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

Protein Folding Quality Check in the RER01:29

Protein Folding Quality Check in the RER

3.7K
ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...
3.7K

You might also read

Related Articles

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

Sort by
Same author

A Computational Model for Determining Labeling Duration in Protein Turnover Studies Using a Single Deuterated Water Labeled Sample.

Journal of the American Society for Mass Spectrometry·2026
Same author

Proteostasis Modelling using Deuterated Water Metabolic Labeling and Data-Independent Acquisition Mass Spectrometry.

bioRxiv : the preprint server for biology·2025
Same author

Duplexing metabolic deuterated water-labeled samples using dimethyl labeling to estimate protein turnover rates.

Communications chemistry·2025
Same author

Turnover Rates and Numbers of Exchangeable Hydrogens in Deuterated Water Labeled Samples.

International journal of molecular sciences·2025
Same author

Numbers of Exchangeable Hydrogens from LC-MS Data of Heavy Water Metabolically Labeled Samples.

Journal of the American Society for Mass Spectrometry·2024
Same author

Exact Integral Formulas for False Discovery Rate and the Variance of False Discovery Proportion.

Journal of proteome research·2024

Related Experiment Video

Updated: Jul 11, 2025

Measurement of Protein Turnover Rates in Senescent and Non-Dividing Cultured Cells with Metabolic Labeling and Mass Spectrometry
08:52

Measurement of Protein Turnover Rates in Senescent and Non-Dividing Cultured Cells with Metabolic Labeling and Mass Spectrometry

Published on: April 6, 2022

3.5K

Flexible Quality Control for Protein Turnover Rates Using d2ome.

Henock M Deberneh1, Rovshan G Sadygov1

  • 1Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, TX 77555-1068, USA.

International Journal of Molecular Sciences
|November 14, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces d2ome, a bioinformatics tool for estimating protein turnover rates. It offers flexible filtering to improve accuracy in high-throughput LC-MS data analysis.

Keywords:
heavy water metabolic labelingisotope profileslabel incorporationprotein turnoverretention time alignment

More Related Videos

In Vivo Quantification of Protein Turnover in Aging C. Elegans using Photoconvertible Dendra2
09:45

In Vivo Quantification of Protein Turnover in Aging C. Elegans using Photoconvertible Dendra2

Published on: June 13, 2020

6.3K
Studying the Protein Quality Control System of D. discoideum Using Temperature-controlled Live Cell Imaging
06:09

Studying the Protein Quality Control System of D. discoideum Using Temperature-controlled Live Cell Imaging

Published on: December 2, 2016

7.1K

Related Experiment Videos

Last Updated: Jul 11, 2025

Measurement of Protein Turnover Rates in Senescent and Non-Dividing Cultured Cells with Metabolic Labeling and Mass Spectrometry
08:52

Measurement of Protein Turnover Rates in Senescent and Non-Dividing Cultured Cells with Metabolic Labeling and Mass Spectrometry

Published on: April 6, 2022

3.5K
In Vivo Quantification of Protein Turnover in Aging C. Elegans using Photoconvertible Dendra2
09:45

In Vivo Quantification of Protein Turnover in Aging C. Elegans using Photoconvertible Dendra2

Published on: June 13, 2020

6.3K
Studying the Protein Quality Control System of D. discoideum Using Temperature-controlled Live Cell Imaging
06:09

Studying the Protein Quality Control System of D. discoideum Using Temperature-controlled Live Cell Imaging

Published on: December 2, 2016

7.1K

Area of Science:

  • Proteomics
  • Bioinformatics
  • Systems Biology

Background:

  • Estimating in vivo protein turnover rates is crucial for understanding cellular dynamics.
  • High-throughput analysis of mammalian proteomes using LC-MS data requires robust quantification.
  • Current bioinformatics tools often use stringent, inflexible filtering criteria for peptide selection.

Purpose of the Study:

  • To develop a computational tool, d2ome, with flexible error control and filtering for accurate protein turnover rate estimation.
  • To improve the automation of protein turnover rate calculations from LC-MS data.
  • To reduce the impact of signal fluctuations and interferences in complex proteomic datasets.

Main Methods:

  • Implementation of flexible error control and filtering measures in the d2ome software.
  • Utilizing spectral properties and signal features for error assessment.
  • Processing LC-MS data from heavy water labeled mammalian proteome samples.

Main Results:

  • The d2ome tool enables automated estimation of protein turnover rates.
  • Flexible filtering reduced the standard deviation of estimated turnover rates.
  • Confidence intervals for turnover rates were significantly tightened, indicating improved precision.

Conclusions:

  • The d2ome approach provides a more adaptable and precise method for calculating protein turnover rates.
  • Flexible error control enhances the reliability of high-throughput proteomic analyses.
  • This tool facilitates a deeper understanding of protein dynamics in biological systems.