Acyl-Enzyme Complexes between Tissue-type Plasminogen Activator and Neuroserpin are Short-lived in Vitro
Bibliographic record
Abstract
The serine protease tissue-type plasminogen activator (t-PA) initiates the fibrinolytic protease cascade and plays a significant role in motor learning, memory, and neuronal cell death induced by excitotoxin and ischemia. In the fibrinolytic system, the serpin PAI-1 negatively regulates the enzymatic activity of both single-chain and two-chain t-PA (sct-PA and tct-PA). In the central nervous system, neuroserpin (NSP) is a serpin thought to regulate t-PA enzymatic activity. We report that although both sct-PA and tct-PA rapidly form acyl-enzyme complexes with NSP in vitro, the interactions are short-lived, rapidly progressing to complete cleavage of NSP and regeneration of fully active enzyme. All NSP molecules appear to transit through the detectable acyl-enzyme intermediate and progress to completion of cleavage; no subpopulation that functions as a pure substrate was detected. Likewise, all molecules were reactive, with no evidence of a latent subpopulation. The interactions between NSP and t-PA were distinct from those between plasmin and NSP, wherein the same peptide bond was cleaved but there was no evidence of a detectable plasmin-NSP acyl-enzyme complex. The interactions between t-PA and NSP contrast with the formation of long-lived, physiologically irreversible acyl-enzyme complexes between t-PA and PAI-1, suggesting that the physiologic effect of t-PA-NSP interactions may be more complex than previously thought.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".