Identification of the Mechanism Responsible for the Increased Fibrin Specificity of TNK-Tissue Plasminogen Activator Relative to Tissue Plasminogen Activator
Bibliographic record
Abstract
TNK-tissue plasminogen activator (TNK-t-PA), a bioengineered variant of tissue-type plasminogen activator (t-PA), has a longer half-life than t-PA because the glycosylation site at amino acid 117 (N117Q, abbreviated N) has been shifted to amino acid 103 (T103N, abbreviated T) and is resistant to inactivation by plasminogen activator inhibitor 1 because of a tetra-alanine substitution in the protease domain (K296A/H297A/R298A/R299A, abbreviated K). TNK-t-PA is more fibrin-specific than t-PA for reasons that are poorly understood. Previously, we demonstrated that the fibrin specificity of t-PA is compromised because t-PA binds to (DD)E, the major degradation product of cross-linked fibrin, with an affinity similar to that for fibrin. To investigate the enhanced fibrin specificity of TNK-t-PA, we compared the kinetics of plasminogen activation for t-PA, TNK-, T-, K-, TK-, and NK-t-PA in the presence of fibrin, (DD)E or fibrinogen. Although the activators have similar catalytic efficiencies in the presence of fibrin, the catalytic efficiency of TNK-t-PA is 15-fold lower than that for t-PA in the presence of (DD)E or fibrinogen. The T and K mutations combine to produce this reduction via distinct mechanisms because T-containing variants have a higher K(M), whereas K-containing variants have a lower k(cat) than t-PA. These results are supported by data indicating that T-containing variants bind (DD)E and fibrinogen with lower affinities than t-PA, whereas the K and N mutations have no effect on binding. Reduced efficiency of plasminogen activation in the presence of (DD)E and fibrinogen but equivalent efficiency in the presence of fibrin explain why TNK-t-PA is more fibrin-specific than t-PA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 teacher head, 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".