Designer Frankenproteins that Halt the Proliferation of Myc-driven Cancer Cells
Bibliographic record
Abstract
Overexpression of the Myc transcription factor is involved in >70% of cancers. Notably, younger women and Black women show higher rates of Myc-driven breast cancers, with 97% of breast tumors from Black women showing elevated levels of Myc. Myc partners with transcription factor Max to bind to the E-box DNA response element (CACGTG). We engineered frankenproteins MEF and MEF/C93, based on the basic region/helix-loop-helix/leucine zipper (bHLHZ) motif, that comprise modules from different protein familes. In bacterial one-hybrid assays and quantitative EMSA, both proteins bound the E-box with Kd values 8-10 nM, which rival native transcription factors. Western blot confirmed high Myc expression in Myc-dependent MDA-MB-231 triple-negative breast cancer cells. MEF and MEF/C93 disrupted the Myc/Max/E-box network in cellulo: cell viability assays revealed IC50 values 1-2 µM in MDA-MB-231 cells vs. ~25 µM IC50 values in Myc-independent MCF-7 breast cancer cells. Furthermore, qPCR showed that our proteins significantly reduced expression of Myc-target genes in MDA-MB-231, but not MCF-7 cells. These findings demonstrate that MEF-based proteins selectively inhibit the Myc/Max/E-box network in Myc-dependent cancer cells, thus offering a promising protein-based therapeutic approach for treating Myc-driven cancers.
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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) | 0.000 | 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 | 0.001 | 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".