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Record W4401275495 · doi:10.1002/bit.28808

Rational design of disulfide bonds to increase thermostability of <i>Rhodococcus opacus</i> catechol 1,2 dioxygenase

2024· article· en· W4401275495 on OpenAlexafffund
Joshua G. R. Lister, Matthew E. Loewen, Michèle C. Loewen, Antony D. St‐Jacques

Bibliographic record

VenueBiotechnology and Bioengineering · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEnzyme Catalysis and Immobilization
Canadian institutionsNational Research Council CanadaUniversity of SaskatchewanUniversity of Ottawa
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsThermostabilityCatecholChemistryDioxygenaseBioconversionRational designStackingStereochemistryEnzymeBiochemistryOrganic chemistryMaterials scienceNanotechnology

Abstract

fetched live from OpenAlex

Abstract Catechol 1,2 dioxygenase is a versatile enzyme with several potential applications. However, due to its low thermostability, its industrial potential is not being met. In this study, the thermostability of a mesophilic catechol 1,2 dioxygenase from the species Rhodococcus opacus was enhanced via the introduction of disulphide bonds into its structure. Engineered designs (56) were obtained using computational prediction applications, with a set of hypothesized selection criteria narrowing the list to 9. Following recombinant production and purification, several of the designs demonstrated substantially improved protein thermostability. Notably, variant K96C‐D278C yielded improvements including a 4.6°C increase in T 50 , a 725% increase in half‐life, a 5.5°C increase in T m , and a &gt;10‐fold increase in total turnover number compared to wild type. Stacking of best designs was not productive. Overall, current state‐of‐the‐art prediction algorithms were effective for design of disulfide‐thermostabilized catechol 1,2 dioxygenase.

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 score0.134
Threshold uncertainty score0.383

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 head0.009
GPT teacher head0.231
Teacher spread0.222 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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