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Record W2416005390 · doi:10.1021/acs.biochem.5b00912

Kinetics of Zinc and Cadmium Exchanges between Metallothionein and Carbonic Anhydrase

2015· article· en· W2416005390 on OpenAlexafffund
Tyler B. J. Pinter, Martin J. Stillman

Bibliographic record

VenueBiochemistry · 2015
Typearticle
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMetallothioneinCarbonic anhydraseCadmiumZincChemistryKineticsBiochemistryMetalloproteinEnzymeOrganic chemistry

Abstract

fetched live from OpenAlex

The flexible coordination stoichiometry of a relatively high number of metal ions is a property unique to the metallothionein (MT) family of proteins. Mammalian MTs, for example, accommodate up to seven divalent metal ions in tetrahedral coordination geometries, using its complement of 20 cysteine ligands. The lability of the metals from these metalloclusters has been used to support the proposal of MTs acting as metal chaperones, by donating to other metal-binding proteins. The metal exchange kinetics between human MT1A and carbonic anhydrase (CA) were examined using time-dependent electrospray ionization mass spectrometry (ESI-MS). The time dependence of three different reaction conditions were studied: (i) zinc donation from partially metalated zinc-MT to apoCA; (ii) metal exchange between zinc saturated MTs and cadmium saturated CA (Cd-CA); and (iii) metal exchange between partially metalated zinc-MTs and Cd-CA. The results show that zinc donation from Zn-MTs to apo-zinc-dependent enzymes is dependent on the metal loading of the Znn-MT (where n = 1-7) and that this is a direct consequence of the increasing metal affinity for smaller values of n. Partially metalated MTs are also shown to extract cadmium from Cd-CA with significantly faster rates than metal saturated MTs and that even under zinc limiting conditions, mammalian Cd-CA would not coexist with MT. On the basis of these and previously published results, we suggest that protein-protein interactions between MT and CA facilitate metal transfers through favorable electrostatic interactions and hypothesize that the metal could be transferred between the MT and the enzyme active site using nearby metal-binding functionalities along the transfer pathway.

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 score0.098
Threshold uncertainty score0.503

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 head0.042
GPT teacher head0.308
Teacher spread0.267 · 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

Citations29
Published2015
Admission routes2
Has abstractyes

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