A High Affinity Interaction of Plasminogen with Fibrin Is Not Essential for Efficient Activation by Tissue-type Plasminogen Activator
Bibliographic record
Abstract
Fibrin (Fn) enhances plasminogen (Pg) activation by tissue-type plasminogen activator (tPA) by serving as a template onto which Pg and tPA assemble. To explore the contribution of the Pg/Fn interaction to Fn cofactor activity, Pg variants were generated and their affinities for Fn were determined using surface plasmon resonance (SPR). Glu-Pg, Lys-Pg (des(1-77)), and Mini-Pg (lacking kringles 1-4) bound Fn with K(d) values of 3.1, 0.21, and 24.5 μm, respectively, whereas Micro-Pg (lacking all kringles) did not bind. The kinetics of activation of the Pg variants by tPA were then examined in the absence or presence of Fn. Whereas Fn had no effect on Micro-Pg activation, the catalytic efficiencies of Glu-Pg, Lys-Pg, and Mini-Pg activation in the presence of Fn were 300- to 600-fold higher than in its absence. The retention of Fn cofactor activity with Mini-Pg, which has low affinity for Fn, suggests that Mini-Pg binds the tPA-Fn complex more tightly than tPA alone. To explore this possibility, SPR was used to examine the interaction of Mini-Pg with Fn in the absence or presence of tPA. There was 50% more Mini-Pg binding to Fn in the presence of tPA than in its absence, suggesting that formation of the tPA-Fn complex exposes a cryptic site that binds Mini-Pg. Thus, our data (a) indicate that high affinity binding of Pg to Fn is not essential for Fn cofactor activity, and (b) suggest that kringle 5 localizes and stabilizes Pg within the tPA-Fn complex and contributes to its efficient activation.
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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 | 0.000 | 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".