Resveratrol Binding to Human Serum Albumin
Bibliographic record
Abstract
Resveratrol (Res), a polyphenolic compound found largely in the skin of red grape and wine, exhibits a wide range of pharmaceutical properties and plays a role in prevention of human cardiovascular diseases [Pendurthi et al., Arterioscler. Thromb. Vasc. Biol. 19, 419-426 (1999)]. It shows a strong affinity towards protein binding and used as inhibitor for cyclooxygenase and ribonuclease reductase. The aim of this study was to examine the interaction of resveratrol with human serum albumin (HSA) in aqueous solution at physiological conditions, using a constant protein concentration (0.3 mM) and various pigment contents (microM to mM). FTIR, UV-Visible, CD, and fluorescence spectroscopic methods were used to determine the resveratrol binding mode, the binding constant and the effects of pigment complexation on protein secondary structure. Structural analysis showed that resveratrol bind non-specifically (H-bonding) via polypeptide polar groups with overall binding constant of K(Res) = 2.56 x 10(5) M(-1). The protein secondary structure, analysed by CD spectroscopy, showed no major alterations at low resveratrol concentrations (0.125 mM), whereas at high pigment content (1 mM), major increase of alpha-helix from 57% (free HSA) to 62% and a decrease of beta-sheet from 10% (free HSA) to 7% occurred in the resveratrol-HSA complexes. The results indicate a partial stabilization of protein secondary structure at high resveratrol content.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".