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Record W2751965336 · doi:10.1002/bit.26445

Constructing arabinofuranosidases for dual arabinoxylan debranching activity

2017· article· en· W2751965336 on OpenAlexafffund
Weijun Wang, Nikola Andric, Cody Sarch, Bruno T. Silva, Maija Tenkanen, Emma R. Master

Bibliographic record

VenueBiotechnology and Bioengineering · 2017
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversity of Toronto
FundersJenny ja Antti Wihurin RahastoNatural Sciences and Engineering Research Council of CanadaGovernment of Ontario
KeywordsArabinoxylanWaxXylanChemistryEnzymeSubstrate (aquarium)BiochemistryBiology

Abstract

fetched live from OpenAlex

Abstract Enzymatic conversion of arabinoxylan requires α‐L‐arabinofuranosidases able to remove α‐L‐arabinofuranosyl residues (α‐L‐Ara f ) from both mono‐ and double‐substituted D‐xylopyranosyl residues (Xyl p ) 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‐Ara f and α‐(1 → 2)‐L‐Ara f substituents attached to disubstituted Xyl p (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‐Ara f from WAX after a 20 hr treatment. 1 H 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‐Ara f compared to m‐α‐(1 → 3)‐L‐Ara f positions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.220
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2017
Admission routes2
Has abstractyes

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