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¹H NMR of Conformationally Flexible Molecules: Temporal Resolution

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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¹H NMR of Conformationally Flexible Molecules: Variable-Temperature NMR

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The axial and equatorial protons in cyclohexane can be distinguished by performing a variable-temperature NMR experiment. In this process, except for one proton, the remaining eleven protons are replaced by deuterium. The deuterium substitution avoids the possible peak splitting caused by the spin-spin coupling between the adjacent protons. The remaining proton flips between the axial and equatorial positions.

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Updated: May 6, 2026

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

Network visualization of conformational sampling during molecular dynamics simulation.

Logan S Ahlstrom¹, Joseph Lee Baker, Kent Ehrlich

¹Department of Chemistry and Biochemistry, University of Arizona, Tucson, AZ 85721, USA.

Journal of Molecular Graphics & Modelling

|November 12, 2013

Summary

This summary is machine-generated.

Network analysis effectively visualizes molecular dynamics simulation data, revealing conformational states and their connections. This method offers a clearer understanding of molecular behavior than traditional clustering alone.

Keywords:

Clustering Conformational sampling Molecular dynamics simulation Network visualization Principal component analysis

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Last Updated: May 6, 2026

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Area of Science:

Computational Biology
Biophysics
Data Science

Background:

Molecular dynamics (MD) simulations generate large datasets.
Analyzing conformational changes in biomolecules requires effective data reduction.
Traditional clustering methods group similar states but may obscure relationships between them.

Purpose of the Study:

To introduce network analysis as a superior method for interpreting MD simulation data.
To visualize dominant conformational states and their connectivity.
To provide a more coherent description of molecular conformational space.

Main Methods:

Network analysis applied to MD trajectory data.
Comparison with 11 clustering algorithms.
Comparison with principal component conformer plots.

Main Results:

Network analysis successfully visualizes dominant conformational states.
The connectivity between states is clearly depicted.
Network visualization provides a more coherent description of conformational space compared to clustering alone.

Conclusions:

Network analysis is an effective tool for understanding molecular conformational dynamics.
This approach enhances the interpretation of MD simulations, particularly for proteins.
Network visualization aids in reaching functional conclusions from simulation data.