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Related Concept Videos

2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)01:19

2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)

Heteronuclear single-quantum correlation spectroscopy (HSQC) is a 2D NMR technique that reveals one-bond correlations between hydrogen and a heteronucleus. The HSQC experiment is similar to the heteronuclear correlation experiment (HETCOR) but is more sensitive. In the HSQC spectrum, the proton chemical shift is plotted on the horizontal F2 axis, while the 13C chemical shift is plotted on the vertical F1 axis. The corresponding proton and 13C spectra are also shown. The HSQC contour plot does...
2D NMR: Overview of Heteronuclear Correlation Techniques01:18

2D NMR: Overview of Heteronuclear Correlation Techniques

Heteronuclear correlation spectroscopy is an analytical technique that investigates the coupling between different types of nuclei, often a proton and an X-nucleus, such as carbon-13 or nitrogen-15. This method is commonly used in nuclear magnetic resonance (NMR) spectroscopy to gain insights into complex chemical compounds' structural and compositional aspects. A typical heteronuclear correlation spectrum displays X-nucleus chemical shifts on one axis and a proton spectrum on the other axis.
High-Resolution Mass Spectrometry (HRMS)01:15

High-Resolution Mass Spectrometry (HRMS)

The resolution of a mass spectrometer depends on the efficiency of separating ions with different ion masses. The mass of an atom is approximated to the sum of the masses of protons and neutrons inside, considering the masses of protons and neutrons as equal. However, the masses of the proton (1.6726 × 10−24 g) and neutron (1.6749 × 10−24 g) are not truly equal. There is a minor error in the expression of atomic masses relative to the simplest atom of hydrogen. For example, the mass of helium...
Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
¹H NMR: Interpreting Distorted and Overlapping Signals01:02

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 slanted or...
2D NMR: Homonuclear Correlation Spectroscopy (COSY)01:06

2D NMR: Homonuclear Correlation Spectroscopy (COSY)

Homonuclear correlation spectroscopy, or COSY, is a 2-dimensional NMR technique that provides information about coupled protons. Typically, the geminal and vicinal coupling are observed. For example, consider the COSY spectrum of ethyl acetate, where its 1D proton NMR spectrum is plotted along the vertical and horizontal axes with their corresponding chemical shift scale. Three spots on the diagonal corresponding to the three peaks in the 1D proton spectrum are called diagonal peaks. The COSY...

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ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
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A new modeling method in feature construction for the HSQC spectra screening problem.

Hiromi Arai1, Satoru Watanabe, Takanori Kigawa

  • 1Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Yokohama, Japan. arai@es.dis.titech.ac.jp

Bioinformatics (Oxford, England)
|July 8, 2008
PubMed
Summary

This study introduces a novel method for analyzing protein nuclear magnetic resonance (NMR) spectra, improving the automated discrimination of folded from unfolded proteins and reducing data complexity.

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Area of Science:

  • Structural Biology
  • Biophysics
  • Computational Biology

Background:

  • Large-scale biological analyses generate vast datasets requiring automated processing.
  • Protein structure analysis using Nuclear Magnetic Resonance (NMR) high-throughput screening presents significant data analysis challenges.
  • Automated analysis of protein (1)H-(15)N heteronuclear single quantum coherence (HSQC) spectra is crucial but often requires expert interpretation.

Purpose of the Study:

  • To develop an automated method for evaluating NMR HSQC spectra for feature construction.
  • To enhance the discrimination between folded and unfolded protein HSQC spectra using machine learning.
  • To reduce the complexity of input data for subsequent analyses.

Main Methods:

  • A novel model was developed to evaluate HSQC spectra by calculating chemical shift similarity against a random coil peak model.
  • Model-based features were generated for machine learning applications.
  • The performance of model-based features was compared against conventional sequence-based and image recognition features.

Main Results:

  • The proposed method demonstrated sufficient discrimination power for classifying folded versus unfolded protein HSQC spectra.
  • Model-based features exhibited less overfitting on training data compared to conventional methods.
  • The approach successfully reduced the complexity of input data for further investigation.

Conclusions:

  • The developed model-based feature construction method offers a robust and less overfitting alternative for analyzing protein HSQC spectra.
  • This approach facilitates more accurate and efficient automated screening in high-throughput protein structure analysis.
  • The method contributes to advancing computational approaches in structural biology and biophysics.