Site-Specific Hydrogen Deuterium Exchange Difference Mass Spectrometry Measurements for Ligand Binding
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Conventional bottom-up HDX-MS experiments are highly suitable for use in drug development; however, a major limitation of this approach is that it generally provides only peptide-level structural resolution. Site specific ( i.e ., single amino acid-resolved) HDX-MS measurements have been achieved using ECD/ETD, but the low efficiency of these fragmentation techniques, combined with poor ion transmission associated with ‘detuning’ the instrument to fully prevent deuterium scrambling, results in sensitivity losses that make ligand binding measurements impractical in a ‘real-world’ ( e.g ., drug development) context. Here we apply a recently developed method for zero scrambling, high efficiency ECD in the challenging context of ligand binding differential HDX experiments, demonstrating a wealth of additional information that can be acquired when HDX-MS analyses are conducted at the amino acid level.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".