Bibliographic record
Abstract
Background: The mammalian proprotein convertases (PCs) constitute a family of nine secretory serine proteases related to bacterial subtilisin and yeast kexin. Seven of them (PC1/3, PC2, furin, PC4, PC5/6, PACE4 and PC7) activate cellular and pathogen precursor proteins by cleavage at single or paired basic residues, while SKI-1/S1P and PCSK9 regulate cholesterol/lipid homeostasis via cleavage at non-basic residues or through induced degradation of receptors. PCs are now considered attractive targets for the development of powerful novel therapeutics. In this poster presentation, I will summarize the physiological functions and pathological implications of the PCs and discuss proposed strategies to control some of their activities, including their therapeutic application and validation in selected disease states. Objectives: The concept of the cleavage of precursor proteins to generate active products was born 47 years ago and the cognate convertases were identified during a 13-year period that ended in 2003 with the identification of PCSK9. Since the discovery of the PCs, efforts have been deployed to identify specific functions of PCs in animal models and humans in both health and disease and they have been implicated in a wide variety of cellular processes that regulate both body homeostasis and multiple disease states. While most PCs exert their functions through cleavage of substrates either at basic or non-basic amino acids, PCSK9 only requires its enzymatic activity to autocatalytically process its prosegment in the endoplasmic reticulum, and is secreted as an inactive protease. The challenging identification of safe orally active small molecule inhibitors of some of the convertases, especially PCSK9, is definitively a future goal that should be pursued vehemently. Conclusions: The use of biologics and desirable automated injection procedures or minimally invasive methods may be the future for effective PC-based therapies. The future is indeed bright and awaits innovative and practical approaches to target these exciting and multifunctional enzymes. The topics discussed here will encourage the investigation and development of potent and effective PC-inhibitors/silencers, and minimally invasive procedures for delivery to patients, which would be highly beneficial in the clinical treatment of diseases in which PCs play a key role.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".