<i>In Vitro</i> and <i>In Vivo</i> Enzyme Activity Screening via RNA-Based Fluorescent Biosensors for <i>S</i>-Adenosyl-<scp>l</scp>-homocysteine (SAH)
Bibliographic record
Abstract
High-throughput enzyme activity screens are essential for target characterization and drug development, but few assays employ techniques or reagents that are applicable to both in vitro and live cell settings. Here, we present a class of selective and sensitive fluorescent biosensors for S-adenosyl-l-homocysteine (SAH) that provide a direct "mix and go" activity assay for methyltransferases (MTases), an enzyme class that includes several cancer therapeutic targets. Our riboswitch-based biosensors required an alternate inverted fusion design strategy, but retained full selectivity for SAH over its close structural analogue, the highly abundant methylation cofactor S-adenosyl-l-methionine (SAM). The level of ligand selectivity for these fluorescent biosensors exceeded that of commercial antibodies for SAH and proved critical to cellular applications, as we employed them to measure methylthioadenosine nucleosidase (MTAN) activity in live Escherichia coli. In particular, we were able to monitor in vivo increase of SAH levels upon chemical inhibition of MTAN using flow cytometry, which demonstrates high-throughput, single cell measurement of an enzyme activity associated with the biosynthesis of quorum sensing signal AI-2. Thus, this study presents RNA-based fluorescent biosensors as promising molecular reagents for high-throughput enzymatic assays that successfully bridge the gap between in vitro and in vivo applications.
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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".