Dividing the Large Glycoside Hydrolase Family 43 into Subfamilies: a Motivation for Detailed Enzyme Characterization
Bibliographic record
Abstract
The rapid rise in DNA sequencing has led to an expansion in the number of glycoside hydrolase (GH) families. The GH43 family currently contains α-l-arabinofuranosidase, β-d-xylosidase, α-l-arabinanase, and β-d-galactosidase enzymes for the debranching and degradation of hemicellulose and pectin polymers. Many studies have revealed finer details about members of GH43 that necessitate the division of GH43 into subfamilies, as was done previously for the GH5 and GH13 families. The work presented here is a robust subfamily classification that assigns over 91% of all complete GH43 domains into 37 subfamilies that correlate with conserved sequence residues and results of biochemical assays and structural studies. Furthermore, cooccurrence analysis of these subfamilies and other functional modules revealed strong associations between some GH43 subfamilies and CBM6 and CBM13 domains. Cooccurrence analysis also revealed the presence of proteins containing up to three GH43 domains and belonging to different subfamilies, suggesting significant functional differences for each subfamily. Overall, the subfamily analysis suggests that the GH43 enzymes probably display a hitherto underestimated variety of subtle specificity features that are not apparent when the enzymes are assayed with simple synthetic substrates, such as pNP-glycosides.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".