Spy1-activated Cyclin Dependent Kinase 2 Shows Reduced Affinity for Current Synthetic Small Molecule Inhibitors of the CDK2-Cyclin Complex
Bibliographic record
Abstract
Cyclin-dependent kinases (CDKs) play a key role in activating essential cell biology processes including cellular proliferation. Inappropriate regulation of CDKs has been implicated in driving several different forms of cancer. One of the regulatory factors is the need to bind to Cyclin-partners before they can be activated and advance the cell cycle. Cyclins are overexpressed in several different cancers, hence activating their relevant CDK. Hyperactive CDK2 in particular is implicated in many cancers, and while many drugs have shown preclinical promise, none have successfully passed through clinical development. Among the complications of targeting CDK2 is the fact that non-classical cyclin partners from the Speedy/RINGO family of proteins can alter the conformation of the kinase. Using computational approaches, we provide data supporting that the active site of CDK2 differs when bound to Spy1 as compared to classical cyclins. Furthermore, combining computational models with experimental techniques we provide data that many small molecule inhibitors have reduced activity against Spy1-bound CDKs. This work supports the need to develop new inhibitors capable of inhibiting the Spy1-CDK2 complex, and suggests that computational tools can be beneficial toward accomplishing this goal.
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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) | 0.004 | 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".