Mechanistic and structural insights into a human carbohydrate degrading enzyme
Bibliographic record
Abstract
Mammalian β‐hexosaminidases have emerged as playing essential roles in cellular physiology and disease. These enzymes are responsible for the cleavage of the monosaccharides N‐acetylglucosamine (GlcNAc) and N‐acetylgalactosamine (GalNAc) from cellular substrates. One of these β‐hexosaminidases, Hexosaminidase D (HexD), encoded by the HEXDC gene, has received little attention. No mechanistic studies have focused on the role of this nucleocytoplasmically localized β‐hexosaminidase and its cellular function remains unknown. We will discuss a series of kinetic, mechanistic and structural investigations into HexD that have aided defining the precise catalytic mechanism of this enzyme. The fundamental insights gained from these studies will aid in the development of potent and selective probes for HexD, which will serve as useful tools to better understand the physiological role played by this enzyme. Support or Funding Information T.M.G. and V.O. are supported by a Wellcome Trust Career Development Fellowship. D.J.V. thanks the Canada Research Chair program for support as a Canada Research Chair in Chemical Glycobiology. M.A. thanks the NSERC CREATE Collaborative Medicinal Chemistry Network in Epigenetics Training (ChemNET) for support. This work was supported by an NSERC Discovery and a CIHR Operating grant (MOP‐123341). I.N. was recipient of an “Aktion Austria‐Slovakia” scholarship from the Österreichischer Austauschdienst.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".