Locating the Binding Sites of Folic Acid with Milk α- and β-Caseins
Bibliographic record
Abstract
We located the binding sites of folic acid with milk α- and β-caseins at physiological conditions, using constant protein concentration and various folic acid contents. FTIR, UV-visible, and fluorescence spectroscopic methods as well as molecular modeling were used to analyze folic acid binding sites, the binding constant, and the effect of folic acid interaction on the stability and conformation of caseins. Structural analysis showed that folic acid binds caseins via both hydrophilic and hydrophobic contacts with overall binding constants of K(folic acid-α-caseins) = 4.8 (±0.6) × 10(4) M(-1) and K(folic acid-β-caseins) = 7.0 (±0.9) × 10(4) M(-1). The number of bound acid molecules per protein was 1.5 (±0.4) for α-casein and 1.4 (±0.3) for β-casein complexes. Molecular modeling showed different binding sites for folic acid on α- and β-caseins. The participation of several amino acids in folic acid-protein complexes was observed, which was stabilized by hydrogen bonding network and the free binding energy of -7.7 kcal/mol (acid-α-casein) and -8.1 kcal/mol (acid-β-casein). Folic acid complexation altered protein secondary structure by the reduction of α-helix from 35% (free α-casein) to 33% (acid-complex) and 32% (free β-casein) to 26% (acid-complex) indicating a partial protein destabilization. Caseins might act as carriers for transportation of folic acid to target molecules.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".