Sulfatases: Critical Enzymes for Algal Polysaccharide Processing
Bibliographic record
Abstract
Microbial sulfatases are important biocatalysts in the marine environment where they play a key role in the catabolic biotransformation of abundant sulphated algal polysaccharides. The sulphate esters decorating algal polysaccharides, such as carrageenan, fucoidan and ulvan, can constitute up to 40% of the biopolymer dry weight. The use of this plentiful carbon and energy source by heterotrophic microbes is enabled in part by the sulfatases encoded in their genomes. Sulfatase catalysed hydrolytic removal of sulphate esters is a key reaction at various stages of the enzymatic cascade that depolymerises sulphated polysaccharides into monosaccharides that can enter energy yielding metabolic pathways. As the critical roles of sulfatases in the metabolism of sulphated polysaccharides from marine algae is increasingly revealed, the structural and functional analysis of these enzymes becomes an important component of understanding these metabolic pathways. The S1 family of formylglycine-dependent sulfatases is the largest and most functionally diverse sulfatase family that is frequently active on polysaccharides. Here, we review this important sulfatase family with emphasis on recent developments in studying the structural and functional relationship between sulfatases and their sulphated algal polysaccharide substrates. This analysis utilises the recently proposed active site nomenclature for sulfatases. We will highlight the key role of sulfatases, not only in marine carbon cycling, but also as potential biocatalysts for the production of a variety of novel tailor made sulphated oligomers, which are useful products in, for example, pharmaceutical or cosmetic applications.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".