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Related Concept Videos

¹H NMR: Long-Range Coupling01:27

¹H NMR: Long-Range Coupling

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The coupling interactions of nuclei across four or more bonds are usually weak, with J values less than 1 Hz. While these are usually not observed in spectra, the presence of multiple bonds along the coupling pathway can result in observable long-range coupling.
In alkenes, spin information is communicated via σ–π overlap, as seen in allylic (four-bond) and homoallylic (five-bond) couplings. These coupling interactions are stronger when the σ bond is parallel to the alkene...
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Double Resonance Techniques: Overview01:12

Double Resonance Techniques: Overview

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Double resonance techniques in Nuclear Magnetic Resonance (NMR) spectroscopy involve the simultaneous application of two different frequencies or radiofrequency pulses to manipulate and observe two distinct nuclear spins. One important application of double resonance is spin decoupling, which selectively suppresses coupling with one type of nucleus while observing the NMR signal from another nucleus, simplifying the spectrum and enhancing resolution.
Spin decoupling is usually achieved by...
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Spin–Spin Coupling: Three-Bond Coupling (Vicinal Coupling)01:22

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Vicinal or three-bond coupling is commonly observed between protons attached to adjacent carbons. Here, nuclear spin information is primarily transferred via electron spin interactions between adjacent C‑H bond orbitals. This generally favors the antiparallel arrangement of spins, so 3J values are usually positive.
The extent of coupling depends on the C‑C bond length, the two H‑C‑C angles, any electron-withdrawing substituents, and the dihedral angle between the involved orbitals. The...
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¹H NMR: Interpreting Distorted and Overlapping Signals01:02

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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...
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NMR Spectroscopy: Spin–Spin Coupling01:08

NMR Spectroscopy: Spin–Spin Coupling

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The spin state of an NMR-active nucleus can have a slight effect on its immediate electronic environment. This effect propagates through the intervening bonds and affects the electronic environments of NMR-active nuclei up to three bonds away; occasionally, even farther. This phenomenon is called spin–spin coupling or J-coupling. Coupling interactions are mutual and result in small changes in the absorption frequencies of both nuclei involved. While nuclei of the same element are involved...
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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Identification of biophysical interaction patterns in direct coupling analysis.

Michael Schmidt1, Kay Hamacher1,2,3

  • 1Department of Physics, TU Darmstadt, Karolinenpl. 5, 64289 Darmstadt, Germany.

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|May 19, 2021
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Summary

Direct-coupling analysis improves protein contact prediction by utilizing full field and coupling information, not just the l2 norm. This enhanced statistical learning method leverages more data for greater accuracy.

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Area of Science:

  • Computational biology
  • Statistical learning
  • Protein structure prediction

Background:

  • Direct-coupling analysis (DCA) is a statistical learning method for predicting protein contacts using sequence data.
  • The maximum entropy principle is applied to formulate an inverse Potts model for DCA.
  • Current methods often use the l2 norm of couplings, potentially discarding valuable information.

Purpose of the Study:

  • To investigate whether using the full field and coupling information in direct-coupling analysis improves protein contact prediction accuracy.
  • To demonstrate the limitations of relying solely on the l2 norm for contact prediction.

Main Methods:

  • Applied direct-coupling analysis using an inverse Potts model based on empirical multiple sequence alignments.
  • Fitted local fields and two-body couplings from sequence data.
  • Compared prediction accuracy using the full coupling information versus only the l2 norm of the couplings.

Main Results:

  • The usage of full fields and coupling information significantly improves protein contact prediction accuracy compared to methods relying solely on the l2 norm.
  • Discarding parts of the coupling information leads to a loss of predictive power.

Conclusions:

  • Full utilization of direct-coupling analysis parameters enhances the accuracy of protein contact prediction.
  • The inverse Potts model framework can be optimized by incorporating all derived field and coupling data.