Endo-fucoidan hydrolases from glycoside hydrolase family 107 (GH107) display structural and mechanistic similarities to α-l-fucosidases from GH29
Bibliographic record
Abstract
Fucoidans are chemically complex and highly heterogeneous sulfated marine fucans from brown macro algae. Possessing a variety of physicochemical and biological activities, fucoidans are used as gelling and thickening agents in the food industry and have anticoagulant, antiviral, antitumor, antibacterial, and immune activities. Although fucoidan-depolymerizing enzymes have been identified, the molecular basis of their activity on these chemically complex polysaccharides remains largely uninvestigated. In this study, we focused on three glycoside hydrolase family 107 (GH107) enzymes: MfFcnA and two newly identified members, P5AFcnA and P19DFcnA, from a bacterial species of the genus Psychromonas . Using carbohydrate-PAGE, we show that P5AFcnA and P19DFcnA are active on fucoidans that differ from those depolymerized by MfFcnA, revealing differential substrate specificity within the GH107 family. Using a combination of X-ray crystallography and NMR analyses, we further show that GH107 family enzymes share features of their structures and catalytic mechanisms with GH29 α-l-fucosidases. However, we found that GH107 enzymes have the distinction of utilizing a histidine side chain as the proposed acid/base catalyst in its retaining mechanism. Further interpretation of the structural data indicated that the active-site architectures within this family are highly variable, likely reflecting the specificity of GH107 enzymes for different fucoidan substructures. Together, these findings begin to illuminate the molecular details underpinning the biological processing of fucoidans.
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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".