Constructing arabinofuranosidases for dual arabinoxylan debranching activity
Bibliographic record
Abstract
Abstract Enzymatic conversion of arabinoxylan requires α‐L‐arabinofuranosidases able to remove α‐L‐arabinofuranosyl residues (α‐L‐Araf) from both mono‐ and double‐substituted D‐xylopyranosyl residues (Xylp) in xylan (i.e., AXH‐m and AXH‐d activity). Herein, SthAbf62A (a family GH62 α‐L‐arabinofuranosidase with AXH‐m activity) and BadAbf43A (a family GH43 α‐L‐arabinofuranosidase with AXH‐d3 activity), were fused to create SthAbf62A_BadAbf43A and BadAbf43A_SthAbf62A. Both fusion enzymes displayed dual AXH‐m,d and synergistic activity toward native, highly branched wheat arabinoxylan (WAX). When using a customized arabinoxylan substrate comprising mainly α‐(1 → 3)‐L‐Araf and α‐(1 → 2)‐L‐Araf substituents attached to disubstituted Xylp (d‐2,3‐WAX), the specific activity of the fusion enzymes was twice that of enzymes added as separate proteins. Moreover, the SthAbf62A_BadAbf43A fusion removed 83% of all α‐L‐Araf from WAX after a 20 hr treatment. 1H NMR analyses further revealed differences in SthAbf62A_BadAbf43 rate of removal of specific α‐L‐Araf substituents from WAX, where 9.4 times higher activity was observed toward d‐α‐(1 → 3)‐L‐Araf compared to m‐α‐(1 → 3)‐L‐Araf positions.
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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 | 0.001 |
| 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".