‘Asynchronous reciprocal coupling of Martini 2.2 coarse-grained and CHARMM36 all-atom simulations in an automated multiscale framework’
Open accessAbstract
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.
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A number of backmapping tools optimized for Martini already exist.
“A number of such backmapping tools, optimized for Martini, already exist (Louison et al., 2021; Vickery and Stansfeld, 2021; López et al., 2022).”
From Molecular dynamics simulation of an entire cell (2023) · cites this paper as López et al., 2022
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Water's Hydrogen Bonds in the Hydrophobic Effect: A Simple Model
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Adaptive Resolution Simulation of MARTINI Solvents
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Machine-learning-based dynamic-importance sampling for adaptive multiscale simulations
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Self-citationUnderstanding Ligand Binding to G-Protein Coupled Receptors Using Multiscale Simulations
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Chiral Recognition of Lipid Bilayer Membranes by Supramolecular Assemblies of Peptide Amphiphiles
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Glimps: A machine learning approach to resolution transformation for multiscale modeling
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Enantiospecific Interactions between Cholesterol and Phospholipids
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Adaptive Resolution Simulations with Self-Adjusting High-Resolution Regions
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Generalizable coordination of large multiscale workflows
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Adaptive resolution simulation of an atomistic DNA molecule in MARTINI salt solution
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Exploring CRD mobility during RAS/RAF engagement at the membrane
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Self-citationTsai, H. G., Lee, J.-B., Li, H.-S., Hou, T.-Y., Chu, W.-Y., Shen, P.-C., Chen, Y., Tan, C.-J., Hu, J.-C., & Chiu, C.-C. (2015) · Biochimica et Biophysica Acta (BBA) - Biomembranes · doi:10.1016/j.bbamem.2015.02.016 · Cited by 26 · see citing works
Comparison of Phosphatidylcholine Vesicle Properties Related to Geometrical Isomerism†
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Dual Resolution Membrane Simulations Using Virtual Sites
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Multiscale Simulation of Protein Hydration Using the SWINGER Dynamical Clustering Algorithm
Zavadlav, J., Marrink, . J., & Praprotnik, M. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b01129 · Cited by 25 · see citing works
ddcMD: A fully GPU-accelerated molecular dynamics program for the Martini force field
Zhang, X., Sundram, S., Oppelstrup, T., Kokkila-Schumacher, S. I. L., Carpenter, T. S., Ingólfsson, H. I., Streitz, F. H., Lightstone, F. C., & Glosli, J. N. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0014500 · Cited by 25 · see citing works
Self-citationHow Anionic Lipids Affect Spatiotemporal Properties of KRAS4B on Model Membranes.
Ngo, V. A., Sarkar, S., Neale, C., & Garcia, A. (2020) · Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.0c02642 · Cited by 23 · see citing works
Self-citationAdaptive resolution simulations of biomolecular systems
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Analysis of a DNA simulation model through hairpin melting experiments
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Combining Coarse-Grained Protein Models with Replica-Exchange All-Atom Molecular Dynamics
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Methionine 170 is an Environmentally Sensitive Membrane Anchor in the Disordered HVR of K-Ras4B
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Self-citationEnantiomers of phospholipids and cholesterol: A key to decipher lipid‐lipid interplay in membrane
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The protonation states of GTP and GppNHp in Ras proteins
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Protein Folding Simulations: Combining Coarse-grained Models and All-atom Molecular Dynamics
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Hybrid Atomistic and Coarse-Grained Model for Surfactants in Apolar Solvents
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Molecular Dynamics Simulations: Difficulties, Solutions and Strategies for Treating Metalloenzymes
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Recapitulation of cell-like KRAS4b membrane dynamics on complex biomimetic membranes
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Self-citationWu, K., Xu, S., Wan, B., Xiu, P., & Zhou, X. (2020) · The Journal of Chemical Physics · doi:10.1063/1.5135309 · Cited by 10 · see citing works
Coarse-grained simulations of proton-dependent conformational changes in lactose permease
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Flagged first, then by citations
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Molecular dynamics simulation of an entire cell
Stevens, J. A., Grünewald, F., van Tilburg, P. A. M., König, M., Gilbert, B. R., Brier, T. A., Thornburg, Z. R., Luthey-Schulten, Z., & Marrink, S. J. (2023) · Frontiers in Chemistry · doi:10.3389/fchem.2023.1106495 · Cited by 156 · see citing works
“A number of such backmapping tools, optimized for Martini, already exist (Louison et al., 2021; Vickery and Stansfeld, 2021; López et al., 2022).”
MethodIngólfsson, H. I., Bhatia, H., Aydin, F., Oppelstrup, T., López, C. A., Stanton, L. G., Carpenter, T. S., Wong, S., Natale, F. D., Zhang, X., et al. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c01018 · Cited by 56 · see citing works
Self-citationNussinov, R. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.169044 · Cited by 33 · see citing works
Exploring CRD mobility during RAS/RAF engagement at the membrane
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Self-citationThe confluence of machine learning and multiscale simulations
Bhatia, H., Aydin, F., Carpenter, T. S., Lightstone, F. C., Bremer, P., Ingólfsson, H. I., Nissley, D. V., & Streitz, F. H. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102569 · Cited by 26 · see citing works
Self-citationRichardson, J. D., & Lehn, R. C. V. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c03276 · Cited by 25 · see citing works
Membrane lipids drive formation of KRAS4b-RAF1 RBDCRD nanoclusters on the membrane
Shrestha, R., Carpenter, T. S., Van, Q. N., Agamasu, C., Tonelli, M., Aydin, F., Chen, D., Gülten, G., Glosli, J. N., López, C. A., et al. (2024) · Communications Biology · doi:10.1038/s42003-024-05916-0 · Cited by 17 · see citing works
Self-citationThe continuous evolution of biomolecular force fields
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CD151 Maintains Endolysosomal Protein Quality to Inhibit Vascular Inflammation
Chen, J., Ding, Y., Jiang, C. Y., Qu, R., Wren, J. D., Georgescu, C., Wang, X., Reuter, D. N., Liu, B., Giles, C. B., et al. (2024) · Circulation Research · doi:10.1161/circresaha.123.323190 · Cited by 15 · see citing works
Self-citationBuilding complex membranes with Martini 3
Öztürk, T. N., König, M., Carpenter, T. S., Pedersen, K. B., Wassenaar, T. A., Ingólfsson, H. I., & Marrink, . J. (2024) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/bs.mie.2024.03.010 · Cited by 13 · see citing works
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Self-citationDynamics and recognition of glycosphingolipids in membranes: insights from different scales
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Challenges of conventional iterative all-atom and coarse-grained multiscale molecular dynamics
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- Authors
- 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., Streitz, F. H., Stephen, A. G., Turbyville, T. J., Lightstone, F. C., Gnanakaran, S., Ingólfsson, H. I., & Neale, C.
- Affiliations
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
- Lawrence Livermore National Laboratory (US)
- Frederick National Laboratory for Cancer Research (US)
- Oak Ridge National Laboratory (US)
- Los Alamos National Laboratory (US)
- Computing Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
- Corresponding author
- Neale, Chris
- ORCID
- Zhang, Xiaohua
- Aydin, Fikret
- Shrestha, Rebika
- Van, Que N.
- Stanley, Christopher B.
- Carpenter, Timothy S.
- Nguyen, Kien
- Patel, Lara A.
- Chen, De
- Burns, Violetta
- Hengartner, Nicolas W.
- Reddy, Tyler J. E.
- Bhatia, Harsh
- Tran, Timothy H.
- Chan, Albert H.
- Simanshu, Dhirendra K.
- Nissley, Dwight V.
- Streitz, Frederick H.
- Stephen, Andrew G.
- Turbyville, Thomas J.
- Lightstone, Felice C.
- Gnanakaran, Sandrasegaram
- Ingólfsson, Helgi I.
- Neale, Chris
- Venue
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Published
- 2022-07-22
- Volume
- 18
- Issue
- 8
- Pages
- 5025-5045
- ISSN
- 1549-9618 (ISSN-L)
- 1549-9626
- OpenAlex
- W4286561119
- Cited by
- 18
- References
- 144
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 5 · 2023: 3 · 2022: 1
- FWCI
- 1.14
- Type
- journal article
- Language
- en
- Open access
- green
- www.osti.gov
- Licence
- Locations
- Journal of Chemical Theory and Computation (publishedVersion)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- National Cancer Institute
- U.S. Department of Energy
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Advanced Mathematical Modeling in Engineering
- Keywords
- multiscale molecular dynamics, CHARMM36, protein secondary structure, Ras, Raf kinase, lipid bilayers, peripheral membrane proteins
- MeSH terms
- Humans, Lipid Bilayers, Proteins, Protein Structure, Secondary, Molecular Dynamics Simulation
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