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Related Concept Videos

NMR Spectroscopy: Chemical Shift Overview01:15

NMR Spectroscopy: Chemical Shift Overview

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.
For instance, the proton...
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...
NMR Spectroscopy Of Amines01:19

NMR Spectroscopy Of Amines

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 broad and...
Proton (¹H) NMR: Chemical Shift01:07

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 in a...
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...
¹H NMR Chemical Shift Equivalence: Enantiotopic and Diastereotopic Protons00:58

¹H NMR Chemical Shift Equivalence: Enantiotopic and Diastereotopic Protons

Replacing each alpha-hydrogen in chloroethane by bromine (or a different functional group) yields a pair of enantiomers. Such protons are called prochiral or enantiotopic and are related by a mirror plane. Enantiotopic protons are chemically equivalent in an achiral environment. Because most proton NMR spectra are recorded using achiral solvents, enantiotopic hydrogens yield a single signal.
In chiral compounds such as 2-butanol, replacing the methylene hydrogens at C3 produces a pair of...

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Related Experiment Video

Updated: Jun 7, 2026

Atomic Scale Structural Studies of Macromolecular Assemblies by Solid-state Nuclear Magnetic Resonance Spectroscopy
14:55

Atomic Scale Structural Studies of Macromolecular Assemblies by Solid-state Nuclear Magnetic Resonance Spectroscopy

Published on: September 17, 2017

Protein secondary structure prediction using NMR chemical shift data.

Yuzhong Zhao1, Babak Alipanahi, Shuai Cheng Li

  • 1Department of Computer Science, University of Science and Technology of China, Hefei, Anhui 230027, China. zyzh@mail.ustc.edu.cn

Journal of Bioinformatics and Computational Biology
|October 29, 2010
PubMed
Summary

We developed SUCCES, a new system for predicting protein secondary structure using NMR chemical shifts. It achieves high accuracy and is easily extendable to new protein datasets without retraining.

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RNA Secondary Structure Prediction Using High-throughput SHAPE
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RNA Secondary Structure Prediction Using High-throughput SHAPE

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A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Related Experiment Videos

Last Updated: Jun 7, 2026

Atomic Scale Structural Studies of Macromolecular Assemblies by Solid-state Nuclear Magnetic Resonance Spectroscopy
14:55

Atomic Scale Structural Studies of Macromolecular Assemblies by Solid-state Nuclear Magnetic Resonance Spectroscopy

Published on: September 17, 2017

RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

  • Biochemistry
  • Structural Biology
  • Computational Biology

Background:

  • Accurate protein secondary structure determination is crucial for NMR tertiary structure determination.
  • Existing methods for this task are limited, necessitating systematic investigation of robust and accurate algorithms.

Purpose of the Study:

  • To systematically investigate and develop algorithms for accurate protein secondary structure prediction from chemical shift information.
  • To create a system that is robust for large datasets, easily extendable to new databases, and approaches the limit of prediction accuracy.

Main Methods:

  • Utilized the k-nearest neighbor algorithm for initial secondary structure prediction.
  • Employed the BCJR algorithm to refine predictions and integrate data from chemical shifts and sequence information.

Main Results:

  • The SUCCES system achieved 86% Q(3) accuracy on a dataset of 805 proteins.
  • Accuracy increased to 92.6% when boundary residues were excluded from evaluation.
  • The system demonstrated robustness and ease of extension to new datasets without retraining.

Conclusions:

  • SUCCES significantly improves upon existing methods for protein secondary structure prediction from NMR chemical shifts.
  • The developed system offers high accuracy and adaptability for diverse protein datasets.
  • SUCCES provides a valuable tool for advancing NMR-based tertiary structure determination.