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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.
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 Networks02:26

Protein Networks

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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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Conserved Binding Sites01:49

Conserved Binding Sites

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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...
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Ligand Binding Sites02:40

Ligand Binding Sites

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
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Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells
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Protein Interaction Networks: Functional and Statistical Approaches.

Mónica Escandón1, Laura Lamelas2, Víctor Roces2

  • 1Agroforestry and Plant Biochemistry, Proteomics and Systems Biology, Department of Biochemistry and Molecular Biology, University of Cordoba, UCO-CeiA3, Cordoba, Spain. bb2esmam@uco.es.

Methods in Molecular Biology (Clifton, N.J.)
|May 29, 2020
PubMed
Summary
This summary is machine-generated.

Integrative omics approaches enhance proteomics by analyzing protein relationships and integrating diverse data. Visualization tools and bioinformatics workflows reveal complex biological networks for deeper functional insights.

Keywords:
CytoscapeDIABLOOmics levelsProtein networksStringsPLS

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Area of Science:

  • Bioinformatics and Computational Biology
  • Proteomics
  • Data Science

Background:

  • Next-generation sequencing and high-throughput technologies generate vast biological data.
  • Traditional proteomics analysis can be enhanced by integrative approaches.
  • Interpreting complex biological data requires effective visualization methods.

Purpose of the Study:

  • To propose bioinformatics and biostatistical workflows for creating protein interaction networks.
  • To demonstrate how integrative approaches can extract relevant biological information.
  • To highlight the importance of visualization for data interpretation.

Main Methods:

  • Integrative analysis of proteomic data with other molecular levels, phenotype, or environmental data.
  • Construction of interaction networks using statistical (PLS) and functional (STRING, shinyGO) methods.
  • Application of advanced integration techniques like coinertial analyses and DIABLO.
  • Network visualization and mining using Cytoscape and Gephi.

Main Results:

  • Proposed workflows reduce dataset complexity by creating interaction networks.
  • Networks depict complex statistical and functional relationships among proteins.
  • Integration of multi-omics data reveals deeper biological connections.
  • Identification of differentially connected modules potentially representing protein complexes and pathways.

Conclusions:

  • Integrative omics approaches offer a powerful framework for advancing proteomics research.
  • Network analysis facilitates the discovery of novel protein functions and interactions.
  • Combined statistical and functional network analysis provides comprehensive biological insights.