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Record W4318620410 · doi:10.1128/aem.01704-22

Thermophilic Carboxylesterases from Hydrothermal Vents of the Volcanic Island of Ischia Active on Synthetic and Biobased Polymers and Mycotoxins

2023· article· en· W4318620410 on OpenAlexaff
Marco A. Distaso, Tatyana N. Chernikova, Rafael Bargiela, Cristina Coscolín, P.J. Stogios, José L. González-Alfonso, Sofia Lemak, Anna N. Khusnutdinova, Francisco J. Plou, E. Evdokimova, Alexei Savchenko, Evgenii A. Lunev, Michail M. Yakimov, Olga V. Golyshina, Manuel Ferrer, Alexander F. Yakunin, Peter N. Golyshin

Bibliographic record

VenueApplied and Environmental Microbiology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEnzyme Catalysis and Immobilization
Canadian institutionsUniversity of CalgaryUniversity of Toronto
FundersAgencia Estatal de InvestigaciónNatural Environment Research CouncilSight Research UKMinisterio de Ciencia e InnovaciónEuropean CommissionLlywodraeth CymruEuropean Regional Development FundHorizon 2020 Framework ProgrammeUK Research and Innovation
KeywordsHydrothermal ventThermophileVolcanoHydrothermal circulationMycotoxinEcologyEnvironmental scienceChemistryBiologyGeologyGeochemistryBotanyBacteriaPaleontology

Abstract

fetched live from OpenAlex

and showed low sequence similarity to known carboxylesterases. Active sites of IS10 and IS12 had the largest effective volumes among the characterized prokaryotic carboxylesterases and exhibited high substrate promiscuity, including hydrolysis of polyesters and mycotoxin T-2 (IS12). Though less promiscuous than IS10 and IS12, IS11 had a higher thermostability with a high temperature optimum (80 to 90°C) for activity and hydrolyzed polyesters, and its crystal structure revealed an unusual lipocalin domain likely involved in substrate binding. The polyesterase activity of these enzymes makes them attractive candidates for further optimization and potential application in plastics recycling.

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 score0.050
Threshold uncertainty score0.295

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 head0.003
GPT teacher head0.170
Teacher spread0.167 · 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

Citations19
Published2023
Admission routes1
Has abstractyes

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