Developments in rapid hydrogen–deuterium exchange methods
Bibliographic record
Abstract
Biological macromolecules, such as proteins, nucleic acids, and carbohydrates, contain heteroatom-bonded hydrogens that undergo exchange with solvent hydrogens on timescales ranging from microseconds to hours. In hydrogen-deuterium exchange mass spectrometry (HDX-MS), this exchange process is used to extract information about biomolecular structure and dynamics. This minireview focuses on millisecond timescale HDX-MS measurements, which, while less common than 'conventional' timescale (seconds to hours) HDX-MS, provide a unique window into weakly structured species, weak (or fast cycling) binding interactions, and subtle shifts in conformational dynamics. This includes intrinsically disordered proteins and regions (IDPs/IDRs) that are associated with cancer and amyloidotic neurodegenerative disease. For nucleic acids and carbohydrates, structures such as isomers, stems, and loops, can be elucidated and overall structural rigidity can be assessed. We will provide a brief overview of technical developments in rapid HDX followed by highlights of various applications, emphasising the importance of broadening the HDX timescale to improve throughput and to capture a wider range of function-relevant dynamic and structural shifts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".