Biosensor optimization using a FRET pair based on mScarlet red fluorescent protein and an mScarlet-derived green fluorescent protein
Bibliographic record
Abstract
Abstract Genetically encoded biosensors based on Förster resonance energy transfer (FRET) are indispensable tools for monitoring biochemical changes in cells. Green and red fluorescent protein-based FRET pairs offer advantages over the classically employed cyan and yellow fluorescent protein pairs, such as better spectral separation, lower phototoxicity, and less autofluorescence. Here, we describe the development of an mScarlet-derived green fluorescent protein (designated as mWatermelon) and its use as a FRET donor to the red fluorescent protein mScarlet-I as a FRET acceptor. We tested the functionality of this FRET pair by engineering biosensors for the detection of protease activity, Ca 2+ , and K + . Furthermore, we described a strategy to enhance the FRET efficiency of these biosensors by modulating the intramolecular association between mWatermelon and mScarlet-I.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".