Low density lipoprotein: structure, dynamics, and interactions of apoB-100 with lipids
Bibliographic record
Abstract
Low-density lipoprotein (LDL) transports cholesterol in the bloodstream and plays an important role in the development of cardiovascular diseases, in particular atherosclerosis. Despite its importance to health, the structure of LDL is not known in detail. This is worrying since the lack of LDL's structural information makes it more difficult to understand its function. In this work, we have combined experimental and theoretical data to construct LDL models comprised of the apoB-100 protein wrapped around a lipid droplet of about 20 nm in size. The models are considered by near-atomistic multi-microsecond simulations to unravel structural as well as dynamical properties of LDL, with particular attention paid to lipids and their interactions with the protein. We find that the distribution and the ordering of the lipids in the LDL particle are rather complex. The previously proposed 2- and 3-layer models turn out to be inadequate to describe the properties of the lipid droplet. At the surface of LDL, apoB-100 is found to interact favorably with cholesterol and its esters. The interactions of apoB-100 with core molecules, in particular cholesteryl esters, are rather frequent and arise from hydrophobic amino acids interacting with the ring of cholesteryl esters, and also in part from the rather loose packing of lipids at the surface of the lipoparticle. The loose packing may foster the function of transfer proteins, which transport lipids between lipoproteins. Finally, the comparison of the several apoB-100 models in our study suggests that the properties of lipids in LDL are rather insensitive to the conformation of apoB-100. Altogether, the findings pave the way for further studies of LDL to better understand the central steps in the emergence of atherosclerosis.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".