Bicyclic Picomolar OGA Inhibitors Enable Chemoproteomic Mapping of Its Endogenous Post-translational Modifications
Bibliographic record
Abstract
-acetylglucosamine residues (O-GlcNAc) has emerged as a topic of great interest. Despite the presence of O-GlcNAc on hundreds of proteins within cells, only two enzymes regulate this modification. One of these enzymes is O-GlcNAcase (OGA), a dimeric glycoside hydrolase that has a deep active site cleft in which diverse substrates are accommodated. Chemical tools to control OGA are emerging as essential resources for helping to decode the biochemical and cellular functions of the O-GlcNAc pathway. Here we describe rationally designed bicyclic thiazolidine inhibitors that exhibit superb selectivity and picomolar inhibition of human OGA. Structures of these inhibitors in complex with human OGA reveal the basis for their exceptional potency and show that they extend out of the enzyme active site cleft. Leveraging this structure, we create a high affinity chemoproteomic probe that enables simple one-step purification of endogenous OGA from brain and targeted proteomic mapping of its post-translational modifications. These data uncover a range of new modifications, including some that are less-known, such as O-ubiquitination and N-formylation. We expect that these inhibitors and chemoproteomics probes will prove useful as fundamental tools to decipher the mechanisms by which OGA is regulated and directed to its diverse cellular substrates. Moreover, the inhibitors and structures described here lay out a blueprint that will enable the creation of chemical probes and tools to interrogate OGA and other carbohydrate active enzymes.
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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 | 0.001 |
| 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".