MS/MS fragmentation-guided search of TMG-chitooligomycins and their structure–activity relationship in specific β-N-acetylglucosaminidase inhibition
Bibliographic record
Abstract
The reducing tetrasaccharide TMG-chitotriomycin (1) is an inhibitor of β-N-acetylglucosaminidase (GlcNAcase), produced by the actinomycete Streptomyces anulatus NBRC13369. The inhibitor shows a unique inhibitory spectrum, that is, selectivity toward enzymes from chitin-containing organisms such as insects and fungi. Nevertheless, its structure-selectivity relationship remains to be clarified. In this study, we conducted a structure-guided search of analogues of 1 in order to obtain diverse N,N,N-trimethylglucosaminium (TMG)-containing chitooligosaccharides. In this approach, the specific fragmentation profile of 1 on ESI-MS/MS analysis was used for the selective detection of desired compounds. As a result, two new analogues, named TMG-chitomonomycin (3) and TMG-chitobiomycin (2), were obtained from a culture filtrate of 1-producing Streptomyces. Their enzyme-inhibiting activity revealed that the potency and selectivity depended on the degree of polymerization of the reducing end GlcNAc units. Furthermore, a computational modeling study inspired the inhibitory mechanism of TMG-related compounds as a mimic of the substrate in the Michaelis complex of the GH20 enzyme. This study is an example of the successful application of a MS/MS experiment for structure-guided isolation of natural products.
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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 | 0.000 |
| 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".