Quantitating the Statistical Distribution of Deuterium Incorporation To Extend the Utility of H/D Exchange MS Data
Bibliographic record
Abstract
Measuring the statistical distribution of deuterium incorporated into enzymatically derived peptide fragments provides a valuable dimension to hydrogen/deuterium exchange mass spectrometry data. In this paper, we will discuss our improvement to the linear least-squares method for determining this distribution, through the addition of "zeroes" to the end of the deuterated isotopic envelope, to partially compensate for data truncation due to finite instrumental signal-to-noise ratios. The value of the distribution is demonstrated in a simple experimental example, where the linearity between average deuteration and percent D2O used to label test peptides hides a more complex relationship between the site-labeling probability and the total number of sites. This method offers the opportunity to resolve cases where a single peptide experiences distinct, independent biochemical states with each bearing a unique average deuteration; this can occur when a protein is modified to substoichiometric levels. From the experimentally determined distribution of a heterogeneously deuterated peptide, it was possible to extract the average deuteration of each component of the mixture.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| 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".