Precursor convertases in the secretory pathway, cytosol and extracellular milieu
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Precursor proteins that transit through the secretory pathway often require processing at specific sites in order to release their bioactive entities. The most prevalent limited proteolysis occurs at single or paired basic residues, and is achieved by one or more of the seven subtilisin-like proprotein convertases (PCs); Furin, PC1, PC2, PACE4 (paired basic amino acid converting enzyme 4), PC4, PC5 and PC7. Other types of cleavages occur at hydophobic residues, some of which are performed by subtilisin/kexin-like isozyme-1 (SKI-1), which is also known as site-1 protease. Together, the PCs and SKI-1 regulate the activity of a large variety of cellular proteins, including growth factors, neuropeptides, receptors, enzymes and even toxins and glycoproteins from infectious retroviruses. These processing events are exquisitely regulated by multiple zymogen-activation steps, as well as by specific subcellular localization signals. The above mentioned convertases are implicated in a number of pathologies such as cancer, neurodegenerative diseases, endocrine disorders and inflammation. Recently, it was recognized that the metalloendopeptidase N-arginine dibasic convertase (NRDc; nardilysin), which cleaves at the N-terminus side of basic residues in dibasic pairs, is localized both in the cytosol and at the cell surface or in the extracellular milieu. While NRDc binds heparin-binding epidermal growth factor (HB-EGF) at the cell surface and potentiates its physiological effect, HB-EGF potently inhibits the NRDc's activity. NRDc could represent the equivalent of the PCs in the cytosol or the extracellular space.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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 it