Nonhydrolyzable ATP Analogues as Selective Inhibitors of Human NPP1: A Combined Computational/Experimental Study
Bibliographic record
Abstract
Elevated nucleotide pyrophosphatase/phosphodiesterase-1 (NPP1) activity is implicated in health disorders including pathological calcification. Specific NPP1 inhibitors would therefore be valuable for studying this enzyme and as potential therapeutic agents. Here we present a combined computational/experimental study characterizing 13 nonhydrolyzable ATP analogues as selective human NPP1 inhibitors. All analogues at 100 μM inhibited (66-99%) the hydrolysis of pnp-TMP by both recombinant NPP1 and cell surface NPP1 activity of osteocarcinoma (HTB-85) cells. These analogues only slightly altered the activity of other ectonucleotidases, NPP3 and NTPDases. The Ki,app values of the seven most potent and selective inhibitors were in the range of 0.5-56 μM, all with mixed type inhibition, predominantly competitive. Those molecules were docked into a newly developed homology model of human NPP1. All adopted ATP-like binding modes, suggesting competitive inhibition with the endogenous ligand. NPP1 selectivity versus NPP3 could be explained in terms of the electrostatic potential of the two proteins that of NPP1 favoring negatively charged ligands. Inhibitor 2 that had the lowest Ki,app (0.5 μM) was also inactive toward P2Y receptors. Overall, analogue 2 is the most potent and selective NPP1 inhibitor described so far.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".