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Related Concept Videos

Proteomics01:33

Proteomics

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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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Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
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A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
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Monitoring Functional Posttranslational Modifications Using a Data-Driven Proteome Informatic Pipeline.

Payman Nickchi1, Uladzislau Vadadokhau2, Mehdi Mirzaie3

  • 1Department of Statistics, University of British Columbia, Vancouver, Biritish Columbia, Canada.

Proteomics
|March 18, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a computational pipeline to predict posttranslational modifications (PTMs) in proteomics data, improving the identification of functional modifications and proteins. The method enhances mass spectrometry analysis for better molecular biomedicine insights.

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Area of Science:

  • Molecular Biomedicine
  • Proteomics
  • Bioinformatics

Background:

  • Posttranslational modifications (PTMs) are critical for cellular signaling but challenging to characterize.
  • Current methods for PTM analysis are often labor-intensive and require specific experimental designs.
  • Understanding PTMs, their functions, and crosstalk is vital in molecular biology.

Purpose of the Study:

  • To develop a PTM-centric computational pipeline for predicting relevant PTMs directly from mass spectrometry proteomics data.
  • To enable the identification and quantification of PTMs without prior experimental information.
  • To demonstrate the pipeline's utility in glycoproteomics and drug-treated cancer cells.

Main Methods:

  • Developed a PTM-centric informatic pipeline for in silico PTM prediction.
  • Applied the pipeline to mass spectrometry-based proteomics data from oral squamous cell carcinoma.
  • Utilized computational enrichment analysis to identify potential PTMs in differentially expressed proteins.
  • Performed refined database searches incorporating predicted PTMs.

Main Results:

  • The pipeline successfully predicted and analyzed enriched PTMs in proteomics data.
  • Identified proteins not detected in initial searches, demonstrating enhanced discovery capabilities.
  • Showcased the pipeline's application in glycoproteomics and analysis of drug-induced protein changes.
  • Validated the effectiveness of PTM-centric searching with computational enrichment.

Conclusions:

  • PTM-centric searching of mass spectrometry data, aided by computational enrichment analysis, is effective.
  • The proposed pipeline significantly improves the identification of PTMs and associated proteins.
  • Integrating this computational approach into future proteomics search engines is recommended.