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Record W2094925476 · doi:10.1021/jp064615f

Theoretical Study of the Cosolvent Effect on the Partial Molar Volume Change of Staphylococcal Nuclease Associated with Pressure Denaturation

2007· article· en· W2094925476 on OpenAlexaff
Takeshi Yamazaki, Takashi Imai, Fumio Hirata, Andriy Kovalenko

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Structure and Dynamics
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
Fundersnot available
KeywordsDenaturation (fissile materials)MolarChemistryNucleasePartial molar propertyVolume (thermodynamics)ChromatographyThermodynamicsBiochemistryDentistryNuclear chemistryMedicineDNAPhysics

Abstract

fetched live from OpenAlex

We explain the molecular mechanism of the effect of urea and glycerol cosolvents on the partial molar volume (PMV) change associated with the pressure denaturation of staphylococcal nuclease (SNase) protein recently observed in experiments. Native and denatured conformations of SNase are produced by using molecular dynamics simulations in water, and the PMV is obtained from the integral equation theory of molecular liquids called 3D-RISM, which is based on statistical mechanics. The PMV of the native SNase in water predicted by 3D-RISM theory is in good agreement with experiment. The PMV changes associated with pressure denaturation in water and in water-urea and water-glycerol mixtures are qualitatively reproduced. By analyzing the results obtained, we found two interesting cosolvent effects on the PMV: (1) both urea and glycerol cosolvents increase the PMVs of both native and denatured SNase compared to those in water and (2) both urea and glycerol cosolvents increase the PMV of denatured SNase more than that of native SNase. We also showed that these two observations can be explained in terms of the thermal volume, which is related to the packing effect of solvent molecules.

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)0.001
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 score0.010
Threshold uncertainty score0.217

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.005
GPT teacher head0.226
Teacher spread0.222 · 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

Citations24
Published2007
Admission routes1
Has abstractyes

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