Peptide–Column Interactions and Their Influence on Back Exchange Rates in Hydrogen/Deuterium Exchange-MS
Bibliographic record
Abstract
Hydrogen/deuterium exchange (HDX) methods generate useful information on protein structure and dynamics, ideally at the individual residue level. Most MS-based HDX methods involve a rapid proteolytic digestion followed by LC/MS analysis, with exchange kinetics monitored at the peptide level. Localizing specific sites of HDX is usually restricted to a resolution the size of the host peptide because gas-phase processes can scramble deuterium throughout the peptide. Subtractive methods may improve resolution, where deuterium levels of overlapping and nested peptides are used in a subtractive manner to localize exchange to smaller segments. In this study, we explore the underlying assumption of the subtractive method, namely, that the measured back exchange kinetics of a given residue is independent of its host peptide. Using a series of deuterated peptides, we show that secondary structure can be partially retained under quenched conditions, and that interactions between peptides and reversed-phase LC columns may both accelerate and decelerate residue HDX, depending upon peptide sequence and length. Secondary structure is induced through column interactions in peptides with a solution-phase propensity for structure, which has the effect of slowing HDX rates relative to predicted random coil values. Conversely, column interactions can orient random-coil peptide conformers to accelerate HDX, the degree to which correlates with peptide charge in solution, and which can be reversed by using stronger ion pairing reagents. The dependency of these effects on sequence and length suggest that subtractive methods for improving structural resolution in HDX-MS will not offer a straightforward solution for increasing exchange site resolution.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".