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Hub Occupancy by Competitively Interacting Proteins Obeys a Simple Queuing Law.

Yuming Jiang1, Antun Skanata1,2, Liviu Movileanu1,2,3,4

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Predicting protein hub occupancy is complex due to multiple protein ligands (PLs) and their interactions. New queuing theory models quantify hub occupancy, revealing how ligand concentrations affect competitive protein-protein interactions (PPIs) and receptor binding.

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Area of Science:

  • Biochemistry
  • Cell Biology
  • Systems Biology

Background:

  • Protein hubs and their ligands mediate crucial cell signaling pathways.
  • Complex mixtures of ligands with varying affinities and concentrations challenge predictions of hub occupancy.
  • Understanding protein-protein interactions (PPIs) is vital for cellular function and disease.

Purpose of the Study:

  • To develop predictive models for protein hub occupancy in complex ligand mixtures.
  • To quantify the impact of ligand concentration and kinetics on competitive PPIs.
  • To provide a mechanistic understanding of receptor binding dynamics.

Main Methods:

  • Application of queuing theory to model PPIs and hub occupancy.
  • Development of analytical strategies for ternary and complex ligand mixtures.
  • Coarse-graining approaches for large-scale ligand competition analysis.

Main Results:

  • Ligand concentration significantly influences competitive PPIs between other ligands and the protein receptor.
  • Models accurately predict hub occupancy based on kinetic rate constants and ligand concentrations.
  • A coarse-graining method simplifies analysis of complex ligand-receptor interactions.

Conclusions:

  • Queuing theory provides a robust framework for understanding competitive PPIs and hub occupancy.
  • The developed models offer quantitative insights into biochemical traffic and signaling dynamics.
  • This work has broad implications for protein analytics, drug development, and understanding cellular processes.