Engineering Dark Chromoprotein Reporters for Photoacoustic Microscopy and FRET Imaging
Bibliographic record
Abstract
A subset of the family of fluorescent proteins are the non-fluorescent chromoproteins which are promising probe molecules for use in photoacoustic imaging and as acceptor chromophores in Förster resonance energy transfer (FRET)-based biosensors. Typical approaches for fluorescent protein optimization by screening of large libraries of variants cannot be effectively applied to chromoproteins due to their characteristic lack of fluorescence. To address this challenge, we have developed a directed evolution method to iteratively screen large libraries of protein variants on the basis of their photoacoustic signal levels. By applying this procedure to the promising Ultramarine and cjBlue chromoprotein templates, we were able to identify improved variants with a 02-04 fold increase in photoacoustic signal-to-noise ratio after only a few evolutionary steps. These improved variants enable more accurate spectral de-mixing and localization of protein-producing bacteria in vivo and serve as effective FRET acceptors for both fluorescence- and photoacoustic-based detection of protease activity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".