High-Resolution Mapping of Carbene-Based Protein Footprints
Bibliographic record
Abstract
Carbene chemistry has been used recently in structural mass spectrometry as a labeling method for mapping protein surfaces. The current study presents a method for quantitating label distribution at the amino acid level and explores the nature and basis for an earlier observation of labeling bias. With the use of a method based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) applied to digests of holo-calmodulin, we developed a quantitation strategy to map site-specific incorporation of carbene, generated from photolysis of ionic label precursors 2-amino-4,4-azipentanoic acid and 4,4-azipentanoic acid. The approach provides reliable incorporation data for fragments generated by electron-transfer dissociation, whereas high-energy collisional dissociation leads to energy and sequence-dependent loss of the label as a neutral. However, both can produce data suitable for mapping residues in the interaction of holo-calmodulin with M13 peptide ligand. Site-specific labeling was monitored as a function of reagent, ionic strength, and temperature, demonstrating that electrostatic interactions at the protein surface can "steer" the distribution of label precursors to sites of surface charge and favor label insertion into residues in the vicinity of the surface charge. A further preference for insertion into carboxylates was observed, based on chemical reactivity. We suggest that decoupling surface partitioning from the chemistry of insertion offers a flexible, tunable labeling strategy for structural mass spectrometry that can be applied to a broad range of protein surface compositions and promotes the design of reagents to simplify the workflow.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".