Fluorescence Resonance Energy Transfer in Gaseous, Mass-Selected Polyproline Peptides
Bibliographic record
Abstract
Despite the many successes of mass spectrometry in the analysis of biological samples, the need to better understand the correlation between condensed-phase properties and those of electrospray species remains. In particular, the link between structures in the condensed phase and in the gaseous environment of the mass spectrometer is still elusive. Here, we show that fluorescence resonance energy transfer (FRET) can be used to probe the conformations of gaseous biopolymers which are formed by electrospray ionization (ESI) and manipulated in a quadrupole ion trap mass spectrometer. A rhodamine dye pair suitable for gas-phase FRET is characterized. Both steady state spectra and lifetime measurements are used to monitor energy transfer in a series of dye-labeled polyproline-based peptides. FRET efficiency is explored as a function of peptide chain length and charge state. For the peptide with eight proline repeats, virtually complete energy transfer is observed. For the peptide with 14 proline repeats, energy transfer decreases as the charge state increases, consistent with Coulomb repulsion induced elongation of the peptide backbone. FRET measurements of the longest peptide examined, which has 20 proline repeats, indicates that the peptide adopts a bent configuration. Evidence for multiple conformations present within the ensemble of trapped ions is provided by fluorescence lifetime measurements. Gas-phase FRET measurements promise to be a new route to probe the conformations of large gaseous ions.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".