Thermodynamics of nanodisc formation mediated by styrene/maleic acid (2:1) copolymer
Bibliographic record
Abstract
Abstract Styrene/maleic acid copolymers (SMA) have recently attracted great interest for in vitro studies of membrane proteins, as they self-insert into and fragment biological membranes to form polymer-bounded nanodiscs that provide a native-like lipid-bilayer environment. SMA copolymers are available in different styrene/maleic acid ratios and chain lengths and, thus, possess different charge densities, hydrophobicities, and solubilisation properties. Here, we studied the equilibrium solubilisation properties of the most commonly used copolymer, SMA(2:1), by monitoring the formation of nanodiscs from phospholipid vesicles using 31P nuclear magnetic resonance spectroscopy, dynamic light scattering, and differential scanning calorimetry. Comparison of SMA(2:1) phase diagrams with those of SMA(3:1) and diisobutylene/maleic acid (DIBMA) revealed that, on a mass concentration scale, SMA(2:1) is the most efficient membrane solubiliser, despite its relatively mild effects on the thermotropic phase behaviour of solubilised lipids. In contrast with previous kinetic studies, our equilibrium experiments demonstrate that the solubilisation of phospholipid bilayers by SMA(2:1) is most efficient at moderately alkaline pH values. This pH dependence was also observed for the solubilisation of native Escherichia coli membranes, for which SMA(2:1) again turned out to be the most powerful solubiliser in terms of the total amounts of membrane proteins extracted.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".