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Updated: Feb 24, 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

Data-driven coarse graining of large biomolecular structures.

Yi-Ling Chen^1,2, Michael Habeck^1,3

¹Statistical Inverse Problems in Biophysics, Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077 Göttingen, Germany.

|August 18, 2017

Summary

This summary is machine-generated.

This study presents a Bayesian approach for coarse-graining biomolecular structures, simplifying complex molecular machines. This method aids in understanding large biological systems by representing them as bead particles for easier analysis.

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Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

Area of Science:

Structural Biology
Computational Biology
Biophysics

Background:

Studying large biomolecular assemblies requires high-resolution experimental and computational techniques.
Atomistic simulations of large systems are computationally expensive and difficult to interpret for functional mechanisms.
Coarse-graining (CG) offers a hierarchical approach to understand biological structures at varying levels of detail.

Purpose of the Study:

To introduce a novel Bayesian approach for coarse-graining biomolecular structures.
To develop a probabilistic model for representing experimental structures as bead particles.
To estimate parameters, bead positions, and the mapping between atoms and beads.

Main Methods:

Developed a probabilistic model for coarse-graining.
Represented molecular structures as clouds of interacting bead particles.
Estimated pairwise potential parameters, bead positions, and atom-to-bead mapping using a Bayesian framework.

Main Results:

The Bayesian approach effectively coarse-grains biomolecular structures.
The model represents structures as bead particles with estimated interactions.
Applicable to both experimental structures and cryo-electron microscopy density maps.

Conclusions:

The Bayesian coarse-graining method provides a scalable approach to study complex molecular machines.
This technique facilitates understanding the function of large biological systems by simplifying their representation.
The method is versatile and applicable to various biomolecular systems and data types.