A Comparative Study of Fe(II) and Fe(III) Interactions with DNA Duplex: Major and Minor Grooves Bindings
Bibliographic record
Abstract
The involvement of the Fe cations in autoxidation in cells and tissues is well documented. DNA is a major target in such reaction, and can chelate Fe cation in many ways. The present study was designed to examine the interaction of calf-thymus DNA with Fe(II) and Fe(III), in aqueous solution at pH 6.5 with cation/DNA (P) (P = phosphate) molar ratios (r) of 1:160 to 1:2. Capillary electrophoresis and Fourier transform infrared (FTIR) difference spectroscopic methods were used to determine the cation binding site, the binding constant, helix stability and DNA conformation in Fe-DNA complexes. Structural analysis showed that at low cation concentration (r = 1/80 and 1/40), Fe(II) binds DNA through guanine N-7 and the backbone PO(2) group with specific binding constants of K(G) = 5.40 x 10(4) M(1) and K(P) = 2.40 x 10(4) M(1). At higher cation content, Fe(II) bindings to adenine N-7 and thymine O-2 are included. The Fe(III) cation shows stronger interaction with DNA bases and the backbone phosphate group. At low cation concentration (r = 1:80), Fe(III) binds mainly to the backbone phosphate group, while at higher metal ion content, cation binding to both guanine N-7 atom and the backbone phosphate group is prevailing with specific binding constants of K(G) = 1.36 x 10(5) M(-1) and K(P) = 5.50 x 10(4) M(-1). At r = 1:10, Fe(II) binding causes a minor helix destabilization, whereas Fe(III) induces DNA condensation. No major DNA conformational changes occurred upon iron complexation and DNA remains in the B-family structure.
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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) | 0.002 | 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".