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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

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Proteomics01:33

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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Protein-protein Interfaces02:04

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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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Tagging and Fusion Proteins01:24

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Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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Integrating Networks and Proteomics: Moving Forward.

Wilson Wen Bin Goh1, Limsoon Wong2

  • 1School of Pharmaceutical Science and Technology, Tianjin University, Tianjin 300072, PR China; Department of Computer Science, National University of Singapore, 117417 Singapore.

Trends in Biotechnology
|June 18, 2016
PubMed
Summary
This summary is machine-generated.

Network-based proteomics offers solutions for data challenges but requires a shift towards quantitative applications and standardized methods for wider adoption and clinical impact.

Keywords:
bioinformaticsnetworksproteomicstranslational research.

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Systems Biology

Background:

  • Network analysis presents significant potential for addressing analytical challenges in proteomics, such as incomplete data coverage and inconsistencies.
  • Despite its promise, network-based proteomics research has seen limited growth, primarily focusing on non-quantitative applications like protein interaction mapping.

Purpose of the Study:

  • To highlight the underutilization of quantitative applications in network-based proteomics.
  • To emphasize the need for standardized evaluation criteria and gold-standard datasets.
  • To advocate for a transition towards quantitative applications to unlock the full potential of network-based proteomics for clinical use.

Main Methods:

  • Review of current network-based proteomics research trends.
  • Analysis of common applications and their limitations.
  • Identification of barriers to adoption and reproducibility.

Main Results:

  • Current research predominantly employs non-quantitative network analyses (e.g., physical interactions, differential expression contextualization).
  • Practical quantitative applications, such as predicting missing proteins or sample classification, remain underexplored.
  • Lack of common standards, including evaluation metrics and benchmark datasets, hinders reproducibility and adoption.

Conclusions:

  • A strategic shift towards quantitative applications is crucial for advancing network-based proteomics.
  • Establishing common standards and evaluation benchmarks is essential for realizing the field's potential.
  • Convergence on standards will facilitate meaningful contributions to clinical proteomics and applications.