The Multi-Leu Peptide Inhibitor Discriminates Between PACE4 and Furin And Exhibits Antiproliferative Effects On Prostate Cancer Cells
Bibliographic record
Abstract
The proprotein convertases (PCs) play an important role in protein precursor activation through processing at paired basic residues. However, significant substrate cleavage redundancy has been reported between PCs. The question remains whether specific PC inhibitors can be designed. This study describes the identification of the sequence LLLLRVKR, named Multi-Leu (ML)-peptide, that displayed a 20-fold selectivity on PACE4 over furin, two enzymes with similar structural characteristics. We have previously demonstrated that PACE4 plays an important role in prostate cancer and could be a druggable target. The present study demonstrates that the ML-peptide significantly reduced the proliferation of DU145 and LNCaP prostate cancer-derived cell lines and induced G0/G1 cell cycle arrest. However, the ML-peptide must enter the cell to inhibit proliferation. It is concluded that peptide-based inhibitors can yield specific PC inhibitors and that the ML-peptide is an important lead compound that could potentially have applications in prostate cancer.
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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 | 0.000 |
| 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".