Loss- and Gain-of-function PCSK9 Variants
Bibliographic record
Abstract
The proprotein convertase PCSK9 is a major target in the treatment of hypercholesterolemia because of its ability bind the LDL receptor (LDLR) and enhance its degradation in endosomes/lysosomes. In the endoplasmic reticulum, the zymogen pro-PCSK9 is first autocatalytically cleaved at its internal Gln(152)↓, resulting in a secreted enzymatically inactive complex of PCSK9 with its inhibitory prosegment (prosegment·PCSK9), which is the active form of PCSK9 on the LDLR. We mutagenized the P1 cleavage site Gln(152) into all other residues except Cys and analyzed the expression and secretion of the resulting mutants. The data demonstrated the following. 1) The only P1 residues recognized by PCSK9 are Gln > Met > Ala > Ser > Thr ≈ Asn, revealing an unsuspected specificity. 2) All other mutations led to the formation of an unprocessed zymogen that acted as a dominant negative retaining the native protein in the endoplasmic reticulum. Analysis of a large panoply of known natural and artificial point mutants revealed that this general dominant negative observation applies to all PCSK9 mutations that result in the inability of the protein to exit the endoplasmic reticulum. Such a tight quality control property of the endoplasmic reticulum may lead to the development of specific PCSK9 small molecule inhibitors that block its autocatalytic processing. Finally, inspired by the most active gain-of-function mutant, D374Y, we evaluated the LDLR degradation activity of 18 Asp(374) variants of PCSK9. All Asp(374) mutations resulted in similar gain-of-function activity on the LDLR except that D374E was as active as native PCSK9, D374G was relatively less active, and D374N and D374P were completely inactive.
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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".