Characterization of Fluorescein Arsenical Hairpin (FlAsH) as a Probe for Single-Molecule Fluorescence Spectroscopy
Bibliographic record
Abstract
In recent years, new labelling strategies have been developed that involve the genetic insertion of small amino-acid sequences for specific attachment of small organic fluorophores. Here, we focus on the tetracysteine FCM motif (FLNCCPGCCMEP), which binds to fluorescein arsenical hairpin (FlAsH), and the ybbR motif (TVLDSLEFIASKLA) which binds fluorophores conjugated to Coenzyme A (CoA) via a phosphoryl transfer reaction. We designed a peptide containing both motifs for orthogonal labelling with FlAsH and Alexa647 (AF647). Molecular dynamics simulations showed that both motifs remain solvent-accessible for labelling reactions. Fluorescence spectra, correlation spectroscopy and anisotropy decay were used to characterize labelling and to obtain photophysical parameters of free and peptide-bound FlAsH. The data demonstrates that FlAsH is a viable probe for single-molecule studies. Single-molecule imaging confirmed dual labeling of the peptide with FlAsH and AF647. Multiparameter single-molecule Förster Resonance Energy Transfer (smFRET) measurements were performed on freely diffusing peptides in solution. The smFRET histogram showed different peaks corresponding to different backbone and dye orientations, in agreement with the molecular dynamics simulations. The tandem of fluorophores and the labelling strategy described here are a promising alternative to bulky fusion fluorescent proteins for smFRET and single-molecule tracking studies of membrane proteins.
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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 | 0.000 |
| 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".