Nonpolar interactions between trans‐membrane helical EGF peptide and phosphatidylcholines, sphingomyelins and cholesterol. Molecular dynamics simulation studies
Bibliographic record
Abstract
A molecular dynamics simulation study of four lipid bilayers with inserted trans-membrane helical fragment of epithelial growth factor (EGF) receptor (EGF peptide) was performed. The lipid bilayers differ in their lipid composition and consist of (i) unsaturated phosphatidylcholine (palmitoyloleoylphosphatidylcholine, POPC), (ii) POPC and 20 mol% of cholesterol (Chol), (iii) sphingomyelin (SM) and 20 mol% of Chol, and (iv) SM and 50 mol% of Chol. Only 1 out of 26 residues in the EGF-peptide sequence is polar (Thr). The hydrophobic thickness of each bilayer is different but shorter than the length of the peptide and so, due to hydrophobic mismatch, the inserted peptide is tilted in each bilayer. Additionally, in the POPC bilayer, which is the thinnest, the peptide loses its helical structure in a short three-amino acid fragment. This facilitates bending of the peptide and burying all hydrophobic amino acids inside the membrane core (Figure 1(b)). Bilayer lipid composition affects interactions between the peptide and lipids in the membrane core. Chol increases packing of atoms relative to the peptide side chains, and thus increases van der Waals interactions. On average, the packing around the peptide is higher in SM-based bilayers than POPC-based bilayers but for certain amino acids, packing depends on their position relative to the bilayer center. In the bilayer center, packing is higher in POPC-based bilayers, while in regions closer to the interface packing is higher in SM-based bilayers. In general, amino acids with larger side chains interact strongly with lipids, and thus the peptide sequence is important for the pattern of interactions at different membrane depths. This pattern closely resembles the shape of recently published lateral pressure profiles [Ollila et alJ. Struct. Biol. DOI:10.1016/j.jsb.2007.01.012].
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".