Developing Light-Switchable Affibody-Based Inhibitors of Key Interactions in the p53 Network
Bibliographic record
Abstract
The tumor suppressor gene TP53 encodes the transcription factor p53, which is crucial for maintaining genomic integrity by regulating cell growth, initiating DNA repair, and inducing apoptosis. The p53 network remains a key target in cancer research due to its pivotal role in tumor suppression. MDM2, a principal negative regulator of p53, suppresses p53 function in most p53 wild-type tumors. Inhibiting the MDM2-p53 interaction stabilizes p53 and activates its tumor-suppressive activity, leading to growth arrest and apoptosis. MDMX, a homolog of MDM2, modulates p53 by stabilizing MDM2. Selective inhibition of MDM2 and MDMX, considering their distinct roles, is of significant interest. Findings suggest that the timing of p53/MDM2 inhibitor administration affects efficacy, highlighting the importance of temporal control in studying these interactions. This research focuses on engineering selective, light-switchable affibody-based inhibitors to probe the p53 network. Rather than drugs, these inhibitors can serve as molecular tools to enable the dynamics of p53 signaling in cells to be measured and will permit development of a quantitative p53 network model, informing a systems-level approach to cancer treatment that can lead to better therapeutic outcomes.
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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) | 0.003 | 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".