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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 Kinases and Phosphatases02:54

Protein Kinases and Phosphatases

Proteins undergo chemical modifications that trigger changes in the charge, structure, and conformation of the proteins. Phosphorylation, acetylation, glycosylation, nitrosylation, ubiquitination, lipidation, methylation, and proteolysis are various protein modifications that regulate protein activity. Such modifications are usually enzyme-driven.
Protein kinases
Many proteins in the cell are regulated by phosphorylation, the addition of a phosphate group. A family of enzymes called kinases...

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A Mass Spectrometry-Based Approach to Identify Phosphoprotein Phosphatases and their Interactors
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Computational analysis of phosphoproteomics: progresses and perspectives.

Jian Ren1, Xinjiao Gao, Zexian Liu

  • 1State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University (SYSU), Guangzhou, Guangdong 510275, China.

Current Protein & Peptide Science
|August 11, 2011
PubMed
Summary

Computational analysis of phosphoproteomics data is crucial for understanding protein phosphorylation, a key post-translational modification regulating cellular signaling. This review highlights methods for extracting insights from large phosphoproteomics datasets.

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Area of Science:

  • Biochemistry
  • Proteomics
  • Bioinformatics

Background:

  • Phosphorylation is a critical post-translational modification (PTM) regulating cellular signaling and biological diversity.
  • Phosphoproteomics has rapidly advanced, identifying over 100,000 phosphorylation sites, with projections exceeding one million.
  • Extracting meaningful biological information from vast phosphoproteomics data presents a significant challenge.

Purpose of the Study:

  • To review the cutting edge of computational analysis in phosphoproteomics.
  • To discuss methods for discovering phosphorylation motifs, modeling networks, analyzing genetic variations affecting phosphorylation, and studying phosphorylation evolution.
  • To propose future research directions integrating experimental and computational approaches.

Main Methods:

  • Review of current computational strategies for phosphoproteomics data analysis.
  • Identification and summarization of techniques for motif discovery.
  • Discussion of network modeling, genetic variation analysis, and evolutionary studies related to phosphorylation.

Main Results:

  • Summarized leading computational methods for phosphoproteomics data analysis.
  • Highlighted key areas including motif discovery, network modeling, genetic variation, and evolution.
  • Identified challenges in data interpretation and proposed future research avenues.

Conclusions:

  • Effective computational analysis is essential for navigating the growing volume of phosphoproteomics data.
  • Integrating experimental and computational methods will drive phosphoproteomics research forward.
  • Future research should focus on advanced computational strategies for deeper biological insights.