Analysis of Keystone Enzyme in Agar Hydrolysis Provides Insight into the Degradation (of a Polysaccharide from) Red Seaweeds
Bibliographic record
Abstract
Agars are abundant polysaccharides from marine red algae, and their chemical structure consists of alternating D-galactose and 3,6-anhydro-L-galactose residues, the latter of which are presumed to make the polymer recalcitrant to degradation by most terrestrial bacteria. Here we study a family 117 glycoside hydrolase (BpGH117) encoded within a recently discovered locus from the human gut bacterium Bacteroides plebeius. Consistent with this locus being involved in agarocolloid degradation, we show that BpGH117 is an exo-acting 3,6-anhydro-α-(1,3)-L-galactosidase that removes the 3,6-anhydrogalactose from the non-reducing end of neoagaro-oligosaccharides. A Michaelis complex of BpGH117 with neoagarobiose reveals the distortion of the constrained 3,6-anhydro-L-galactose into a conformation that favors catalysis. Furthermore, this complex, supported by analysis of site-directed mutants, provides evidence for an organization of the active site and positioning of the catalytic residues that are consistent with an inverting mechanism of catalysis and suggests that a histidine residue acts as the general acid. This latter feature differs from the vast majority of glycoside hydrolases, which use a carboxylic acid, highlighting the alternative strategies that enzymes may utilize in catalyzing the cleavage of glycosidic bonds.
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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 | 0.000 |
| 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".