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Related Concept Videos

Protein Networks02:26

Protein Networks

3.9K
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,...
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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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Overview of Protein Sorting and Transport01:45

Overview of Protein Sorting and Transport

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

Conservation of Protein Domains Over Different Proteins

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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...
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Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

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Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order...
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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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Heterogeneous network approaches to protein pathway prediction.

Gowri Nayar1, Russ B Altman1,2,3,4

  • 1Department of Biomedical Data Science, Stanford University, United States.

Computational and Structural Biotechnology Journal
|July 22, 2024
PubMed
Summary

Heterogeneous networks integrate diverse biological data to model complex protein-protein interactions (PPIs). This approach enhances pathway prediction accuracy, advancing cellular function understanding and aiding disease research.

Keywords:
EmbeddingsFunctional proteomicsHeterogeneous networksPathway predictionProtein pathways

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Understanding protein-protein interactions (PPIs) is crucial for cellular function and phenotype.
  • High-throughput sequencing generates vast molecular data, but translating it into functional pathway information remains a challenge.

Purpose of the Study:

  • To analyze heterogeneous network methodologies for modeling protein pathways.
  • To highlight the importance of integrating multifaceted biological data for pathway analysis.

Main Methods:

  • Detailed analysis of heterogeneous network construction, from data representation to machine learning predictions.
  • Integration of diverse biological data types to model complex interaction networks.

Main Results:

  • Heterogeneous networks effectively capture the complexity of proteomic interactions.
  • This methodology offers enhanced accuracy in predicting biological pathways.

Conclusions:

  • Heterogeneous networks provide a powerful framework for understanding cellular processes.
  • This approach has significant potential for disease treatment and drug discovery through advanced proteomic data analysis.