Structural analysis of DNA and RNA interactions with antioxidant flavonoids
Bibliographic record
Abstract
Flavonoids are natural polyphynolic compounds with major antioxidant activity that can prevent DNA damage. The anticancer and antiviral activities of these natural products are attributed to their potential biomedical applications. In this review we are examining how the antioxidant flavonoids bind DNA and RNA and what mechanism of action is involved in preventing DNA damage. Detailed spectroscopic data on the interactions of morin (mor), apigenin (api), naringin (nar), quercetin (que), kaempferol (kae) and delphinidin (del) with DNA and transfer RNA in aqueous solution at physiological conditions were analysed. The structural analysis showed flavonoids mainly intercalate into DNA and RNA duplexes with minor external binding to the major or minor groove and the backbone phosphate group with overall binding constants for DNA adducts Kmor═5.99×103 M–1, Kapi═7.10×104 M–1, and Knar═3.10×103 M–1, Kque═7.25×104 M–1, Kkae═3.60×104 M–1 and Kdel═1.66×104 M–1, and for tRNA adducts Kmor═9.15×103 M–1, Kapi═4.96×104 M–1, and Knar═1.14×104 M–1, Kque═4.80×104 M–1, Kkae═4.65×104 M–1 and Kdel═9.47×104 M–1. The stability of adduct formation is in the order of que > api > kae > del >mor > nar for DNA and del > api > que > kae > nar > mor for tRNA. Low flavonoid concentration induces helical stabilization, whereas high pigment content causes helix opening. Flavonoids induce a partial B to A–DNA transition at high pigment concentration, while tRNA remains in A-family structure upon flavonoid complexation. The antioxidant activity of flavonoids changes in the order delphinidin > quercetin > kaempferol > morin > naringin > apigenin. The results show intercalated flavonoid molecule can act as an antioxidant and prevent DNA damage.
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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 | 0.000 |
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".