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. In this work, 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.
Lopez Bautista, Cesar Augusto, et al. "Asynchronous Reciprocal Coupling of Martini 2.2 Coarse-Grained and CHARMM36 All-Atom Simulations in an Automated Multiscale Framework." Journal of Chemical Theory and Computation, vol. 18, no. 8, Jul. 2022. https://doi.org/10.1021/acs.jctc.2c00168
Lopez Bautista, Cesar Augusto, Zhang, Xiaohua, Aydin, Fikret, Shrestha, Rebika, Van, Que N., Stanley, Christopher B., Carpenter, Timothy S., Nguyen, Kien, Patel, Lara Anne, Chen, De, Burns, Violetta, Hengartner, Nicolas W., Reddy, Tyler John Edward, Bhatia, Harsh, Di Natale, Francesco, Tran, Timothy H., Chan, Albert H., Simanshu, Dhirendra K., ... Neale, Christopher Andrew (2022). Asynchronous Reciprocal Coupling of Martini 2.2 Coarse-Grained and CHARMM36 All-Atom Simulations in an Automated Multiscale Framework. Journal of Chemical Theory and Computation, 18(8). https://doi.org/10.1021/acs.jctc.2c00168
Lopez Bautista, Cesar Augusto, Zhang, Xiaohua, Aydin, Fikret, et al., "Asynchronous Reciprocal Coupling of Martini 2.2 Coarse-Grained and CHARMM36 All-Atom Simulations in an Automated Multiscale Framework," Journal of Chemical Theory and Computation 18, no. 8 (2022), https://doi.org/10.1021/acs.jctc.2c00168
@article{osti_1885350,
author = {Lopez Bautista, Cesar Augusto and Zhang, Xiaohua and Aydin, Fikret and Shrestha, Rebika and Van, Que N. and Stanley, Christopher B. and Carpenter, Timothy S. and Nguyen, Kien and Patel, Lara Anne and Chen, De and others},
title = {Asynchronous Reciprocal Coupling of Martini 2.2 Coarse-Grained and CHARMM36 All-Atom Simulations in an Automated Multiscale Framework},
annote = {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. In this work, 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.},
doi = {10.1021/acs.jctc.2c00168},
url = {https://www.osti.gov/biblio/1885350},
journal = {Journal of Chemical Theory and Computation},
issn = {ISSN 1549-9618},
number = {8},
volume = {18},
place = {United States},
publisher = {American Chemical Society},
year = {2022},
month = {07}}
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Frederick National Laboratory for Cancer Research, Frederick, MD (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA); National Cancer Institute (NCI); USDOE Office of Science (SC)
Grant/Contract Number:
AC05-00OR22725; AC52-07NA27344; AC52-06NA25396
OSTI ID:
1885350
Alternate ID(s):
OSTI ID: 1894845 OSTI ID: 2204111
Report Number(s):
LLNL--JRNL-830356
Journal Information:
Journal of Chemical Theory and Computation, Journal Name: Journal of Chemical Theory and Computation Journal Issue: 8 Vol. 18; ISSN 1549-9618
Di Natale, Francesco; Bhatia, Harsh; Carpenter, Timothy S.
SC '19: The International Conference for High Performance Computing, Networking, Storage, and Analysis, Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysishttps://doi.org/10.1145/3295500.3356197
Bhatia, Harsh; Di Natale, Francesco; Moon, Joseph Y.
SC '21: The International Conference for High Performance Computing, Networking, Storage and Analysis, Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysishttps://doi.org/10.1145/3458817.3476210