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Record W2024506703 · doi:10.1002/poc.1687

Model for the analysis of enzymatic proton‐transfer reactions with an application to soybean lipoxygenase‐1 and six mutants

2010· article· en· W2024506703 on OpenAlexaff
Willem Siebrand, Zorka Smedarchina

Bibliographic record

VenueJournal of Physical Organic Chemistry · 2010
Typearticle
Languageen
FieldChemistry
TopicPhotochemistry and Electron Transfer Studies
Canadian institutionsSteacie Institute for Molecular Sciences
Fundersnot available
KeywordsChemistryKinetic isotope effectDeuteriumProtonReaction rate constantKinetic energyQuantum tunnellingEnzyme catalysisReaction rateThermodynamicsComputational chemistryKineticsEnzymeAtomic physicsCatalysisBiochemistryQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract A general analytical model is introduced for the analysis of temperature‐dependent rate constants in enzymatic hydrogen and deuterium transfer reactions. It exploits the relationship between kinetic isotope effects (KIEs) and their temperature dependence in tunneling reaction to derive criteria that indicate whether a data set can be assigned to one rate‐determining tunneling step in the enzymatic reaction sequence. If so, the model evaluates the relative contributions of the tunneling mode and supporting skeletal modes to transfer and provides information on these modes. Recently reported kinetic data on proton transfer in linoleic acid catalyzed by soybean lipoxygenase‐1 (SLO1) and six mutants are analyzed by the model, which includes two oscillators, one representing proton motion relative to the skeletal framework and the other the supporting framework motions. It is concluded that most but not all components of this data set can be assigned to a single tunneling step, possible exceptions being associated with the highest observed rates, in agreement with evolutionary expectations. This still allows a complete analysis, namely in terms of the deuteron rate constants. The analysis evaluates how the contributions to proton transfer of the two modes and the electronic term depend on transfer distances in the enzyme and its mutants. It also provides rationalizations for several apparent anomalies, such as the vanishing of the activation energy for some mutants and the observed invariance of the KIE among mutants with very different activity. Copyright © 2010 John Wiley & Sons, Ltd.

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 score0.039
Threshold uncertainty score0.482

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 head0.009
GPT teacher head0.252
Teacher spread0.243 · 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

Citations4
Published2010
Admission routes1
Has abstractyes

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