FRep: A Fluorescent Protein-Based Bioprobe for <i>in Vivo</i> Detection of Protein–DNA Interactions
Bibliographic record
Abstract
We describe a bacterial reporter system, FRep, for rapid and facile detection of protein-DNA recognition. The bioprobe reporter comprises genes of two fluorescent proteins (FPs) separated by a potential DNA target. If a coexpressed transcription factor binds the DNA target, transcription of the second FP is impeded, resulting in loss of FRET partner. Using ratiometric FRET, we show that evaluation of protein-DNA recognition can be reliably made on bZIP and bHLHZ transcription factors and their DNA targets. FRep displays similar thresholds of detection regarding protein-DNA binding affinities, as compared to well-established electrophoretic and yeast assays, although we observed variations in the intensity of fluorescence signals and detection thresholds that may depend on differences between DNA-binding protein production levels and/or stability in the cell, or the expressed bioprobe linker between the two FPs. FRep can potentially be applied to high-throughput searches of both protein and DNA libraries; in a mock library screen, binding and nonbinding complexes can even be distinguished by visual inspection of colonies on plates. FRep presents notable advantages over existing technologies when applied to assessing protein-DNA interactions in vivo, and this approach has the potential for applications in assaying protein-protein interactions and screening molecules that influence specific macromolecular interactions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 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".