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Related Concept Videos

Applications Of NMR In Biology01:25

Applications Of NMR In Biology

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Nuclear magnetic resonance (NMR) spectroscopy is a very valuable analytical technique for researchers. It has been used for more than 50 years as an analytical tool. F. Bloch and E. Purcell formulated NMR in 1946 and won the 1952 Nobel Prize in Physics  for their work. Biological macromolecules such as proteins, nucleic acids, lipids, and organic molecules including pharmaceutical compounds, can be studied using this versatile tool that exploits the magnetic properties of certain nuclei.
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Nuclear Magnetic Resonance (NMR): Overview01:07

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Nuclear magnetic resonance (NMR) is a phenomenon exhibited by certain nuclei that can absorb characteristic radio frequency radiation under certain conditions. NMR has been extensively applied in molecular spectroscopy and medical diagnostic imaging. In both these applications, the molecule or subject under study is placed in a magnetic field and irradiated with radio frequency energy.
NMR spectroscopy generates a spectrum where the characteristic absorption frequencies of the sample are...
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NMR Spectroscopy and Mass Spectrometry of Aldehydes and Ketones01:15

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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...
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nmRanalysis: An Open-Source Web Application for Semi-automated NMR Metabolite Profiling.

Javier E Flores1, Anastasiya V Prymolenna2, Logan A Lewis1

  • 1Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.

Analytical Chemistry
|March 25, 2025
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Summary

This study introduces nmRanalysis, a web application automating nuclear magnetic resonance (NMR) spectral profiling. It integrates existing tools and adds a machine-learning recommender for metabolite identification, overcoming workflow bottlenecks.

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Area of Science:

  • Analytical Chemistry
  • Biochemistry
  • Computational Biology

Background:

  • Nuclear Magnetic Resonance (NMR) spectral data acquisition and preprocessing are automated.
  • Downstream spectral profiling remains a bottleneck in NMR analysis workflows.
  • Existing solutions for profiling automation often introduce other limitations.

Purpose of the Study:

  • To develop a user-friendly web application, nmRanalysis, for automated NMR spectral profiling.
  • To integrate existing profiling tools and introduce novel features to enhance the NMR analysis workflow.
  • To address the bottleneck in downstream NMR data processing.

Main Methods:

  • Development of a web application integrating multiple NMR spectral profiling tools.
  • Implementation of a machine-learning-driven recommender system for metabolite identification.
  • User-friendly interface design for streamlined analysis.

Main Results:

  • nmRanalysis provides a more automated spectral profiling workflow.
  • The application successfully integrates strengths of existing tools.
  • The novel recommender system enhances metabolite identification accuracy and efficiency.

Conclusions:

  • nmRanalysis offers a significant improvement over individual profiling tools.
  • The application effectively mitigates the downstream processing bottleneck in NMR analysis.
  • nmRanalysis enhances the overall utility and automation of NMR data analysis.