Limitations of conventional inhibitor classifications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Enzyme inhibitors are usually classified as competitive, non-competitive or mixed non-competitive. Each of these designations has a serious limitation in that it only describes an extreme of inhibitory behaviour. The non-competitive inhibition equation only considers an approach to complete inhibition of the catalytic turnover rate, while the competitive inhibition equation predicts an infinite increase in the Michaelis-Menten constant (decrease in enzyme affinity for substrate), resulting from increased inhibitor concentration. Both of these models exclude the possibility of a finite inhibitor-induced change in the kinetic parameters of the enzyme they are affecting. They also exclude the possibility of an inhibitor affecting both the substrate affinity and the catalytic turnover at the same time. Mixed non-competitive inhibition describes a hybrid form of inhibition displaying some characteristics of both competitive and non-competitive inhibition. It also suffers from an inability to describe finite changes in activity and to describe concomitant changes in substrate affinity and catalytic turnover. Two inhibitor binding constants are invoked in this equation, suggesting that such inhibitors interact with the enzyme in two completely independent manners. From these considerations, it is suggested here that conventional equations do not adequately describe observed kinetic data due to a lack of distinction between the mass action binding term describing inhibitor-enzyme association and the terms representing the actual effect of the inhibitor on the enzyme. Herein we describe an alternate approach for representing enzyme activity modulation based on a re-examination of conventional inhibition equations. The arguments presented are illustrated using the known competitive inhibition of Kallikrein with benzamidine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it