Determination of the inhibitor dissociation constant of an individual unmodified enzyme molecule in free solution
Bibliographic record
Abstract
Single enzyme molecule assays on E. coli β-galactosidase were performed using a capillary electrophoresis-based method. Three types of assays were performed. The catalytic rate of 20 individual molecules was assayed in duplicate in the presence of 50 μM substrate. The ratio of rates for the second incubation relative to the first was 0.96 ± 0.03, showing the reproducibility of the method. In the second assay, the rates were determined in the absence and presence of 210 μM L-ribose, a competitive inhibitor. The ratio of the rate in the presence of inhibitor to that in its absence for 19 individual molecules was 0.44 ± 0.23. This large relative standard deviation suggests that each individual enzyme molecule was affected to a different extent by the presence of the inhibitor, which is consistent with KI being heterogeneous. To estimate KI for individual molecules, a third assay was performed. Each molecule was incubated in the presence of 30 and 50 μM substrate and then in the presence of 50 μM substrate plus 210 μM inhibitor. Comparison of the rates in the two substrate concentrations allowed for the determination of the individual Km of each molecule. From this value and the difference in rates in the presence and absence of inhibitor, the individual molecule KI values were determined. This value was found to differ between individual molecules and was found to increase with an increase in Km . Modeling showed that a heterogeneity in KI results in an alteration in the Michaelis-Menten curve for a population of enzymes in the presence of a competitive inhibitor.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".