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Record W2012654011 · doi:10.1021/bi0269963

Mechanism of S-Nitrosation of Recombinant Human Brain Calbindin D<sub>28K</sub>

2003· article· en· W2012654011 on OpenAlexaff
Limei Tao

Bibliographic record

VenueBiochemistry · 2003
Typearticle
Languageen
FieldMedicine
TopicRenin-Angiotensin System Studies
Canadian institutionsConcordia University
Fundersnot available
KeywordsChemistryNitrosationRadicalGlutathioneSuperoxide dismutaseCysteineSuperoxideBiochemistryAntioxidantEnzyme

Abstract

fetched live from OpenAlex

Mass spectrometry and UV-vis absorption results support a mechanism for NO donation by S-nitrosoglutathione (GSNO) to recombinant human brain calbindin D(28K) (rHCaBP) that requires the presence of trace copper, added as either Cu,Zn-superoxide dismutase (CuZnSOD) or CuSO(4). The extent of copper-catalyzed rHCaBP S-nitrosation depends on the ratio of protein to GSNO and on the reaction time, and NO-transfer is prevented when copper chelators are present. CuZnSOD is an efficient catalyst of rHCaBP S-nitrosation, and the mechanism of CuZnSOD-catalyzed S-nitrosation involves reduction of the active-site Cu(II) by a number of the five free thiols in rHCaBP, giving rise to thiyl radicals. The Cu(I)ZnSOD formed catalyzes the reductive cleavage of GSNO present in solution to give GSH and release NO. rHCaBP thiyl radicals react with NO to yield the S-nitrosoprotein. Cu(II)ZnSOD is also reduced by GSH in a concentration-dependent manner up to 5 mM but not at higher GSH concentrations. However, unlike the rHCaBP thiyl radicals, GS(*) radicals dimerize to GSSG faster than their reaction with NO. The data presented here provide a biologically relevant mechanism for protein S-nitrosation by small S-nitrosothiols. S-nitrosation is rapidly gaining recognition as a major form of protein posttranslational modification, and the efficient S-nitrosation of CaBP by CuZnSOD/GSNO is speculated to be of neurochemical importance given that CaBP and CuZnSOD are abundant in neurons.

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 score0.582

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 head0.017
GPT teacher head0.261
Teacher spread0.245 · 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

Citations24
Published2003
Admission routes1
Has abstractyes

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