Identification of Polo-like kinase 1 interaction inhibitors using a novel cell-based assay
Bibliographic record
Abstract
Polo-like kinase 1 (Plk1) plays several roles in cell division and it is a recognized cancer drug target. Plk1 levels are elevated in cancer and several types of cancer cells are hypersensitive to Plk1 inhibition. Small molecule inhibitors of the kinase domain (KD) of Plk1 have been developed. Their selectivity is limited, which likely contributes to their toxicity. Polo-like kinases are characterized by a Polo-Box Domain (PBD), which mediates interactions with phosphorylation substrates or regulators. Inhibition of the PBD could allow better selectivity or result in different effects than inhibition of the KD. In vitro screens have been used to identify PBD inhibitors with mixed results. We developed the first cell-based assay to screen for PBD inhibitors, using Bioluminescence Resonance Energy Transfer (BRET). We screened through 112 983 compounds and characterized hits in secondary biochemical and biological assays. Subsequent Structure-Activity Relationship (SAR) analysis on our most promising hit revealed that it requires an alkylating function for its activity. In addition, we show that the previously reported PBD inhibitors thymoquinone and Poloxin are also alkylating agents. Our cell-based assay is a promising tool for the identification of new PBD inhibitors with more drug-like profiles using larger and more diverse chemical libraries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".