Multifunctional Compounds for Activation of the p53‐Y220C Mutant in Cancer
Bibliographic record
Abstract
Abstract The p53 protein plays a major role in cancer prevention, and over 50 % of cancer diagnoses can be attributed to p53 malfunction. The common p53 mutation Y220C causes local protein unfolding, aggregation, and can result in a loss of Zn in the DNA‐binding domain. Structural analysis has shown that this mutant creates a surface site that can be stabilized using small molecules, and herein a multifunctional approach to restore function to p53‐Y220C is reported. A series of compounds has been designed that contain iodinated phenols aimed for interaction and stabilization of the p53‐Y220C surface cavity, and Zn‐binding fragments for metallochaperone activity. Their Zn‐binding affinity was characterized using spectroscopic methods and demonstrate the ability of compounds L4 and L5 to increase intracellular levels of Zn2+ in a p53‐Y220C‐mutant cell line. The in vitro cytotoxicity of our compounds was initially screened by the National Cancer Institute (NCI‐60), followed by testing in three stomach cancer cell lines with varying p53 status’, including AGS (WTp53), MKN1 (V143A), and NUGC3 (Y220C). Our most promising ligand, L5, is nearly 3‐fold more cytotoxic than cisplatin in a large number of cell lines. The impressive cytotoxicity of L5 is further maintained in a NUGC3 3D spheroid model. L5 also induces Y220C‐specific apoptosis in a cleaved caspase‐3 assay, reduces levels of unfolded mutant p53, and recovers p53 transcriptional function in the NUGC3 cell line. These results show that these multifunctional scaffolds have the potential to restore wild‐type function in mutant p53‐Y220C.
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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 | 0.000 |
| 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".