Martini compatible coarse-grained model of polyethylenimine for pulmonary gene delivery
Bibliographic record
Abstract
Pulmonary gene delivery has demonstrated high specificity for respiratory diseases, offering great control on dosage of therapeutics and side effects. On the other hand, intrinsic barriers in pulmonary systems impose new challenges such as crossing the pulmonary surfactant and evading mucus entrapment. Differences in hydrophobicity of plasma membrane and pulmonary surfactant require different chemistries of gene carriers to improve efficacy. Large-scale coarse-grained (CG) molecular dynamics simulations would facilitate the screening of gene carriers and understanding of the molecular mechanisms involved in pulmonary delivery. Among non-viral carriers, polyethyleneimine (PEI) has been a promising candidate that can be synthesized with various molecular weight, degree of branching, and functionalization. In this work, CG models are developed for PEI and its lipid-functionalized form, within the Martini framework, to provide a platform for exploring structure-function relationships of PEI-based pulmonary delivery systems. Special attention is focused on parameterizing the non-bonded interactions associated with CG PEI, to ensure compatibility with Martini proteins, short interfering RNA, and phospholipids that are essential components in pulmonary gene delivery. The non-bonded parameters are validated by comparing all-atom (AA) and CG potential of mean force (PMF) curves, where the root-mean-square deviations between the AA and CG PMF curves are shown to be comparable to or smaller than those reported in Martini literature.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".