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

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

10.9K
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...
10.9K
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 Networks02:26

Protein Networks

4.0K
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,...
4.0K
Conservation of Protein Domains02:26

Conservation of Protein Domains

3.1K
3.1K
Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

1.9K
1.9K
Protein and Protein Structure02:15

Protein and Protein Structure

79.6K
Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
A protein's shape is critical to its function. For example, an enzyme...
79.6K

You might also read

Related Articles

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

Sort by
Same author

ProSiteHunter: A Unified Framework for Sequence-Based Prediction of Protein-Nucleic Acid and Protein-Protein Binding Sites.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

On the state of protein function prediction: a report on the fourth CAFA challenge.

bioRxiv : the preprint server for biology·2026
Same author

When cryo-EM modeling meets structure prediction.

Nature structural & molecular biology·2026
Same author

High-accuracy protein complex structure modeling based on sequence-derived structure complementarity.

Nature communications·2025
Same author

MultiSAAl: Sequence-Informed Antibody-Antigen Interaction Prediction Using Multiscale Deep Learning.

Journal of chemical information and modeling·2025
Same author

Highlights of Model Quality Assessment in CASP16.

Proteins·2025
Same journal

Ciliary flow and morphology shape mass transport at the surface and within gastrovascular cavities of black corals.

Communications biology·2026
Same journal

Virus-mediated prokaryotic community adaptation dynamics under thermal stress in municipal organic solid waste microbiomes.

Communications biology·2026
Same journal

Multi-omics insights into the woolly trait of Saussurea medusa and the plant's coordinated regulation of flavonoid biosynthesis.

Communications biology·2026
Same journal

Loss contexts enhance dorsolateral prefrontal interpersonal neural synchrony during successful human deceptive recommendations.

Communications biology·2026
Same journal

Neuro-regulator role of H<sub>2</sub>S in astrocyte activation and its effects on neurological damage and behavior of VPA-exposed rats.

Communications biology·2026
Same journal

Temporal orchestration of transcriptional and epigenomic programming underlying maternal embryonic diapause in a cricket model.

Communications biology·2026
See all related articles

Related Experiment Video

Updated: Jul 9, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

1.9K

Multi-domain and complex protein structure prediction using inter-domain interactions from deep learning.

Yuhao Xia1, Kailong Zhao1, Dong Liu1

  • 1College of Information Engineering, Zhejiang University of Technology, HangZhou, 310023, China.

Communications Biology
|December 1, 2023
PubMed
Summary
This summary is machine-generated.

DeepAssembly enhances protein structure prediction for multi-domain proteins and complexes. This new method improves accuracy over AlphaFold2, aiding drug design and understanding protein function.

More Related Videos

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

68.7K
Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
09:51

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

15.5K

Related Experiment Videos

Last Updated: Jul 9, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

1.9K
A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

68.7K
Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
09:51

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

15.5K

Area of Science:

  • Structural Biology
  • Computational Biology
  • Drug Discovery

Background:

  • Accurate protein structure modeling is crucial for understanding biological functions and developing structure-based drugs.
  • While AlphaFold2 excels at single-domain protein structure prediction, challenges remain in modeling multi-domain proteins and protein complexes.
  • Existing methods struggle with the accurate assembly of multiple protein domains and the prediction of complex structures.

Purpose of the Study:

  • To develop a novel computational protocol, DeepAssembly, for accurate structure modeling of multi-domain proteins and protein complexes.
  • To improve upon existing structure prediction methods, particularly AlphaFold2, in handling multi-domain and complex protein assemblies.
  • To provide a more efficient approach for predicting protein complex structures by utilizing domain-level assembly.

Main Methods:

  • Developed DeepAssembly, a protocol integrating domain segmentation and single-domain modeling.
  • Employed a population-based evolutionary algorithm for assembling multi-domain proteins, guided by deep learning-inferred inter-domain interactions.
  • Adapted DeepAssembly for protein complex assembly by treating domains as the fundamental units instead of entire chains.

Main Results:

  • DeepAssembly achieved a 22.7% higher average inter-domain distance precision compared to AlphaFold2 on 219 multi-domain proteins.
  • Improved accuracy by 13.1% for 164 multi-domain protein structures with low confidence in the AlphaFold database.
  • Successfully predicted interfaces for 32.4% of 247 tested heterodimers (DockQ ≥ 0.23), demonstrating utility in complex structure prediction.

Conclusions:

  • DeepAssembly offers a significant advancement in modeling multi-domain and complex protein structures.
  • The domain-based assembly approach, leveraging learned inter-domain interactions, provides a lighter and effective strategy for complex prediction.
  • DeepAssembly's performance improvements over AlphaFold2 highlight its potential for accelerating protein structure-based research and drug discovery.