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Updated: Aug 5, 2025

Assaying Protein Kinase Activity with Radiolabeled ATP
Published on: May 26, 2017
Kinetics of diffusion-influenced multisite phosphorylation with enzyme reactivation.
Irina V Gopich1, Attila Szabo1
1Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, 20892, USA.
Accounting for diffusion in multisite phosphorylation requires more than modifying rate constants. Introducing enzyme reactivation leads to negative rate constants and necessitates a non-Markovian theory for accurate kinetic modeling.
Area of Science:
- Biochemistry
- Chemical Kinetics
- Enzyme Kinetics
Background:
- Multisite phosphorylation kinetics are complex and influenced by diffusion.
- Conventional rate equations may not fully capture diffusion effects.
- Enzyme inactivation and reactivation add further complexity.
Purpose of the Study:
- To extend diffusion-modified kinetic models to include enzyme reactivation.
- To develop a more accurate theoretical framework for short-time kinetics.
- To investigate the impact of enzyme reactivation on phosphorylation cycles.
Main Methods:
- Modification of conventional rate equations to include diffusion.
- Introduction of new transitions and enzyme reactivation steps.
- Development of a non-Markovian theory with memory kernels.
- Application to double phosphorylation and phosphorylation-dephosphorylation cycles.
Main Results:
- A diffusion-modified kinetic scheme with a negative rate constant emerged due to enzyme reactivation.
- Non-Markovian effects are significant at short times.
- The non-Markovian theory accurately describes short-time kinetics.
- Loss of bistability in a phosphorylation-dephosphorylation cycle was reproduced with decreasing enzyme reactivation time.
Conclusions:
- Enzyme reactivation significantly alters diffusion-influenced phosphorylation kinetics.
- A non-Markovian approach is essential for accurate modeling at short timescales.
- The developed theory provides a more robust framework for understanding complex enzymatic reactions.
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