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Record W2165739140 · doi:10.1186/1754-6834-6-112

The synergistic action of accessory enzymes enhances the hydrolytic potential of a “cellulase mixture” but is highly substrate specific

2013· article· en· W2165739140 on OpenAlexafffund
Jinguang Hu, Valdeir Arantes, Amadeus Pribowo, Jack Saddler

Bibliographic record

VenueBiotechnology for Biofuels · 2013
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaNatural Resources CanadaChina Scholarship CouncilMinistry of Education of the People's Republic of ChinaGenome British Columbia
KeywordsCellulaseXylanaseHydrolysisChemistryCelluloseBiochemistrySubstrate (aquarium)Enzymatic hydrolysisFood scienceEnzymeBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Currently, the amount of protein/enzyme required to achieve effective cellulose hydrolysis is still too high. One way to reduce the amount of protein/enzyme required is to formulate a more efficient enzyme cocktail by adding so-called accessory enzymes such as xylanase, lytic polysaccharide monooxygenase (AA9, formerly known as GH61), etc., to the cellulase mixture. Previous work has shown the strong synergism that can occur between cellulase and xylanase mixtures during the hydrolysis of steam pretreated corn stover, requiring lower protein loading to achieve effective hydrolysis. However, relatively high loadings of xylanases were required. When family 10 and 11 endo-xylanases and family 5 xyloglucanase were supplemented to a commercial cellulase mixture varying degrees of improved hydrolysis over a range of pretreated, lignocellulosic substrates were observed. RESULTS: The potential synergistic interactions between cellulase monocomponents and hemicellulases from family 10 and 11 endo-xylanases (GH10 EX and GH11 EX) and family 5 xyloglucanase (GH5 XG), during hydrolysis of various steam pretreated lignocellulosic substrates, were assessed. It was apparent that the hydrolytic activity of cellulase monocomponents was enhanced by the addition of accessory enzymes although the "boosting" effect was highly substrate specific. The GH10 EX and GH5 XG both exhibited broad substrate specificity and showed strong synergistic interaction with the cellulases when added individually. The GH10 EX was more effective on steam pretreated agriculture residues and hardwood substrates whereas GH5 XG addition was more effective on softwood substrates. The synergistic interaction between GH10 EX and GH5 XG when added together further enhanced the hydrolytic activity of the cellulase enzymes over a range of pretreated lignocellulosic substrates. GH10 EX addition could also stimulate further cellulose hydrolysis when added to the hydrolysis reactions when the rate of hydrolysis had levelled off. CONCLUSIONS: Endo-xylanases and xyloglucanases interacted synergistically with cellulases to improve the hydrolysis of a range of pretreated lignocellulosic substrates. However, the extent of improved hydrolysis was highly substrate dependent. It appears that those accessory enzymes, such as GH10 EX and GH5 XG, with broader substrate specificities promoted the greatest improvements in the hydrolytic performance of the cellulase mixture on all of the pretreated biomass substrates.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.214
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), 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

Citations223
Published2013
Admission routes2
Has abstractyes

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