Distinct antithrombin activation modes for fondaparinux and natural heparins detected using millisecond hydrogen deuterium exchange and collision induced unfolding
Bibliographic record
Abstract
Low molecular weight heparin and synthetic mimetics such as fondaparinux show different binding kinetics, protease specificity, and clinical effects. A combination of allosteric and template-mediated bridging mechanisms have been proposed to explain the differences in rate acceleration and specificity. The difficulty in working with heterogeneous heparin species has rendered a crystallographic interpretation of the differences in antithrombin activation between mimetics and natural heparin inaccessible. In this study, we examine the allosteric changes in antithrombin caused by binding fondaparinux, enoxaparin and depolymerized natural heparins using millisecond hydrogen deuterium exchange mass spectrometry (TRESI-HDX MS) and relate these conformational changes to complex stability in the gas phase using collision induced unfolding (CIU). This exploration reveals that in addition to the dynamic changes caused by fondaparinux, long chain heparins reduce structural flexibility proximal to Arg393, the cleavable residue in the reactive centre loop of the protein. These local changes in protein dynamics are associated with an increase in overall complex stability that increases with heparin chain length. Ultimately, these results shed light on the molecular mechanisms underlying differences in activity and specificity between heparin mimetics and natural heparins.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".