Introducing Quantitative Assessment of Michaelis Constant (K_m) Accuracy.

Area of Science:

• Enzyme kinetics and biochemical analysis.
• Computational biology and bioinformatics.
• Quantitative biochemistry.

Background:

• The Michaelis constant (K_m) is crucial for enzyme kinetics, variant selection, inhibitor screening, and metabolic modeling.
• Current methods using nonlinear regression often yield inaccurate K_m values, with reported standard errors (SE) underestimating true uncertainty.
• Existing software lacks a metric to assess the accuracy of determined K_m values.

Purpose of the Study:

• To extend the Accuracy Confidence Interval (ACI) framework to K_m determination (ACI-K_m).
• To quantify the propagation of systematic uncertainties in enzyme (E₀) and substrate (S₀) concentrations into K_m values.
• To provide a reliable accuracy metric for K_m that complements traditional precision measures.

Main Methods:

• Formulation of ACI-K_m using a binding-isotherm approach to the enzyme kinetics velocity-substrate curve.
• Integration of confidence intervals for concentration accuracy to assess uncertainty propagation.
• Application to existing datasets without requiring new experiments.

Main Results:

• ACI-K_m provides a probabilistic interval expected to contain the accurate K_m value.
• Standard error (SE) alone can severely underestimate the uncertainty in K_m.
• ACI-K_m offers more dependable accuracy bounds for critical decision-making in research and development.

Conclusions:

• ACI-K_m is a valuable tool for assessing the accuracy of K_m values derived from kinetic data.
• The method is applicable across various enzyme concentrations (E₀/K_m ratios) and requires only existing data.
• A web application is available to implement ACI-K_m, facilitating its widespread adoption.