Optimized Phage Display-Based Selection for the Development of Heterodimerizing Optogenetic Tools
Bibliographic record
Abstract
Multiple display techniques, including phage display, mRNA display, and ribosome display, have been used to expand the optogenetic toolbox beyond what nature provides. These techniques are most often applied to the development of binding partners that selectively recognize different conformational states of photoswitchable proteins. However, for some targets, in particular the spectrally diverse cyanobacteriochrome (CBCR) GAF domain family, the subtle differences between conformational states pose a significant challenge to discovering highly selective binders. We present an optimized phage display-based protocol designed to effectively capture these subtle changes. This optimized protocol applies high selection pressure by changing the elution method and tightening negative selection, leading to the enrichment of selective binders. Through multiple selection campaigns, we demonstrate the utility of this protocol for identifying highly selective binders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".