Introducing Quantitative Assessment of Michaelis Constant (Km) Accuracy

Wang, Tong; Dhillon, Parmeetpal; Krylova, Svetlana; Bijlani, Amit; Schreiber, Sebastian; Golemi-Kotra; Dasantila; Jose, Joachim; Krylov, Sergey

Abstract

The Michaelis constant (Km) underpins enzyme kinetics and is widely used to compare enzyme variants, guide inhibitor screens, set assay conditions, and inform metabolic-flux models. However, even when experiments follow standard guidelines, the Km obtained from nonlinear regression can be substantially inaccurate (differing markedly from the parameter value that would describe noise-free data under the Michaelis–Menten model) while still appearing precise, as indicated by a small standard error (SE). Standard software tools such as GraphPad Prism and Origin report only standard error and offer no way to gauge accuracy. As a result, inaccurate Km values can lead to selecting suboptimal enzyme variants, misestimating inhibitor potency, mispredicting pathway fluxes, or introducing costly inefficiencies in bioprocesses. Here, we address this gap by demonstrating that the binding-isotherm framework, commonly used for affinity constants, can be directly transferred to Km determination, enabling accuracy assessment using the recently developed Accuracy Confidence Interval (ACI) framework. By recasting the classical velocity-versus-substrate fit as a binding-isotherm regression, the method propagates routine concentration uncertainties (δS0/S0 and δE0/E0, as supplied by the user) into an interval expected to enclose the model-implied true Km. The workflow requires no additional kinetic experiments and applies across a wide range of enzyme concentrations, including cases where E0 is equal to or greater than Km, thus overcoming key limitations of the traditional Michaelis–Menten formulation. A free, user-friendly web application (https://aci.sci.yorku.ca) fully automates the analysis without requiring custom coding or advanced mathematical expertise. We analyzed synthetic data with known kinetic parameters and showed that Km ± SE values from standard software can severely underestimate the true uncertainty in Km, whereas the ACI provides reliable bounds for decision-making. We then applied the ACI framework to experimental data, further illustrating its practical value. The ACI thus provides an actionable accuracy metric, complementing traditional precision statistics and alerting researchers when stricter concentration calibration or additional replicates are warranted.