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

Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

7.9K
Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein....
7.9K
Regulated Protein Degradation02:58

Regulated Protein Degradation

7.9K
It is vital to regulate the activity of enzymatic as well as non-enzymatic proteins inside the cell. This can be achieved either through creating a balance between their rate of synthesis and degradation or regulating the intrinsic activity of the protein. Both these regulation mechanisms play an essential role in the normal functioning of cells.
Protein degradation plays two important roles in the cells. It helps to protect cells from misfolded or damaged proteins before they lead to a...
7.9K
The Proteasome01:13

The Proteasome

1.2K
Eukaryotic cells can degrade proteins through several pathways. One of the most important among these is the ubiquitin-proteasome pathway. It helps the cell eliminate the misfolded, damaged, or unwarranted cytoplasmic proteins in a highly specific manner.
In this pathway, the target proteins are first tagged with small proteins called ubiquitin. This involves participation of a series of enzymes including— E1 (ubiquitin-activating enzyme), E2 (ubiquitin-conjugating enzyme), and E3...
1.2K
Ligand Binding Sites02:40

Ligand Binding Sites

14.2K
Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
14.2K
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

5.1K
Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
5.1K
Oligosaccharide Assembly01:24

Oligosaccharide Assembly

3.1K
Protein glycosylation starts in the ER lumen and continues in the Golgi apparatus. Glycosyltransferases catalyze the addition of sugar molecules or glycosylation of proteins. Usually, these enzymes add sugars to the hydroxyl groups of selected serine or threonine residues to form O-linked glycans or the amino groups of asparagine residues to form N-linked glycans. Different positions on the same polypeptide chain can contain differently linked glycans.
Multiple sugar molecules that may or may...
3.1K

You might also read

Related Articles

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

Sort by
Same author

Cardiorespiratory Fitness Reference Standards and Prognostic Stratification in Chinese Patients With Cardiovascular Disease.

JACC. Asia·2026
Same author

An Interpretable Deep Learning Framework Leveraging RNA Foundation Model and Capsule Networks for Accurate Prediction of RNA 2'-O-Methylation Sites.

Journal of chemical information and modeling·2026
Same author

Photovoltaic power prediction and fault diagnosis method based on LSTM and transformer.

Scientific reports·2026
Same author

3D-Printing Starfish-Inspired Gas-Evolving Electrode Scaffolds Enable Ampere-Level Alkaline Water Electrolysis.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Integrative multi-omics combined with molecular docking and molecular dynamics simulations elucidates the mechanism driving the transition from green notes to fruity aroma during musang king durian ripening.

Food research international (Ottawa, Ont.)·2026
Same author

EnAcrPred: A robust ensemble machine learning framework for identifying anti-CRISPR proteins.

Protein science : a publication of the Protein Society·2026

Related Experiment Video

Updated: Oct 16, 2025

Evaluation of Substrate Ubiquitylation by E3 Ubiquitin-ligase in Mammalian Cell Lysates
09:47

Evaluation of Substrate Ubiquitylation by E3 Ubiquitin-ligase in Mammalian Cell Lysates

Published on: May 10, 2022

2.8K

A representation and deep learning model for annotating ubiquitylation sentences stating E3 ligase - substrate

Mengqi Luo1,2, Zhongyan Li3, Shangfu Li1

  • 1Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, China.

BMC Bioinformatics
|October 19, 2021
PubMed
Summary
This summary is machine-generated.

Researchers developed a deep learning model to automatically identify E3 ligase-substrate interactions in ubiquitylation research. This tool aids in analyzing vast biomedical literature, accelerating discoveries in disease-related ubiquitylation processes.

Keywords:
Deep learningE3 ligaseInformation representationNatural language processingText miningUbiquitylation sentences annotation

More Related Videos

In Vitro Analysis of E3 Ubiquitin Ligase Function
06:06

In Vitro Analysis of E3 Ubiquitin Ligase Function

Published on: May 14, 2021

5.5K
Functional Characterization of RING-Type E3 Ubiquitin Ligases In Vitro and In Planta
10:27

Functional Characterization of RING-Type E3 Ubiquitin Ligases In Vitro and In Planta

Published on: December 5, 2019

9.1K

Related Experiment Videos

Last Updated: Oct 16, 2025

Evaluation of Substrate Ubiquitylation by E3 Ubiquitin-ligase in Mammalian Cell Lysates
09:47

Evaluation of Substrate Ubiquitylation by E3 Ubiquitin-ligase in Mammalian Cell Lysates

Published on: May 10, 2022

2.8K
In Vitro Analysis of E3 Ubiquitin Ligase Function
06:06

In Vitro Analysis of E3 Ubiquitin Ligase Function

Published on: May 14, 2021

5.5K
Functional Characterization of RING-Type E3 Ubiquitin Ligases In Vitro and In Planta
10:27

Functional Characterization of RING-Type E3 Ubiquitin Ligases In Vitro and In Planta

Published on: December 5, 2019

9.1K

Area of Science:

  • Biochemistry
  • Bioinformatics
  • Computational Biology

Background:

  • Ubiquitylation is a crucial post-translational modification regulating cellular processes and implicated in various diseases.
  • Identifying E3 ligase-substrate interactions is key to understanding ubiquitylation, but the growing volume of biomedical literature presents a data access challenge.
  • Automatic annotation of E3-substrate interactions from literature is essential for efficient research progression.

Purpose of the Study:

  • To develop an automated model for annotating E3 ligase-substrate interaction sentences within biomedical literature.
  • To leverage deep learning and natural language processing for accurate identification of these interactions.
  • To create a valuable resource for researchers studying ubiquitylation.

Main Methods:

  • Utilized representation and attention mechanism-based deep learning methods.
  • Applied natural language processing techniques to sentences containing E3 proteins.
  • Employed a Long Short-Term Memory (LSTM)-based deep learning classifier.
  • Constructed a manual corpus of E3-substrate interaction sentences with labeled proteins.

Main Results:

  • The proposed deep learning model effectively annotates E3-substrate interaction sentences.
  • The attention mechanism-based deep learning approach demonstrated superior performance compared to statistical machine learning methods.
  • The developed model and corpus serve as valuable resources for ubiquitylation research.

Conclusions:

  • An electronic manual corpus of E3-substrate interaction sentences facilitates text mining and machine learning.
  • Deep learning and semantic representation significantly enhance automatic annotation of ubiquitylation sentences.
  • The model enables faster information retrieval and aids in identifying key E3 ligase substrates for further study.