Analysis of Inhibition Rate Enhancement by Covalent Linkage of Antithrombin to Heparin as a Potential Predictor of Reaction Mechanism
Bibliographic record
Abstract
Antithrombin (AT) inhibition of coagulation enzymes is catalyzed by unfractionated heparin (UFH) and other heparinoids. Reaction proceeds either via conformational activation of the inhibitor or template-mediated binding of both inhibitor and protease. We investigated if the relative inhibition rates of AT + UFH and covalent AT-heparin conjugate (ATH) with coagulation factors might be indicative of the mechanism involved. Rates were determined by discontinuous assay and mechanisms were probed by a variety of binding studies with UFH or ATH heparin chains. Rates were increased more than 2-fold with ATH over AT + UFH in reactions with thrombin, factor (F) VIIa + tissue factor + Ca2+ + lipid, FIXa and FXIa, but not with FXa or FXIIa. In comparison, UFH or ATH heparin binding (evidence of a template mechanism) was only observed with thrombin, tissue factor, FIXa and FXIa. Thus, inhibition rate enhancement by conjugation of AT with heparin were predictive of inhibitor.enzyme template bridging by heparin. Rationales behind this novel concept are discussed.
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.
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 | 0.001 | 0.002 |
| 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) | 0.001 | 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 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".