Hi, I'm Parmeet

The Michaelis constant (Km) is central to enzyme kinetics, guiding variant selection, inhibitor screening, and metabolic modeling. However, Km 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 Km (ACI-Km) 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 Km 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-Km is valid across a wide range of E0/Km conditions, including relatively high E0. A free web application (https://aci.sci.yorku.ca) implements ACI-Km. Tests on synthetic and experimental datasets show that Km ± 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 Km determination.

Accurate determination of equilibrium dissociation constants (Kd) from kinetic measurements requires understanding how systematic errors in concentration and signal propagate into errors in the rate constants kon and koff, and thus into Kd calculated as koff/kon. Here, we present a deterministic, platform-agnostic error-propagation framework applicable to any method that monitors reversible 1:1 binding between a ligand (L) and a target/analyte (T) via time-resolved association and dissociation traces under pseudo-first-order conditions, where the total concentration of T (T0) remains effectively constant during association. The analysis yields closed-form expressions for the relative error in Kd and reveals a triphasic dependence of |ΔKd/Kd| on T0/Kd, including a low T0/Kd regime in which the determined Kd is intrinsically robust to systematic error. Because these features arise from the structure of the kinetic equations, they apply broadly to both solution- and surface-based assays regardless of detection modality. Within this framework, the measured signal is proportional to the amount of LT complex (C), and the maximal proportional response Smax corresponds to the signal expected at full occupancy of ligand binding sites by T. To illustrate how the general theory manifests in practice, we apply it to surface-based measurements where Smax is established through external constraints and show that accurate kon, koff, and Kd values can be obtained from time-resolved traces acquired at T0 values far below the true Kd. We also outline practical limitations, including the requirement that kon and koff be resolvable from the measured traces, and discuss how the theoretical conclusions relate to established experimental strategies such as reference binders, off-rate screening, and software-based correlation diagnostics. This work provides a general theoretical foundation for understanding accuracy constraints in kinetic Kd determination and clarifies how these principles translate to widely used surface-based measurements for molecular interaction analysis.