Binding of Plasminogen and Tissue Plasminogen Activator to Plasmin-Modulated Factor X and Factor Xa
Bibliographic record
Abstract
Previous work in our laboratory has suggested that the fibrinolytic enzyme plasmin (Pn) inactivates coagulation factors X (FX) and Xa (FXa) in the presence of Ca(2+) and anionic phospholipid (aPL), producing fragments which bind plasminogen (Pg) and accelerate tissue plasminogen activator (t-PA). Our goals here were to determine if the Pn-mediated fragments of FX or FXa remain associated, whether they directly bind t-PA, and to quantify their interaction with Pg. Binding to aPL, benzamidine-Sepharose, or the active-site inhibitor dansyl-Glu-Gly-Arg-chloromethyl ketone demonstrated that Pn cleavage yielded noncovalent heterodimers of a fragment containing the aPL-binding domain (FXgamma(47) or FXagamma(33)) and a 13-kDa fragment (FXgamma(13) or FXagamma(13)). Both ligand blotting and surface plasmon resonance (SPR) showed that Pn-cleaved FX and FXa bound t-PA directly when Pn-treatment was effected in the presence of aPL and Ca(2+). Using SPR, apparent K(d) values of 1-3 microM and 0.3-0.4 microM were measured directly and by competition for the FXgamma(47/13)-Pg and FXagamma(33/13)-Pg interactions, respectively. For the first time, Pg-binding to a receptor was shown to be Ca(2+) enhanced, although primarily mediated by C-terminal lysine residues. Mathematical modeling of kinetic data suggesting two Pg per FXgamma(47/13) or FXagamma(33/13) was consistent with our conclusion that each subunit of FXgamma(47/13) or FXagamma(33/13) contains a C-terminal lysine. Earlier X-ray structures show that these Lys residues are distal from each other and the membrane, supporting the model where each interacts with a separate Pg. t-PA acceleration by FXgamma(47/13) or FXagamma(33/13) may therefore involve simultaneous presentation of two substrate molecules.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".