Amino Acid Insertion Frequencies Arising from Photoproducts Generated Using Aliphatic Diazirines
Bibliographic record
Abstract
Mapping proteins with chemical reagents and mass spectrometry can generate a measure of accessible surface area, which in turn can be used to support the modeling and refinement of protein structures. Photolytically generated carbenes are a promising class of reagent for this purpose. Substituent effects appear to influence surface mapping properties, allowing for a useful measure of design control. However, to use carbene labeling data in a quantitative manner for modeling activities, we require a better understanding of their inherent amino acid reactivity, so that incorporation data can be normalized. The current study presents an analysis of the amino acid insertion frequency of aliphatic carbenes generated by the photolysis of three different diazirines: 3,3'-azibutyl-1-ammonium, 3,3’-azibutan-1-ol, and 4,4'-azipentan-1-oate. Leveraging an improved photolysis system for single-shot labeling of sub-microliter frozen samples, we used EThCD to localize insertion products in a large population of labeled peptides. Counting statistics were drawn from data-dependent LC-MS 2 experiments and used to estimate the frequencies of insertion as a function of amino acid. We observed labeling of all 20 amino acids over a remarkably narrow range of insertion frequencies. However, the nature of the substituent could influence relative insertion frequencies, within a general preference for larger polar amino acids. We confirm a large (6-fold) increase in labeling yield when carbenes were photogenerated in the solid phase (77 K) relative to the liquid phase (293 K), and we suggest that carbene labeling should always be conducted in the frozen state to avoid information loss in surface mapping experiments.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".