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 Networks02:26

Protein Networks

4.1K
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.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.1K
Protein-protein Interfaces02:04

Protein-protein Interfaces

13.1K
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
13.1K
Proteomics01:33

Proteomics

7.8K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
7.8K
Protein Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

2.2K
Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...
2.2K

You might also read

Related Articles

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

Sort by
Same author

SARS-CoV-2 infection in female sex workers from Nairobi, Kenya early in the COVID-19 pandemic: Seroincidence and behavioural associations.

PloS one·2026
Same author

SCM-1/SCAMP Maintains Microdomain Boundaries and Cargo Sorting within the Endosomal System.

bioRxiv : the preprint server for biology·2026
Same author

A functional map of the human intrinsically disordered proteome.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Serological Benefit of SARS-CoV-2 Vaccination Relative to Infection in Children With Acute Lymphoblastic Leukemia.

Pediatric blood & cancer·2026
Same author

Stress Granule Sequestration of CCR4-NOT Promotes Poly(A) Lengthening of Stress-Survival Transcripts.

bioRxiv : the preprint server for biology·2026
Same author

SARS-CoV-2 Infection and COVID-19 Vaccine Antibody Responses in Two Canadian Cohorts of Persons Living with HIV.

Antibodies (Basel, Switzerland)·2026
Same journal

Machine Learning-Assisted Label-Free SERS Decoding of Mitochondrial Molecular Dynamics in Ovarian Granulosa Cells during Aging.

Analytical chemistry·2026
Same journal

Revealing the Regulatory Interplay of NHE1 mRNA and Na<sup>+</sup> in Cancer Cells Using a DNA Nanosensor.

Analytical chemistry·2026
Same journal

Towards Cellular Resolution of Tryptic Peptides in Tissue Sections by MALDI MS Imaging: A Focus on Enzyme Application and Reproducibility.

Analytical chemistry·2026
Same journal

Bioinspired Bilayer Hydrogel Colorimetric Sensor Array for Low-Temperature Food Freshness Analysis.

Analytical chemistry·2026
Same journal

Quartz Crystal Microbalance-Based Point-of-Care Testing Systems: Principles, Device Design, and Applications.

Analytical chemistry·2026
Same journal

Heterojunction Gate-Empowered OPECT Aptasensing: A Valid Protocol for Realizing High Current Gain at Low Electron Donor Dependency.

Analytical chemistry·2026
See all related articles

Related Experiment Video

Updated: Sep 4, 2025

Analyzing Dynamic Protein Complexes Assembled On and Released From Biolayer Interferometry Biosensor Using Mass Spectrometry and Electron Microscopy
09:30

Analyzing Dynamic Protein Complexes Assembled On and Released From Biolayer Interferometry Biosensor Using Mass Spectrometry and Electron Microscopy

Published on: August 6, 2018

9.5K

Mapping Protein-Protein Interactions Using Data-Dependent Acquisition without Dynamic Exclusion.

Shen Zhang1, Brett Larsen1, Karen Colwill1

  • 1Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario M5G 1X5, Canada.

Analytical Chemistry
|July 18, 2022
PubMed
Summary
This summary is machine-generated.

We developed turboDDA, a simple method enhancing protein interactome analysis. This approach improves sensitivity and identifies more high-confidence interactors compared to standard data-dependent acquisition and data-independent acquisition methods.

More Related Videos

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization
12:11

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization

Published on: February 27, 2020

7.0K
Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling
11:19

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling

Published on: November 17, 2019

16.3K

Related Experiment Videos

Last Updated: Sep 4, 2025

Analyzing Dynamic Protein Complexes Assembled On and Released From Biolayer Interferometry Biosensor Using Mass Spectrometry and Electron Microscopy
09:30

Analyzing Dynamic Protein Complexes Assembled On and Released From Biolayer Interferometry Biosensor Using Mass Spectrometry and Electron Microscopy

Published on: August 6, 2018

9.5K
Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization
12:11

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization

Published on: February 27, 2020

7.0K
Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling
11:19

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling

Published on: November 17, 2019

16.3K

Area of Science:

  • Proteomics
  • Mass Spectrometry
  • Molecular Interactions

Background:

  • Liquid chromatography-mass spectrometry (LC-MS) is crucial for systematic analysis of affinity-purified samples.
  • Data-independent acquisition (DIA) offers improved reproducibility over data-dependent acquisition (DDA) for protein-protein interaction detection.
  • Current DIA methods face challenges with throughput due to library generation and ongoing optimization of analysis pipelines.

Purpose of the Study:

  • To develop a simple, robust approach, turboDDA, to enhance interactome analysis using spectral counting and DDA.
  • To improve sensitivity and reproducibility in identifying protein interactors.
  • To overcome limitations of existing DDA and DIA methods in proteomics studies.

Main Methods:

  • Development of turboDDA by eliminating the dynamic exclusion (DE) step in DDA.
  • Optimization of acquisition parameters for DDA.
  • Application of turboDDA to interaction and proximity proteomics samples.
  • Comparison with standard DDA (with DE) and DIA (MSPLIT-DIA spectral counting).

Main Results:

  • turboDDA identified 18-71% more interactors compared to standard DDA with DE.
  • turboDDA demonstrated comparable or better sensitivity than DIA spectral counting approaches.
  • The method increased the number of identified high-confidence interactors.

Conclusions:

  • turboDDA offers a significant improvement in sensitivity and interactor identification for proteomics.
  • This approach enhances interactome analysis by optimizing DDA without library generation.
  • turboDDA presents a valuable alternative for high-throughput, sensitive protein interaction studies.