Introducing Quantitative Assessment of Michaelis Constant (Km) Accuracy
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.117 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".