Introducing Quantitative Assessment of Michaelis Constant (K_m) Accuracy

The Michaelis constant (K_m) is central to enzyme kinetics, guiding variant selection, inhibitor screening, and metabolic modeling. However, K_m obtained by nonlinear regression can be substantially inaccurate even when the reported standard error (SE) appears small. Common software reports SE but provides no accuracy metric. This gap is addressed by extending the accuracy confidence interval (ACI) framework to K_m (ACI-K_m) through a binding-isotherm formulation of the velocity-substrate fit. Given confidence intervals for concentration accuracy, the method quantifies how residual systematic uncertainties in enzyme and substrate concentrations ( E₀ and S₀) propagate into the determined K_m values and provides a probabilistic interval expected to enclose the accurate value. The approach requires no additional kinetic experiments and is directly applicable to existing datasets. Concentration-accuracy intervals can be estimated from calibration data, reagent specifications, or quality-control records. ACI-K_m is valid across a wide range of E₀/K_m conditions, including relatively high E₀. A free web application (https://aci.sci.yorku.ca) implements ACI-K_m. Tests on synthetic and experimental datasets show that K_m ± SE can severely underestimate uncertainty, whereas ACI provides more reliable accuracy bounds for decision-making, complementing rather than replacing traditional precision metrics by providing quantitative diagnostic bounds for concentration-related uncertainties in K_m determination.