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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...

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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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Compressing proteomes: the relevance of medium range correlations.

Dario Benedetto1, Emanuele Caglioti, Claudia Chica

  • 1Dipartimento di Matematica, Università di Roma La Sapienza, Piazzale Aldo Moro 5, Rome, Italy.

EURASIP Journal on Bioinformatics & Systems Biology
|February 8, 2008
PubMed
Summary

Protein sequences exhibit nonrandom patterns. Statistical models analyzing short and medium-range amino acid correlations improve sequence information capture and compression, suggesting evolutionary origins for these proteome patterns.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Proteome sequences are fundamental to biological function.
  • Understanding sequence nonrandomness can reveal underlying biological principles.
  • Previous studies have explored sequence correlations at various ranges.

Purpose of the Study:

  • To investigate the nonrandomness of proteome sequences by analyzing amino acid correlations.
  • To evaluate the effectiveness of statistical models incorporating short and medium-range correlations for sequence analysis.
  • To explore the evolutionary implications of observed sequence redundancy.

Main Methods:

  • Analysis of short-range (10 residues apart) and medium-range (100 residues apart) amino acid correlations in proteome sequences.
  • Development and application of statistical models to capture these correlations.
  • Assessment of model performance based on information capture and compression rates.

Main Results:

  • Significant correlations between amino acids at both short and medium ranges were identified in proteome sequences.
  • Statistical models incorporating both correlation types demonstrated superior performance in capturing sequence information.
  • These models achieved improved compression rates compared to models without these correlations.

Conclusions:

  • The nonrandomness of proteome sequences is characterized by specific short and medium-range amino acid correlations.
  • These correlations are crucial for accurate modeling and compression of protein sequence data.
  • The observed redundancy likely stems from the evolutionary history of proteomes and protein sequences.