Functional metagenomics reveals an alternative, broad-specificity pathway for metabolism of carbohydrates in human gut commensal bacteria
Bibliographic record
Abstract
The vast majority of the glycosidases characterised so far follow one of the variations of the “Koshland” mechanisms to hydrolyse glycosidic bonds. Herein we describe a large-scale screen of a human gut microbiome metagenomic library using an assay that selectively identifies non-Koshland glycosidase activities. This screen led to identification of a commonly occurring cluster of enzymes with unprecedentedly broad substrate specificities that is thoroughly characterised, mechanistically and structurally. Not only do these enzymes break glycosidic linkages of both α and β stereochemistry and multiple connectivities, but also substrates that are not cleaved by standard glycosidases. These include thioglycosides such as glucosinolates and pseudo-glycosidic bonds of pharmaceuticals such as acarbose. This is achieved via a distinct mechanism of hydrolysis that involves stepwise oxidation, elimination and hydration steps, each catalysed by enzyme modules that are in many cases interchangeable between organisms and substrate classes. These appear to constitute a substantial alternative pathway for glycan degradation.
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) | 0.001 | 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 | 0.000 | 0.001 |
| 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".