AC05-00OR22725 |
Oak Ridge National Laboratory | |
Los Alamos National Laboratory | DE-AC5206NA25396 |
Los Alamos National Laboratory | |
Frederick National Laboratory for Cancer Research | LLNL-JRNL-830356, HHSN261200800001E |
Frederick National Laboratory for Cancer Research |