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

Cesar A. López; Xiaohua Zhang; Fikret Aydin; Rebika Shrestha; Que N. Van; Christopher B. Stanley; Timothy S. Carpenter; Kien Nguyen; Lara A. Patel; De Chen; Violetta Burns; Nicolas W. Hengartner; Tyler J.E. Reddy; Harsh Bhatia; Francesco Di Natale; Timothy H. Tran; Albert H. Chan; Dhirendra K. Simanshu; Dwight V. Nissley; Frederick H. Streitz; Andrew G. Stephen; Thomas J. Turbyville; Felice C. Lightstone; Sandrasegaram Gnanakaran; Helgi I. Ingólfsson; Chris Neale

doi:10.1021/acs.jctc.2c00168

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

Cesar A. López, Xiaohua Zhang, Fikret Aydin, Rebika Shrestha, Que N. Van, Christopher B. Stanley, Timothy S. Carpenter, Kien Nguyen, Lara A. Patel, De Chen, Violetta Burns, Nicolas W. Hengartner, Tyler J.E. Reddy, Harsh Bhatia, Francesco Di Natale, Timothy H. Tran, Albert H. Chan, Dhirendra K. Simanshu, Dwight V. Nissley, Frederick H. StreitzAndrew G. Stephen, Thomas J. Turbyville, Felice C. Lightstone, Sandrasegaram Gnanakaran, Helgi I. Ingólfsson, Chris Neale

Biostatistics and Biomedical Informatics

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

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
Pages (from-to)	5025-5045
Number of pages	21
Journal	Journal of Chemical Theory and Computation
Volume	18
Issue number	8
DOIs	https://doi.org/10.1021/acs.jctc.2c00168
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
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

Access to Document

10.1021/acs.jctc.2c00168

Cite this

López, C. A., Zhang, X., Aydin, F., Shrestha, R., Van, Q. N., Stanley, C. B., Carpenter, T. S., Nguyen, K., Patel, L. A., Chen, D., Burns, V., Hengartner, N. W., Reddy, T. J. E., Bhatia, H., Di Natale, F., Tran, T. H., Chan, A. H., Simanshu, D. K., Nissley, D. V., ... Neale, C. (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), 5025-5045. https://doi.org/10.1021/acs.jctc.2c00168

@article{8313010906a3457cb1c3f6023fc56c67,

title = "Asynchronous Reciprocal Coupling of Martini 2.2 Coarse-Grained and CHARMM36 All-Atom Simulations in an Automated Multiscale Framework",

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.",

author = "L{\'o}pez, {Cesar A.} and Xiaohua Zhang and Fikret Aydin and Rebika Shrestha and Van, {Que N.} and Stanley, {Christopher B.} and Carpenter, {Timothy S.} and Kien Nguyen and Patel, {Lara A.} and De Chen and Violetta Burns and Hengartner, {Nicolas W.} and Reddy, {Tyler J.E.} and Harsh Bhatia and {Di Natale}, Francesco and Tran, {Timothy H.} and Chan, {Albert H.} and Simanshu, {Dhirendra K.} and Nissley, {Dwight V.} and Streitz, {Frederick H.} and Stephen, {Andrew G.} and Turbyville, {Thomas J.} and Lightstone, {Felice C.} and Sandrasegaram Gnanakaran and Ing{\'o}lfsson, {Helgi I.} and Chris Neale",

note = "Publisher Copyright: {\textcopyright} 2022 American Chemical Society.",

year = "2022",

month = aug,

day = "9",

doi = "10.1021/acs.jctc.2c00168",

language = "English",

volume = "18",

pages = "5025--5045",

journal = "Journal of Chemical Theory and Computation",

issn = "1549-9618",

publisher = "American Chemical Society",

number = "8",

}

López, CA, Zhang, X, Aydin, F, Shrestha, R, Van, QN, Stanley, CB, Carpenter, TS, Nguyen, K, Patel, LA, Chen, D, Burns, V, Hengartner, NW, Reddy, TJE, Bhatia, H, Di Natale, F, Tran, TH, Chan, AH, Simanshu, DK, Nissley, DV, Streitz, FH, Stephen, AG, Turbyville, TJ, Lightstone, FC, Gnanakaran, S, Ingólfsson, HI & Neale, C 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, vol. 18, no. 8, pp. 5025-5045. https://doi.org/10.1021/acs.jctc.2c00168

TY - JOUR

T1 - Asynchronous Reciprocal Coupling of Martini 2.2 Coarse-Grained and CHARMM36 All-Atom Simulations in an Automated Multiscale Framework

AU - López, Cesar A.

AU - Zhang, Xiaohua

AU - Aydin, Fikret

AU - Shrestha, Rebika

AU - Van, Que N.

AU - Stanley, Christopher B.

AU - Carpenter, Timothy S.

AU - Nguyen, Kien

AU - Patel, Lara A.

AU - Chen, De

AU - Burns, Violetta

AU - Hengartner, Nicolas W.

AU - Reddy, Tyler J.E.

AU - Bhatia, Harsh

AU - Di Natale, Francesco

AU - Tran, Timothy H.

AU - Chan, Albert H.

AU - Simanshu, Dhirendra K.

AU - Nissley, Dwight V.

AU - Streitz, Frederick H.

AU - Stephen, Andrew G.

AU - Turbyville, Thomas J.

AU - Lightstone, Felice C.

AU - Gnanakaran, Sandrasegaram

AU - Ingólfsson, Helgi I.

AU - Neale, Chris

PY - 2022/8/9

Y1 - 2022/8/9

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85134527114&partnerID=8YFLogxK

U2 - 10.1021/acs.jctc.2c00168

DO - 10.1021/acs.jctc.2c00168

M3 - Article

C2 - 35866871

AN - SCOPUS:85134527114

SN - 1549-9618

VL - 18

SP - 5025

EP - 5045

JO - Journal of Chemical Theory and Computation

JF - Journal of Chemical Theory and Computation

IS - 8

ER -

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

Abstract

Funding

Access to Document

Other files and links

Fingerprint

Cite this