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Record W4415263006 · doi:10.1002/pro.70326

Structural mechanisms for cold‐adapted activity of phosphoenolpyruvate carboxykinase

2025· article· en· W4415263006 on OpenAlexafffund
Matthew J. McLeod, Shauhin Yazdani, Sarah Barwell, Todd Holyoak

Bibliographic record

VenueProtein Science · 2025
Typearticle
Languageen
FieldMaterials Science
TopicEnzyme Structure and Function
Canadian institutionsUniversity of Waterloo
FundersNational Institute of General Medical SciencesNatural Sciences and Engineering Research Council of Canada
KeywordsPsychrophileThermostabilityEnzymeActive sitePhosphoenolpyruvate carboxykinaseMesophileThermophileProtein structureProtein engineering

Abstract

fetched live from OpenAlex

Abstract Temperature is a critical factor in enzyme function, as most enzymes are thermally activated. Across Earth's diverse environments (−20 to 120°C), enzymes have evolved to function optimally at their organism's growth temperature. Thermophilic enzymes must resist denaturation, while psychrophilic enzymes must maintain activity with limited thermal energy. Although principles underlying thermostability are well established, the mechanisms governing kinetic adaptation to temperature remain less understood. To investigate this, we characterized the kinetics and determined a comprehensive series of X‐ray crystal structures of a psychrophilic, GTP‐dependent phosphoenolpyruvate carboxykinase (PEPCK) bound to substrates and non‐reactive mimics of the reaction coordinate. These structures were compared to those of a mesophilic PEPCK. PEPCK is a dynamic enzyme requiring substantial conformational changes during catalysis, particularly ordering of the active site Ω‐loop lid. The psychrophilic enzyme exhibited a reduced catalytic efficiency ( k cat / K M ) and lower optimal temperature ( T opt ) relative to its mesophilic counterpart. Structural comparisons revealed substitutions in the Ω‐loop that likely increase the entropic cost of loop ordering and reduce enthalpic stabilization, hindering efficient active site closure. These results provide a mechanistic basis for cold adaptation in enzyme catalysis, linking specific structural features to altered kinetic behavior. Understanding such adaptations not only advances our knowledge of enzyme evolution but also informs protein engineering efforts aimed at designing efficient biocatalysts for industrial applications operating at non‐physiological temperatures.

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)0.001
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 score0.047
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.259
Teacher spread0.247 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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