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Record W2161365787 · doi:10.1002/cphc.201000774

A Simple Model for Barrier Frequencies for Enzymatic Reactions

2010· article· en· W2161365787 on OpenAlexaff
Iñaki Tuñón, James T. Hynes

Bibliographic record

VenueChemPhysChem · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhotosynthetic Processes and Mechanisms
Canadian institutionsCanadian Nautical Research Society
FundersDivision of ChemistryNational Science Foundation
KeywordsChemistryReaction rateChemical physicsReaction rate constantEnzyme catalysisComputational chemistrySimple (philosophy)Chemical reactionActivation energyThermodynamicsMolecular dynamicsInverseReaction mechanismBiological systemEnzymePhysical chemistryKineticsCatalysisOrganic chemistryClassical mechanicsPhysics

Abstract

fetched live from OpenAlex

We present a simple model to rationalize the effects of environment on the reaction barrier frequencies derived from free energy profiles. These frequencies are relevant in deviations of a rate constant from its transition state theory value and in determining which environmental dynamics participate in the reaction. In particular, this simple model can be used to understand the changes in the reaction barrier frequencies of an enzymatic catalyzed reaction and the corresponding uncatalyzed process in aqueous solution, a change which has implications for dynamical environmental effects on the enzymatic reaction. Two possible cases are analyzed, in which the polarity (charge separation/localization) of the reacting system increases or decreases as the reaction advances. A simple modeling of the environment's effects allows the explanation of an unusual "inverse" effect on the reaction frequencies, that is, a free energy barrier lowering accompanied by an increase of the reaction frequency, a behavior observed in some enzymes. The model predictions are successfully compared with results from full simulations for four different enzyme reactions.

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 score0.227
Threshold uncertainty score0.517

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 head0.267
Teacher spread0.250 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

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