The Functional and Structural Analysis of a Mimetic Peptide of Human Hepatic Lipase
Bibliographic record
Abstract
Human hepatic lipase (HL) hydrolyzes triglycerides and phospholipids within circulating lipoproteins. HL is bound to heparan sulfate proteoglycans (HPSG) and is believed to function as a head to tail homodimer. The hydrolysis of lipids within high-density lipoproteins (HDL) liberates preβ1-HDL that can accept extrahepatic cholesterol during the anti-atherogenic process of reverse cholesterol transport. Displacing HL from HSPG to the bloodstream allows HL to access more HDL, ultimately generating more preβ1-HDL. We hypothesized that a peptide mimicking the major heparin binding domain (HBD) of HL would displace HL from HSPG. To test this hypothesis, we used a fusion protein with a cleavable peptide containing the major HBD of HL. The fusion protein retained the heparin binding properties of human HL, which suggests that the HL-HSPG association may not require a homodimer structure. Using HEK293 cells expressing HL, we showed that the fusion protein displaces HL from the cell surface at 4°C. We also sought to determine structural properties of the cleaved peptide. Circular dichroism studies showed the mimetic peptide had a propensity to become α-helical in the presence of trifluoroethanol. Proton nuclear magnetic resonance experiments further show that the peptide undergoes a structural change in the presence of heparin. Overall, we have generated a functional peptide that mimics the HBD properties of HL and it can displace cell surface HL. We anticipate testing whether it can ultimately increase the generation of preβ1-HDL and improve cholesterol efflux. Funded by the Canadian Institutes of Health Research (#RNL-125110), and the Research & Development Corporation of Newfoundland and Labrador (#5404.1358.102).
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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 | 0.001 |
| 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".