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NMR Spectroscopy and Mass Spectrometry of Aldehydes and Ketones01:15

NMR Spectroscopy and Mass Spectrometry of Aldehydes and Ketones

3.7K
In aldehydes, the hydrogen atom connected to the carbonyl carbon helps distinguish aldehydes from other carbonyl compounds using ¹H NMR spectroscopy. The closeness of aldehydic hydrogen to the electrophilic carbonyl carbon highly deshields the hydrogen atom causing its signal to appear around 10 ppm in the ¹H NMR spectra. α hydrogens split the aldehydic proton signal, which helps identify the number of α hydrogens in the molecule. For instance, one α hydrogen creates a...
3.7K
¹H NMR of Conformationally Flexible Molecules: Temporal Resolution00:52

¹H NMR of Conformationally Flexible Molecules: Temporal Resolution

800
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...
800
NMR Spectroscopy Of Amines01:19

NMR Spectroscopy Of Amines

8.4K
In proton NMR spectroscopy, primary amines and secondary amines showcase their N–H protons as a broad signal in the chemical shift range between δ 0.5 and 5 ppm. The exact position in this range depends on several factors, including sample concentration, hydrogen bonding, and the type of solvent used. Since amine protons undergo fast proton exchange in solution, the protons are labile and therefore do not participate in any splitting with adjacent protons. Thus, the observed peak is...
8.4K
¹H NMR of Conformationally Flexible Molecules: Variable-Temperature NMR01:15

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

1.0K
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.
1.0K
¹H NMR: Complex Splitting01:13

¹H NMR: Complex Splitting

1.2K
A proton M that is coupled to a proton X results in doublet signals for M. However, NMR-active nuclei can be simultaneously coupled to more than one nonequivalent nucleus. When M is coupled to a second proton A, such as in styrene oxide, each peak in the doublet is split into another doublet.
Splitting diagrams or splitting tree diagrams are routinely used to depict such complex couplings. While drawing splitting diagrams, the splitting with the larger coupling constant is usually applied...
1.2K
¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

991
Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
991

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Related Experiment Video

Updated: May 27, 2025

Atomic Scale Structural Studies of Macromolecular Assemblies by Solid-state Nuclear Magnetic Resonance Spectroscopy
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Atomic Scale Structural Studies of Macromolecular Assemblies by Solid-state Nuclear Magnetic Resonance Spectroscopy

Published on: September 17, 2017

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NMR Spectroscopy for the Validation of AlphaFold2 Structures.

Jake Williams1, Isabelle A Gagnon2, Joseph R Sachleben3

  • 1Department of Computer Science, University of Chicago, Chicago, IL.

Biorxiv : the Preprint Server for Biology
|February 20, 2025
PubMed
Summary
This summary is machine-generated.

Hybrid methods combining artificial intelligence (AI) and nuclear magnetic resonance (NMR) spectroscopy can improve protein structure prediction accuracy. This study develops and validates heuristics to integrate AI predictions with experimental NMR data for enhanced structural determination.

Keywords:
AlphaFoldArtificial IntelligenceMachine LearningNMR spectroscopyNOESYSPANRstructure validation

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Area of Science:

  • Biochemistry
  • Structural Biology
  • Computational Biology

Background:

  • AlphaFold revolutionized protein structure prediction using artificial intelligence (AI).
  • Combining AI predictions with experimental data offers potential for higher accuracy and reduced experimental effort.
  • Nuclear Magnetic Resonance (NMR) spectroscopy provides valuable experimental structural information.

Purpose of the Study:

  • To develop and test hybrid computational-experimental methods for protein structure determination.
  • To assess the accuracy of AI-predicted protein structures using experimental NMR data.
  • To establish a framework for integrating AlphaFold predictions with NMR spectra.

Main Methods:

  • Developed heuristics to compare N-edited NOESY spectra with AlphaFold predicted structures.
  • Compiled a dataset linking the Biological Magnetic Resonance Data Bank (BMRB), Protein Data Bank (PDB), and AlphaFold Database.
  • Utilized a support vector machine to evaluate NMR data consistency with predicted structures.

Main Results:

  • Demonstrated the ability of new heuristics to identify inaccurate AlphaFold structures.
  • Established a comprehensive dataset for developing and testing hybrid AI-NMR methods.
  • Successfully applied the developed methods to determine the structure of the engineered protein LoTOP.

Conclusions:

  • Hybrid approaches leveraging AI and NMR spectroscopy can enhance protein structure prediction.
  • The developed heuristics and machine learning models provide a robust framework for validating AI-based structures.
  • This work facilitates more accurate and efficient protein structure determination through integrated computational and experimental strategies.