A novel cell‐based sensor detecting the activity of individual basic proprotein convertases
Bibliographic record
Abstract
The basic proprotein convertases (PCs) furin, PC1/3, PC2, PC5/6, PACE4, PC4, and PC7 are promising drug targets for human diseases. However, developing selective inhibitors remains challenging due to overlapping substrate recognition motifs and limited structural information. Classical drug screening approaches for basic PC inhibitors involve homogeneous biochemical assays using soluble recombinant enzymes combined with fluorogenic substrate peptides that may not accurately recapitulate the complex cellular context of the basic PC-substrate interaction. Herein we report basic PC sensor (BPCS), a novel cell-based molecular sensor that allows rapid screening of candidate inhibitors and their selectivity toward individual basic PCs within mammalian cells. BPCS consists of Gaussia luciferase linked to a sortilin-1 membrane anchor via a cleavage motif that allows efficient release of luciferase specifically if individual basic PCs are provided in the same membrane. Screening of selected candidate peptidomimetic inhibitors revealed that BPCS can readily distinguish between general and selective PC inhibitors in a high-throughput screening format. The robust and cost-effective assay format of BPCS makes it suitable to identify novel specific small-molecule inhibitors against basic PCs for therapeutic application. Its cell-based nature will allow screening for drug targets in addition to the catalytically active mature enzyme, including maturation, transport, and cellular factors that modulate the enzyme's activity. This broadened 'target range' will enhance the likelihood to identify novel small-molecule compounds that inhibit basic PCs in a direct or indirect manner and represents a conceptual advantage.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".