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

Autor:	López CA; Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States., Zhang X; Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States., Aydin F; Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States., Shrestha R; NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States., Van QN; NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States., Stanley CB; Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States., Carpenter TS; Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States., Nguyen K; Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States., Patel LA; 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., Chen; NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States., Burns V; Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States., Hengartner NW; Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States., Reddy TJE; Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States., Bhatia H; Computing Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States., Di Natale F; Computing Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States., Tran TH; NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States., Chan AH; NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States., Simanshu DK; NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States., Nissley DV; NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States., Streitz FH; Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States., Stephen AG; NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States., Turbyville TJ; NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States., Lightstone FC; Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States., Gnanakaran S; Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States., Ingólfsson HI; Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States., Neale C; Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.
Jazyk:	angličtina
Zdroj:	Journal of chemical theory and computation [J Chem Theory Comput] 2022 Aug 09; Vol. 18 (8), pp. 5025-5045. Date of Electronic Publication: 2022 Jul 22.
DOI:	10.1021/acs.jctc.2c00168
Abstrakt:	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.
Databáze:	MEDLINE
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