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Related Concept Videos

¹H NMR of Conformationally Flexible Molecules: Temporal Resolution00:52

¹H NMR of Conformationally Flexible Molecules: Temporal Resolution

At room temperature, the chair conformer of cyclohexane undergoes rapid ring flipping between two equivalent chair conformers at a rate of approximately 105 times per second. These two chair conformers are in equilibrium. The rapid ring flipping results in the interconversion of the axial proton to an equatorial proton and an equatorial to the axial proton. Such interconversions are too rapid and cannot be detected on the NMR timescale. Hence, the NMR spectrometer cannot distinguish between the...
Stability of Equilibrium Configuration01:23

Stability of Equilibrium Configuration

Understanding the stability of equilibrium configurations is a fundamental part of mechanical engineering. In any system, there are three distinct types of equilibrium: stable, neutral, and unstable.
A stable equilibrium occurs when a system tends to return to its original position when given a small displacement, and the potential energy is at its minimum. An example of a stable equilibrium is when a cantilever beam is fixed at one end and a weight is attached to the other end. If the weight...
Conformations of Ethane and Propane02:18

Conformations of Ethane and Propane

In an organic molecule, free rotation about the carbon-carbon single bond results in energetically different conformers of the molecule. Due to this rotation, called the internal rotation, ethane has two major conformations — staggered and eclipsed.
Staggered conformation is a low energy and more stable conformation with the C-H bonds on the front carbon placed at 60°dihedral angles relative to the C-H bonds on the back carbon, leading to a reduced torsional strain. In staggered ethane, the...
Chair Conformation of Cyclohexane02:02

Chair Conformation of Cyclohexane

The chair conformation is the most stable form of cyclohexane due to the absence of angle and torsional strain. The absence of angle strain is a result of cyclohexane’s bond angle being very close to the ideal tetrahedral bond angle of 109.5° in its chair conformer. Similarly, the torsional strain is also absent owing to the perfectly staggered arrangement of bonds.
The hydrogen atoms linked to carbons are arranged in two different axial and equatorial orientations to achieve this staggered...
Cooperative Allosteric Transitions01:58

Cooperative Allosteric Transitions

Cooperative allosteric transitions can occur in multimeric proteins, where each subunit of the protein has its own ligand-binding site. When a ligand binds to any of these subunits, it triggers a conformational change that affects the binding sites in the other subunits; this can change the affinity of the other sites for their respective ligands. The ability of the protein to change the shape of its binding site is attributed to the presence of a mix of flexible and stable segments in the...
Cooperative Allosteric Transitions01:58

Cooperative Allosteric Transitions

Cooperative allosteric transitions can occur in multimeric proteins, where each subunit of the protein has its own ligand-binding site. When a ligand binds to any of these subunits, it triggers a conformational change that affects the binding sites in the other subunits; this can change the affinity of the other sites for their respective ligands. The ability of the protein to change the shape of its binding site is attributed to the presence of a mix of flexible and stable segments in the...

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Updated: May 12, 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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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

Persistent topology and metastable state in conformational dynamics.

Huang-Wei Chang1, Sergio Bacallado, Vijay S Pande

  • 1Institute for Computational and Mathematical Engineering, Stanford University, Stanford, California, United States of America. huangwei@stanford.edu

Plos One
|April 9, 2013
PubMed
Summary

This study introduces Multi-Persistent Clustering, a novel tool for analyzing molecular dynamics simulation data. It robustly identifies stable biomolecular states by exploring clustering structures based on scale and density.

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Last Updated: May 12, 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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Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Area of Science:

  • Computational Biology
  • Biophysics
  • Data Science

Background:

  • Molecular dynamics simulations generate vast datasets crucial for understanding biomolecular kinetics.
  • Traditional clustering algorithms struggle with the complex free energy landscapes of biomolecular systems, showing high sensitivity to data perturbations.
  • The Markov state model framework relies heavily on accurate clustering of simulation data.

Purpose of the Study:

  • To develop a robust data-exploratory tool for analyzing molecular dynamics simulation data.
  • To overcome the limitations of common clustering algorithms in handling rugged free energy landscapes.
  • To provide a systematic method for identifying stable and robust clusters representing dominant biomolecular states.

Main Methods:

  • Introduction of Multi-Persistent Clustering analysis, integrating concepts from metastable state dynamics and multi-dimensional persistence in computational topology.
  • Exploration of data clustering structure based on persistence across varying scales and densities.
  • Multi-resolution analysis to reveal relative cluster potential and hierarchical relationships.

Main Results:

  • The Multi-Persistent Clustering analysis systematically identifies clusters robust to data perturbations.
  • Dominant biomolecular states can be identified with increased confidence.
  • The method provides insights into borderline clusters, guiding decisions on further simulation or structural analysis.
  • Hierarchical relationships and relative potentials of clusters are elucidated.

Conclusions:

  • Multi-Persistent Clustering offers a reliable approach for analyzing complex biomolecular simulation data.
  • The method enhances confidence in identifying key biomolecular states from simulation trajectories.
  • This tool aids researchers in making informed decisions regarding simulation parameters and data interpretation.