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Applications Of NMR In Biology

Applications Of NMR In Biology

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.

¹H NMR Signal Integration: Overview

¹H NMR Signal Integration: Overview

The intensity of a signal, which can be represented by the area under the peak, depends on the number of protons contributing to that signal. The area under each peak is shown as a vertical line called an integral, with the integral value listed under it, as seen in the proton NMR spectrum of benzyl acetate. Each integral value is divided by the smallest integral value to obtain the ratio of the number of protons producing each signal. The ratio reveals the relative number of protons and not...

Proton (¹H) NMR: Chemical Shift

Proton (¹H) NMR: Chemical Shift

Organic molecules primarily contain carbon and hydrogen atoms. While all the hydrogen isotopes are NMR-active, protium or hydrogen-1 is the most abundant. It has a significant energy separation between its nuclear spin states due to its large gyromagnetic ratio. As per Boltzmann's distribution, an increase in the energy separation implies a greater excess population of nuclei available for excitation, resulting in a strong NMR absorption signal.
Absorption signals of all the protium nuclei...

¹H NMR: Interpreting Distorted and Overlapping Signals

¹H NMR: Interpreting Distorted and Overlapping Signals

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...

¹³C NMR: ¹H–¹³C Decoupling

¹³C NMR: ¹H–¹³C Decoupling

The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
A broadband decoupling technique is used to simplify these complex, sometimes overlapping, signals. Broadband decoupling relies on a...

NMR Spectroscopy and Mass Spectrometry of Aldehydes and Ketones

NMR Spectroscopy and Mass Spectrometry of Aldehydes and Ketones

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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Related Experiment Video

Updated: Jun 23, 2025

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Published on: November 29, 2024

Explainable AI to Facilitate Understanding of Neural Network-Based Metabolite Profiling Using NMR Spectroscopy.

Hayden Johnson¹, Aaryani Tipirneni-Sajja¹

¹Magnetic Resonance Imaging and Spectroscopy Lab, Department of Biomedical Engineering, The University of Memphis, Memphis, TN 38152, USA.

|June 26, 2024

Summary

Explainable AI methods like integrated gradients help understand how neural networks quantify metabolites from NMR spectra. This approach reveals how NNs interpret spectral data, improving trust and debugging for metabolomics applications.

Keywords:

NMR metabolomics NMR spectroscopy explainable AI integrated gradients machine learning metabolite profiling neural networks

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Last Updated: Jun 23, 2025

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Area of Science:

Analytical Chemistry
Computational Biology
Artificial Intelligence

Background:

Neural networks (NNs) offer rapid metabolite quantification from NMR spectra.
The "black box" nature of NNs hinders understanding of their predictive mechanisms.
Explainable AI (XAI) is crucial for interpreting complex model behaviors.

Purpose of the Study:

To implement and validate the integrated gradients (IG) algorithm for explaining NN predictions in NMR metabolomics.
To elucidate spectral regions critical for metabolite quantification by NNs.
To enhance the transparency and reliability of NN-based metabolomics.

Main Methods:

Application of the integrated gradients (IG) XAI algorithm to NN models.
Validation using simulated NMR spectra of aqueous metabolites.
Testing on experimentally acquired lipid spectra from a reference standard and murine hepatic extract.

Main Results:

IG successfully identified spectral regions (line-shapes, amplitudes, frequencies) used by NNs for quantification.
NNs were shown to handle peak overlap and prioritize key resonances.
The study demonstrated how training data influences NN decision-making and aids in debugging.

Conclusions:

The IG technique provides visual and quantitative insights into NN-NMR metabolomics.
This explainability can increase confidence in NN applications for automated and targeted metabolomics.
XAI facilitates a deeper understanding of NN performance and potential biases.