Fluorescent biosensors for probing CDK4/cyclin D activity and developing non‐ATP pocket Inhibitors for melanoma, lung cancer and lymphoma (972.2)
Bibliographic record
Abstract
CDK4/cyclin D kinase constitutes an attractive pharmacological target in lung cancer, melanoma and lymphoma, associated with mutation or amplification of CDK4, cyclin D or p16INK4a, but efforts to develop tools for detection of this kinase in its native environment, as well as selective inhibitors for therapeutic purposes have remained limited. To this aim we have engineered a fluorescent polypeptide biosensor that reports on CDK4/cyclin D activity in a sensitive and continuous fashion in vitro, in living cells and in biopsies and which allows to monitor response to therapeutics in animal tumour models. We have further designed two biosensors which have been applied to identify competitors of essential protein/protein interfaces between CDK4 and Cyclin D, and allosteric inhibitors that perturb the conformational dynamics of CDK4, respectively, by high throughput screening. These studies highlight the importance of fluorescent biosensors for fundamental research, biomedical developments and drug discovery programmes, providing novel and sensitive approaches to monitor cancer‐associated alterations in protein kinase activities and develop non‐ATP pocket inhibitors. Grant Funding Source : Grants to MCM “Chercheuse d’Avenir” Région Languedoc‐Roussillon, ARC and INCA
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".