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Record W2335543360 · doi:10.1021/ja106731e

Evidence that Water Can Reduce the Kinetic Stability of Protein−Hydrophobic Ligand Interactions

2010· article· en· W2335543360 on OpenAlexafffund
Lan Liu, Klaus Michelsen, Elena N. Kitova, Paul D. Schnier, John S. Klassen

Bibliographic record

VenueJournal of the American Chemical Society · 2010
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryDissociation (chemistry)Reaction rate constantAqueous solutionDeprotonationIonPhysical chemistryAnalytical Chemistry (journal)KineticsOrganic chemistry

Abstract

fetched live from OpenAlex

The first quantitative comparison of the thermal dissociation rate constants measured for protein-ligand complexes in their hydrated and dehydrated states is described. Rate constants, measured using surface plasmon resonance spectroscopy, are reported for the dissociation of the 1:1 complexes of bovine β-lactoglobulin (Lg) with the fatty acids (FA), palmitic acid (PA), and stearic acid (SA), in aqueous solution at pH 8 and at temperatures ranging from 5 to 45 °C. The rate constants are compared to values determined from time-resolved blackbody infrared radiative dissociation measurements for the gaseous deprotonated (Lg+FA)(n-) ions, where n = 6 and 7, at temperatures ranging from 25 to 66 °C. Notably, the hydrated (Lg+PA) complex is kinetically less stable than the corresponding gas phase (Lg+PA)(n-) ions at all temperatures investigated; the hydrated (Lg+SA) complex is kinetically less stable than the gaseous (Lg+SA)(n-) ions at temperatures <45 °C. The greater kinetic stability of the gaseous (Lg+FA)(n-) ions originates from significantly larger, by 11-12 kcal mol(-1), E(a) values. It is proposed that the differences in the dissociation E(a) values measured in solution and the gas phase reflect the differential hydration of the reactant and the dissociative transition state.

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 score0.009
Threshold uncertainty score0.669

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.284
Teacher spread0.263 · 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

Citations29
Published2010
Admission routes2
Has abstractyes

Explore more

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