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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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Updated: Sep 10, 2025

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diaPASEF-Powered Chemoproteomics Enables Deep Kinome Interaction Profiling.

Kathryn Woods1, Alexandria M Chan1,2, Thankhoe A Rants'o1,2

  • 1Department of Pharmacology & Toxicology, University of Utah, Salt Lake City 84112, United States.

Journal of Proteome Research
|August 27, 2025
PubMed
Summary
This summary is machine-generated.

A new method, 2nd gen kinobead competition and correlation analysis (kiCCA), maps protein kinase interactions. This approach identifies more kinase interactions, reveals signaling pathways, and aids drug discovery for diseases like neuroblastoma.

Keywords:
ALK inhibitorchemoproteomicskinomeneuroblastomanoradrenergic-mesenchymal transitionprotein−protein interaction

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Area of Science:

  • Cellular signaling and molecular biology
  • Biochemistry and Proteomics
  • Cancer Biology and Drug Discovery

Background:

  • Protein kinases (kinome) regulate cellular functions via phosphorylation and are implicated in human diseases.
  • Kinase activity and signaling are governed by dynamic protein-protein interaction (PPI) networks.
  • Mapping these networks is crucial for understanding kinase activation and identifying drug targets.

Purpose of the Study:

  • To introduce and validate a second-generation kinobead competition and correlation analysis (kiCCA) for comprehensive kinome PPI mapping.
  • To apply 2nd gen kiCCA to neuroblastoma models to uncover kinase-mediated signaling in the noradrenergic-mesenchymal transition (NMT).
  • To demonstrate the utility of 2nd gen kiCCA for prioritizing kinases in drug discovery.

Main Methods:

  • Utilized kinome affinity purification coupled with kinase inhibitor soluble competition.
  • Employed data-independent acquisition with parallel accumulation serial fragmentation (diaPASEF) mass spectrometry (MS).
  • Developed a redesigned correlation coefficient analysis (CCA) algorithm for enhanced PPI prediction.

Main Results:

  • 2nd gen kiCCA identified six times more kinase PPIs compared to the first-generation method in native cell extracts.
  • The study elucidated kinase-mediated signaling pathways underlying the neuroblastoma NMT.
  • Pharmacological targets for modulating NMT states were accurately predicted using the new approach.

Conclusions:

  • The 2nd gen kiCCA is a powerful and broadly applicable tool for kinome PPI mapping.
  • This method significantly advances cell signaling research and kinase-targeted drug discovery.
  • The findings provide insights into neuroblastoma progression and potential therapeutic strategies.