Combining Rational Design and Continuous Evolution on Minimalist Proteins That Target the E-box DNA Site
Bibliographic record
Abstract
Protein-based therapeutics are part of the next-generation arsenal of drugs being developed against proto-oncoprotein Myc. We designed protein MEF to mimic the basic region/helix-loop-helix/leucine zipper (bHLHZ) domain of Max and Myc, which bind to the E-box motif (enhancer box, CACGTG). To make MEF, we started with our rationally designed ME47, a hybrid of the Max basic region and E47 HLH, that effectively inhibited tumor growth in a mouse model of breast cancer. We used phage-assisted continuous evolution (PACE), which uncovered mutations at Arg12 that contact the DNA phosphodiester backbone. The Arg12 mutations improved ME47’s stability. We replaced Cys29 with Ala to eliminate potential undesired disulfide formation and fused the designed FosW leucine zipper to mutated ME47 to increase the dimerization interface and E-box targeting activity. This “franken-protein” MEF comprises the Max basic region, E47 HLH, and FosW leucine zipper. Compared with ME47, MEF gives 2-fold stronger binding to E-box and 4-fold increased specificity for E-box over nonspecific DNA. The synergistic combination of rational design and PACE allowed us to make MEF and demonstrates the power and utility of our two-pronged approach toward development of promising protein drugs with robust structure and DNA-binding function.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".