Steered molecular dynamics simulations reveal a self-protecting configuration of nanoparticles during membrane penetration
Bibliographic record
Abstract
Cell entry of polynucleotide-based therapeutic agents can be facilitated by nanoparticle (NP) mediated delivery. In this work, using steered molecular dynamics simulations, we simulated the membrane penetration process of a NP formed by 2 short interfering RNA (siRNA) and 6 polyethylenimine (PEI) molecules. To the best of our knowledge, this is the first set of simulations that explore the direct penetration of an siRNA/PEI NP through a membrane at an all-atom scale. Three types of PEI molecules were used for NP formation: a native PEI, a PEI modified with caprylic acids and a PEI modified with linoleic acids. We found that hydrogen bond formation between the PEIs and the membrane did not lead to instability of the siRNA/PEI NPs during the internalization process. Instead, our results suggested adoption of a "self-protecting" configuration by the siRNA/PEI NP during membrane penetration, where the siRNA/PEI NP becomes more compact and siRNAs become aligned, leading to more stable configurations while detaching from the membrane. The siRNA/PEI NP modified with linoleic acid showed the smallest structural change due to its strong intra-particle lipid associations and the resulting rigidity, while NP modified with caprylic acid showed the largest structural changes. Our observations provide unique insight into the structural changes of siRNA/PEI NPs when crossing the cell membrane, which can be important for the design of new NP carriers for nucleic acid delivery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".