Time-resolved fluorescence of tryptophan in biophysical chemistry and pharmaceutical research – the pleasures and nightmares dealing with nature’s own fluorophore
Bibliographic record
Abstract
Time-resolved (TR) intrinsic fluorescence of tryptophan (Trp) provides a wealth of information on the structure and localization of proteins and peptides and their interactions with one another, with drugs, lipid membranes, lipid- and surfactant-based drug delivery systems, et cetera. Intrinsic Trp eliminates the need for labeling and avoids the perturbation of the system by the label; introduced Trp is a rather conservative and small label compared to others. Whereas custom-tailored fluorophores are often optimized for a special technique, Trp can be employed to monitor a wide variety of effects. We address interactions of Trp with surrounding molecules, dynamic quenchers and Förster resonance energy transfer (FRET) acceptors that affect the fluorescence decay. Speed and range of angular motion of Trp are characterized by TR anisotropy. Electrostatic interactions of Trp with charged and polar molecules, including water, are monitored by decay-associated spectra (DAS) or TR emission spectra (TRES) and quantified in terms of TR shifts of the spectral center of gravity. This versatility is a great advantage and, at the same time, comes with a complexity of the behavior that can render it a challenge to interpret the data in detail properly. This review provides an overview of applications of TR fluorescence of Trp bulk samples in biomolecular, biophysical, and pharmaceutical studies. The aim is not only to point out the diversity of the read-out of these techniques, but also critically examine their current use. Therefore, we identify most common technical pitfalls and evaluate the degree of reliability of the interpretational approaches. This should aid a more extensive and meaningful use of TR fluorescence of Trp.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".