Rat cathepsin K: Enzymatic specificity and regulation of its collagenolytic activity
Bibliographic record
Abstract
Human cathepsin K (hCatK), which is highly expressed in osteoclasts, has the noteworthy ability to cleave type I and II collagens in their helical domain. Its collagenase potency depends strictly on the formation of an oligomeric complex with chondroitin 4-sulfate (C4-S). Accordingly, hCatK is a pivotal protease involved in bone resorption and is an attractive target for the treatment of osteoporosis. As rat is a common animal model for the evaluation of hCatK inhibitors, we conducted a comparative analysis of rat CatK (rCatK) and hCatK, which share a high degree of identity (88%) and similarity (93%). The pH activity profile of both enzymes displayed a similar bell-shaped curve (optimal pH: 6.4). Presence of Ser134 and Val160 in the S2 pocket of rCatK instead of Ala and Leu residues, respectively, in hCatK, led to a weaker peptidase activity, as observed for mouse CatK. Also, regardless of the presence of C4-S, rCatK cleaved in the nonhelical telopeptide regions of both type I (tail) and type II (articular joint) rat collagens. Structure-based computational analyses (electrostatic potential, molecular docking, molecular dynamics, free energy calculations) sustained that the C4-S mediated collagenolytic activity of rCatK obeys distinct molecular interactions from those of hCatK. Additionally, T-kininogen (a.k.a. thiostatin), a unique rat serum acute phase molecule, acted as a tight-binding inhibitor of hCatK (Ki = 0.11 ± 0.05 nM). Taken into account the increase of T-Kininogen level in inflamed rat sera, this may raise the question of the appropriateness to evaluate pharmacological hCatK inhibitors in this peculiar animal model.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".