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

4.4K
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,...
4.4K
Protein Networks02:26

Protein Networks

2.8K
2.8K
Protein-protein Interfaces02:04

Protein-protein Interfaces

14.4K
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...
14.4K
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

4.4K
4.4K
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

14.0K
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...
14.0K
Overview of Protein Sorting and Transport01:45

Overview of Protein Sorting and Transport

21.4K
Eukaryotic cells have different membrane-bound organelles with distinct protein requirements. The process by which proteins are targeted to a specific organelle is called protein sorting.
Protein sorting can be of two types: signal-based sorting and vesicle-based trafficking. In signal-based sorting, specific amino acid sequences called sorting signals target proteins to the proper location inside the cell either via gated transport or by protein translocation.  In gated transport, folded...
21.4K

You might also read

Related Articles

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

Sort by
Same author

ANXA11 recruits tumor-associated neutrophils to promote the progression of colorectal cancer.

International immunopharmacology·2026
Same author

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
Same author

UniRES-GO: Unified residue-level early fusion of sequence and predicted structure for protein function prediction.

Analytical biochemistry·2026
Same author

Comparative metabolomics provides insights into the metabolic reprogramming of the predatory <i>Arma custos</i> (Fabricius) during low-temperature storage.

Frontiers in insect science·2026
Same author

Bayesian Hyperparameter Optimization Improves scGPT Fine-Tuning for Single-Cell Multi-Omics Integration.

Bioinformatics (Oxford, England)·2026
Same author

Long Non-Coding RNAs in HER2-Positive Breast Cancer: From Resistance Mechanisms to Translational Potential.

Oncology research·2026
Same journal

DeepDPM: A Deep Learning Method for MoRFs Prediction Based on Wavelet Transform and Dynamic Convolutional Attention Mechanism.

Journal of chemical information and modeling·2026
Same journal

Graph-Based Generation and Reduction of Complex Chemical Reaction Networks.

Journal of chemical information and modeling·2026
Same journal

Modeling the Sensitivity of Large-Scale Virtual Screening to Scoring Function Accuracy, Artifacts, and Library Composition.

Journal of chemical information and modeling·2026
Same journal

Machine Learning-Driven Discovery of Indole/Oxoindole-Piperazine Scaffolds as Dual MAO-B/Sig-1R Ligands for Neurodegenerative Disorders.

Journal of chemical information and modeling·2026
Same journal

Mapping Evolution of Molecules across Biochemistry with Assembly Theory.

Journal of chemical information and modeling·2026
Same journal

Structural Proteomics-Based Deciphering of Hydrophobic Packing Fingerprints Informing Protein Thermostability in TIM Barrels.

Journal of chemical information and modeling·2026
See all related articles

Related Experiment Video

Updated: Jan 7, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

2.1K

Struct2GO-Enhanced: Multimodal Graph Attention Improves Protein Function Prediction.

Zihan Shi1, Thanh Hoa Vo2,3, Nguyen Quoc Khanh Le4,5,6

  • 1NUS-ISS, National University of Singapore, Singapore 119615, Singapore.

Journal of Chemical Information and Modeling
|January 2, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced framework for protein function prediction using AlphaFold2 structural data, improving multimodal feature fusion and attention mechanisms for better accuracy.

More Related Videos

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

2.5K
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.6K

Related Experiment Videos

Last Updated: Jan 7, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

2.1K
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

2.5K
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.6K

Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Machine Learning

Background:

  • Protein function prediction is crucial for understanding biological systems.
  • Current models struggle with multimodal feature fusion and attention for structure-function relationships.
  • AlphaFold2 structural information has advanced prediction but requires better integration.

Purpose of the Study:

  • To develop an enhanced framework for protein function prediction.
  • To improve multimodal feature fusion and attention mechanisms.
  • To leverage AlphaFold2 structural data more effectively.

Main Methods:

  • Introduced Graph-CBAM for graph neural network attention.
  • Implemented complete multimodal fusion of Node2vec embeddings and one-hot encodings.
  • Utilized a dual-head self-attention pooling module for node importance stabilization.

Main Results:

  • The enhanced model consistently outperformed existing benchmarks on human protein datasets.
  • Achieved a 2.9% increase in Fmax for Biological Process (BP) and 3.9% AUPR enhancement for Cellular Component (CC).
  • Demonstrated the independent contributions of structural embeddings, one-hot encodings, and Graph-CBAM via ablation studies.

Conclusions:

  • The proposed framework offers a more complete and practical solution for AlphaFold2-based protein function prediction.
  • The model shows particular advantages for proteins lacking protein-protein interaction data.
  • This work advances the field by improving the capture of structural-functional relationships.