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

4.7K
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,...
4.7K
Protein Networks02:26

Protein Networks

2.9K
2.9K
Protein-protein Interfaces02:04

Protein-protein Interfaces

15.0K
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...
15.0K
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

15.0K
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...
15.0K
Conserved Binding Sites01:49

Conserved Binding Sites

5.3K
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...
5.3K
Protein Organization01:24

Protein Organization

10.0K
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....
10.0K

You might also read

Related Articles

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

Sort by
Same author

Synergistic Energetics and Exciton Management Driven by Interfacial Dipoles Enable 20.1% Efficient Organic Solar Cells.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

WTop: A Wavelet-Driven Framework for Comprehensive Proteoform Characterization in Top-Down Mass Spectrometry.

Analytical chemistry·2026
Same author

Cosolvent-Modulated Donor Preaggregation Enhances Molecular Order in 20% Efficient Bilayer Organic Solar Cells.

ACS applied materials & interfaces·2026
Same author

ResNeXt-Based Rescoring Model for Proteoform Characterization in Top-Down Mass Spectra.

Interdisciplinary sciences, computational life sciences·2025
Same author

ScFold: a GNN-based model for efficient inverse folding of short-chain proteins via spatial reduction.

Briefings in bioinformatics·2025
Same author

Modulating Acceptor Phase Leads to 19.59% Efficiency Organic Solar Cells.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2024
Same journal

circ2DGNN: circRNA-Disease Association Prediction via Transformer-Based Graph Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Hierarchical Hypergraph Learning in Association- Weighted Heterogeneous Network for miRNA- Disease Association Identification.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Discriminative Domain Adaption Network for Simultaneously Removing Batch Effects and Annotating Cell Types in Single-Cell RNA-Seq.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

MLW-BFECF: A Multi-Weighted Dynamic Cascade Forest Based on Bilinear Feature Extraction for Predicting the Stage of Kidney Renal Clear Cell Carcinoma on Multi-Modal Gene Data.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

An End-to-End Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Generative Biomedical Event Extraction With Constrained Decoding Strategy.

IEEE/ACM transactions on computational biology and bioinformatics·2024
See all related articles

Related Experiment Video

Updated: Apr 4, 2026

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

2.7K

Predicting Essential Proteins Based on Weighted Degree Centrality.

Xiwei Tang, Jianxin Wang, Jiancheng Zhong

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |September 11, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces Weighted Degree Centrality (WDC), a novel method integrating protein-protein interaction and gene expression data to accurately predict essential proteins. WDC outperforms existing computational approaches for identifying crucial proteins in organisms.

    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

    70.1K
    Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
    07:08

    Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

    Published on: July 14, 2015

    7.8K

    Related Experiment Videos

    Last Updated: Apr 4, 2026

    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

    2.7K
    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

    70.1K
    Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
    07:08

    Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

    Published on: July 14, 2015

    7.8K

    Area of Science:

    • Computational Biology
    • Systems Biology
    • Bioinformatics

    Background:

    • Identifying essential proteins is crucial for understanding organism viability.
    • Existing computational methods for essential protein prediction are limited by insufficient protein-protein interaction (PPI) data.
    • Gene expression profiles can compensate for the lack of comprehensive PPI data.

    Purpose of the Study:

    • To develop a reliable computational method for predicting essential proteins by integrating PPI and gene expression data.
    • To introduce Weighted Degree Centrality (WDC) as a new centrality measure for essential protein identification.
    • To evaluate the performance of WDC against existing prediction methods.

    Main Methods:

    • Pearson correlation coefficient (PCC) was used to integrate PPI and gene expression data.
    • A novel centrality measure, Weighted Degree Centrality (WDC), was developed based on PCC and edge clustering coefficient (ECC).
    • WDC was applied to yeast and E. coli PPI networks, and its performance was compared with other methods (DC, BC, CC, SC, EC, IC, NC, PeC).

    Main Results:

    • WDC demonstrated superior performance in identifying essential proteins compared to existing methods.
    • The study analyzed the parameter λ in WDC, identifying an optimal value.
    • Integration of diverse data sources proved effective for essential protein prediction.

    Conclusions:

    • Weighted Degree Centrality (WDC) is an effective and reliable method for predicting essential proteins.
    • Integrating protein-protein interaction and gene expression data significantly enhances prediction accuracy.
    • The developed WDC method offers an improved approach for computational essential protein identification.