Illuminating Photochemistry of an Excitation Ratiometric Fluorescent Protein Calcium Biosensor
Bibliographic record
Abstract
Fluorescent protein (FP)-based biosensors have become an important and promising tool to track metal ion movement inside living systems. Their working principles after light irradiation, however, remain elusive. To facilitate the rational design of biosensors, we dissect the fluorescence modulation mechanism of a newly developed excitation ratiometric green FP-based Ca 2+ biosensor, GEX-GECO1, using femtosecond stimulated Raman spectroscopy (FSRS) in the electronic excited state. Upon 400 nm photoexcitation, characteristic vibrational marker bands at ∼1180 and 1300 cm –1 show concomitant decay and rise dynamics, probing the progression of an ultrafast excited state proton transfer (ESPT) reaction. The Ca 2+ -bound biosensor exhibits two distinct populations that undergo ESPT with ∼6 and 80 ps time constants, in contrast to one dominant population with a 25 ps time constant in the Ca 2+ -free biosensor. This result is supported by key structural constraints from molecular dynamics simulations with and without Ca 2+ . The blueshift of the ∼1265 cm –1 C–O stretch mode unravels the vibrational cooling dynamics of the protonated chromophore regardless of Ca 2+ binding events. This unique line of inquiry reveals the essential structural dynamics basis of fluorescence modulation inside an excitation ratiometric protein biosensor, correlating the uncovered chromophore structural heterogeneity with different H-bonding configurations and intrinsic proton transfer rate in the photoexcited state.
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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".