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Record W2334497030 · doi:10.1021/la401524w

White Wine Proteins: How Does the pH Affect Their Conformation at Room Temperature?

2013· article· en· W2334497030 on OpenAlexfundno aff
Marie Dufrechou, Aude Vernhet, Pierre Roblin, François‐Xavier Sauvage, Céline Poncet‐Legrand

Bibliographic record

VenueLangmuir · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsnot available
FundersInstitut National de la Recherche AgronomiqueAlberta Water Research Institute
KeywordsChemistryWineCircular dichroismThaumatinWhite WineProtein tertiary structureInvertaseNative stateChitinaseProtein structureProtein aggregationCrystallographyBiophysicsBiochemistryEnzymeFood scienceBiology

Abstract

fetched live from OpenAlex

Our studies focused on the determination of aggregation mechanisms of proteins occurring in wine at room temperature. Even if the wine pH range is narrow (2.8 to 3.7), some proteins are affected by this parameter. At low pH, the formation of aggregates and the development of a haze due to proteins sometimes occur. The objective of this work was to determine if the pH impacted the conformational stability of wine proteins. Different techniques were used: circular dichroism and fluorescence spectroscopy to investigate the modification of their secondary and tertiary structure and also SAXS to determine their global shape. Four pure proteins were used, two considered to be stable (invertase and thaumatin-like proteins) and two considered to be unstable (two chitinase isoforms). Two pH values were tested to emphasize their behavior (pH 2.5 and 4.0). The present work highlighted the fact that the conformational stability of some wine proteins (chitinases) was impacted by partial modifications, related to the exposure of some hydrophobic sites. These modifications were enough to destabilize the native state of the protein. These modifications were not observed on wine proteins determined to be stable (invertase and thaumatin-like proteins).

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.893
Threshold uncertainty score0.999

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)0.0020.000

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 head0.010
GPT teacher head0.185
Teacher spread0.175 · 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.

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

Citations28
Published2013
Admission routes1
Has abstractyes

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