A composite docking approach for the identification and characterization of ectosteric inhibitors of cathepsin K
Bibliographic record
Abstract
Cathepsin K (CatK) is a cysteine protease that plays an important role in mammalian intra- and extracellular protein turnover and is known for its unique and potent collagenase activity. Through studies on the mechanism of its collagenase activity, selective ectosteric sites were identified that are remote from the active site. Inhibitors targeting these ectosteric sites are collagenase selective and do not interfere with other proteolytic activities of the enzyme. Potential ectosteric inhibitors were identified using a computational approach to screen the druggable subset of and the entire 281,987 compounds comprising Chemical Repository library of the National Cancer Institute-Developmental Therapeutics Program (NCI-DTP). Compounds were scored based on their affinity for the ectosteric site. Here we compared the scores of three individual molecular docking methods with that of a composite score of all three methods together. The composite docking method was up to five-fold more effective at identifying potent collagenase inhibitors (IC50 < 20 μM) than the individual methods. Of 160 top compounds tested in enzymatic assays, 28 compounds revealed blocking of the collagenase activity of CatK at 100 μM. Two compounds exhibited IC50 values below 5 μM corresponding to a molar protease:inhibitor concentration of <1:12. Both compounds were subsequently tested in osteoclast bone resorption assays where the most potent inhibitor, 10-[2-[bis(2-hydroxyethyl)amino]ethyl]-7,8-diethylbenzo[g]pteridine-2,4-dione, (NSC-374902), displayed an inhibition of bone resorption with an IC50-value of approximately 300 nM and no cell toxicity effects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".