Investigating how intrinsically disordered regions contribute to protein function using HDX-MS
Bibliographic record
Abstract
A large amount of the human proteome is composed of highly dynamic regions that do not adopt a single static conformation. These regions are defined as intrinsically disordered, and they are found in a third of all eukaryotic proteins. They play instrumental roles in many aspects of protein signaling, but can be challenging to characterize by biophysical methods. Intriguingly, many of these regions can adopt stable secondary structure upon interaction with a variety of binding partners, including proteins, lipids, and ligands. This review will discuss the application of Hydrogen-deuterium exchange mass spectrometry (HDX-MS) as a powerful biophysical tool that is particularly well suited for structural and functional characterization of intrinsically disordered regions in proteins. A focus will be on the theory of hydrogen exchange, and its practical application to identify disordered regions, as well as characterize how they participate in protein-protein and protein-membrane interfaces. A particular emphasis will be on how HDX-MS data can be presented specifically tailored for analysis of intrinsically disordered regions, as well as the technical aspects that are critical to consider when designing HDX-MS experiments for proteins containing intrinsically disordered regions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".