A bacteria colony-based screen for optimal linker combinations in genetically encoded biosensors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Fluorescent protein (FP)-based biosensors based on the principle of intramolecular Förster resonance energy transfer (FRET) enable the visualization of a variety of biochemical events in living cells. The construction of these biosensors requires the genetic insertion of a judiciously chosen molecular recognition element between two distinct hues of FP. When the molecular recognition element interacts with the analyte of interest and undergoes a conformational change, the ratiometric emission of the construct is altered due to a change in the FRET efficiency. The sensitivity of such biosensors is proportional to the change in ratiometric emission, and so there is a pressing need for methods to maximize the ratiometric change of existing biosensor constructs in order to increase the breadth of their utility. RESULTS: To accelerate the development and optimization of improved FRET-based biosensors, we have developed a method for function-based high-throughput screening of biosensor variants in colonies of Escherichia coli. We have demonstrated this technology by undertaking the optimization of a biosensor for detection of methylation of lysine 27 of histone H3 (H3K27). This effort involved the construction and screening of 3 distinct libraries: a domain library that included several engineered binding domains isolated by phage-display; a lower-resolution linker library; and a higher-resolution linker library. CONCLUSION: Application of this library screening methodology led to the identification of an optimized H3K27-trimethylation biosensor that exhibited an emission ratio change (66%) that was 2.3 × improved relative to that of the initially constructed biosensor (29%).
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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 | 0.001 | 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 it