Mechanism, Structure, and Inhibition of O-GlcNAc Processing Enzymes
Bibliographic record
Abstract
The post-translational modification of nucleocytoplasmic proteins with O-linked 2-acetamido-2-deoxy-d-glucopyranose (O-GlcNAc) is a topic of considerable interest and attracts a great deal of research effort. O-GlcNAcylation is a dynamic process which can occur multiple times over the lifetime of a protein, sometimes in a reciprocal relationship with phosphorylation. Several hundred proteins, which are involved in a diverse range of cellular processes, have been identified as being modified with the monosaccharide. The control of the O-GlcNAc modification state on different protein targets appears to be important in the aetiology of a number of diseases, including type II diabetes, neurodegenerative diseases and cancer. Two enzymes are responsible for the addition and removal of the O-GlcNAc modification: uridine diphospho-N-acetylglucosamine:polypeptide beta-N-acetylglucosaminyltransferase (OGT) and O-GlcNAcase (OGA), respectively. Over the past decade the volume of information known about these two enzymes has increased significantly. In particular, mechanistic studies of OGA, in conjunction with structural studies of bacterial homologues of OGA have stimulated the design of inhibitors and offered a rationale for the binding of certain potent and selective inhibitors. Mechanistic information about OGT lags a little way behind OGA, but the recent deduction of the structure of an OGT bacterial homologue should now drive these studies forward.
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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 | 0.000 | 0.001 |
| 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".