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Record W1968461149 · doi:10.1515/bc.2001.102

Cathepsins X and B Display Distinct Activity Profiles That Can Be Exploited for Inhibitor Design

2001· article· en· W1968461149 on OpenAlexaff
Robert Ménard, Christian Therrien, Paule Lachance, Traian Sulea, Haiping Qi, William Roush

Bibliographic record

VenueBiological Chemistry · 2001
Typearticle
Languageen
FieldMedicine
TopicPeptidase Inhibition and Analysis
Canadian institutionsBiotechnology Research Institute
Fundersnot available
KeywordsCathepsin BCathepsin ACathepsin OCarboxypeptidaseCathepsinChemistryDipeptideBiochemistryCathepsin CEndopeptidaseEnzymeCleavage (geology)Cathepsin LCathepsin HCathepsin L1StereochemistryBiologyPeptide

Abstract

fetched live from OpenAlex

The carboxypeptidase and endopeptidase activities of cathepsins X and B, as well as their inhibition by E-64 derivatives, have been investigated in detail and compared. The results clearly demonstrate that cathepsins X and B do not share similar activity profiles against substrates and inhibitors. Using quenched fluorogenic substrates, we show that cathepsin X preferentially cleaves substrates through a monopeptidyl carboxypeptidase pathway, while cathepsin B displays a preference for the dipeptidyl pathway. The preference for one or the other pathway is about the same for both enzymes, i. e. approximately 2 orders of magnitude. Cleavage of a C-terminal dipeptide of a substrate by cathepsin X can be observed under conditions that preclude efficient monopeptidyl carboxypeptidase activity. In addition, an inhibitor designed to exploit the unique structural features responsible for the carboxypeptidase activity of cathepsin X has been synthesized and tested against cathepsins X, B and L. Although of moderate potency, this E-64 derivative is the first reported example of a cathepsin X-specific inhibitor. By comparison, CA074 was found to inactivate cathepsin B at least 34000-fold more efficiently than cathepsin X.

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 score0.004
Threshold uncertainty score0.418

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 head0.103
GPT teacher head0.302
Teacher spread0.199 · 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

Citations13
Published2001
Admission routes1
Has abstractyes

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