Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

NMR Spectroscopy: Spin–Spin Coupling01:08

NMR Spectroscopy: Spin–Spin Coupling

3.2K
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...
3.2K
Inverse Trigonometric Functions01:29

Inverse Trigonometric Functions

275
Inverse trigonometric functions are fundamental mathematical tools that reverse the actions of standard trigonometric functions. While trigonometric functions map angles to ratios, inverse trigonometric functions perform the opposite operation by mapping a ratio back to its corresponding angle. These functions are essential in various applications, particularly in determining angles when given specific distances, such as calculating elevation angles in navigation and engineering.For a function...
275
Inverse Hyperbolic Functions and Their Derivatives01:25

Inverse Hyperbolic Functions and Their Derivatives

72
The shape of a suspension bridge cable hanging under its own weight is described by a catenary curve, which is modeled using the hyperbolic cosine function. This mathematical model accurately captures the balance between gravity and tension acting along the cable. When a particular vertical position on the cable is known, the corresponding horizontal position can be determined using the inverse hyperbolic cosine function, allowing for a detailed analysis of the cable's geometry.Inverse...
72
Derivatives of Inverse Trigonometric Functions01:30

Derivatives of Inverse Trigonometric Functions

422
A ship tracking an approaching aircraft relies on geometric measurements to find out the aircraft’s position relative to the observer. By measuring the slant distance to the aircraft and the angle of elevation, the horizontal and vertical components of the distance can be obtained using trigonometric relationships. This geometric approach provides a basis for analyzing how the observed angle changes as the aircraft moves closer to the ship.To examine the mathematical behavior of the angle...
422
Data: Types and Distribution01:19

Data: Types and Distribution

1.8K
In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
1.8K
Hyperbolic and Inverse Hyperbolic Functions: Problem Solving01:30

Hyperbolic and Inverse Hyperbolic Functions: Problem Solving

124
An arched gate can be effectively modeled using a hyperbolic cosine profile because this type of function is smooth and symmetric about the vertical axis. When the arch is centered at the origin, its maximum height occurs at the center point. This symmetry ensures that any height below the crown of the arch is reached at two horizontal positions that are equal in distance from the centerline but lie on opposite sides.To determine where the gate reaches a height of five meters, the height of the...
124

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Direct Determination of Protein Rotational Diffusion Tensors and Generalized Order Parameters from Multifield <sup>15</sup>N NMR Spin Relaxation.

Journal of the American Chemical Society·2026
Same author

Patient and Physician Experiences in Immune Thrombocytopenia.

Advances in therapy·2026
Same author

Acquired Hemophilia A After Neoadjuvant Immunotherapy for Renal Cell Carcinoma.

Rhode Island medical journal (2013)·2026
Same author

A fast sample shuttle to couple high and low magnetic fields and applications in high-resolution relaxometry.

Magnetic resonance (Gottingen, Germany)·2025
Same author

A steady-state approach for analysis of high-resolution relaxometry.

Journal of magnetic resonance (San Diego, Calif. : 1997)·2025
Same author

Evaluating Negative Margins in Foot Amputations for Diabetic Osteomyelitis: How Do We Decide?

Foot & ankle specialist·2025

Related Experiment Video

Updated: Feb 1, 2026

Spin Saturation Transfer Difference NMR SSTD NMR: A New Tool to Obtain Kinetic Parameters of Chemical Exchange Processes
11:44

Spin Saturation Transfer Difference NMR SSTD NMR: A New Tool to Obtain Kinetic Parameters of Chemical Exchange Processes

Published on: November 12, 2016

18.6K

Analysis of NMR Spin-Relaxation Data Using an Inverse Gaussian Distribution Function.

Andrew Hsu1, Fabien Ferrage2, Arthur G Palmer3

  • 1Department of Chemistry, Columbia University, New York, New York.

Biophysical Journal
|December 4, 2018
PubMed
Summary

This study introduces the inverse Gaussian probability distribution function to accurately model macromolecular dynamics using spin relaxation in solution-state NMR spectroscopy. This method enhances understanding of protein conformational dynamics, particularly for intrinsically disordered regions.

More Related Videos

Author Spotlight: Exploring Intrinsically Disordered Protein Dynamics Through NMR Relaxation Experiments
09:25

Author Spotlight: Exploring Intrinsically Disordered Protein Dynamics Through NMR Relaxation Experiments

Published on: November 1, 2024

2.8K
Measuring the Spin-Lattice Relaxation Magnetic Field Dependence of Hyperpolarized [1-13C]pyruvate
11:57

Measuring the Spin-Lattice Relaxation Magnetic Field Dependence of Hyperpolarized [1-13C]pyruvate

Published on: September 13, 2019

7.0K

Related Experiment Videos

Last Updated: Feb 1, 2026

Spin Saturation Transfer Difference NMR SSTD NMR: A New Tool to Obtain Kinetic Parameters of Chemical Exchange Processes
11:44

Spin Saturation Transfer Difference NMR SSTD NMR: A New Tool to Obtain Kinetic Parameters of Chemical Exchange Processes

Published on: November 12, 2016

18.6K
Author Spotlight: Exploring Intrinsically Disordered Protein Dynamics Through NMR Relaxation Experiments
09:25

Author Spotlight: Exploring Intrinsically Disordered Protein Dynamics Through NMR Relaxation Experiments

Published on: November 1, 2024

2.8K
Measuring the Spin-Lattice Relaxation Magnetic Field Dependence of Hyperpolarized [1-13C]pyruvate
11:57

Measuring the Spin-Lattice Relaxation Magnetic Field Dependence of Hyperpolarized [1-13C]pyruvate

Published on: September 13, 2019

7.0K

Area of Science:

  • Biophysical Chemistry
  • Structural Biology
  • Nuclear Magnetic Resonance (NMR) Spectroscopy

Background:

  • Spin relaxation in solution-state NMR is crucial for studying biological macromolecule dynamics.
  • Existing models for spectral density functions often struggle with convergence at zero frequency, especially for nanosecond timescale motions.
  • Accurate modeling of correlation times is essential for interpreting NMR relaxation data.

Purpose of the Study:

  • To derive and validate a new probability distribution function, the inverse Gaussian, for modeling spectral density functions in NMR spin relaxation.
  • To ensure accuracy and convergence of spectral density functions at zero frequency for enhanced analysis of macromolecular dynamics.
  • To apply this new model to understand the conformational dynamics of intrinsically disordered protein regions.

Main Methods:

  • Derivation of the inverse Gaussian probability distribution function using the principle of maximal entropy.
  • Calculation of correlation and spectral density functions based on the derived distribution.
  • Application of the model-free spectral density functions to analyze 15N spin-relaxation data from the bZip transcription factor domain of Saccharomyces cerevisiae protein GCN4.

Main Results:

  • The inverse Gaussian probability distribution function provides spectral density functions that are finite at zero frequency.
  • The model successfully describes distributions of overall or internal correlation times using the model-free ansatz.
  • Analysis of 15N spin-relaxation data revealed insights into the conformational dynamics of the bZip transcription factor domain.

Conclusions:

  • The inverse Gaussian distribution function offers an improved approach for modeling spectral density in NMR spin relaxation studies.
  • This method enhances the characterization of conformational dynamics, particularly for intrinsically disordered protein regions.
  • The findings extend current models for understanding the dynamics of biological macromolecules in solution.