Bioisosteres at C9 of 2,3-didehydro-5-N-acetyl neuraminic acid identify selective inhibitors of NEU3
Bibliographic record
Abstract
Human neuraminidases play critical roles in many physiological and pathological processes. These enzymes catalyze the hydrolysis of terminal sialic acids (also known as neuraminic acids) from glycan chains. Humans have four isoenzymes of NEU: NEU1, NEU2, NEU3, and NEU4, making selective inhibitors important tools to investigate the function of individual isoenzymes. A typical scaffold used for the development of NEU inhibitors is 2-deoxy-2,3-dehydro-N-acetylneuraminic acid (DANA), which is a general inhibitor of viral, bacterial, and human neuraminidases. Modifications at the C9 position of DANA have been critical for the potency and selectivity of inhibitors that target human NEU. For example, C9-amides bearing alkyl substituents have been used to develop inhibitors with preference for NEU1, while C9-triazoles with aromatic substituents have higher potency for NEU3 and NEU4. To design improved DANA analogs, we generated a library of compounds with either a short alkyl chain or a biphenyl substituent linked to the C9 position through one of six amide bioisosteres. Bioisostere linkers included triazole, urea, thiourea, carbamate, thiocarbamate, and sulfonamide groups. Within this library, we identified a C9-biphenyl carbamate derivative (963) that showed high potency for NEU3 (K¬i = 0.12 ± 0.01 μM). Additionally, the carbamate analog had enhanced selectivity for NEU3 over all other human isoenzymes. In contrast, NEU1 and NEU4 isoenzymes preferred amide and triazole linkers, respectively. Finally, analogs with urea, sulfonamide, and amide linkers showed enhanced inhibitory activity for a bacterial NEU, NanI from Clostridium perfringens.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".