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Record W2606615303 · doi:10.1021/acs.biochem.6b01276

The Catalase Activity of Catalase-Peroxidases Is Modulated by Changes in the p<i>K</i><sub>a</sub> of the Distal Histidine

2017· article· en· W2606615303 on OpenAlexafffund
Miguel Machuqueiro, Bruno L. Victor, Jacek Switala, Jacylyn Villanueva, Carme Rovira, Ignacio Fita, P.C. Loewen

Bibliographic record

VenueBiochemistry · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEnzyme-mediated dye degradation
Canadian institutionsUniversity of Manitoba
FundersFundação para a Ciência e a TecnologiaNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsCatalaseHemeChemistryAdductPeroxidaseEnzymeHistidineProtonationStereochemistryBiochemistryOrganic chemistryIon

Abstract

fetched live from OpenAlex

The unusual Met-Tyr-Trp adduct composed of cross-linked side chains along with an associated mobile Arg is essential for catalase activity in catalase-peroxidases. In addition, acidic residues in the entrance channel, in particular an Asp and a Glu ∼7 and ∼15 Å, respectively, from the heme, significantly enhance catalase activity. The mechanism by which these channel carboxylates influence catalase activity is the focus of this work. Seventeen new variants with fewer and additional acidic residues have been constructed and characterized structurally and for enzymatic activity, revealing that their effect on activity is roughly inversely proportional to their distance from the heme and adduct, suggesting that the electrostatic potential of the heme cavity may be affected. A discrete group of protonable residues are contained within a 15 Å sphere surrounding the heme iron, and a computational analysis reveals that the p K a of the distal His 112, alone, is modulated within the pH range of catalase activity by the remote acidic residues in a pattern consistent with its protonated form having a key role in the catalase reaction cycle. The electrostatic potential also impacts the catalatic reaction through its influence on the charged status of the Met-Tyr-Trp adduct.

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 score0.076
Threshold uncertainty score0.248

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 science0.0010.000
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 head0.010
GPT teacher head0.204
Teacher spread0.193 · 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
Published2017
Admission routes2
Has abstractyes

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