Several inhibitors of the Plk1 Polo-Box Domain turn out to be non-specific protein alkylators
Bibliographic record
Abstract
For almost a decade, there has been much interest in the development of chemical inhibitors of Polo-like kinase 1 (Plk1) protein interactions. Plk1 is a master regulator of the cell division cycle that controls numerous substrates. It is a promising target for cancer drug development. Inhibitors of the kinase domain of Plk1 had some success in clinical trials. However, they are not perfectly selective. In principle, Plk1 can also be inhibited by interfering with its protein interaction domain, the Polo-Box Domain (PBD). Selective chemical inhibitors of the PBD would constitute tools to probe for PBD-dependent functions of Plk1 and could be advantageous in cancer therapy. The discovery of Poloxin and thymoquinone as PBD inhibitors indicated that small, cell-permeable chemical inhibitors could be identified. Other efforts followed, including ours, reporting additional molecules capable of blocking the PBD. It is now clear that, unfortunately, most of these compounds are non-specific protein alkylators (defined here as groups covalently added via a carbon) that have little or no potential for the development of real Plk1 PBD-specific drugs. This situation should be minded by biologists potentially interested in using these compounds to study Plk1. Further efforts are needed to develop selective, cell-permeable PBD inhibitors.
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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) | 0.001 | 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".