Structural characterization of cationic lipid-tRNA complexes
Bibliographic record
Abstract
Despite considerable interest and investigations on cationic lipid-DNA complexes, reports on lipid-RNA interaction are very limited. In contrast to lipid-DNA complexes where lipid binding induces partial B to A and B to C conformational changes, lipid-tRNA complexation preserves tRNA folded state. This study is the first attempt to investigate the binding of cationic lipid with transfer RNA and the effect of lipid complexation on tRNA aggregation and condensation. We examine the interaction of tRNA with cholesterol (Chol), 1,2-dioleoyl-3-trimethylammonium-propane (DOTAP), dioctadecyldimethylammoniumbromide (DDAB) and dioleoylphosphatidylethanolamine (DOPE), at physiological condition, using constant tRNA concentration and various lipid contents. FTIR, UV-visible, CD spectroscopic methods and atomic force microscopy (AFM) were used to analyze lipid binding site, the binding constant and the effects of lipid interaction on tRNA stability, conformation and condensation. Structural analysis showed lipid-tRNA interactions with G-C and A-U base pairs as well as the backbone phosphate group with overall binding constants of K(Chol) = 5.94 (+/- 0.8) x 10(4) M(-1), K(DDAB) = 8.33 (+/- 0.90) x 10(5) M(-1), K(DOTAP) = 1.05 (+/- 0.30) x 10(5) M(-1) and K(DOPE) = 2.75 (+/- 0.50) x 10(4) M(-1). The order of stability of lipid-tRNA complexation is DDAB > DOTAP > Chol > DOPE. Hydrophobic interactions between lipid aliphatic tails and tRNA were observed. RNA remains in A-family structure, while biopolymer aggregation and condensation occurred at high lipid concentrations.
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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 | 0.001 |
| 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".