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Effects of calcium and magnesium on a 41-kDa serine-dependent protease possessing collagen-cleavage activity

2000· article· en· W1991602799 on OpenAlexaff
John J. Robinson

Bibliographic record

VenueJournal of Cellular Biochemistry · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtease and Inhibitor Mechanisms
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsProteaseSerine proteaseBiochemistryChemistryCleavage (geology)CalciumGelatinaseProteasesPMSFMetalloproteinaseEnzymeBiology

Abstract

fetched live from OpenAlex

We report here the continued characterization of a 41-kDa protease expressed in the early stage of the sea urchin embryo. This protease was previously shown to possess both a gelatin-cleavage activity and an echinoderm-specific collagen-cleavage activity. In the experiments reported here, we have explored the biochemical nature of this proteolytic activity. Pepstatin A (an acidic protease inhibitor), 1,10-phenanthroline (a metalloprotease inhibitor), and E-64 (a thiol protease inhibitor) were without effect on the gelatin-cleavage activity of the 41-kDa species. Using a gelatin substrate gel zymographic assay, the serine protease inhibitors phenylmethylsulfonyl fluoride and benzamide appeared to partially inhibit gelatin-cleavage activity. This result was confirmed in a quantitative gelatin-cleavage assay using the water soluble, serine protease inhibitor [4-(2-aminoethyl)benzenesulfonylfluoride]. The biochemical character of this protease was further explored by examining the effects of calcium and magnesium, the major divalent cations present in sea water, on the gelatin-cleavage activity. Calcium and magnesium competed for binding to the 41-kDa collagenase/gelatinase, and prebound calcium was displaced by magnesium. Cleavage activity was inhibited by magnesium, and calcium protected the protease against this inhibition. These results identify calcium and magnesium as antagonistic agents that may regulate the proteolytic activity of the 41-kDa species.

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 score0.002
Threshold uncertainty score0.853

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 head0.005
GPT teacher head0.218
Teacher spread0.214 · 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

Citations12
Published2000
Admission routes1
Has abstractyes

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