Fluorescence monitoring of riboswitch transcription regulation using a dual molecular beacon assay
Bibliographic record
Abstract
Riboswitches are mRNA elements that specifically bind cellular metabolites and control gene expression by modifying their structure. As riboswitches often control essential genes in pathogenic bacteria, riboswitches have been proposed as new targets for antibiotics. High-throughput screening provides a powerful approach to identify riboswitch ligand analogs that could act as powerful antibacterial drugs. Biochemical assays have already been used to find riboswitch-binding analogs, but those methods do take into account the transcriptional context for riboswitch regulation. As the importance of co-transcriptional ligand binding has been shown for several riboswitches, it is vital to develop an assay that screens riboswitch-binding analogs during the transcriptional process. Here, we describe the development of a dual molecular beacon system monitoring the transcriptional regulation activity of the Bacillus subtilis pbuE adenine riboswitch. This system relies on two molecular beacons that enable the monitoring of transcription efficiency, as well as the regulatory activity of the riboswitch. Different analogs were tested using our system, and a good correlation was observed between riboswitch activity and reported metabolite affinities. This method is specific, reliable and could be applied at the high-throughput level for the identification of new potential antibiotics targeting any riboswitch-regulating gene expression at the mRNA level.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".