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Related Experiment Video

Updated: Mar 30, 2026

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

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Modeling Structural Dynamics of Biomolecular Complexes by Coarse-Grained Molecular Simulations.

Shoji Takada¹, Ryo Kanada¹, Cheng Tan¹

¹Department of Biophysics, Graduate School of Science, Kyoto University , Sakyo, Kyoto 6068502, Japan.

Accounts of Chemical Research

|November 18, 2015

Summary

This review details coarse-grained (CG) simulation methods for biomolecules like proteins and DNA, enabling efficient study of large-scale dynamics. Applications demonstrate CG modeling

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Area of Science:

Biophysics
Computational Biology
Biomolecular Modeling

Background:

Biomolecular systems exhibit hierarchical structures necessitating multiscale computational approaches.
Coarse-grained (CG) simulations are crucial for modeling long-timescale dynamics in large biological systems.

Purpose of the Study:

To review recent advancements and applications of CG modeling methods for proteins, DNA, and their complexes.
To present the CafeMol CG biomolecular simulator and its underlying models.

Main Methods:

Development of CG models where ~10 non-hydrogen atoms form a single particle (e.g., one particle per amino acid for proteins).
Utilized structure-based potential energy functions for proteins, incorporating sequence-dependent flexibility and nonlocal contacts.
Employed a DNA model tuned to experimental data and modeled protein-DNA interactions via structure-based or electrostatic/excluded volume terms.
Implemented CG molecular dynamics (CGMD) with Langevin dynamics for accelerated simulations.

Main Results:

Demonstrated CG modeling of protein energy landscapes, DNA structural/thermodynamic properties, and protein-DNA interactions.
Applied CGMD to simulate kinesin motor function, nonspecific protein-DNA binding, p53 transcription factor dynamics, and dinucleosome structural behavior.
Achieved significant acceleration of large-scale motions in CGMD compared to atomistic simulations.

Conclusions:

CG modeling provides a powerful framework for investigating complex biomolecular dynamics and interactions.
Future directions include improving electrostatic treatments and developing compatible phospholipid models for CG simulations.