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Record W2032986967 · doi:10.1021/bm050229c

Thermodynamics of Binding Interactions between Bovine β-Lactoglobulin A and the Antihypertensive Peptide β-Lg f142-148

2006· article· en· W2032986967 on OpenAlexaff
Samira Roufik, Sylvie F. Gauthier, Xiao-jing Leng, Sylvie L. Turgeon

Bibliographic record

VenueBiomacromolecules · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsChemistryIsothermal titration calorimetryPeptideEnthalpyBinding siteBinding energyCrystallographyStereochemistryBiochemistryThermodynamics

Abstract

fetched live from OpenAlex

The binding capacity of bovine beta-lactoglobulin variant A (beta-Lg A) for six peptides derived from beta-Lg was evaluated using an ultrafiltration method under the following conditions: pH 6.8, 40 degrees C, and a beta-Lg A/peptide molar ratio of 1:5. Only peptides beta-Lg f102-105, f142-148, and f69-83 bound in significant amounts to beta-Lg A corresponding to 1.5, 1.1, and 0.7 mol of peptide per mole of beta-Lg A, respectively. The interaction between beta-Lg A and the antihypertensive peptide beta-Lg f142-148 was investigated further by isothermal titration calorimetry. The binding isotherms at pH 6.8 and 25 degrees C confirmed that beta-Lg f142-148 bound to beta-Lg A and that the interaction followed a sequential three-site binding model with constants of association of 2 x 10(3), 1 x 10(3), and 0.4 x 10(3) M(-1) for the first, second, and third binding sites, respectively. The enthalpy of binding was exothermic for the first and second binding sites and endothermic for the third binding site. Binding of the peptide to all three sites was spontaneous as shown by the negative free energy values. These results show for the first time that beta-Lg A can bind bioactive peptides. This potential could be exploited to transport bioactive peptides and protect them in the gastrointestinal tract following their oral administration as nutraceuticals.

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 score0.029
Threshold uncertainty score0.547

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 head0.008
GPT teacher head0.232
Teacher spread0.224 · 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

Citations69
Published2006
Admission routes1
Has abstractyes

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