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

Intrinsically Disordered Proteins02:18

Intrinsically Disordered Proteins

17.7K
Intrinsically disordered proteins are a group of proteins that do not fold into specific three-dimensional structures. Their structural flexibility allows them to complement ordered proteins to perform functions that are inaccessible to rigid structures. They are more common in eukaryotes than prokaryotes and may either be exclusively intrinsically disordered or hybrid proteins, consisting of a mix of ordered and disordered regions. The absence of a rigid structure in these proteins can be...
17.7K
Conserved Binding Sites01:49

Conserved Binding Sites

4.2K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
4.2K
Protein Folding01:25

Protein Folding

7.8K
Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
Protein Structure Is Critical to Its Biological Function
Proteins perform a wide range of biological functions such as catalyzing chemical reactions, providing...
7.8K
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

10.8K
Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
10.8K
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.5K
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...
12.5K
Protein Organization01:24

Protein Organization

6.3K
Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....
6.3K

You might also read

Related Articles

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

Sort by
Same author

Beyond processed EEG: Towards brain-informed anaesthesia.

Journal of clinical monitoring and computing·2026
Same author

Pathogenic variations illuminate functional constraints in intrinsically disordered proteins.

iScience·2026
Same author

Toward a unified framework for determining conformational ensembles of disordered proteins.

Nature methods·2026
Same author

The non-catalytic DNA polymerase ε subunit is an NPF motif recognition protein.

Nature communications·2025
Same author

Protocol of the exploratory prospective observational PEAK study: PrEcision medicine in the management of cardiovascular surgery associated Acute Kidney Injury (AKI).

BMJ open·2025
Same author

AIUPred - Binding: Energy Embedding to Identify Disordered Binding Regions.

Journal of molecular biology·2025

Related Experiment Video

Updated: Jun 7, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.0K

Deep learning for intrinsically disordered proteins: From improved predictions to deciphering conformational

Gábor Erdős1, Zsuzsanna Dosztányi1

  • 1Department of Biochemistry, Eötvös Loránd University, Pázmány Péter stny 1/c, Budapest H-1117, Hungary.

Current Opinion in Structural Biology
|November 10, 2024
PubMed
Summary

Deep learning is advancing predictions for intrinsically disordered proteins (IDPs), which lack stable structures. This review covers new methods and data efforts crucial for understanding protein disorder from sequence data.

More Related Videos

Paramagnetic Relaxation Enhancement for Detecting and Characterizing Self-Associations of Intrinsically Disordered Proteins
07:24

Paramagnetic Relaxation Enhancement for Detecting and Characterizing Self-Associations of Intrinsically Disordered Proteins

Published on: September 23, 2021

1.7K
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

1.8K

Related Experiment Videos

Last Updated: Jun 7, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.0K
Paramagnetic Relaxation Enhancement for Detecting and Characterizing Self-Associations of Intrinsically Disordered Proteins
07:24

Paramagnetic Relaxation Enhancement for Detecting and Characterizing Self-Associations of Intrinsically Disordered Proteins

Published on: September 23, 2021

1.7K
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

1.8K

Area of Science:

  • Biochemistry
  • Computational Biology
  • Bioinformatics

Background:

  • Intrinsically disordered proteins (IDPs) lack stable 3D structures, posing challenges for traditional prediction methods.
  • Deep learning has transformed protein structure prediction for globular proteins.

Purpose of the Study:

  • To review the impact of deep learning on predicting protein disorder.
  • To highlight community efforts in data curation and benchmarking.
  • To discuss novel machine learning methods for characterizing protein conformational ensembles.

Main Methods:

  • Review of deep learning methodologies applied to protein disorder prediction.
  • Analysis of community-driven data resources and performance assessments.
  • Exploration of novel machine learning techniques for sequence-based conformational analysis.

Main Results:

  • Deep learning approaches show significant promise in improving protein disorder predictions.
  • Community efforts are vital for data standardization and evaluating state-of-the-art methods.
  • New machine learning models enable direct characterization of protein conformational ensembles from sequence.

Conclusions:

  • Deep learning is a powerful tool for advancing the study of intrinsically disordered proteins.
  • Continued community collaboration is essential for progress in protein disorder research.
  • Machine learning offers new avenues for understanding protein dynamics and function directly from sequence.