Two-Site Ionic Labeling with Pyranine: Implications for Structural Dynamics Studies of Polymers and Polypeptides by Time-Resolved Fluorescence Anisotropy
Bibliographic record
Abstract
Time-resolved fluorescence anisotropy (TRFA) is widely used to study dynamic motions of biomolecules in a variety of environments. However, depolarization due to rapid side chain motions often complicates the interpretation of anisotropy decay data and interferes with the accurate observation of segmental motions. Here, we demonstrate a new method for two-point ionic labeling of polymers and biomolecules that have appropriately spaced amino groups using the fluorescent probe 8-hydroxyl-1,3,6-trisulfonated pyrene (pyranine). TRFA analysis shows that such labeling provides a more rigid attachment of the fluorophore to the macromolecule than the covalent or single-point ionic labeling of amino groups, leading to time-resolved anisotropy decays that better reflect the backbone motion of the labeled polymer segment. Optimal coupling of pyranine to biomolecule dynamics is shown to be obtained for appropriately spaced Arg groups, and in such cases the ionic binding is stable up to 150 mM ionic strength. TRFA was used to monitor the behavior of pyranine-labeled poly(allylamine) (PAM) and poly-d-lysine (PL) in sodium silicate derived sol-gel materials and revealed significant restriction of backbone motion upon entrapment for both polymers, an observation that was not readily apparent in a previous study with entrapped fluorescein-labeled PAM and PL. The implications of these findings for fluorescence studies of polymer and biomolecule dynamics are discussed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".