The Roles of Selected Arginine and Lysine Residues of TAFI (Pro-CPU) in Its Activation to TAFIa by the Thrombin-Thrombomodulin Complex
Bibliographic record
Abstract
Thrombomodulin (TM) increases the catalytic efficiency of thrombin (IIa)-mediated activation of thrombin-activable fibrinolysis inhibitor (TAFI) 1250-fold. Negatively charged residues of the C-loop of TM-EGF-like domain 3 are required for TAFI activation. Molecular models suggested several positively charged residues of TAFI with which the C-loop residues could interact. Seven TAFI mutants were constructed to determine if these residues are required for efficient TAFI activation. TAFI wild-type or mutants were activated in the presence or absence of TM and the kinetic parameters of TAFI activation were determined. When the three consecutive lysine residues in the activation peptide of TAFI were substituted with alanine (K42/43/44A), the catalytic efficiencies for TAFI activation with TM decreased 8-fold. When other positively charged surface residues of TAFI (Lys-133, Lys-211, Lys-212, Arg-220, Lys-240, or Arg-275) were mutated to alanine, the catalytic efficiencies for TAFI activation with TM decreased by 1.7-2.7-fold. All decreases were highly statistically significant. In the absence of TM, catalytic efficiencies ranged from 2.8-fold lower to 1.24-fold higher than wild-type. None of these, except the 2.8-fold lower value, was statistically significant. The average half-life of the TAFIa mutants was 8.1+/-0.6 min, and that of wild type was 8.4+/-0.3 min at 37 degrees C. Our data show that these residues are important in the activation of TAFI by IIa, especially in the presence of TM. Whether the mutated residues promote a TAFI-TM or TAFI-IIa interaction remains to be determined. In addition, these residues do not influence spontaneous inactivation of TAFIa.
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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) | 0.000 | 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".