Determination of isotopic labeling of proteins by precursor ion scanning liquid chromatography/tandem mass spectrometry of derivatized amino acids applied to nuclear magnetic resonance studies
Bibliographic record
Abstract
RATIONALE: A method has been developed for the quantitation of isotopic labeling of proteins using liquid chromatography/tandem mass spectrometry (LC/MS/MS) for the application of protein nuclear magnetic resonance (NMR) studies. NMR relies on specific isotopic nuclei, such as (13)C and (15)N, for detection and, therefore, isotopic labeling is an important sample preparation step prior to in-depth structural characterization of proteins. The goal of this study was to develop a robust quantitative assay for assessing isotopic labeling in proteins while retaining information on the extent of labeling for individual amino acids. METHODS: Complete digestion of proteins by acid hydrolysis was followed by derivatization of free amino acids with 6-aminoquinolyl N-hydroxysuccinimidyl carbamate (AQC) forming derivatives having identical MS/MS fragmentation behavior. Precursor ion scanning on a hybrid quadrupole-linear ion trap platform was used for amino acid analysis and determining isotopic labeling of proteins. RESULTS: Using a set of isotope-labeled amino acid standards mixed with their unlabeled counterparts, the method was validated for accurately measuring % isotopic contribution. We then applied the method for determining the (13)C isotopic content of algal proteins during a feeding study using (13)C(6)-glucose- or (13)C-bicarbonate-supplemented culture media as well as the level of labeling in mussel byssal threads obtained after feeding with labeled algae. CONCLUSIONS: This method is ideally suited for assessing the extent of protein labeling prior to NMR studies, where the isotopic labeling is a determining factor in the quality of resulting protein spectra, and can be applied to a multitude of different biological samples.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".