Fragment screening of <i>N</i>-acetylmannosamine kinase reveals noncarbohydrate inhibitors
Bibliographic record
Abstract
Many biological processes from infection to tumor immune evasion are controlled by cell surface sialylation. To gather further insight into these processes, methods to alter cell surface sialylation are required. One way to achieve this is inhibiting the key enzyme of sialic acid de novo biosynthesis, the intracellular bifunctional UDP-N-acetylglucosamine epimerase/N-acetylmannosamine kinase (GNE/MNK). Here, we present low molecular weight inhibitors of MNK activity based on picolinic acid derivatives. They were identified in a fragment screening using 19F NMR and validated in a biochemical inhibition assay followed by a structure–activity relationship analysis and docking. The optimized compound 6-carbamoylpicolinic acid inhibits MNK with a double-digit micromolar affinity. Its low molecular weight (166 Da) renders this picolinic acid derivative an exquisite starting point for the development of high-affinity MNK inhibitors, which may serve as molecular probes or lead candidates in future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".