Combining Laser Spectroscopy and Mass Spectrometry for Structural Analysis of Biologically Relevant Molecules in the Gas Phase
Bibliographic record
Abstract
The combination of laser spectroscopy with electrospray ionization (ESI) mass spectrometry has enabled conformational analysis of gaseous ions that retain some solution-phase characteristics. Mixtures of thermodynamically favoured gas-phase conformers and kinetically trapped conformers from solution may be probed simultaneously to reveal distinct photophysical properties. This body of work exploits the kinetic trapping of biologically relevant molecules to elucidate structural properties intrinsic (i.e., without solvent interactions) to systems including fluorescent sensors and DNA-drug complexes. Chapters 2 and 3 explore the trapping of multiple protomers of two fluorophores: a DNA minor groove binder and a ratiometric fluorogenic probe. The presence of multiple protomers was supported by fluorescence, photodissociation and quantum mechanical calculations. A previously unreported protomer of the DNA minor groove binder was uncovered while excitation-dependent and vibronically resolved fluorescence emission facilitated spectral assignments for the fluorogenic probe. Chapter 4 describes a collaborative effort to understand the origin of fluorescence emission enhancement for an amyloid-sensing dye by altering the local environment through macrocycle encapsulation. A hybrid computational method was used to predict gas-phase structures of the host-guest complex, with the most stable complex showing favourable electrostatic interactions that could attenuate access to non-radiative relaxation pathways. Lastly, Chapter 5 details the extension of gas-phase Förster Resonance Energy Transfer (FRET) towards detecting conformational changes in DNA duplexes induced by the binding of small molecule drugs. Changes in FRET response were detected with the incremental addition of single drug molecules to the duplex, highlighting the advantage of measuring fluorescence from mass-selected complex ions. The work in this thesis develops the utility of optical spectroscopy performed on mass-selected ions with key results showing that distinguishable deactivation pathways result from different protonation sites or non-covalent interactions and that gas-phase FRET can detect conformational effects of individual drug binding events on DNA.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".