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Protein Folding

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Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
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Updated: Apr 3, 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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Wavelet-Enhanced Data-Driven Collective Variables for Efficient Sampling of Protein Folding Landscapes.

Zhiteng Zhang¹, Xueguang Shao^1,2, Wensheng Cai^1,2

¹Research Center for Analytical Sciences, Tianjin Key Laboratory of Biosensing and Molecular Recognition, College of Chemistry, Nankai University, Tianjin 300071, China.

Journal of Chemical Information and Modeling

|April 2, 2026

Summary

This study introduces a novel feature extraction method using discrete wavelet transform to filter molecular dynamics trajectories. This approach effectively identifies slow degrees of freedom for enhanced sampling, accelerating the exploration of complex protein folding landscapes.

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

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Published on: July 25, 2013

Area of Science:

Computational chemistry and biophysics
Molecular dynamics simulations
Enhanced sampling techniques

Background:

Collective variables (CVs) are crucial for enhanced sampling methods in biomolecular simulations.
Selecting appropriate geometric CVs is challenging and system-dependent for complex free-energy landscapes.
Data-driven CVs are susceptible to fast fluctuations, obscuring slow degrees of freedom (DOFs).

Purpose of the Study:

To develop a robust method for identifying biologically relevant slow modes in molecular simulations.
To overcome the limitations of existing data-driven CVs in handling fast stochastic fluctuations.
To enhance the efficiency of enhanced sampling methods for complex biomolecular systems.

Main Methods:

Utilized discrete wavelet transform for feature extraction to filter fast-mode motions from molecular dynamics (MD) trajectories.
Applied dimensionality reduction to the filtered descriptors.
Constructed data-driven low-dimensional CVs using the refined feature subset.

Main Results:

The proposed strategy successfully identified slow DOFs with high fidelity.
CVs derived from this method facilitated rapid transitions between folded and unfolded states.
Demonstrated applicability to both chignolin and the complex BBA protein system.

Conclusions:

The feature extraction strategy effectively filters noise and reveals slow dynamics in MD simulations.
This approach significantly accelerates the exploration of protein folding landscapes.
The method provides a reliable way to construct accurate CVs for enhanced sampling across diverse systems.