Apparatus for Automated Continuous Hydrogen Deuterium Exchange Mass Spectrometry Measurements from Milliseconds to Hours
Bibliographic record
Abstract
Hydrogen deuterium exchange mass spectrometry (HDX-MS) is a rapidly growing technique for protein characterization in industry and academia, complementing the "static" picture provided by classical structural biology with information about the dynamic structural changes that accompany biological function. Conventional hydrogen deuterium exchange experiments, carried out on commercially available systems, typically collect 4-5 exchange timepoints on a timescale ranging from tens of seconds to hours using a workflow that can require 24 h or more of continuous data collection for triplicate measurements. A small number of groups have developed setups for millisecond timescale HDX, allowing for the characterization of dynamic shifts in weakly structured or disordered regions of proteins. This capability is particularly important given the central role that weakly ordered protein regions often play in protein function and pathogenesis. In this work, we introduce a new continuous flow injection setup for time-resolved HDX-MS (CFI-TRESI-HDX) that allows automated, continuous or discrete labeling time measurements from milliseconds to hours. The device is composed almost entirely of "off-the-shelf" LC components and can acquire an essentially unlimited number of timepoints with substantially reduced runtimes compared to conventional systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.011 |
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".