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Related Experiment Video

Updated: Jun 18, 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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Published on: March 1, 2022

"Cross-graining": efficient multi-scale simulation via Markov state models.

Peter M Kasson¹, Vijay S Pande

¹Departments of Chemistry and Structural Biology, Stanford University Stanford, CA 94305, USA.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

|November 13, 2009

Summary

This study introduces a novel multi-scale simulation method, "cross-graining," for biomolecular systems. It enables efficient simulation of complex systems by combining coarse-grained and atomistic details using Markov state models.

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Area of Science:

Computational Biology
Biophysics
Molecular Dynamics

Background:

Simulating biomolecular systems at multiple levels of detail simultaneously presents significant challenges.
Developing accurate and efficient computational methods is crucial for understanding complex biological processes.

Purpose of the Study:

To present a new method for multi-scale simulation of biomolecular systems.
To enable the simultaneous simulation of systems at both coarse-grained and atomistic levels.

Main Methods:

A novel "cross-graining" methodology is introduced.
The method partitions a complex system into two loosely-coupled sub-systems: one coarse-grained and one atomistic.
A Markov state model is constructed by approximating transitions in the joint space as a product of orthogonal transitions, applicable when coupling is slow and discrete.

Main Results:

The proposed formalism allows for the construction of Markov state models for mixed-scale systems.
Demonstrates the application of this methodology to the multi-scale simulation of membrane proteins.
The cross-graining approach offers a general and efficient strategy for simulating systems with varying levels of detail.

Conclusions:

The developed cross-graining methodology provides an efficient approach for multi-scale simulations.
This method is particularly applicable to complex systems like membrane proteins.
It offers a general solution for simulating mixed-scale biomolecular systems.