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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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0030.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0080.002

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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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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