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

π Electron Effects on Chemical Shift: Overview01:27

π Electron Effects on Chemical Shift: Overview

An applied magnetic field causes loosely bound π-electrons in organic molecules to circulate, producing a local or induced diamagnetic field over a large spatial volume. As the molecules tumble in solution, the field generated by π-electrons in spherical substituents results in a zero net field. However, the net field generated by π-electrons in non-spherical substituents is not zero. The effect of this induced field depends on the orientation of the molecule with respect to B0, resulting in...
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...
Chemical Shift: Internal References and Solvent Effects01:17

Chemical Shift: Internal References and Solvent Effects

In an NMR sample, precise measurement of the absolute absorption frequencies of nuclei is difficult. A standard internal reference compound is added, and the frequency difference between the reference signal and sample signals is measured.
The internal reference compound generally used in NMR spectroscopy is tetramethylsilane (TMS). TMS is preferred because it is chemically inert, soluble in NMR solvents, and easily removable. Also, the highly shielded methyl protons in TMS yield an intense...
¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

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

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...

You might also read

Related Articles

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

Sort by
Same author

CASP11 refinement experiments with ROSETTA.

Proteins·2015
Same author

Structure of a designed tetrahedral protein assembly variant engineered to have improved soluble expression.

Protein science : a publication of the Protein Society·2015
Same author

Unique double-ring structure of the peroxisomal Pex1/Pex6 ATPase complex revealed by cryo-electron microscopy.

Proceedings of the National Academy of Sciences of the United States of America·2015
Same author

Mechanistic Analysis of an Engineered Enzyme that Catalyzes the Formose Reaction.

Chembiochem : a European journal of chemical biology·2015
Same author

Design of ordered two-dimensional arrays mediated by noncovalent protein-protein interfaces.

Science (New York, N.Y.)·2015
Same author

Designing Two-Dimensional Protein Arrays through Fusion of Multimers and Interface Mutations.

Nano letters·2015

Related Experiment Video

Updated: May 8, 2026

NMR-Based Fragment Screening in a Minimum Sample but Maximum Automation Mode
09:19

NMR-Based Fragment Screening in a Minimum Sample but Maximum Automation Mode

Published on: June 4, 2021

Improved chemical shift based fragment selection for CS-Rosetta using Rosetta3 fragment picker.

Robert Vernon1, Yang Shen, David Baker

  • 1Department Chemie, Biomolecular NMR and Munich Center for Integrated Protein Science, Technische Universität München, Lichtenbergstrasse 4, 85747, Garching, Germany.

Journal of Biomolecular NMR
|August 27, 2013
PubMed
Summary

A new fragment picker for CS-Rosetta protein structure prediction was developed, improving accuracy and robustness. This tool enhances the selection of protein fragments, leading to more reliable de novo structure predictions even with imperfect data.

More Related Videos

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source
08:35

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source

Published on: May 29, 2021

Workflow and Tools for Crystallographic Fragment Screening at the Helmholtz-Zentrum Berlin
06:29

Workflow and Tools for Crystallographic Fragment Screening at the Helmholtz-Zentrum Berlin

Published on: March 3, 2021

Related Experiment Videos

Last Updated: May 8, 2026

NMR-Based Fragment Screening in a Minimum Sample but Maximum Automation Mode
09:19

NMR-Based Fragment Screening in a Minimum Sample but Maximum Automation Mode

Published on: June 4, 2021

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source
08:35

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source

Published on: May 29, 2021

Workflow and Tools for Crystallographic Fragment Screening at the Helmholtz-Zentrum Berlin
06:29

Workflow and Tools for Crystallographic Fragment Screening at the Helmholtz-Zentrum Berlin

Published on: March 3, 2021

Area of Science:

  • Computational Biology
  • Structural Biology
  • Bioinformatics

Background:

  • CS-Rosetta is a computational tool for protein structure prediction.
  • Previous fragment selection methods, MFR and NNMake, had limitations.
  • De novo structure prediction requires accurate fragment selection.

Purpose of the Study:

  • To develop an improved fragment picker for CS-Rosetta.
  • To combine the strengths of MFR and NNMake.
  • To enhance robustness against noisy or missing data.

Main Methods:

  • Developed a new fragment picker integrating features of MFR and NNMake.
  • Reduced sensitivity to outliers and data inconsistencies.
  • Tested on CS-Rosetta targets and an independent benchmark set.

Main Results:

  • The new fragment picker showed significant improvements on 6 of 23 CS-Rosetta targets.
  • Independent benchmarking on 39 protein targets confirmed significantly improved performance.
  • The protocol is more robust and less prone to bias from poor data.

Conclusions:

  • The new fragment picker enhances CS-Rosetta's de novo protein structure prediction capabilities.
  • Improved fragment selection leads to more reliable structural models.
  • The tool offers a more robust approach for handling challenging NMR datasets.