Phospholipid Regulates the Activation of Factor X by Tissue Factor/Factor VIIa (TF/VIIa) via Substrate and Product Interactions
Bibliographic record
Abstract
Although the phospholipid requirement for tissue factor (TF) activity has been well-established, the mechanism by which the surface regulates enzymatic activity remains unclear. We added phospholipid vesicles to already relipidated TF (30/70 PS/PC) and found that added lipid can both enhance and inhibit the rate of factor X (F.X) activation. Using active-site-inhibited F.Xa we demonstrate that F.Xa is a more potent inhibitor of TF/VIIa at lower lipid concentrations, and that this inhibition is attributable to high surface occupancy by F.Xa near the enzyme. We also find that exactly twice as many F.Xa molecules are bound to a lipid surface at saturation as F.X, and that a dimer model of F.Xa binding to the lipid can account for the experimentally observed, preferential binding of F.Xa (compared to F.X) to phospholipid surfaces. We manipulated the amount of phospholipid available to each TF molecule by controlling vesicle size and the number of TF molecules per vesicle and found that, as the 2D radius of phospholipid available to each TF molecule was increased, the observed k(cat) increased hyperbolically toward a maximum or "true k(cat)". At a 2D lipid radius of approximately 37 nm, the observed k(cat) was 50% of the "true k(cat)". Thus, phospholipid surface serves as a conduit for F.X presentation and F.Xa removal, and the rate at which F.Xa leaves the vicinity of the enzyme, either by lateral diffusion or desorption from the surface, regulates the rate of F.X activation. We argue that these findings require reevaluation of existing models of coagulation.
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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 | 0.000 | 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 | 0.001 | 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".