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Record W2946054052 · doi:10.1021/acscentsci.9b00221

Dynamic and Functional Profiling of Xylan-Degrading Enzymes in <i>Aspergillus</i> Secretomes Using Activity-Based Probes

2019· article· en· W2946054052 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueACS Central Science · 2019
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsnot available
FundersH2020 European Research CouncilBiotechnology and Biological Sciences Research CouncilEngineering and Physical Sciences Research CouncilNatural Sciences and Engineering Research Council of CanadaDiamond Light SourceGeneralitat de CatalunyaMinisterio de Ciencia e InnovaciónAgence Nationale de la RechercheNederlandse Organisatie voor Wetenschappelijk OnderzoekMinisterio de Ciencia, Innovación y UniversidadesDirectorate for Biological SciencesAgència de Gestió d'Ajuts Universitaris i de RecercaRoyal SocietyMinisterio de Economía y CompetitividadYorkshire Forward
KeywordsEnzymeAspergillus nigerXylanGlycoside hydrolaseChemistryProfiling (computer programming)BiochemistryComputational biologyBiologyComputer science

Abstract

fetched live from OpenAlex

, by the use of chemical probes inspired by the β-glucosidase inhibitor cyclophellitol. Furthermore, we demonstrate the use of these activity-based probes (ABPs) to assess enzyme-substrate specificities, thermal stabilities, and other biotechnologically relevant parameters. Our experiments highlight the utility of ABPs as promising tools for the discovery of relevant enzymes useful for biomass breakdown.

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.

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 score0.295
Threshold uncertainty score0.298

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 head0.010
GPT teacher head0.210
Teacher spread0.201 · 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