Partial interdigitation of lipid bilayers
Bibliographic record
Abstract
Abstract A methodology has been developed to detect partial interdigitation of lipid bilayers when a bioactive molecule is intercalated between the polar, interface, or hydrophobic segments. This methodology uses the easily accessible differential scanning calorimetry (DSC) technique as a screening one and the increase of ΔH due to the incorporated drug in lipid bilayers as a diagnostic thermodynamic parameter. The combined use of X‐ray diffraction and Raman spectroscopy complement and confirm the provided by DSC information as it is shown in three classes of molecules, namely AT1 antagonists, vinca alkaloids, and anesthetic steroids. For the two classes of molecules, AT1 antagonists and vinca alkaloids, their presence in lipid bilayers results in the increase of ΔH and it is accompanied by the increase of trans:gauche ratio and the decrease of d‐spacing as depicted by Raman spectroscopy and small‐angle X‐ray diffraction correspondingly, confirming the predictive ability of DSC experiments. When an anesthetic steroid is incorporated in lipid bilayers, neither increase of ΔH nor decrease of d‐spacing was observed, confirming again the DSC results that show the absence of partial interdigitation of this class of molecules. Molecular dynamics simulations have been carried out for a representative system [(5S)‐1‐benzylo‐5‐(1H‐benzimidazol‐1‐ylo‐methylo)‐2‐pyrrolidinone (MMK3) ligands at 1,2‐dimyristoyl‐sn‐glycero‐3‐phosphocholine (DMPC) lipid bilayer], and the results confirmed the experimental findings. The change of distance at z‐axis of oxygen atoms at head group of lipid molecule has been measured throughout the simulations. Statistical analysis has shown ∼8.8 Å interdigitation. Derived computational results are encouraging and can be performed to another ligand/lipid system. The development of a theoretical methodology will lead to advance the field and save a valuable time and effort. © 2010 Wiley Periodicals, Inc. Int J Quantum Chem, 2011
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".