Design and Prototyping of Genetically Encoded Arsenic Biosensors Based on Transcriptional Regulator AfArsR
Bibliographic record
Abstract
Genetically encoded biosensors based on engineered fluorescent proteins (FPs) are essential tools for monitoring the dynamics of specific ions and molecules in biological systems. Arsenic ion in the +3 oxidation state (As3+) is highly toxic to cells due to its ability to bind to protein thiol groups, leading to inhibition of protein function, disruption of protein–protein interactions, and eventually to cell death. A genetically encoded biosensor for the detection of As3+ could potentially facilitate the investigation of such toxicity both in vitro and in vivo. Here, we designed and developed two prototype genetically encoded arsenic biosensors (GEARs), based on a bacterial As3+ responsive transcriptional factor AfArsR from Acidithiobacillus ferrooxidans. We constructed FRET-based GEAR biosensors by insertion of AfArsR between FP acceptor/donor FRET pairs. We further designed and engineered single FP-based GEAR biosensors by insertion of AfArsR into GFP. These constructs represent prototypes for a new family of biosensors based on the ArsR transcriptional factor scaffold. Further improvements of the GEAR biosensor family could lead to variants with suitable performance for detection of As3+ in various biological and environmental systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".