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NMR Spectroscopy of Aromatic Compounds01:14

NMR Spectroscopy of Aromatic Compounds

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Aromatic compounds can be identified or analyzed using proton NMR and carbon‐13 NMR. Typically, aromatic hydrogens or hydrogens directly bonded to the aromatic rings are strongly deshielded by the aromatic ring current. Therefore, they absorb in the range of 6.5–8.0 ppm in proton NMR spectra. For instance, aromatic hydrogens directly bonded to the benzene ring absorb at 7.3 ppm. However, aromatic hydrogens of larger rings absorb farther upfield or downfield than the ideal range.
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NMR Spectrometers: Overview01:20

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NMR spectrometers consist of a strong magnet, a radiofrequency transmitter, and a detector attached to a computer console for recording spectra of samples containing NMR-active nuclei. In first-generation NMR instruments called continuous-wave spectrometers, the resonance frequencies of the nuclei are determined by frequency-sweep or field-sweep methods. The magnetic field strength is fixed and the rf signal is swept in the former, while the radiofrequency signal is fixed and the magnetic field...
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NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences01:17

NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences

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A pulse is a short burst of radio waves distributed over a range of frequencies that simultaneously excites all the nuclei in the sample. Upon passing a radio frequency pulse along the x-axis, the nuclei absorb energy corresponding to their Larmor frequencies and achieve resonance. This shifts the net magnetization vector from the z-axis toward the transverse plane. This angle of rotation of the magnetization vector, or the flip angle, is proportional to the duration and intensity of the pulse.
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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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NMR Spectroscopy: Chemical Shift Overview01:15

NMR Spectroscopy: Chemical Shift Overview

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The position of the absorption signal of a sample is reported relative to the position of the signal of tetramethylsilane (TMS), which is added as an internal reference while recording spectra. The difference between the absorption frequencies of the sample and TMS (in Hz) is divided by the spectrometer operating frequency (in MHz) to obtain a dimensionless quantity called the chemical shift. It is reported on the δ (delta) scale and expressed in parts per million.
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NMR Spectroscopy Of Amines01:19

NMR Spectroscopy Of Amines

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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...
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SpecDB: A relational database for archiving biomolecular NMR spectral data.

Keith J Fraga1, Yuanpeng J Huang2, Theresa A Ramelot2

  • 1Department of Molecular and Cellular Biology, University of California, Davis, CA 95616, USA.

Journal of Magnetic Resonance (San Diego, Calif. : 1997)
|August 5, 2022
PubMed
Summary

Researchers developed SpecDB, a tool for archiving Nuclear Magnetic Resonance (NMR) free-induction decay (FID) data. This system organizes NMR data and metadata, enabling machine learning applications in structural biology.

Keywords:
Biomolecular NMRMachine learningSQLSpectrum database

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Area of Science:

  • Structural Biology
  • Biophysics
  • Computational Biology

Background:

  • Nuclear Magnetic Resonance (NMR) is crucial for determining biomolecular structures, functions, and dynamics.
  • Advancements in machine learning highlight the need for extensive, high-quality datasets in structural biology.
  • Biomolecular NMR generates substantial data, necessitating better organization for machine learning model training and improved analysis.

Purpose of the Study:

  • To create a lightweight, accessible tool for archiving NMR free-induction decay (FID) data at the point of generation.
  • To establish a centralized resource for FID data and associated metadata.
  • To facilitate the development of advanced machine learning methods for NMR data processing and structure determination.

Main Methods:

  • Development of a relational schema, named Spectral Database (SpecDB), for storing NMR sample and FID data metadata.
  • Implementation of SpecDB using SQLite.
  • Creation of a Python software library with a command-line application for database management (creation, organization, querying, backup, sharing, maintenance).

Main Results:

  • SpecDB provides a structured method for archiving and organizing NMR FID data and metadata.
  • The associated Python tools enable efficient database management and data sharing.
  • The system supports the creation of a valuable, shareable resource for the biomolecular NMR community.

Conclusions:

  • SpecDB offers a practical solution for managing NMR time-domain data.
  • The tool and schema empower researchers to store, organize, share, and learn from NMR FID data.
  • This initiative supports the integration of machine learning in biomolecular NMR analysis.