By Increasing the Affinity of Heparin for Fibrin, Zn<sup>2+</sup> Promotes the Formation of a Ternary Heparin–Thrombin–Fibrin Complex That Protects Thrombin from Inhibition by Antithrombin
Bibliographic record
Abstract
Heparin binds fibrin and, by bridging thrombin onto fibrin, promotes the formation of a ternary heparin-thrombin-fibrin complex that protects thrombin from inhibition by antithrombin. Because thrombin binds γ(A)/γ'-fibrin, a variant with an extended γ-chain, with higher affinity than the bulk γ(A)/γ(A)-fibrin, γ(A)/γ'-fibrin affords bound thrombin more protection from inhibition by antithrombin-heparin. We examined the effect of Zn(2+) on heparin-thrombin-fibrin complex formation because Zn(2+) modulates heparin-protein interactions. Zn(2+) increased the affinity of heparin for γ(A)/γ(A)- and γ(A)/γ'-fibrin by 4.3- and 3.7-fold, respectively, but had no effect on the affinity of thrombin for either form of fibrin. In contrast, in the presence of heparin, Zn(2+) increased the affinity of thrombin for γ(A)/γ(A)-fibrin 4-fold (from a K(d) value of 0.8 to 0.2 μM) and slowed the rate of thrombin dissociation from γ(A)/γ(A)-fibrin clots. These findings suggest that Zn(2+) enhances the formation of ternary heparin-thrombin-fibrin complexes with γ(A)/γ(A)-fibrin but does not influence the already high affinity interaction of thrombin with γ(A)/γ'-fibrin. Consistent with this concept, in the presence of Zn(2+), γ(A)/γ(A)-fibrin protected thrombin from inhibition by antithrombin-heparin to a similar extent as γ(A)/γ'-fibrin. Therefore, by enhancing the binding of heparin to fibrin, physiological concentrations of Zn(2+) render fibrin-bound thrombin more protected from inhibition by antithrombin. Because fibrin-bound thrombin can trigger thrombus expansion, these findings help to explain why recurrent thrombosis can occur despite heparin treatment.
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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 | 0.001 | 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".