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 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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".