Structural Effects of a Covalent Linkage Between Antithrombin and Heparin: Covalent N-Terminus Attachment of Heparin Enhances the Maintenance of Antithrombin's Activated State
Bibliographic record
Abstract
We have produced a molecule comprising of permanently-activated covalently linked antithrombin and heparin (ATH). This study was designed to elucidate the covalent linkage point(s) for heparin on antithrombin and conformational properties of the ATH molecule. ATH was produced using Schiff base/Amadori rearrangement by incubating antithrombin with unfractionated heparin for 14 d at 40 degrees C. ATH was then digested using Proteinase K, and the heparin-peptide was reacted with NaIO4/NaBH4/mild acid to degrade the heparin moiety. Sequencing of the remaining peptide was performed by Edman degradation with linkage point confirmation by LC-MS. The degree of insertion of the reactive center loop (RCL) of antithrombin into the A-sheet of ATH was examined using synthesized antithrombin RCL peptides. Binding between the peptides and ATH, and the formation of ATH in the presence of the peptides were tested. CD was used to further examine the secondary and tertiary structures of ATH. The results suggest that heparin is conjugated to the amino terminal of antithrombin in the majority of ATH molecules, proximal to the previously determined heparin binding domain of antithrombin. From the linkage data, a model is proposed for the structure of ATH. Studies using the RCL peptides and CD analysis of ATH support this model.
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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".