Dissecting Functional Interactions in Coagulation Protein Complexes by Use of NMR Spectroscopy
Bibliographic record
Abstract
The blood coagulation cascade can be considered as a system of well-orchestrated protein activation reactions involving and leading to the formation of large macromolecular assemblies. NMR investigations performed during the last six years have focused on the structural, motional and binding properties of some protein domains and interfaces critical for the formation of these protein complexes, outlining sophisticated intermolecular adaptations. The studied protein domains are either single molecules or covalently-linked heterodimers of the epidermal growth factor (EGF) homology domains, calcium-binding EGF domains and gamma-carboxyglutamic(Gla)-containing domains responsible for calcium-dependent binding to cell membranes. The characterized binding interfaces have included those between thrombin and fibrinogen, between thrombin and thrombomodulin, between factor VIIIa and the cell membrane, between tissue factor and factor VIIa, and most recently between factor Va and prothrombin. The obtained results indicate that the regulation of blood coagulation by protein and low molecular weight cofactors may involve a significant degree of protein folding transitions with changes in molecular and conformational motions coupled to enzymatic activities. This new level of complexity of the molecular processes controlling coagulation may lead to novel strategies for the development of more effective therapeutic anticoagulants.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".