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Shrestha R, Carpenter TS, Van QN, Agamasu C, Tonelli M, Aydin F, Chen D, Gulten G, Glosli JN, López CA, Oppelstrup T, Neale C, Gnanakaran S, Gillette WK, Ingólfsson HI, Lightstone FC, Stephen AG, Streitz FH, Nissley DV, Turbyville TJ. Author Correction: Membrane lipids drive formation of KRAS4b-RAF1 RBDCRD nanoclusters on the membrane. Commun Biol 2024; 7:445. [PMID: 38605143 PMCID: PMC11009335 DOI: 10.1038/s42003-024-06061-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2024] Open
Affiliation(s)
- Rebika Shrestha
- RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD, 21701, USA
| | - Timothy S Carpenter
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - Que N Van
- RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD, 21701, USA
| | - Constance Agamasu
- RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD, 21701, USA
| | - Marco Tonelli
- National Magnetic Resonance Facility at Madison, Biochemistry Department, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Fikret Aydin
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - De Chen
- RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD, 21701, USA
| | - Gulcin Gulten
- RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD, 21701, USA
| | - James N Glosli
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - Cesar A López
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
| | - Tomas Oppelstrup
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - Chris Neale
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
| | - Sandrasegaram Gnanakaran
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
| | - William K Gillette
- RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD, 21701, USA
| | - Helgi I Ingólfsson
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - Felice C Lightstone
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - Andrew G Stephen
- RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD, 21701, USA
| | - Frederick H Streitz
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - Dwight V Nissley
- RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD, 21701, USA
| | - Thomas J Turbyville
- RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD, 21701, USA.
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Shrestha R, Carpenter TS, Van QN, Agamasu C, Tonelli M, Aydin F, Chen D, Gulten G, Glosli JN, López CA, Oppelstrup T, Neale C, Gnanakaran S, Gillette WK, Ingólfsson HI, Lightstone FC, Stephen AG, Streitz FH, Nissley DV, Turbyville TJ. Membrane lipids drive formation of KRAS4b-RAF1 RBDCRD nanoclusters on the membrane. Commun Biol 2024; 7:242. [PMID: 38418613 PMCID: PMC10902389 DOI: 10.1038/s42003-024-05916-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 02/14/2024] [Indexed: 03/02/2024] Open
Abstract
The oncogene RAS, extensively studied for decades, presents persistent gaps in understanding, hindering the development of effective therapeutic strategies due to a lack of precise details on how RAS initiates MAPK signaling with RAF effector proteins at the plasma membrane. Recent advances in X-ray crystallography, cryo-EM, and super-resolution fluorescence microscopy offer structural and spatial insights, yet the molecular mechanisms involving protein-protein and protein-lipid interactions in RAS-mediated signaling require further characterization. This study utilizes single-molecule experimental techniques, nuclear magnetic resonance spectroscopy, and the computational Machine-Learned Modeling Infrastructure (MuMMI) to examine KRAS4b and RAF1 on a biologically relevant lipid bilayer. MuMMI captures long-timescale events while preserving detailed atomic descriptions, providing testable models for experimental validation. Both in vitro and computational studies reveal that RBDCRD binding alters KRAS lateral diffusion on the lipid bilayer, increasing cluster size and decreasing diffusion. RAS and membrane binding cause hydrophobic residues in the CRD region to penetrate the bilayer, stabilizing complexes through β-strand elongation. These cooperative interactions among lipids, KRAS4b, and RAF1 are proposed as essential for forming nanoclusters, potentially a critical step in MAP kinase signal activation.
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Affiliation(s)
- Rebika Shrestha
- RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD, 21701, USA
| | - Timothy S Carpenter
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - Que N Van
- RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD, 21701, USA
| | - Constance Agamasu
- RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD, 21701, USA
| | - Marco Tonelli
- National Magnetic Resonance Facility at Madison, Biochemistry Department, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Fikret Aydin
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - De Chen
- RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD, 21701, USA
| | - Gulcin Gulten
- RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD, 21701, USA
| | - James N Glosli
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - Cesar A López
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
| | - Tomas Oppelstrup
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - Chris Neale
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
| | - Sandrasegaram Gnanakaran
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
| | - William K Gillette
- RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD, 21701, USA
| | - Helgi I Ingólfsson
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - Felice C Lightstone
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - Andrew G Stephen
- RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD, 21701, USA
| | - Frederick H Streitz
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - Dwight V Nissley
- RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD, 21701, USA
| | - Thomas J Turbyville
- RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD, 21701, USA.
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Bhatia H, Aydin F, Carpenter TS, Lightstone FC, Bremer PT, Ingólfsson HI, Nissley DV, Streitz FH. The confluence of machine learning and multiscale simulations. Curr Opin Struct Biol 2023; 80:102569. [PMID: 36966691 DOI: 10.1016/j.sbi.2023.102569] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 01/31/2023] [Accepted: 02/08/2023] [Indexed: 06/04/2023]
Abstract
Multiscale modeling has a long history of use in structural biology, as computational biologists strive to overcome the time- and length-scale limits of atomistic molecular dynamics. Contemporary machine learning techniques, such as deep learning, have promoted advances in virtually every field of science and engineering and are revitalizing the traditional notions of multiscale modeling. Deep learning has found success in various approaches for distilling information from fine-scale models, such as building surrogate models and guiding the development of coarse-grained potentials. However, perhaps its most powerful use in multiscale modeling is in defining latent spaces that enable efficient exploration of conformational space. This confluence of machine learning and multiscale simulation with modern high-performance computing promises a new era of discovery and innovation in structural biology.
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Affiliation(s)
- Harsh Bhatia
- Computing Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA. https://twitter.com/@harshbhatia85
| | - Fikret Aydin
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - Timothy S Carpenter
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - Felice C Lightstone
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - Peer-Timo Bremer
- Computing Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - Helgi I Ingólfsson
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - Dwight V Nissley
- RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, MD, 21701, USA.
| | - Frederick H Streitz
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA.
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Ingólfsson HI, Bhatia H, Aydin F, Oppelstrup T, López CA, Stanton LG, Carpenter TS, Wong S, Di Natale F, Zhang X, Moon JY, Stanley CB, Chavez JR, Nguyen K, Dharuman G, Burns V, Shrestha R, Goswami D, Gulten G, Van QN, Ramanathan A, Van Essen B, Hengartner NW, Stephen AG, Turbyville T, Bremer PT, Gnanakaran S, Glosli JN, Lightstone FC, Nissley DV, Streitz FH. Machine Learning-Driven Multiscale Modeling: Bridging the Scales with a Next-Generation Simulation Infrastructure. J Chem Theory Comput 2023; 19:2658-2675. [PMID: 37075065 PMCID: PMC10173464 DOI: 10.1021/acs.jctc.2c01018] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Abstract
Interdependence across time and length scales is common in biology, where atomic interactions can impact larger-scale phenomenon. Such dependence is especially true for a well-known cancer signaling pathway, where the membrane-bound RAS protein binds an effector protein called RAF. To capture the driving forces that bring RAS and RAF (represented as two domains, RBD and CRD) together on the plasma membrane, simulations with the ability to calculate atomic detail while having long time and large length- scales are needed. The Multiscale Machine-Learned Modeling Infrastructure (MuMMI) is able to resolve RAS/RAF protein-membrane interactions that identify specific lipid-protein fingerprints that enhance protein orientations viable for effector binding. MuMMI is a fully automated, ensemble-based multiscale approach connecting three resolution scales: (1) the coarsest scale is a continuum model able to simulate milliseconds of time for a 1 μm2 membrane, (2) the middle scale is a coarse-grained (CG) Martini bead model to explore protein-lipid interactions, and (3) the finest scale is an all-atom (AA) model capturing specific interactions between lipids and proteins. MuMMI dynamically couples adjacent scales in a pairwise manner using machine learning (ML). The dynamic coupling allows for better sampling of the refined scale from the adjacent coarse scale (forward) and on-the-fly feedback to improve the fidelity of the coarser scale from the adjacent refined scale (backward). MuMMI operates efficiently at any scale, from a few compute nodes to the largest supercomputers in the world, and is generalizable to simulate different systems. As computing resources continue to increase and multiscale methods continue to advance, fully automated multiscale simulations (like MuMMI) will be commonly used to address complex science questions.
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Affiliation(s)
- Helgi I Ingólfsson
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Harsh Bhatia
- Computing Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Fikret Aydin
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Tomas Oppelstrup
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Cesar A López
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Liam G Stanton
- Department of Mathematics and Statistics, San José State University, San José, California 95192, United States
| | - Timothy S Carpenter
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Sergio Wong
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Francesco Di Natale
- Computing Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Xiaohua Zhang
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Joseph Y Moon
- Computing Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Christopher B Stanley
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
| | - Joseph R Chavez
- Computing Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Kien Nguyen
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Gautham Dharuman
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Violetta Burns
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Rebika Shrestha
- RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, Maryland 21701, United States
| | - Debanjan Goswami
- RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, Maryland 21701, United States
| | - Gulcin Gulten
- RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, Maryland 21701, United States
| | - Que N Van
- RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, Maryland 21701, United States
| | - Arvind Ramanathan
- Computing, Environment & Life Sciences (CELS) Directorate, Argonne National Laboratory, Lemont, Illinois 60439, United States
| | - Brian Van Essen
- Computing Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Nicolas W Hengartner
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Andrew G Stephen
- RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, Maryland 21701, United States
| | - Thomas Turbyville
- RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, Maryland 21701, United States
| | - Peer-Timo Bremer
- Computing Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - S Gnanakaran
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - James N Glosli
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Felice C Lightstone
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Dwight V Nissley
- RAS Initiative, The Cancer Research Technology Program, Frederick National Laboratory, Frederick, Maryland 21701, United States
| | - Frederick H Streitz
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
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5
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Nguyen K, López CA, Neale C, Van QN, Carpenter TS, Di Natale F, Travers T, Tran TH, Chan AH, Bhatia H, Frank PH, Tonelli M, Zhang X, Gulten G, Reddy T, Burns V, Oppelstrup T, Hengartner N, Simanshu DK, Bremer PT, Chen D, Glosli JN, Shrestha R, Turbyville T, Streitz FH, Nissley DV, Ingólfsson HI, Stephen AG, Lightstone FC, Gnanakaran S. Exploring CRD mobility during RAS/RAF engagement at the membrane. Biophys J 2022; 121:3630-3650. [PMID: 35778842 PMCID: PMC9617161 DOI: 10.1016/j.bpj.2022.06.035] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 06/21/2022] [Accepted: 06/28/2022] [Indexed: 11/25/2022] Open
Abstract
During the activation of mitogen-activated protein kinase (MAPK) signaling, the RAS-binding domain (RBD) and cysteine-rich domain (CRD) of RAF bind to active RAS at the plasma membrane. The orientation of RAS at the membrane may be critical for formation of the RAS-RBDCRD complex and subsequent signaling. To explore how RAS membrane orientation relates to the protein dynamics within the RAS-RBDCRD complex, we perform multiscale coarse-grained and all-atom molecular dynamics (MD) simulations of KRAS4b bound to the RBD and CRD domains of RAF-1, both in solution and anchored to a model plasma membrane. Solution MD simulations describe dynamic KRAS4b-CRD conformations, suggesting that the CRD has sufficient flexibility in this environment to substantially change its binding interface with KRAS4b. In contrast, when the ternary complex is anchored to the membrane, the mobility of the CRD relative to KRAS4b is restricted, resulting in fewer distinct KRAS4b-CRD conformations. These simulations implicate membrane orientations of the ternary complex that are consistent with NMR measurements. While a crystal structure-like conformation is observed in both solution and membrane simulations, a particular intermolecular rearrangement of the ternary complex is observed only when it is anchored to the membrane. This configuration emerges when the CRD hydrophobic loops are inserted into the membrane and helices α3-5 of KRAS4b are solvent exposed. This membrane-specific configuration is stabilized by KRAS4b-CRD contacts that are not observed in the crystal structure. These results suggest modulatory interplay between the CRD and plasma membrane that correlate with RAS/RAF complex structure and dynamics, and potentially influence subsequent steps in the activation of MAPK signaling.
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Affiliation(s)
- Kien Nguyen
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico
| | - Cesar A López
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico
| | - Chris Neale
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico
| | - Que N Van
- National Cancer Institute RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | - Timothy S Carpenter
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California
| | - Francesco Di Natale
- Applications, Simulations, and Quality, Lawrence Livermore National Laboratory, Livermore, California
| | | | - Timothy H Tran
- National Cancer Institute RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | - Albert H Chan
- National Cancer Institute RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | - Harsh Bhatia
- Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, California
| | - Peter H Frank
- National Cancer Institute RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | - Marco Tonelli
- National Magnetic Resonance Facility at Madison, Biochemistry Department, University of Wisconsin-Madison, Madison, Wisconsin
| | - Xiaohua Zhang
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California
| | - Gulcin Gulten
- National Cancer Institute RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | - Tyler Reddy
- CCS-7, Los Alamos National Laboratory, Los Alamos, New Mexico
| | - Violetta Burns
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico
| | - Tomas Oppelstrup
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California
| | - Nick Hengartner
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico
| | - Dhirendra K Simanshu
- National Cancer Institute RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | - Peer-Timo Bremer
- Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, California
| | - De Chen
- National Cancer Institute RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | - James N Glosli
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California
| | - Rebika Shrestha
- National Cancer Institute RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | - Thomas Turbyville
- National Cancer Institute RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | - Frederick H Streitz
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California
| | - Dwight V Nissley
- National Cancer Institute RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | - Helgi I Ingólfsson
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California
| | - Andrew G Stephen
- National Cancer Institute RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland
| | - Felice C Lightstone
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California
| | - Sandrasegaram Gnanakaran
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico.
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6
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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. Asynchronous Reciprocal Coupling of Martini 2.2 Coarse-Grained and CHARMM36 All-Atom Simulations in an Automated Multiscale Framework. J Chem Theory Comput 2022; 18:5025-5045. [PMID: 35866871 DOI: 10.1021/acs.jctc.2c00168] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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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Affiliation(s)
- Cesar A López
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Xiaohua Zhang
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Fikret Aydin
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Rebika Shrestha
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Que N Van
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Christopher B Stanley
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
| | - Timothy S Carpenter
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Kien Nguyen
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Lara A Patel
- 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
| | - De Chen
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Violetta Burns
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Nicolas W Hengartner
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Tyler J E Reddy
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Harsh Bhatia
- Computing Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Francesco Di Natale
- Computing Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Timothy H Tran
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Albert H Chan
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Dhirendra K Simanshu
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Dwight V Nissley
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Frederick H Streitz
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Andrew G Stephen
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Thomas J Turbyville
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Felice C Lightstone
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Sandrasegaram Gnanakaran
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Helgi I Ingólfsson
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Chris Neale
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
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Zhang X, Sundram S, Oppelstrup T, Kokkila-Schumacher SIL, Carpenter TS, Ingólfsson HI, Streitz FH, Lightstone FC, Glosli JN. ddcMD: A fully GPU-accelerated molecular dynamics program for the Martini force field. J Chem Phys 2020; 153:045103. [PMID: 32752727 DOI: 10.1063/5.0014500] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
We have implemented the Martini force field within Lawrence Livermore National Laboratory's molecular dynamics program, ddcMD. The program is extended to a heterogeneous programming model so that it can exploit graphics processing unit (GPU) accelerators. In addition to the Martini force field being ported to the GPU, the entire integration step, including thermostat, barostat, and constraint solver, is ported as well, which speeds up the simulations to 278-fold using one GPU vs one central processing unit (CPU) core. A benchmark study is performed with several test cases, comparing ddcMD and GROMACS Martini simulations. The average performance of ddcMD for a protein-lipid simulation system of 136k particles achieves 1.04 µs/day on one NVIDIA V100 GPU and aggregates 6.19 µs/day on one Summit node with six GPUs. The GPU implementation in ddcMD offloads all computations to the GPU and only requires one CPU core per simulation to manage the inputs and outputs, freeing up remaining CPU resources on the compute node for alternative tasks often required in complex simulation campaigns. The ddcMD code has been made open source and is available on GitHub at https://github.com/LLNL/ddcMD.
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Affiliation(s)
- Xiaohua Zhang
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
| | - Shiv Sundram
- Computing Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
| | - Tomas Oppelstrup
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
| | | | - Timothy S Carpenter
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
| | - Helgi I Ingólfsson
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
| | - Frederick H Streitz
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
| | - Felice C Lightstone
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
| | - James N Glosli
- Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
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Streitz FH. Abstract IA10: Multiscale simulations of k-Ras on lipid bilayers. Mol Cancer Res 2020. [DOI: 10.1158/1557-3125.ras18-ia10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Simulating proteins on lipid membranes could provide unprecedented insights into cancer biology and a host of other phenomena. However, such simulations face conflicting computational constraints: reaching biologically relevant time and length scales (milliseconds and microns) requires continuum-level models but understanding the processes of interest requires molecular level detail. I will present a new type of massively parallel, multiscale simulation framework that brings together these two modeling paradigms. Using a machine learning algorithm, we couple a novel continuum model to an ensemble of molecular dynamics simulations. The result is a simulation at macro length- and time-scales that incorporates microscale precision. We demonstrate this capability with a large-scale simulation of several hundred k-Ras proteins on a realistic lipid bilayer spanning 1 μm2.
Citation Format: Frederick H. Streitz. Multiscale simulations of k-Ras on lipid bilayers [abstract]. In: Proceedings of the AACR Special Conference on Targeting RAS-Driven Cancers; 2018 Dec 9-12; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2020;18(5_Suppl):Abstract nr IA10.
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Rudd RE, Cabot WH, Caspersen KJ, Greenough JA, Richards DF, Streitz FH, Miller PL. Self-diffusivity and interdiffusivity of molten aluminum-copper alloys under pressure, derived from molecular dynamics. Phys Rev E Stat Nonlin Soft Matter Phys 2012; 85:031202. [PMID: 22587084 DOI: 10.1103/physreve.85.031202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2010] [Revised: 12/09/2011] [Indexed: 05/31/2023]
Abstract
We use molecular dynamics (MD) to simulate diffusion in molten aluminum-copper (AlCu) alloys. The self-diffusivities and Maxwell-Stefan diffusivities are calculated for AlCu mixtures using the Green-Kubo formulas at temperatures from 1000 to 4000 K and pressures from 0 to 25 GPa, along with additional points at higher temperatures and pressures. The diffusivities are corrected for finite-size effects. The Maxwell-Stefan diffusivity is compared to the diffusivity calculated from the self-diffusivities using a generalization of the Darken equation. We find that the effects of cross-correlation are small. Using the calculated self-diffusivities, we have assessed whether dilute hard-sphere and dilute Lennard-Jones models apply to the molten mixture. Neither of the two dilute gas diffusivities describes the diffusivity in molten Al and Cu. We report generalized analytic models for the self-diffusivities and interdiffusivity (mutual diffusivity) that fit the MD results well. The MD-derived transport coefficients are in good agreement with the available experimental data. We also report MD calculations of the viscosity and an analytic fit to those results. The ionic thermal conductivity is discussed briefly.
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Affiliation(s)
- Robert E Rudd
- Lawrence Livermore National Laboratory, Livermore, California 94551, USA.
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Benedict LX, Glosli JN, Richards DF, Streitz FH, Hau-Riege SP, London RA, Graziani FR, Murillo MS, Benage JF. Molecular dynamics simulations of electron-ion temperature equilibration in an SF6 plasma. Phys Rev Lett 2009; 102:205004. [PMID: 19519037 DOI: 10.1103/physrevlett.102.205004] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2008] [Indexed: 05/27/2023]
Abstract
We use classical molecular dynamics to investigate electron-ion temperature equilibration in a two-temperature SF6 plasma. We choose a density of 1.0 x 10;{19}SF_{6} molecules per cm;{3} and initial temperatures of T_{e} = 100 eV and T_{S} = T_{F} = 15 eV, in accordance with experiments currently underway at Los Alamos National Laboratory. Our computed relaxation time lies between two oft-used variants of the Landau-Spitzer relaxation formula which invoke static screening. Discrepancies are also found when comparing to the predictions made by more recent theoretical approaches. These differences should be large enough to be measured in the upcoming experiments.
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Affiliation(s)
- Lorin X Benedict
- Lawrence Livermore National Laboratory, Livermore, California 94550, USA
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Glosli JN, Graziani FR, More RM, Murillo MS, Streitz FH, Surh MP, Benedict LX, Hau-Riege S, Langdon AB, London RA. Molecular dynamics simulations of temperature equilibration in dense hydrogen. Phys Rev E Stat Nonlin Soft Matter Phys 2008; 78:025401. [PMID: 18850889 DOI: 10.1103/physreve.78.025401] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2008] [Indexed: 05/26/2023]
Abstract
The temperature equilibration rate between electrons and protons in dense hydrogen has been calculated with molecular dynamics simulations for temperatures between 10 and 600eV and densities between 10;{20}cm;{-3}to10;{24}cm;{-3} . Careful attention has been devoted to convergence of the simulations, including the role of semiclassical potentials. We find that for Coulomb logarithms L greater, similar1 , a model by Gericke-Murillo-Schlanges (GMS) [D. O. Gericke, Phys. Rev. E 65, 036418 (2002)] based on a T -matrix method and the approach by Brown-Preston-Singleton [L. S. Brown, Phys. Rep. 410, 237 (2005)] agrees with the simulation data to within the error bars of the simulation. For smaller Coulomb logarithms, the GMS model is consistent with the simulation results. Landau-Spitzer models are consistent with the simulation data for L>4 .
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Affiliation(s)
- J N Glosli
- Lawrence Livermore National Laboratory, Livermore, California 94550, USA
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12
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Streitz FH, Glosli JN, Patel MV, Chan B, Yates RK, Supinski BRD, Sexton J, Gunnels JA. Simulating solidification in metals at high pressure: The drive to petascale computing. ACTA ACUST UNITED AC 2006. [DOI: 10.1088/1742-6596/46/1/037] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Abstract
Although computer simulation has played a central role in the study of nucleation and growth since the earliest molecular dynamics simulations almost 50 years ago, confusion surrounding the effect of finite size on such simulations has limited their applicability. Modeling solidification in molten tantalum on the Blue Gene/L computer, we report here on the first atomistic simulation of solidification that verifies independence from finite-size effects during the entire nucleation and growth process, up to the onset of coarsening. We show that finite-size scaling theory explains the observed maximal grain sizes for systems up to about 8 000 000 atoms. For larger simulations, a crossover from finite-size scaling to more physical size-independent behavior is observed.
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Streitz FH, Mintmire JW. Electrostatic potentials for metal-oxide surfaces and interfaces. Phys Rev B Condens Matter 1994; 50:11996-12003. [PMID: 9975340 DOI: 10.1103/physrevb.50.11996] [Citation(s) in RCA: 179] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
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Streitz FH, Cammarata RC, Sieradzki K. Surface-stress effects on elastic properties. I. Thin metal films. Phys Rev B Condens Matter 1994; 49:10699-10706. [PMID: 10009898 DOI: 10.1103/physrevb.49.10699] [Citation(s) in RCA: 27] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
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Streitz FH, Cammarata RC, Sieradzki K. Surface-stress effects on elastic properties. II. Metallic multilayers. Phys Rev B Condens Matter 1994; 49:10707-10716. [PMID: 10009899 DOI: 10.1103/physrevb.49.10707] [Citation(s) in RCA: 24] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
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Sieradzki K, Streitz FH. Elastic interactions of defects on (111) Au surfaces. Phys Rev B Condens Matter 1992; 45:11433-11436. [PMID: 10001089 DOI: 10.1103/physrevb.45.11433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
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Xiao G, Streitz FH, Cieplak MZ, Bakhshai A, Gavrin A, Chien CL. Electrical transport and superconductivity in a Au-YBa2Cu3O7 percolation system. Phys Rev B Condens Matter 1988; 38:776-779. [PMID: 9945252 DOI: 10.1103/physrevb.38.776] [Citation(s) in RCA: 32] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
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Xiao G, Cieplak MZ, Gavrin A, Streitz FH, Bakhshai A, Chien CL. High-temperature superconductivity in tetragonal perovskite structures: Is oxygen-vacancy order important? Phys Rev Lett 1988; 60:1446-1449. [PMID: 10038040 DOI: 10.1103/physrevlett.60.1446] [Citation(s) in RCA: 73] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
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Xiao G, Streitz FH, Gavrin A, Cieplak MZ, Childress J, Lu M, Zwicker A, Chien CL. Flux pinning and critical current density in YBa2Cu3O6+y and EuBa2Cu3O6+y superconductors. Phys Rev B Condens Matter 1987; 36:2382-2385. [PMID: 9943108 DOI: 10.1103/physrevb.36.2382] [Citation(s) in RCA: 20] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
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Xiao G, Streitz FH, Gavrin A, Du YW, Chien CL. Effect of transition-metal elements on the superconductivity of Y-Ba-Cu-O. Phys Rev B Condens Matter 1987; 35:8782-8784. [PMID: 9941251 DOI: 10.1103/physrevb.35.8782] [Citation(s) in RCA: 235] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
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