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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...

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Updated: May 15, 2026

Visualizing the Conformational Dynamics of Membrane Receptors Using Single-Molecule FRET
10:59

Visualizing the Conformational Dynamics of Membrane Receptors Using Single-Molecule FRET

Published on: August 17, 2022

Extracting conformational memory from single-molecule kinetic data.

Steve Pressé1, Julian Lee, Ken A Dill

  • 1Department of Physics, Indiana University-Purdue University, Indianapolis, Indiana, USA.

The Journal of Physical Chemistry. B
|December 25, 2012
PubMed
Summary
This summary is machine-generated.

The non-Markov memory kernel (NMMK) method extracts kinetic models from single-molecule data without assuming Markovian dynamics. This approach directly uses data to build a unique, error-bounded model, revealing conformational memory for mechanistic insights.

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Last Updated: May 15, 2026

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15N CPMG Relaxation Dispersion for the Investigation of Protein Conformational Dynamics on the µs-ms Timescale

Published on: April 19, 2021

Area of Science:

  • Biophysics
  • Physical Chemistry
  • Computational Biology

Background:

  • Single-molecule experiments generate stochastic time trajectories requiring kinetic model extraction.
  • Traditional methods often assume discrete states and Markovian transitions, which may oversimplify complex biological systems.
  • Apparent states in experimental data can represent ensembles or hide interconverting substates.

Purpose of the Study:

  • To introduce a generalized method for kinetic model extraction from single-molecule data.
  • To overcome limitations of traditional Markovian and discrete-state models.
  • To enable direct data-driven model selection and reveal non-Markovian dynamics.

Main Methods:

  • Developed the non-Markov memory kernel (NMMK) method.
  • Adapted a maximum entropy-based image reconstruction approach.
  • Applied NMMK to analyze conformational memory in single-molecule trajectories.

Main Results:

  • NMMK yields a unique kinetic model directly from data, including error bars.
  • The method does not assume Markov dynamics, accommodating complex state behaviors.
  • NMMK effectively utilizes entire datasets, avoiding data wastage.
  • Extracted conformational memory provides mechanistic insights.

Conclusions:

  • The NMMK method offers a more general and data-driven approach to kinetic modeling of single-molecule trajectories.
  • It accurately captures non-Markovian dynamics and provides valuable mechanistic information.
  • NMMK represents a significant advancement over traditional methods for analyzing complex stochastic processes.