Bottom-up hydrogen deuterium exchange mass spectrometry: data analysis and interpretation
Bibliographic record
Abstract
Hydrogen Deuterium Exchange (HDX) Mass Spectrometry (MS) is a sensitive analytical technique that provides information on protein conformation and dynamics in solution. It is commonly used in the study of protein-ligand and protein-protein interactions and more recently in the pharmaceutical industry for epitope mapping, screening drug candidates and in the comparison of biopharmaceuticals to biosimilars. HDX-MS monitors the exchange of protein backbone hydrogen atoms with deuterium in solution. Recent advancements in HDX automation and data analysis, have taken the emphasis off developing a fundamental understanding of HDX, which is still lacking. This tutorial review will cover the different mechanisms of exchange and how the exchange reaction is affected by various factors. We also explore the basis of data analysis and the difficulties that often arise in the interpretation of site-specific and segment-averaged HDX data, such as overlapping isotopic distributions and correct identification of peptides. Initial data analysis generates a list of peptides and the deuterium incorporation of each peptide at each labeling time point, i.e., a set of deuterium uptake profiles. Data interpretation and error analysis is subsequently required to ensure that deuterium uptake profiles accurately reflect conformational dynamics in solution. Finally, this review will also discuss the different ways in which HDX data can be represented and how the data can be interpreted.
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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 | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".