Click display: a rapid and efficient <i>in vitro</i> protein display method for directed evolution
Bibliographic record
Abstract
We describe a novel method for in vitro protein display-click display-that does not depend on maintaining RNA integrity during biopanning and yields covalently linked protein-cDNA complexes from double-stranded input DNA within 2 h. The display is achieved in a one-pot format encompassing transcription, translation and reverse transcription reactions in series. Stable linkage between proteins and the encoding cDNA is mediated by a modified DNA linker-ML-generated via a click chemistry reaction between a puromycin-containing oligo and a cDNA synthesis primer. Biopanning of a click-displayed mock library coupled with next-generation sequencing analysis revealed >600-fold enrichment of target binders within a single round of panning. A synthetic library of Designed Ankyrin Repeat Proteins (DARPins) with ∼1012 individual members was generated using click display in a 25-μl reaction and six rounds of library panning against a model protein yielded a panel of nanomolar binders. This study establishes click display as a powerful tool for protein binder discovery/engineering and provides a convenient platform for in vitro biopanning selection even in RNase-rich environments such as on whole cells.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".