Molecular basis for LDL receptor recognition by PCSK9
Bibliographic record
Abstract
Proprotein convertase subtilisin/kexin type 9 (PCSK9) posttranslationally regulates hepatic low-density lipoprotein receptors (LDLRs) by binding to LDLRs on the cell surface, leading to their degradation. The binding site of PCSK9 has been localized to the epidermal growth factor-like repeat A (EGF-A) domain of the LDLR. Here, we describe the crystal structure of a complex between PCSK9 and the EGF-A domain of the LDLR. The binding site for the LDLR EGF-A domain resides on the surface of PCSK9's subtilisin-like catalytic domain containing Asp-374, a residue for which a gain-of-function mutation (Asp-374-Tyr) increases the affinity of PCSK9 toward LDLR and increases plasma LDL-cholesterol (LDL-C) levels in humans. The binding surface on PCSK9 is distant from its catalytic site, and the EGF-A domain makes no contact with either the C-terminal domain or the prodomain. Point mutations in PCSK9 that altered key residues contributing to EGF-A binding (Arg-194 and Phe-379) greatly diminished binding to the LDLR's extracellular domain. The structure of PCSK9 in complex with the LDLR EGF-A domain defines potential therapeutic target sites for blocking agents that could interfere with this interaction in vivo, thereby increasing LDLR function and reducing plasma LDL-C levels.
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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) | 0.002 | 0.001 |
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".