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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.
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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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The addition or removal of phosphate groups from proteins is the most common chemical modification that regulates cellular processes. These modifications can affect the structure, activity, stability, and localization of proteins within cells as well as their interactions with other proteins.
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Identification of Kinase-substrate Pairs Using High Throughput Screening
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PhosphoPICK: modelling cellular context to map kinase-substrate phosphorylation events.

Ralph Patrick1, Kim-Anh Lê Cao2, Bostjan Kobe2

  • 1School of Chemistry and Molecular Biosciences and Queensland Facility for Advanced Bioinformatics, The University of Queensland, St Lucia 4072, Translational Research Institute, The University of Queensland Diamantina Institute, Brisbane, St Lucia 4102, Institute for Molecular Bioscience and Australian Infectious Diseases Research Centre, The University of Queensland, St Lucia, 4072, Australia.

Bioinformatics (Oxford, England)
|October 12, 2014
PubMed
Summary

This study introduces a new computational model that predicts kinase substrates by integrating protein interaction and abundance data. The model improves phosphorylation site prediction accuracy, offering a systems biology approach to understand kinase regulation.

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Area of Science:

  • Systems biology
  • Computational biology
  • Molecular biology

Background:

  • Kinase-substrate phosphorylation specificity is influenced by both substrate sequence and cellular context.
  • Existing sequence-based methods for predicting phosphorylation sites have limitations, including high false-positive rates.
  • Systems biology approaches to understanding phosphorylation are underexplored.

Purpose of the Study:

  • To develop and validate a computational model for predicting kinase substrates using protein-protein interaction and cell cycle-dependent protein abundance data.
  • To assess the model's performance in predicting kinase substrates for 59 human kinases.
  • To evaluate the model's ability to enhance existing sequence-based prediction methods and identify functional overlaps.

Main Methods:

  • A predictive model was developed integrating protein-protein interaction networks and cell cycle-specific protein abundance data.
  • The model was applied to predict kinase substrates for 59 human kinases.
  • Performance was evaluated using Area Under the Curve (AUC) metrics.
  • The model's predictions were compared with sequence-based methods, particularly for the CMGC kinase family.
  • Functional overlaps between predicted CDK2 substrates and E2F transcription factors were investigated.

Main Results:

  • The model achieved high accuracy in substrate prediction, with an average AUC of 0.86 across 59 kinases.
  • Integrating context data significantly improved prediction performance compared to sequence-based methods alone, especially for CMGC family kinases.
  • The study successfully identified functional overlaps between predicted CDK2 substrates and E2F transcription factor targets.

Conclusions:

  • A systems biology approach integrating protein interaction and abundance data provides a robust method for predicting kinase substrates.
  • This context-aware model overcomes limitations of sequence-based prediction methods, reducing false positives.
  • The findings offer insights into the regulation of protein phosphorylation and functional relationships within cellular pathways.