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Record W2019380853 · doi:10.1042/bj20140809

Structural requirements for the collagenase and elastase activity of cathepsin K and its selective inhibition by an exosite inhibitor

2014· article· en· W2019380853 on OpenAlexafffund
Vidhu Sharma, Preety Panwar, Anthony J. O’Donoghue, Haoran Cui, Rafael V. C. Guido, Charles S. Craik, Dieter Brömme

Bibliographic record

VenueBiochemical Journal · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBone Metabolism and Diseases
Canadian institutionsUniversity of British Columbia
FundersNational Cancer InstituteCanadian Institutes of Health Research
KeywordsCollagenaseCathepsinChemistryElastaseBiochemistryCathepsin KElastinCathepsin GProteaseSerine proteaseProteolysisProteasesEnzymeBiologyIn vitro

Abstract

fetched live from OpenAlex

Human cathepsin K (CatK) is a major drug target for the treatment of osteoporosis. Although its collagenase activity is unique, CatK also exerts a potent elastolytic activity that is shared with human cathepsins V and S. Other members of the cysteine cathepsin family, which are structurally similar, do not exhibit significant collagen and elastin degrading activities. This raises the question of the presence of specific structural elements, exosites, that are required for these activities. CatK has two exosites that control its collagenolytic and elastolytic activity. Modifications of exosites 1 and 2 block the elastase activity of CatK, whereas only exosite-1 alterations prevent collagenolysis. Neither exosite affects the catalytic activity, protease stability, subsite specificity of CatK or the degradation of other biological substrates by this protease. A low-molecular-mass inhibitor that docks into exosite-1 inhibits the elastase and collagenase activity of CatK without interfering with the degradation of other protein substrates. The identification of CatK exosites opens up the prospect of designing highly potent inhibitors that selectively inhibit the degradation of therapeutically relevant substrates by this multifunctional protease.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.302

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.262
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations46
Published2014
Admission routes2
Has abstractyes

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