Oligopeptides impairing the Myc‐Max heterodimerization inhibit lung cancer cell proliferation by reducing Myc transcriptional activity
Bibliographic record
Abstract
Deregulated CMYC gene causes cell transformation and is often correlated with tumor progression and a worse clinical outcome of cancer patients. The transcription factor Myc functions by heterodimerizing with its partner, Max. As a strategy to inhibit Myc activity, we have synthesized three small peptides corresponding to segments of the leucine zipper (LZ) region of Max. The purpose of these peptides is to occupy the site of recognition between Myc and Max located in the LZ and inhibit-specific heterodimerization between these proteins. We have used the synthesized oligopeptides in two lung cancer cell lines with different levels of Myc expression. Results demonstrate that: (i) the three peptides resulted equally effective in competing the interaction between Myc and Max in vitro; (ii) they were efficiently internalized into the cells and significantly inhibited cell growth in the cells showing the highest Myc expression; (iii) one specific peptide, only nine aminoacids long, efficiently impaired the transcriptional activity of Myc in vivo, showing a more stable interaction with this protein. Our results are relevant to the development of novel anti-tumoral therapeutic strategies, directed to Myc-overexpressing tumors.
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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) | 0.001 | 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".