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Related Concept Videos

¹H NMR Signal Integration: Overview00:58

¹H NMR Signal Integration: Overview

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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...
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Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule01:10

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In the AX proton spin system, proton A can sense the two spin states of a coupled proton X, resulting in a doublet NMR signal with two peaks of equal (1:1) intensity. When proton A is coupled to two equivalent protons (AX2 spin system), the spin states of each X can be aligned with or against the external field, creating three possible scenarios. This results in a 1:2:1  triplet signal, where the central peak corresponds to the chemical shift of A and is twice as large or intense as the...
2.9K
¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

1.3K
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...
1.3K
¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

1.7K
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...
1.7K
NMR Spectrometers: Resolution and Error Correction01:14

NMR Spectrometers: Resolution and Error Correction

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When magnetic nuclei in a sample achieve resonance and undergo relaxation, the signal detected in NMR is an approximately exponential free induction decay. Fourier transform of an exponential decay yields a Lorentzian peak in the frequency domain. Lorentzian peaks in an NMR spectrum are defined by their amplitude, full width at half maximum, and position, where the peak width is governed by the spin-spin relaxation time alone. In real experiments, however, the applied magnetic field is rendered...
999
¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

1.3K
When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
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Paramagnetic Relaxation Enhancement for Detecting and Characterizing Self-Associations of Intrinsically Disordered Proteins
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Bayesian peak picking for NMR spectra.

Yichen Cheng1, Xin Gao2, Faming Liang1

  • 1Department of Statistics, Texas A&M University, College Station, TX 77843, USA.

Genomics, Proteomics & Bioinformatics
|November 5, 2013
PubMed
Summary
This summary is machine-generated.

This study automates nuclear magnetic resonance (NMR) peak picking for protein structure determination. A novel Bayesian method accurately identifies true peaks in NMR spectra, advancing structural genomics.

Keywords:
Markov chain Monte CarloNuclear magnetic resonancePeak picking

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Area of Science:

  • Structural biology and genomics
  • Biophysics and computational biology

Background:

  • Protein structure determination is crucial for understanding biological functions.
  • Nuclear Magnetic Resonance (NMR) is a key technique for in vivo protein structure determination.
  • Automating the peak picking step in NMR is essential for efficiency and accuracy.

Purpose of the Study:

  • To develop an automated method for the critical peak picking step in NMR structure determination.
  • To address the challenges of distinguishing true peaks from false positives in NMR spectra.

Main Methods:

  • Modeling NMR spectra using a mixture of bivariate Gaussian densities.
  • Employing a stochastic approximation Monte Carlo algorithm within a Bayesian framework.
  • Framing peak picking as a variable selection problem.

Main Results:

  • The proposed Bayesian method automatically distinguishes true peaks from false ones.
  • The approach eliminates the need for data preprocessing.
  • This represents the first application of Bayesian methods to NMR spectrum peak picking.

Conclusions:

  • Automated peak picking using Bayesian inference is feasible and effective for NMR data.
  • This method enhances the accuracy and efficiency of protein structure determination.
  • The technique offers a significant advancement in structural genomics workflows.