Examining Instrumental Factors Influencing the Performance of Data-Independent Acquisition Methods in Hydrogen/Deuterium Exchange Mass Spectrometry
Bibliographic record
Abstract
Hydrogen/deuterium exchange mass spectrometry (HX-MS) is a method used to study solution-phase protein structure and dynamics. Despite its many applications, HX-MS is limited in throughput because manual data analysis is still the norm. We previously developed HX-MS 2 technology to add a second dimension of deuteration data and promote automated data processing. Data-independent acquisition (DIA) techniques enable this approach, but we require optimized methods for best performance. Using an Orbitrap Eclipse for illustration, we show that ion optics and collision energy settings typical of a proteomics DIA experiment generate maximal peptide retrieval from the DIA library. As few as three MS 2 sequence ions are sufficient to generate a deuteration measurement with a precision that exceeds what is possible in traditional HX-MS. DIA window sizes are based on the chromatographic resolution of the method. An inter-scan window offset method is the recommended default configuration for most HX-DIA applications but an intra-scan overlap method can be tuned for highest performance and is recommended when maximum peptide retrieval is desired. We demonstrate the robustness of one HX-MS 2 configuration (consisting of Trajan HDX automation technology, an Orbitrap Eclipse mass spectrometer and AutoHX software) on an extensive time-course analysis of phosphorylase B and an epitope analysis of single-domain antibodies (V H Hs, nanobodies) specific to the receptor binding domain of SARS-CoV-2 spike protein.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".