Lipid Concentration and Molar Ratio Boundaries for the Use of Isotropic Bicelles
Bibliographic record
Abstract
Bicelles are model membranes generally made of long-chain dimyristoylphosphatidylcholine (DMPC) and short-chain dihexanoyl-PC (DHPC). They are extensively used in the study of membrane interactions and structure determination of membrane-associated peptides, since their composition and morphology mimic the widespread PC-rich natural eukaryotic membranes. At low DMPC/DHPC (q) molar ratios, fast-tumbling bicelles are formed in which the DMPC bilayer is stabilized by DHPC molecules in the high-curvature rim region. Experimental constraints imposed by techniques such as circular dichroism, dynamic light scattering, or microscopy may require the use of bicelles at high dilutions. Studies have shown that such conditions induce the formation of small aggregates and alter the lipid-to-detergent ratio of the bicelle assemblies. The objectives of this work were to determine the exact composition of those DMPC/DHPC isotropic bicelles and study the lipid miscibility. This was done using (31)P nuclear magnetic resonance (NMR) and exploring a wide range of lipid concentrations (2-400 mM) and q ratios (0.15-2). Our data demonstrate how dilution modifies the actual DMPC/DHPC molar ratio in the bicelles. Care must be taken for samples with a total lipid concentration ≤250 mM and especially at q ∼ 1.5-2, since moderate dilutions could lead to the formation of large and slow-tumbling lipid structures that could hinder the use of solution NMR methods, circular dichroism or dynamic light scattering studies. Our results, supported by infrared spectroscopy and molecular dynamics simulations, also show that phospholipids in bicelles are largely segregated only when q > 1. Boundaries are presented within which control of the bicelles' q ratio is possible. This work, thus, intends to guide the choice of q ratio and total phospholipid concentration when using isotropic bicelles.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".