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Record W2164426756 · doi:10.1039/c2cp42759a

The effect of urea on aqueous hydrophobic contact-pair interactions

2012· article· en· W2164426756 on OpenAlexaff
Tanner A. Shpiruk, Mazdak Khajehpour

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Structure and Dynamics
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsUreaChemistryvan der Waals forceHydrophobic effectAqueous solutionDenaturation (fissile materials)Hydrogen bondSolventHydrophobeCrystallographyChemical physicsMoleculeOrganic chemistryBiochemistryNuclear chemistry

Abstract

fetched live from OpenAlex

Urea is perhaps the most common denaturant used for studying proteins. However the mechanism of denaturation is still not well understood. Recent theoretical work suggests that van der Waals interactions between urea and non-polar amino acid residues are a major contributor to the protein denaturation process. However, there are few experimental data measuring the effect of urea on hydrophobic interactions. In this work we have determined how the addition of urea to the aqueous solvent affects the contact-pair formed between alkyl and phenyl groups in model compounds: the data indicate that for solutes having a radius smaller than 2.87 Å cavity formation energetics dominate, therefore the addition of urea promotes the formation of hydrophobic contact pairs; while for larger solutes, van der Waals interactions have the largest magnitude, causing urea to disrupt the formation of contact pairs. The influence of urea on hydrophobic interactions is shown to be continuous in the 1-8 M concentration range and is well-correlated with the predictions of scaled particle theory. This demonstrates that the effect of urea on hydrophobic contact pairs can be explained by the changes observed in the solvent packing density, without having to invoke changes in the hydrogen bonding network of water.

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 score0.005
Threshold uncertainty score0.751

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 head0.004
GPT teacher head0.241
Teacher spread0.238 · 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

Citations20
Published2012
Admission routes1
Has abstractyes

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