Comparing enzyme activity modifier equations through the development of global data fitting templates in Excel.

Area of Science:

• Biochemistry
• Enzyme kinetics
• Pharmacology

Background:

• Classical enzyme inhibition definitions conflate inhibitory effect and binding constants.
• Standard inhibitory terms may oversimplify complex biomolecular interactions.
• Distinguishing between inhibitor binding and its effect is crucial for accurate kinetic analysis.

Purpose of the Study:

• To examine the relationship between binding curves and standard inhibitory terms.
• To develop and validate a novel modifier equation that differentiates inhibitor binding from inhibitory effect.
• To compare the performance of this new equation against existing models for enzyme inhibition and activation.

Main Methods:

• Analysis of the relationship between biomolecular interaction binding curves and the term (1 + ([I]/K)).
• Development of a modifier equation capable of describing both enzyme activation and inhibition.
• Global data fitting using Excel templates and simulated/published datasets.

Main Results:

• The novel modifier equation accurately distinguishes between inhibitor binding and inhibitory effect.
• This single equation demonstrated superior or equivalent fits compared to standard inhibitory equations across various datasets.
• The modifier equation effectively models both enzyme inhibition and activation.

Conclusions:

• The proposed modifier equation offers a more accurate and potentially simpler framework for enzyme kinetic studies.
• Current enzyme inhibition models may be unnecessarily complex.
• The developed equation and Excel template provide valuable tools for researchers studying enzyme inhibition and activation.