Development of Protein-based Inhibitors of the Proprotein of Convertase SKI-1/S1P
Bibliographic record
Abstract
Processing of membrane-bound transcription factors such as sterol regulatory element-binding proteins (SREBPs) and the ER-stress response factor ATF6, and glycoproteins of some hemorrhagic fever viruses are initiated by the proprotein convertase SKI-1/S1P. So far, no cellular protein-based inhibitor of the hydrophobic-amino acid specific SKI-1 is known. The prosegment of the basic-amino acid specific convertases (e.g. furin and PC5) or alpha(1)-PDX, a variant of alpha(1)-antitrypsin (alpha(1)-AT) exhibiting an RIPR(358) sequence at the reactive site loop, were shown to potently inhibit these secretory proteinases. Accordingly, we tested the SKI-1-inhibitory potential of various point mutants of either the 198 amino acid preprosegment of SKI-1-(1-198) or alpha(1)-AT. Transient transfections data showed that, out of numerous mutants studied, the R134E prosegment mutant or the alpha(1)-AT reactive site loop variants RRVL(358), RRYL(358) and RRIL(358) are the best specific cellular inhibitors of SKI-1. The observed inhibition of the processing of endogenous SREBP-2, exogenous ATF6 and a PDGF-A (RRLL(86)) variant were >55% and reach approximately 80% in stable transfectants. We also show that SKI-1 forms SDS-stable complexes with these alpha(1)-AT variants, but not with wild-type alpha(1)-AT or alpha(1)-PDX. Finally, these inhibitors were also shown to affect the processing and stability of the Crimean-Congo hemorrhagic fever virus glycoprotein.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".