Stability of mutant serpin/furin complexes: Dependence on pH and regulation at the deacylation step
Bibliographic record
Abstract
Furin proteolytically cleaves a wide variety of proprotein substrates mainly within the trans-Golgi network (TGN) but also at the cell membrane and in endosomal compartments where pH is more acidic. Incorporation of furin recognition sequences within the reactive site loop (RSL) of alpha(1)-antitrypsin (AT) leads to the production of furin inhibitors. In an attempt to design more stable, potent, and specific serpin-based inhibitors, we constructed a series of AT and alpha(1)-antichymotrypsin (ACT) mutants by modifying the P(7)-P(1) region of their RSLs. The biochemical properties of these variants were assessed by evaluating their propensity to establish SDS-resistant complexes with furin in a variety of conditions (pH 6.0-9.0) and by measuring their association rate constants. The effect of pH during the initial steps of complex formation was minimal, suggesting that the acylation step is not rate-limiting. The decrease in stoichiometry of inhibition (SI) values observed in AT variants at high pHs was a result of the reduced pH-dependent deacylation rate, which is rate-limiting in this mechanism and which suggests increased complex stability. Conversely, the SI values for ACT mutants had a tendency to be lower at acidic pH. Transiently transfecting HEK293 cells with these mutants abolished processing of the pro-von Willebrand factor precursor but, interestingly, only the ACT variants were secreted in the media as uncleaved forms. Our results suggest that reengineering the reactive site loops of serpins to accommodate and target furin or other serine proteases must take into account the intrinsic physicochemical properties of the serpin.
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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".