Chromophore of an Enhanced Green Fluorescent Protein Can Play a Photoprotective Role Due to Photobleaching
Bibliographic record
Abstract
Under stress conditions, elevated levels of cellular reactive oxygen species (ROS) may impair crucial cellular structures. To counteract the resulting oxidative damage, living cells are equipped with several defense mechanisms, including photoprotective functions of specific proteins. Here, we discuss the plausible ROS scavenging mechanisms by the enhanced green fluorescent protein, EGFP. To check if this protein could fulfill a photoprotective function, we employed electron spin resonance (ESR) in combination with spin-trapping. Two organic photosensitizers, rose bengal and methylene blue, as well as an inorganic photocatalyst, nano-TiO2, were used to photogenerate ROS. Spin-traps, TMP-OH and DMPO, and a nitroxide radical, TEMPOL, served as molecular targets for ROS. Our results show that EGFP quenches various forms of ROS, including superoxide radicals and singlet oxygen. Compared to the three proteins PNP, papain, and BSA, EGFP revealed high ROS quenching ability, which suggests its photoprotective role in living systems. Damage to the EGFP chromophore was also observed under strong photo-oxidative conditions. This study contributes to the discussion on the protective function of fluorescent proteins homologous to the green fluorescent protein (GFP). It also draws attention to the possible interactions of GFP-like proteins with ROS in systems where such proteins are used as biological markers.
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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 | 0.001 | 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".