Lipid tail chemistry regulates selective membrane interactions with DNA nanoprobes and DNA-based coacervates
Bibliographic record
Abstract
Abstract Biological membranes actively regulate their composition to fine-tune their packing, fluidity, phase and surface charge, key properties that influence biomolecular interactions driving essential cellular pathways. While membrane surface charge is often attributed to specific lipid headgroups, the role of acyl-chain chemistry in modulating the interplay between these biophysical membrane properties remains unexplored. Here, we systematically investigate how acyl-chain length and saturation modulate lipid packing, fluidity, and membrane surface charge in zwitterionic lipid membranes. Using amphiphilic DNA nanoprobes as model charged biomolecules, we describe the interplay between packing, fluidity, phase and charge, identifying a packing-dependent guiding principle for membrane interactions that persists in the presence of anionic lipids. We also demonstrate that the identity and hydrophobicity of membrane anchors in nanoprobes significantly influence their binding to membranes. By integrating acyl-chain chemistry and membrane biophysical properties into design criteria for biomolecular attachment, our findings provide a mechanistic framework for engineering membrane interactions with both DNA nanoprobes and DNA-based coacervates. Beyond direct application to biomimetic platforms and synthetic cell engineering, these insights are relevant to lipid-based vaccine nanotechnologies and a fundamental understanding of membrane-biomolecule interactions in living cells. Abstract Figure
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".