Phage-Assisted Continuous Evolution (PACE): A Guide Focused on Evolving Protein–DNA Interactions
Bibliographic record
Abstract
Abstract The uptake of directed evolution methods is increasing, as these powerful systems can be utilized to develop new biomolecules with altered/novel activities, for example, proteins with new catalytic functions or substrate specificities and nucleic acids that recognize an intended target. Especially useful are systems that incorporate continuous evolution, where the protein under selective pressure undergoes continuous mutagenesis with little-to-no input from the researcher once the system is started. However, continuous evolution methods can be challenging to implement and a daunting investment of time and resources. Our intent is to provide basic information and helpful suggestions that we have gained from our experience with bacterial phage-assisted continuous evolution (PACE) toward the evolution of proteins that bind to a specific DNA target. We discuss factors to consider before adopting PACE for a given evolution scheme with focus on the PACE bacterial one-hybrid selection system and what optimization of a PACE selection circuit may look like using the evolution of the DNA-binding protein ME47 as a case study. We outline different types of selection circuits and techniques that may be added onto a basic PACE setup. With this information, researchers will be better equipped to determine whether PACE is a valid strategy to adopt for their research program and how to set up a valid selection circuit.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".