Computational Modeling of a New Fluorescent Biosensor for Caspase Proteolytic Activity Improves Dynamic Range
Bibliographic record
Abstract
The class of fluorescence resonance energy transfer (FRET) protein biosensors that are useful for measuring protease activity is composed of a tandem fusion of yellow fluorescent protein (YFP), a cleavage recognition sequence, and cyan fluorescent protein (CFP). The dynamic range of these FRET-based protein biosensors is often weak, but applications such as high throughput drug screening require stronger dynamic ranges. Using the biosensor for the caspase-3 protease as an example, here we showed a computational approach to improve the FRET dynamic range based on the atomic structure of caspase-3 bound to its inhibitor. This result was verified from our experiments where the FRET dynamic range improved by at least 60% on average in both in vitro and in vivo contexts. In concept, the same strategy can be applied to improve dynamic range of other FRET-based protein biosensors for protease activity where there exist solved atomic structures for protein complexes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".