Mechanism of Cationic Lipid-Induced DNA Condensation: Lipid-DNA Coordination and Divalent Cation Charge Fluctuations
Bibliographic record
Abstract
Abstract The condensation of nucleic acids by lipids is a widespread phenomenon in biology with crucial implications for drug delivery. However, due to the challenges in measuring and assessing the contribution of each component in the lipid-DNA-cation system, the intricate physical mechanisms underlying the assembly of DNAs in lipid bilayers remain insufficiently understood. Specifically, the role of divalent cations in the like-charge attraction of DNA pairs remains a topic of debate. This study utilizes all-atom molecular dynamics simulations and free energy calculations to investigate the condensation of DNA duplexes in cationic lipid bilayers. Our exhaustive exploration of the thermodynamic factors inducing DNA condensation reveals unique roles for phospholipid head groups and cations. We observed that bridging cations between the lipid head groups and DNA, drastically reduce DNA charges, while mobile magnesium cations ping-ponging between DNA double strands, create charge fluctuations. While the first factor stabilizes the DNA-lipid complex, the latter creates attractive forces to induce the condensation of DNA pairs. This novel mechanism not only sheds light on the current data regarding cationic lipid-induced DNA condensation but also provides potential design strategies for creating efficient gene delivery vectors for drug delivery.
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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 | 0.001 |
| 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".