Nanoparticle self-assembly in mixtures of phospholipids with styrene/maleic acid copolymers or fluorinated surfactants
Bibliographic record
Abstract
Self-assembling nanostructures in aqueous mixtures of bilayer-forming lipids and micelle-forming surfactants are relevant to in vitro studies on biological and synthetic membranes and membrane proteins. Considerable efforts are currently underway to replace conventional detergents by milder alternatives such as styrene/maleic acid (SMA) copolymers and fluorinated surfactants. However, these compounds and their nanosized assemblies remain poorly understood as regards their interactions with lipid membranes, particularly, the thermodynamics of membrane partitioning and solubilisation. Using (19)F and (31)P nuclear magnetic resonance spectroscopy, static and dynamic light scattering, and isothermal titration calorimetry, we have systematically investigated the aggregational state of a zwitterionic bilayer-forming phospholipid upon exposure to an SMA polymer with a styrene/maleic acid ratio of 3 : 1 or to a fluorinated octyl phosphocholine derivative called F(6)OPC. The lipid interactions of SMA(3 : 1) and F(6)OPC can be thermodynamically conceptualised within the framework of a three-stage model that treats bilayer vesicles, discoidal or micellar nanostructures, and the aqueous solution as distinct pseudophases. The exceptional solubilising power of SMA(3 : 1) is reflected in very low membrane-saturating and solubilising polymer/lipid molar ratios of 0.10 and 0.15, respectively. Although F(6)OPC saturates bilayers at an even lower molar ratio of 0.031, this nondetergent does not solubilise lipids even at >1000-fold molar excess, thus highlighting fundamental differences between these two types of mild membrane-mimetic systems. We rationalise these findings in terms of a new classification of surfactants based on bilayer-to-micelle transfer free energies and discuss practical implications for membrane-protein research.
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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 | 0.000 |
| 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 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".