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Related Concept Videos

Proteomics01:33

Proteomics

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 proteomics...
Protein Networks02:26

Protein Networks

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

Conservation of Protein Domains Over Different Proteins

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 form...
Overview of Protein Sorting and Transport01:45

Overview of Protein Sorting and Transport

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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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Study of proteome maps using partial ordering.

Milan Randić1, Marjana Novic, Marjan Vracko

  • 1National Institute of Chemistry, Hajdrihova 19, SI-1000, Ljubljana, Slovenia. mrandic@msn.com

Journal of Theoretical Biology
|June 15, 2010
PubMed
Summary
This summary is machine-generated.

This study numerically characterizes proteome maps using partial ordering and adjacency matrices. This method quantifies similarities between rat liver cell proteome maps exposed to peroxisome proliferators.

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A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

Area of Science:

  • Proteomics
  • Bioinformatics
  • Systems Biology

Background:

  • Proteome maps provide a snapshot of protein expression but require robust characterization methods.
  • Analyzing changes in proteome maps is crucial for understanding cellular responses to stimuli, such as peroxisome proliferators.

Purpose of the Study:

  • To develop a numerical method for characterizing and comparing proteome maps.
  • To assess the similarity between proteome maps derived from rat liver cells exposed to peroxisome proliferators.

Main Methods:

  • Numerical characterization of proteome maps using partial ordering of protein spots based on mass and charge.
  • Embedding a partial ordering diagram onto 2D maps and constructing corresponding adjacency matrices.
  • Augmenting adjacency matrices with protein abundance information as scaled diagonal entries.
  • Utilizing leading eigenvectors of adjacency matrices as descriptors for map comparison.

Main Results:

  • The described method allows for quantitative comparison of proteome maps.
  • The approach was successfully illustrated on proteome maps from rat liver cells exposed to peroxisome proliferators.
  • Leading eigenvectors effectively served as descriptors to determine proteome map similarity.

Conclusions:

  • Partial ordering and eigenvector analysis offer a robust framework for numerical characterization of proteome maps.
  • This methodology facilitates the quantitative assessment of proteome map similarity, aiding in the analysis of biological responses.