Generation of a Stable Activated Thrombin Activable Fibrinolysis Inhibitor Variant
Bibliographic record
Abstract
Activated thrombin activable fibrinolysis inhibitor (TAFIa), generated upon activation of TAFI, exerts an antifibrinolytic effect. TAFIa is a thermolabile enzyme, inactivated through a conformational change. The objective of the current study was to generate a stable variant of human TAFIa. Using a site-directed as well as a random mutagenesis approach to generate a library of TAFI mutants, we identified two mutations that increase TAFIa stability, i.e. a Ser305 to Cys and a Thr329 to Ile mutation, respectively. Combining these mutations in TAFI-Ala147-Ile325, the most stable isoform of TAFIa (half-life of 9.4 +/- 0.4 min), revealed a TAFIa half-life of 70 +/- 3.1 min (i.e. an 11-fold increase versus 6.3 +/- 0.3 min for TAFIa-Ala147-Thr325, the most frequently occurring isoform of TAFI in humans) at 37 degrees C. Moreover, clot lysis (induced by tissue plasminogen activator) experiments in which TAFI-Ala147-Cys305-Ile325-Ile329 was added to TAFI-depleted plasma revealed a 50% clot lysis time of 313 +/- 77 min (i.e. a 3.0-fold increase versus 117 +/- 10 min for TAFI-Ala147-Thr325). The availability of a more stable TAFIa variant will facilitate the search for inhibitors and allow further structural analysis to elucidate the mechanisms of the instability 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".