RNA Adducts with Chlorophyll and Chlorophyllin: Stability and Structural Features
Bibliographic record
Abstract
Porphyrins and their metal derivatives are strong nucleic acids binders. Some of these compounds have been used for radiation sensitization therapy of cancer and are targeted to interact with cellular DNA. Chlorophyll (Chl) binds DNA via guanine N-7 atom (major groove) and the backbone phosphate group (Neault and Tajmir-Riahi. Biophys. J. 76, 2177, 1999), whereas chlorophyllin (Chln) intercalates into A-T and G-C regions (Neault and Tajmir-Riahi. J. Phys. Chem. B. 102, 1610, 1998). This study was designed to examine the interaction of RNA with chlorophyll a and chlorophyllin in aqueous solution at physiological pH with pigment/RNA(phosphate) ratios (r) of 1/80 to 1/2. Fourier transform infrared (FTIR) and UV-visible difference spectroscopic methods were used to characterize the nature of pigment-RNA interaction and to establish correlation between spectral changes and the pigment binding mode, binding constant, RNA secondary structure and structural variations of pigment-RNA complexes in aqueous solution. Spectroscopic results showed that Chl and Chln bind RNA through G-C and A-U bases and the backbone phosphate group with overall binding constants of KChl = 1.95 x 10(5) M(-1) and KChln = 1.61 x 10(5) M(-1). The larger K value obtained for Chl-RNA complexes is attributed to the formation of more stable five or six-coordinate Mg cation in the RNA adducts, while the four-coordination Cu(II) in Chln can be more stable than that of the five or six-coordinated copper ion in the Chln-RNA complexes. Aggregation of pigment-RNA complexes occurs at high metalloporphyrin concentrations. No biopolymer secondary structural changes were observed upon pigment interaction and RNA remains in the A-family structure in these pigment complexes.
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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".