Fluorescence Properties and Functional Roles of Tryptophan Residues 60d, 96, 148, 207, and 215 of Thrombin
Bibliographic record
Abstract
Conservative Trp-to-Phe mutations were individually created in human thrombin at positions 60d, 96, 148, 207, and 215. Fluorescence intensities for these residues varied by a factor of 6. Residues 60d, 96, 148, and 215 transferred energy to the thrombin inhibitor 5-dimethylaminonaphthalene-1-sulfonylarginine-N-(3-ethyl-1,5- pentanediyl)amide efficiently, but residue 207 did not. Intensities correlated inversely with exposure to solvent, and measured and theoretical energy transfer efficiencies agreed well. Function was measured with respect to fibrinogen clotting, platelet and factor V activation, inhibition by antithrombin, and the thrombomodulin-dependent activation of protein C and thrombin-activable fibrinolysis inhibitor (TAFI). All activities of W96F and W207F ranged from 74 to 154% of the wild-type activity. This was also true for W148F, except for inhibition by antithrombin, where it showed 60% activity. W60dF was deficient by 30, 57, and 43% with fibrinogen clotting, platelet activation, and factor V cleavage (Arg(1006)), respectively. W215F was deficient by 90, 55, and 56% with fibrinogen clotting, platelet activation, and factor V cleavage (Arg(1536)). With protein C and TAFI, W96F, W148F, and W207F were normal. W60dF, however, was 76 and 23% of normal levels with protein C and TAFI, respectively. In contrast, W215F was 25 and 124% of normal levels in these reactions. Thus, many activities of thrombin are retained upon substitution of Trp with Phe at positions 96, 148, and 207. Trp(60d), however, appears to be very important for TAFI activation, and Trp(215) appears to very important for clotting and protein C 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".