Mass Spectrometry of Laser-Initiated Carbene Reactions for Protein Topographic Analysis
Bibliographic record
Abstract
We report a protein labeling method using nonselective carbene reactions of sufficiently high efficiency to permit detection by mass spectrometric methods. The approach uses a diazirine-modified amino acid (l-2-amino-4,4'-azipentanoic acid, "photoleucine") as a label source, which is converted to a highly reactive carbene by pulsed laser photolysis at 355 nm. Labeling of standard proteins and peptides (CaM, Mb, M13) was achieved with yields up to 390-fold higher than previous studies using methylene. Carbene labeling is sensitive to changes in protein topography brought about by conformational change and ligand binding. The modification of apo-CaM was 45 ± 7% higher than that of holo-CaM. Modification of the CaM-M13 complex reflected a 39 ± 1% reduction in labeling for bound holo-CaM relative to free holo-CaM. Labeling yield is independent of protein concentration over approximately 2 orders of magnitude but is weakly dependent on the presence of other chromophores in a photon-limited apparatus. The current configuration required 2 min of irradiation for full reagent conversion; however, it is shown that comparable yields can be achieved with a single high-energy laser pulse (>100 mJ/pulse, <10 ns), offering a labeling method with high temporal resolution. We suggest a mechanism of labeling governed by limited carbene diffusion and the protein surface activity of the diazirine precursor. This surface activity is speculated to return a measure of selectivity relative to methylene labeling, which ultimately may be tunable.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".