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Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

Bridging coarse-grained models by jump-in-sample simulations.

Xin Zhou1, Yi Jiang, Steen Rasmussen

  • 1Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA. xzhou@lanl.gov

The Journal of Chemical Physics
|May 10, 2008
PubMed
Summary
This summary is machine-generated.

We developed an efficient method to create coarse-grained (CG) models from finer resolution data. This approach accurately estimates free energies and effective potentials, improving molecular modeling.

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Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

Area of Science:

  • Computational chemistry
  • Statistical mechanics
  • Molecular modeling

Background:

  • Developing accurate coarse-grained (CG) models is crucial for simulating complex molecular systems.
  • Traditional methods for CG model construction can be computationally intensive and challenging in high dimensions.

Purpose of the Study:

  • To present an efficient and robust method for constructing CG models from finer resolution data.
  • To accurately estimate free energies and fit the effective potential surface in high-dimensional conformational spaces.

Main Methods:

  • A novel jump-in-sample algorithm utilizing random walks within the CG conformational space.
  • Iterative estimation of free energies using an improved Wang-Landau (WL) algorithm.
  • Application to a tetrahedral molecular fluid to construct intermolecular effective potentials.

Main Results:

  • Demonstrated an efficient method for CG model construction.
  • Successfully estimated free energies and fitted the effective potential surface in high-dimensional spaces.
  • The improved WL algorithm showed enhanced efficiency and applicability in high-dimensional settings.

Conclusions:

  • The proposed method provides an efficient approach for generating accurate CG models.
  • The enhanced WL algorithm is effective for free energy calculations in high-dimensional systems.
  • This work advances molecular modeling capabilities through improved CG model construction.