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Related Experiment Video

Updated: Jun 6, 2026

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

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

Computing ensembles of transitions from stable states: Dynamic importance sampling.

Juan R Perilla¹, Oliver Beckstein, Elizabeth J Denning

¹Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA. jrperillaj@jhu.edu

Journal of Computational Chemistry

|December 7, 2010

Summary

A new dynamic importance sampling (DIMS) algorithm enhances understanding of biomolecular intermediate states. This method improves sampling beyond stable states, offering insights into crucial conformational changes for protein function.

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Last Updated: Jun 6, 2026

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

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

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Published on: September 5, 2019

Area of Science:

Biophysics
Computational Biology
Structural Biology

Background:

Biomolecular structures exist in multiple conformations, critical for function.
Understanding intermediate states between known structures is essential but challenging.

Purpose of the Study:

To implement and test a dynamic importance sampling (DIMS) algorithm.
To improve the understanding of intermediate states in biomolecular conformational changes.

Main Methods:

Implementation of the dynamic importance sampling (DIMS) algorithm within the CHARMM program.
Testing the algorithm on six diverse systems, including protein folding and conformational changes.

Main Results:

The DIMS algorithm successfully sampled diverse intermediate states across all tested systems.
The method demonstrated an ability to control bias for analyzing intermediate state distributions.

Conclusions:

DIMS provides a valuable approach to complement traditional molecular dynamics for studying functionally relevant conformational changes.
The algorithm enhances insights into the dynamics of biomolecules by exploring states beyond stable energy wells.