MBO(N)D: A multibody method for long-time molecular dynamics simulations

Chun, Hon M.; Padilla, C.; Chin, Donovan N.; Watanabe, Masakatsu; Karlov, Valeri Ivanovic; Alper, Howard; Soosaar, K.; Blair, Kim B.; Becker, Oren M.; Caves, Leo S. D.; Nagle, Robert J.; Haney, D N; Farmer, Barry L.

doi:10.1002/(sici)1096-987x(200002)21:3<159::aid-jcc1>3.0.co;2-j

Cited by 85 publications

(69 citation statements)

References 102 publications

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“…From Eqs. (5), (6), and (19) follows that in the replacement model with only hybrid molecules a hybrid molecule experiences only translational kicks from other molecules in the coarse-grained region (w = 0) and hence its center of mass moves exactly as the respective oneparticle coarse-grained molecule in the original excg model. Similarly, in the explicit region (w = 1) a hybrid molecule experiences only atomistic forces and hence its explicit atoms move exactly as the explicit atoms in the respective tetrahedral molecule in the original model.…”

Section: Multiscale Simulation Detailsmentioning

confidence: 99%

“…Therefore, the model containing only hybrid molecules interacting via the pair force defined by Eq. (19) together with the applied Langevin thermostat [15] acting on each particle in the system (to assure that the atom velocities are thermalized in accordance with the equipartition principle) exactly mimics the original ex-cg model in which the temperature would also be held constant by the Langevin thermostat. From the methodology development point of view, these two models are therefore identical.…”

Section: Multiscale Simulation Detailsmentioning

confidence: 99%

“…The interaction range in the system is given by the range of the effective pair potential between molecules and the geometry of tetrahedral molecules, i.e., the most outer atoms of two tetrahedral molecules with centers of mass slightly less than 2.31σ apart still experience the effective potential contribution in Eq. (19). Hence, the actual interaction range in the system is approximately 3.5σ.…”

Section: Multiscale Simulation Detailsmentioning

confidence: 99%

“…Since some specific chemical details are usually lost in the coarse-graining procedure, much effort has been devoted recently to the development of multiscale modeling approaches, where different parts of the system are modeled at different levels of detail to account for the local resolution requirement [19,20,21,22,23,24]. In the dualresolution modeling approach for studying the behavior of polymers near metal surfaces [22,23,24], for example, polymer chain ends that interact with the metal surface are represented partially atomistically while the remaining parts of the polymers, where lower resolution is adequate, are represented as bead-spring chains.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive resolution molecular-dynamics simulation: Changing the degrees of freedom on the fly

Praprotnik

Site

Kremer

2005

The Journal of Chemical Physics

371

514

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We present a new adaptive resolution technique for efficient particle-based multiscale molecular dynamics (MD) simulations. The presented approach is tailor-made for molecular systems where atomistic resolution is required only in spatially localized domains whereas a lower mesoscopic level of detail is sufficient for the rest of the system. Our method allows an on-the-fly interchange between a given molecule's atomic and coarse-grained level of description, enabling us to reach large length and time scales while spatially retaining atomistic details of the system. The new approach is tested on a model system of a liquid of tetrahedral molecules. The simulation box is divided into two regions: one containing only atomistically resolved tetrahedral molecules, the other containing only one particle coarse-grained spherical molecules. The molecules can freely move between the two regions while changing their level of resolution accordingly. The coarse-grained and the atomistically resolved systems have the same statistical properties at the same physical conditions.

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Section: Multiscale Simulation Detailsmentioning

confidence: 99%

Section: Multiscale Simulation Detailsmentioning

confidence: 99%

Section: Multiscale Simulation Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive resolution molecular-dynamics simulation: Changing the degrees of freedom on the fly

Praprotnik

Site

Kremer

2005

The Journal of Chemical Physics

371

514

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“…The output of StoneHinge can also be used as a starting point for MBO(N)D molecular dynamics simulations. 46 By dividing the protein into rigid bodies and flexible regions (e.g., using the StoneHinge predictions), this algorithm reduces the computational time required for molecular dynamics simulations. We also foresee other advances and applications of the StoneHinge algorithm, including recognizing and sampling active-site loop motion.…”

Section: Using Stonehinge To Guide Flexible Dockingmentioning

confidence: 99%

StoneHinge: Hinge prediction by network analysis of individual protein structures

et al. 2009

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Hinge motions are important for molecular recognition, and knowledge of their location can guide the sampling of protein conformations for docking. Predicting domains and intervening hinges is also important for identifying structurally self-determinate units and anticipating the influence of mutations on protein flexibility and stability. Here we present StoneHinge, a novel approach for predicting hinges between domains using input from two complementary analyses of noncovalent bond networks: StoneHingeP, which identifies domain-hinge-domain signatures in ProFlex constraint counting results, and StoneHingeD, which does the same for DomDecomp Gaussian network analyses. Predictions for the two methods are compared to hinges defined in the literature and by visual inspection of interpolated motions between conformations in a series of proteins. For StoneHingeP, all the predicted hinges agree with hinge sites reported in the literature or observed visually, although some predictions include extra residues. Furthermore, no hinges are predicted in six hinge-free proteins. On the other hand, StoneHingeD tends to overpredict the number of hinges, while accurately pinpointing hinge locations. By determining the consensus of their results, StoneHinge improves the specificity, predicting 11 of 13 hinges found both visually and in the literature for nine different open protein structures, and making no false-positive predictions. By comparison, a popular hinge detection method that requires knowledge of both the open and closed conformations finds 10 of the 13 known hinges, while predicting four additional, false hinges.

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Modal dynamics of proteins in water

Elezgaray

Sanejouand

2000

J. Comput. Chem.

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We studied a dynamical model for the motion of the large scales of proteins in water. The model was obtained by projecting the (averaged) Newton equations onto some set of harmonic modes. We compared the statistics of the so-obtained trajectories with those obtained by standard techniques, and concluded that our dynamical model is able to fairly reproduce the average properties of the large-scale motion of the protein, and at the same time allow time steps one order of magnitude larger than the standard ones.

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MBO(N)D: A multibody method for long-time molecular dynamics simulations

Cited by 85 publications

References 102 publications

Adaptive resolution molecular-dynamics simulation: Changing the degrees of freedom on the fly

Adaptive resolution molecular-dynamics simulation: Changing the degrees of freedom on the fly

StoneHinge: Hinge prediction by network analysis of individual protein structures

Modal dynamics of proteins in water

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