Inhibitors of the Subtilase-Like Pro-Protein Convertases (SPCs)
Bibliographic record
Abstract
Following protein biosynthesis, some of the most important cellular mechanisms that generate biological diversity are the enzymatically driven post-translational modifications that ultimately lead to the formation of bioactive molecules. Within the secretory pathway, a multitude of precursor proteins are thus modified resulting in hormones, neuropeptides, growth factors, receptors and even enzymes. These modifications include cleavage at specific sites through endo- or exo-peptidase action, amidation, glycosylation and sulfation. In recent years, an important family of these processing enzymes was discovered and characterized. The so-called proprotein convertases are the products of seven distinct genes and function as endopeptidases that cleave protein precursors C-terminal to basic residue sites. They are structurally related to the bacterial subtilisin family of enzymes and are thus referred to as the subtilisin-like proprotein convertases (SPCs). Many studies have examined the inhibition of this family of enzymes, through the search of endogenous inhibitors or through the development of peptidyl, non-peptidyl or protein inhibitors. Some potent inhibitors have been discovered or engineered. While it is certain that potent inhibitors could serve as important tools to further elucidate the specific functions of each SPC, it has also been suggested that such inhibitors may be developed into lead compounds that could have important therapeutic applications. This review examines the progress made in regards to endogenous and engineered inhibitors and evidence for possible uses as molecular tools or in therapeutic applications. It is noted that although important inhibitory potencies have often been reported, there is generally insufficient evidence to demonstrate high levels of specificity. It is thus suggested that an important short-term challenge before the field will be a better understanding of the catalytic specificity of each SPC.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".