Restraining Expansion of the Peak Envelope in H/D Exchange-MS and Its Application in Detecting Perturbations of Protein Structure/Dynamics
Bibliographic record
Abstract
Hydrogen/deuterium exchange (H/DX) mass spectrometry (MS) is increasingly applied to problems in protein structural biology in order to map protein dynamics and identify sites of interactions. In theory, an MS-based readout of deuterium label incorporation can overcome the concentration, size, purity, and complexity limitations inherent in NMR-based measurements of exchange; however, in practice, these advantages are reduced due to spectral interference and dilution of the sample in deuterium oxide (D 2O). In this study, we demonstrate that popular H/DX labeling strategies aggravate the interference problem and that significant recovery of spectral capacity may be achieved with a "minimalist" strategy. Simulations of peptide deuteration justify large reductions in the level of D 2O used in labeling experiments, as well as reduced numbers of peaks used in making relative labeling measurements between biochemical states of a protein. To demonstrate the utility of a minimalist approach, calmodulin was interrogated in a bottom-up H/DX-MS workflow, and sensitivity to the addition of Ca (2+) as a structural perturbation was measured as a function of % D 2O and the number of peaks used in quantitating deuteration level. It is shown that high sensitivity to change is preserved with deuteration levels of 5.0 +/- 1.1 (apo-CaM) and 1.4 +/- 1.3% (holo-CaM) using 10% D 2O in the labeling experiment. Further, only two peaks of a peptide peak distribution are needed to sensitively monitor changes in protein structure, dynamics, or both.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".