Beyond small molecules: advancing MYC-targeted cancer therapies through protein engineering
Bibliographic record
Abstract
Protein engineering has emerged as a powerful approach toward the development of novel therapeutics targeting the MYC/MAX/E-box network, an active driver of >70% of cancers. The MYC/MAX heterodimer regulates numerous genes in our cells by binding the Enhancer box (E-box) DNA site and activating the transcription of downstream genes. Traditional small molecules that inhibit MYC face significant limitations that include toxic effects, drug delivery challenges, and resistance. Recent advances in protein engineering offer promising alternatives by creating protein-based drugs that directly disrupt the MYC/MAX dimerization interface and/or MYC/MAX's binding to specific DNA targets. Designed DNA binding proteins like Omomyc, DuoMyc, ME47, MEF, and Mad inhibit MYC activity through specific dimerization, sequestration, and DNA-binding mechanisms. Compared to small molecules, these engineered proteins can offer superior specificity and efficacy and provide a potential pathway for overcoming the limitations of traditional cancer therapies. The success of these protein therapeutics highlights the importance of protein engineering in developing cancer treatments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".