Bibliographic record
Abstract
Membrane proteins that bind and transport lipids face special challenges. Since lipids typically have low water solubility, both accessibility of the substrate to the protein and delivery to the desired destination are problematical. The amphipathic nature of membrane lipids, and their relatively large molecular size, also means that these proteins must possess substrate-binding sites of a different nature than those designed to handle small polar molecules. This review considers two integral proteins whose function is to bind and transfer membrane lipids within or across a membrane. The first protein, MsbA, is a putative lipid flippase that is a member of the ATP-binding cassette (ABC) superfamily. The protein is found in the inner (cytoplasmic) membrane (IM) of Gram-negative bacteria such as E. coli, where it is proposed to move lipid A from the inner to the outer membrane (OM) leaflet, an important step in the lipopolysaccharide biosynthetic pathway. Cholesterol is a major component of the plasma membrane in eukaryotic cells, where it regulates bilayer fluidity. The other lipid-binding protein discussed here, mammalian NPC1 (Niemann-Pick disease, Type C1), binds cholesterol inside late endosomes/lysosomes (LE/LY) and is involved in its transfer to the cytosol as part of a key intracellular sterol-trafficking pathway. Mutations in NPC1 lead to a devastating neurodegenerative condition, Niemann-Pick Type C disease, which is characterized by massive cholesterol accumulation in LE/LY. The accelerating pace of membrane protein structure determination over the past decade has allowed us a glimpse of how lipid binding and transfer by membrane proteins such as MsbA and NPC1 might be achieved.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".