Biosynthesis and Enzymatic Characterization of Human SKI-1/S1P and the Processing of Its Inhibitory Prosegment
Bibliographic record
Abstract
Biochemical and enzymatic characterization of the novel human subtilase hSKI-1 was carried out in various cell lines. Within the endoplasmic reticulum of LoVo cells, proSKI-1 is converted to SKI-1 by processing of its prosegment into 26-, 24-, 14-, 10-, and 8-kDa products, some of which remain tightly associated with the enzyme. N-terminal sequencing and mass spectrometric analysis were used to map the cleavage sites of the most abundant fragments, which were confirmed by synthetic peptide processing. To characterize its in vitro enzymatic properties, we generated a secreted form of SKI-1. Our data demonstrate that SKI-1 is a Ca(2+)-dependent proteinase exhibiting optimal cleavage at pH 6.5. We present evidence that SKI-1 processes peptides mimicking the cleavage sites of the SKI-1 prosegment, pro-brain-derived neurotrophic factor, and the sterol regulatory element-binding protein SREBP-2. Among the candidate peptides encompassing sections of the SKI-1 prosegment, the RSLK(137)- and RRLL(186)-containing peptides were best cleaved by this enzyme. Mutagenesis of the latter peptide allowed us to develop an efficiently processed SKI-1 substrate and to assess the importance of several P and P' residues. Finally, we demonstrate that, in vitro, recombinant prosegments of SKI-1 inhibit its activity with apparent inhibitor constants of 100-200 nM.
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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 | 0.001 |
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".