In Vitro Assay for Protease Activity of Proprotein Convertase Subtilisin Kexins (PCSKs): An Overall Review of Existing and New Methodologies
Bibliographic record
Abstract
The mammalian proprotein convertase subtilisin kexins (PCSKs) previously called proprotein or prohormone convertases (PCs) are a family of Ca(+2)-dependent endoproteases in the subtilisin family. These proteolytic enzymes exert their many crucial physiological and biological functions in vivo via their ability to cleave larger inactive precursor proteins into their biologically active mature forms. This event takes place in a highly efficient and selective manner. Such actions of PCSKs either alone or in combination to cleave specific protein bonds are the hallmark events that not only define the normal functions and metabolism of the body but also may lead to a variety of diseases or disorders with associated conditions. These include among others, diabetes, obesity, cancer, cardiovascular diseases, reproduction abnormalities as well as viral bacterial infections. These conditions were the direct consequences of an enhanced level of enzymatic activity of one or more PCSKs except only PCSK9, whose protease activity in relation to its physiological substrate has yet to be characterized. Owing to this finding, a large number of research studies have been exclusively devoted to develop rapid, efficient and reliable in vitro methods for examining the protease activity of these enzymes. Several assays have been developed to monitor PCSK activity and these are widely used in chemical, biochemical, cellular and animal studies. This review will cover various methodologies and protocols that are currently available in the literature for PCSK activity assays. These include liquid phase methods using fluorogenic, chromogenic and intramolecularly quenched fluorescent substrates as well as a newly developed novel solid phase fluorescence method. This review will also highlight the usefulness of these methodologies and finally a comparative analysis has been made to examine their merits and demerits with some key examples.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".