Identification of substrate-specific inhibitors of cathepsin K through high-throughput screening
Bibliographic record
Abstract
Cathepsin K (CatK) is a cysteine protease and drug target for skeletal disorders that is known for its potent collagenase and elastase activity. The formation of oligomeric complexes of CatK in the presence of glycosaminoglycans has been associated with its collagenase activity. Inhibitors that disrupt these complexes can selectively block the collagenase activity without interfering with the other regulatory proteolytic activities of the enzyme. Here, we have developed a fluorescence polarization (FP) assay to screen 4761 compounds for substrate-specific ectosteric collagenase inhibitors of CatK. A total of 38 compounds were identified that block the collagenase activity without interfering with the hydrolysis of active site substrates such as the synthetic peptide substrate, benzyloxycarbonyl-Phe-Arg-7-amido-4-methylcoumarin, and gelatin. The identified inhibitors can be divided into two main classes, negatively charged and polyaromatic compounds which suggest the binding to different ectosteric sites. Two of the inhibitors were highly effective in preventing the bone-resorption activity of CatK in osteoclasts. Interestingly, some of the ectosteric inhibitors were capable of differentiating between the collagenase and elastase activity of CatK depending on the ectosteric site utilized by the compound. Owing to their substrate-specific selectivity, ectosteric inhibitors represent a viable alternative to side effect-prone active site-directed inhibitors.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".