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Updated: Jul 17, 2026
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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
Protein folding dynamics in lattice model with physical movement
Sema Kachalo1, Hsiao-Mei Lu, Jie Liang
1Dept. of Bioeng., Illinois Univ., Chicago, IL.
Summary
Protein folding dynamics reveal that kinetic energy landscapes, not native structure properties, predict folding rates. Analysis identifies intermediate states without macrostate approximations.
Area of Science:
- Computational Biology
- Biophysics
- Statistical Mechanics
Background:
- Understanding protein folding dynamics is crucial for molecular biology and drug discovery.
- Predicting protein folding rates from sequence and structure remains a significant challenge.
- Existing models often rely on simplified assumptions or approximations.
Purpose of the Study:
- To investigate the folding dynamics of protein-like sequences on a square lattice.
- To develop a physically realizable move set that captures all conformational changes.
- To identify key factors governing protein folding rates and intermediate state formation.
Main Methods:
- Constructed a comprehensive move set for conformational changes on a square lattice.
- Solved the master equation for a 16-mer using an 802,075x802,075 transition matrix.
- Monitored time-dependent occupancy probabilities across nine orders of magnitude in time scale.
- Analyzed the kinetic energy landscape and its connection graph.
Main Results:
- Folding rates varied up to 200-fold for sequences with the same ground state conformation.
- Native structure parameters, designability, and thermodynamics were poor predictors of folding rates.
- Kinetic energy landscape properties effectively explained observed folding rates.
- Identified transient intermediate states using basin analysis of the kinetic landscape.
Conclusions:
- The kinetic energy landscape is a superior predictor of protein folding rates compared to static structural or thermodynamic properties.
- A detailed kinetic analysis, without macrostate approximations, reveals the dynamics of intermediate states.
- This approach offers a more accurate framework for understanding and predicting protein folding behavior.
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