Asynchronous Reciprocal Coupling of Martini 2.2 Coarse-Grained and CHARMM36 All-Atom Simulations in an Automated Multiscale FrameworkClick to copy article linkArticle link copied!
- Cesar A. LópezCesar A. LópezTheoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United StatesMore by Cesar A. López
- Xiaohua ZhangXiaohua ZhangPhysical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United StatesMore by Xiaohua Zhang
- Fikret AydinFikret AydinPhysical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United StatesMore by Fikret Aydin
- Rebika ShresthaRebika ShresthaNCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United StatesMore by Rebika Shrestha
- Que N. VanQue N. VanNCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United StatesMore by Que N. Van
- Christopher B. StanleyChristopher B. StanleyComputational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United StatesMore by Christopher B. Stanley
- Timothy S. CarpenterTimothy S. CarpenterPhysical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United StatesMore by Timothy S. Carpenter
- Kien NguyenKien NguyenTheoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United StatesMore by Kien Nguyen
- Lara A. PatelLara A. PatelTheoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United StatesCenter for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United StatesMore by Lara A. Patel
- De ChenDe ChenNCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United StatesMore by De Chen
- Violetta BurnsVioletta BurnsTheoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United StatesMore by Violetta Burns
- Nicolas W. HengartnerNicolas W. HengartnerTheoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United StatesMore by Nicolas W. Hengartner
- Tyler J. E. ReddyTyler J. E. ReddyTheoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United StatesMore by Tyler J. E. Reddy
- Harsh BhatiaHarsh BhatiaComputing Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United StatesMore by Harsh Bhatia
- Francesco Di NataleFrancesco Di NataleComputing Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United StatesMore by Francesco Di Natale
- Timothy H. TranTimothy H. TranNCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United StatesMore by Timothy H. Tran
- Albert H. ChanAlbert H. ChanNCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United StatesMore by Albert H. Chan
- Dhirendra K. SimanshuDhirendra K. SimanshuNCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United StatesMore by Dhirendra K. Simanshu
- Dwight V. NissleyDwight V. NissleyNCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United StatesMore by Dwight V. Nissley
- Frederick H. StreitzFrederick H. StreitzPhysical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United StatesMore by Frederick H. Streitz
- Andrew G. StephenAndrew G. StephenNCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United StatesMore by Andrew G. Stephen
- Thomas J. TurbyvilleThomas J. TurbyvilleNCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United StatesMore by Thomas J. Turbyville
- Felice C. LightstoneFelice C. LightstonePhysical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United StatesMore by Felice C. Lightstone
- Sandrasegaram GnanakaranSandrasegaram GnanakaranTheoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United StatesMore by Sandrasegaram Gnanakaran
- Helgi I. IngólfssonHelgi I. IngólfssonPhysical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United StatesMore by Helgi I. Ingólfsson
- Chris Neale*Chris Neale*Email: [email protected]. Tel: 1-505-667-8715.Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United StatesMore by Chris Neale
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.
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