Two-Dimensional Potentials of Mean Force of Nile Red in Intact and Damaged Model Bilayers. Application to Calculations of Fluorescence Spectra
Bibliographic record
Abstract
Fluorescent dyes revolutionized and expanded our understanding of biological membranes. The interpretation of experimental fluorescence data in terms of membrane structure, however, requires detailed information about the molecular environment of the dyes. Nile red is a fluorescent molecule whose excitation and emission maxima depend on the polarity of the solvent. It is mainly used as a probe to study lipid microenvironments, for example in imaging the progression of damage to the myelin sheath in multiple sclerosis. In this study, we determine the position and orientation of Nile red in lipid bilayers by calculating two-dimensional Potential of Mean Force (2D-PMF) profiles in a defect-free 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) bilayer and in damaged bilayers containing two mixtures of the oxidized lipid 1-palmitoyl-2-(9'-oxo-nonanoyl)-sn-glycero-3-phosphocholine and POPC. From 2D-PMF simulations we obtain positions and orientations of Nile Red corresponding to the minimum on the binding free energy surface in three different membrane environments with increasing amounts of water, mimicking damage in biological tissue. Using representative snapshots from the simulations, we use combined quantum mechanical/molecular mechanical (QM/MM) models to calculate the emission spectrum of Nile red as a function of its local solvation environment. The results of QM and QM/MM computations are in qualitative agreement with the experimentally observed shift in fluorescence for the dye moving from aqueous solution to the more hydrophobic environment of the lipid interiors. The range of the conformation dependent values of the computed absorption-emission spectra and the lack of solvent relaxation effects in the QM/MM calculations made it challenging to delineate specific differences between the intact and damaged bilayers.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".