The Proprotein Convertase SKI-1/S1P
Bibliographic record
Abstract
Herein we designed, synthesized, tested, and validated fluorogenic methylcoumarinamide (MCA) and chloromethylketone-peptides spanning the Lassa virus GPC cleavage site as substrates and inhibitors for the proprotein convertase SKI-1/S1P. The 7-mer MCA (YISRRLL-MCA) and 8-mer MCA (IYISRRLL-MCA) are very efficiently cleaved with respect to both the 6-mer MCA (ISRRLL-MCA) and point mutated fluorogenic analogues, except for the 7-mer mutant Y253F. The importance of the P7 phenylic residue was confirmed by digestions of two 16-mer non-fluorogenic peptidyl substrates that differ by a single point mutation (Y253A). Because NMR analysis of these 16-mer peptides did not reveal significant structural differences at recognition motif RRLL, the P7 Tyr residue is likely important in establishing key interactions within the catalytic pocket of SKI-1. Based on these data, we established through analysis of pro-ATF6 and pro-SREBP-2 cellular processing that decanoylated chloromethylketone 7-mer, 6-mer, and 4-mer peptides containing the core RRLL sequence are irreversible and potent ex vivo SKI-1 inhibitors. Although caution must be exercised in using these inhibitors in in vitro reactions, as they can also inhibit the basic amino acid-specific convertase furin, within cells and when used at concentrations < or = 100 microM these inhibitors are relatively specific for inhibition of SKI-1 processing events, as opposed to those performed by furin-like convertases.
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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".