SuperNova, a monomeric photosensitizing fluorescent protein for chromophore-assisted light inactivation
Bibliographic record
Abstract
Chromophore-assisted light inactivation (CALI) is a powerful technique for acute perturbation of biomolecules in a spatio-temporally defined manner in living specimen with reactive oxygen species (ROS). Whereas a chemical photosensitizer including fluorescein must be added to specimens exogenously and cannot be restricted to particular cells or sub-cellular compartments, a genetically-encoded photosensitizer, KillerRed, can be controlled in its expression by tissue specific promoters or subcellular localization tags. Despite of this superiority, KillerRed hasn't yet become a versatile tool because its dimerization tendency prevents fusion with proteins of interest. Here, we report the development of monomeric variant of KillerRed (SuperNova) by direct evolution using random mutagenesis. In contrast to KillerRed, SuperNova in fusion with target proteins shows proper localization. Furthermore, unlike KillerRed, SuperNova expression alone doesn't perturb mitotic cell division. Supernova retains the ability to generate ROS, and hence promote CALI-based functional analysis of target proteins overcoming the major drawbacks of KillerRed.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".