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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.014 | 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 teacher head, 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".