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Related Concept Videos

Protein Networks02:26

Protein Networks

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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.
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Protein-protein Interfaces02:04

Protein-protein Interfaces

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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...
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Protein Complex Assembly02:41

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Proteins can form homomeric complexes with another unit of the same protein or heteromeric complexes with different types.  Most protein complexes self-assemble spontaneously via ordered pathways, while some proteins need assembly factors that guide their proper assembly. Despite the crowded intracellular environment, proteins usually interact with their correct partners and form functional complexes.
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Protein Organization01:24

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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.
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Identification of Protein Complexes in Escherichia coli using Sequential Peptide Affinity Purification in Combination with Tandem Mass Spectrometry
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Multi-source biological knowledge-guided hypergraph spatiotemporal subnetwork embedding for protein complex

Shilong Wang1, Hai Cui1, Yanchen Qu1

  • 1Information Science and Technology College, Dalian Maritime University, No.1 Linghai Road, 116026, Dalian, Liaoning, China.

Briefings in Bioinformatics
|January 15, 2025
PubMed
Summary
This summary is machine-generated.

We developed HGST, a novel method using hypergraph spatiotemporal subnetworks to identify protein complexes. This approach integrates multi-source data and models complex interactions for better biological significance in protein-protein interaction networks.

Keywords:
biological knowledgehypergraph embeddingprotein complexspatiotemporal subnetwork

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Area of Science:

  • Systems biology
  • Bioinformatics
  • Computational biology

Background:

  • Protein complexes are crucial for cellular functions and disease mechanisms.
  • Existing protein-protein interaction (PPI) network analysis methods are limited by static data and pairwise relationship assumptions.
  • Dynamic and higher-order interactions in biological systems are not fully captured by current models.

Purpose of the Study:

  • To propose HGST, a multi-source biological knowledge-guided hypergraph spatiotemporal subnetwork embedding method.
  • To overcome limitations of static PPI networks and model non-pairwise interactions.
  • To identify biologically significant protein complexes more effectively.

Main Methods:

  • Constructing spatiotemporal PPI subnetworks incorporating protein dynamics and multi-source knowledge.
  • Transforming subnetworks into hypergraphs to model higher-order interactions.
  • Integrating amino acid sequence and gene ontology features for multi-dimensional representation.
  • Identifying protein complexes using a core-attachment strategy on reweighted subnetworks.

Main Results:

  • HGST demonstrated competitive performance across four real PPI datasets.
  • The method successfully identified protein complexes with high biological significance.
  • Biological analyses validated the effectiveness of HGST in complex identification.

Conclusions:

  • HGST offers an advanced approach for identifying protein complexes by leveraging spatiotemporal dynamics and higher-order interactions.
  • The integration of multi-source biological knowledge and multi-dimensional features enhances the accuracy and biological relevance of identified complexes.
  • This method provides a valuable tool for understanding protein functions and disease mechanisms.