Two Homologous Apolipoprotein AI Mimetic Peptides
Bibliographic record
Abstract
Two related 18-amino acid, class A, amphipathic helical peptides termed 3F-2 and 3F14 were chosen for this study. Although they have identical amino acid compositions and many similar biophysical properties, 3F-2 is more potent than 3F14 as an apolipoprotein AI mimetic peptide. The two peptides exhibit similar gross conformational properties, forming structures of high helical content on a membrane surface. However, the thermal denaturation transition of 3F-2 is more cooperative, suggesting a higher degree of oligomerization on the membrane. Both 3F-2 and 3F14 promote the segregation of cholesterol in membranes containing phosphatidylcholine and cholesterol, but 3F-2 exhibits a greater selectivity for partitioning into cholesterol-depleted regions of the membrane. Magic angle spinning/NMR studies indicate that the aromatic residues of 3F-2 are stacked in the presence of lipid. The aromatic side chains of this peptide also penetrate more deeply into membranes of phosphatidylcholine with cholesterol compared with 3F14. Using the fluorescent probe, 1,3-dipyrenylpropane, we monitored the properties of the lipid hydrocarbon environment. 3F-2 had a greater effect in altering the properties of the hydrocarbon region of the membrane. The results are consistent with our proposed model of the effect of peptide shape on the nature of the difference in peptide insertion into the bilayer.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".