Combining rational design and continuous evolution on minimalist proteins that target DNA
Bibliographic record
Abstract
ABSTRACT We designed MEF to mimic the basic region/helix-loop-helix/leucine zipper (bHLHZ) domain of transcription factors Max and Myc, which bind with high DNA sequence specificity and affinity 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. ME47, however, displays propensity for instability and misfolding. We therefore sought to improve ME47’s structural and functional features. We used phage-assisted continuous evolution (PACE) to uncover “nonrational” changes to complement our rational design. PACE mutated Arg12 that contacts the DNA phosphodiester backbone. We would not have rationally made such a change, but this mutation improved ME47’s stability with little change in DNA-binding function. We mutated Cys29 to Ser and Ala in ME47’s HLH to eliminate undesired disulfide formation; these mutations reduced E-box binding activity. To compensate, we fused the designed FosW leucine zipper to ME47 to increase the dimerization interface and improve protein stability and E-box targeting activity. This “franken-protein” MEF comprises the Max basic region, E47 HLH, and FosW leucine zipper—plus mutations that arose during PACE and rational design—and is a tractable, reliable protein in vivo and in vitro. Compared with ME47, MEF gives three-fold stronger binding to E-box with four-fold increased specificity for E-box over nonspecific DNA. Generation of MEF demonstrates that combining rational design and continuous evolution can be a powerful tool for designing proteins with robust structure and strong 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 | 0.001 |
| 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 | 0.001 | 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".