Structural Modeling of NTPDase-Substrate Complexes Preserving Catalytic Experimental Features
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Members of the ecto-nucleoside triphosphate diphosphohydrolase (E-NTPDase) family play a pivotal role in hydrolyzing nucleoside triphosphates and diphosphates, modulating purinergic and pyrimidinergic signaling pathways. The NTPDases have therapeutic potential; gaining structural insights into NTPDase-substrate complexes would be valuable for optimizing these enzymes for therapeutic applications. However, such insights remain limited, posing challenges for effective optimization. Molecular docking often fails to capture experimentally characterized substrate conformations, leading to biologically irrelevant models. To address this, we developed a computational strategy that preserves experimentally observed substrate features while leveraging the active site’s conservation across NTPDases. Our method identifies a canonical linear-like substrate conformation encompassing the phosphate tail and nucleobase ring conserved across experimental NTPDase structures. This approach enabled the modeling of Homo sapiens (Hs) NTPDases (HsNTPDase1–8) complexed with ATP, ADP, GTP, GDP, UTP, and UDP, accurately positioning metal ion cofactor and catalytic water molecules. The resulting models offer a reliable framework for studying enzyme–substrate interactions, paving the way for rational enzyme engineering and therapeutic exploration.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".