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Record W2576153484 · doi:10.1126/science.aag2355

The role of dimer asymmetry and protomer dynamics in enzyme catalysis

2017· article· en· W2576153484 on OpenAlexafffund
Tae Hun Kim, P. Mehrabi, Zhong Ren, Adnan Sljoka, Christopher Ing, Alexandr Bezginov, Libin Ye, Régis Pomès, R. Scott Prosser, E.F. Pai

Bibliographic record

VenueScience · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Structure and Dynamics
Canadian institutionsHospital for Sick ChildrenOntario Institute for Cancer ResearchUniversity of Toronto
FundersCanadian Institutes of Health ResearchHospital for Sick ChildrenNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsChemistryDimerEnzymeEnzyme catalysisSubstrate (aquarium)CatalysisStereochemistryDehalogenaseBiochemistryBiologyOrganic chemistryEcology

Abstract

fetched live from OpenAlex

Freeze-trapping x-ray crystallography, nuclear magnetic resonance, and computational techniques reveal the distribution of states and their interconversion rates along the reaction pathway of a bacterial homodimeric enzyme, fluoroacetate dehalogenase (FAcD). The crystal structure of apo-FAcD exhibits asymmetry around the dimer interface and cap domain, priming one protomer for substrate binding. This asymmetry is dynamically averaged through conformational exchange on a millisecond time scale. During catalysis, the protomer conformational exchange rate becomes enhanced, the empty protomer exhibits increased local disorder, and water egresses. Computational studies identify allosteric pathways between protomers. Water release and enhanced dynamics associated with catalysis compensate for entropic losses from substrate binding while facilitating sampling of the transition state. The studies provide insights into how substrate-coupled allosteric modulation of structure and dynamics facilitates catalysis in a homodimeric enzyme.

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)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.244
Teacher spread0.242 · 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

Citations205
Published2017
Admission routes2
Has abstractyes

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