Potent and Selective Cell‐Active Iminosugar Inhibitors of Human α‐<i>N</i>‐Acetylgalactosaminidase (α‐NAGAL)
Bibliographic record
Abstract
Abstract Glycoside hydrolases (GHs) are a class of enzymes with emerging roles in a range of disease. Selective GH inhibitors are sought to better understand their functions and assess the therapeutic potential of modulating their activities. Iminosugars are a promising class of GH inhibitors but typically lack the selectivity required to accurately perturb biological systems. Here, we describe a concise synthesis of iminosugar inhibitors of N‐acetyl‐α‐galactosaminidase (α‐NAGAL), the GH responsible for cleaving terminal α‐N‐acetylgalactosamine residues from glycoproteins and other glycoconjugates. Starting from non‐carbohydrate precursors, this modular synthesis supported the identification of a potent (490 nM) and α‐NAGAL selective (∼200‐fold) guanidino‐containing derivative DGJNGuan. To illustrate the cellular activity of this new inhibitor, we developed a quantitative fluorescence image‐based method to measure levels of the Tn‐antigen, a cellular glycoprotein substrate of α‐NAGAL. Using this assay, we show that DGJNGuan exhibits excellent inhibition of α‐NAGAL within cells using patient derived fibroblasts (EC50=150 nM). Moreover, in vitro and in cell assays to assess levels of lysosomal β‐hexosaminidase substrate ganglioside GM2 show that DGJNGuan is selective whereas DGJNAc exhibits off‐target inhibition both in vitro and within cells. DGJNGuan is a readily produced and selective tool compound that should prove useful for investigating the physiological roles of α‐NAGAL.
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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) | 0.000 | 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".