Bibliographic record
Abstract
Proteins are major components of biological membranes that affect the formation of membrane domains. We have been interested in the role of protein segments that partition at the membrane interface in modifying the distribution of cholesterol in membranes. It has been suggested that the sequence ‐L/V‐(X)(1‐5)‐Y‐(X)(1‐5)‐R/K‐, in which (X)(1‐5) represents between one to five residues of any amino acid, promotes interaction with cholesterol. This pattern of amino acids has been termed the CRAC motif. Certain CRAC sequences cause the formation of cholesterol‐rich regions, even in membranes not containing high melting lipids. These peptides also insert more deeply into membranes containing cholesterol compared with membranes of pure phosphatidylcholine. Molecular modeling provides an explanation for the role of Tyr in the CRAC motif. The CRAC motif is very general and encompasses a large number of sequences, not all of which are equally effective in promoting cholesterol sequestration. We have found that the segment YIYF of the sterol sensing domain is sufficient by itself to interact with cholesterol‐rich domains. The segment YIYF is present at the end of transmembrane helices of a number of proteins that interact with cholesterol. In some case this segment is part of a CRAC sequence and in others it is not. There are also other peptides and proteins that have the opposite effect, i.e. they insert more deeply into membranes devoid of cholesterol. Because cholesterol increases the tightness of packing of membrane components, this is expected to be a common situation and a thermodynamic consequence of the unequal distribution of proteins between cholesterol‐rich and cholesterol‐depleted domains will also lead to the segregation of cholesterol into domains, without the protein having any direct interaction with cholesterol. These phenomena are dependent on cholesterol chirality.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.010 |
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".