Role of chirality in peptide-induced formation of cholesterol-rich domains
Bibliographic record
Abstract
The chiral specificity of the interactions of peptides that induce the formation of cholesterol-rich domains has not been extensively investigated. Both the peptide and most lipids are chiral, so there is a possibility that interactions between peptide and lipid could require chiral recognition. On the other hand, in our models with small peptides, the extent of folding of the peptide to form a specific binding pocket is limited. We have determined that replacing cholesterol with its enantiomer, ent-cholesterol, alters the modulation of lipid organization by peptides. The phase-transition properties of SOPC (1-stearoyl-2-oleoylphosphatidylcholine):cholesterol [in a 6:4 ratio with 0.2 mol% PtdIns(4,5)P2] are not significantly altered when ent-cholesterol replaces cholesterol. However, in the presence of 10 mol% of a 19-amino-acid, N-terminally myristoylated fragment (myristoyl-GGKLSKKKKGYNVNDEKAK-amide) of the protein NAP-22 (neuronal axonal membrane protein), the lipid mixture containing cholesterol undergoes separation into cholesterol-rich and cholesterol-depleted domains. This does not occur when ent-cholesterol replaces cholesterol. In another example, when N-acetyl-Leu-Trp-Tyr-Ile-Lys-amide (N-acetyl-LWYIK-amide) is added to SOPC:cholesterol (7:3 ratio), there is a marked increase in the transition enthalpy of the phospholipid, indicating separation of a cholesterol-depleted domain of SOPC. This phenomenon completely disappears when ent-cholesterol replaces cholesterol. The all-D-isomer of N-acetyl-LWYIK-amide also induces the formation of cholesterol-rich domains with natural cholesterol, but does so to a lesser extent with ent-cholesterol. Thus specific peptide chirality is not required for interaction with cholesterol-containing membranes. However, a specific chirality of membrane lipids is required for peptide-induced formation of cholesterol-rich domains.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".