CRIF1–CDK2 Interface Inhibitors: An Unprecedented Strategy for Modulation of Cell Radiosensitivity
Bibliographic record
Abstract
Cyclin-dependent kinases (CDKs) are historic therapeutic targets implicated in tumorigenic events due to their critical involvement in the cell cycle phase. However, selectivity has proven to be a bottleneck, causing repeated failures. Previously, we reported CR6-interacting factor 1 (CRIF1), acting as a cell cycle negative regulator through interaction with CDK2. In the current report, we identified the CRIF1-CDK2 interaction interface by in silico studies and shortlisted interface inhibitors through virtual screening on CRIF1 using 40 678 drug-like compounds. These compounds were tested by cell proliferation assay, and four of these molecules were found to selectively inhibit the proliferation of osteosarcoma (OS) cell lines, but do not affect normal bone mesenchymal stem cells (BMSC). A binding study reveals significant affinities of the inhibitors on CRIF1. More importantly, treatment of the OS cells with a combination of ionizing radiation (IR) and the best-performing inhibitors remarkably increased IR inhibition potential from 19.9% to 59.6%. This occurred by selectively promoting G2/M arrest and apoptosis related to CDK2 overactivation in OS cells but not in BMSC and was supported by significant CDK2 phosphorylation modifications. Knocking down of CRIF1 by siRNA treatment showed similar effects to the interface inhibitors. Together we substantiate the identification of novel lead molecules, which may provide a new treatment to overcome selectivity issues and enhance the radiosensitivity of tumor cells, opening a conceptually novel strategy of CDK-targeting for different cancer types.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".