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Record W2519427278 · doi:10.1038/srep33179

The Mechanism of Hsp90 ATPase Stimulation by Aha1

2016· article· en· W2519427278 on OpenAlexafffund
Annemarie Wolmarans, Brian Lee, Leo Spyracopoulos, Paul LaPointe

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchAlberta Innovates
KeywordsHsp90Mechanism (biology)StimulationATPaseChemistryComputational biologyComputer scienceNeuroscienceBiologyBiochemistryHeat shock proteinEnzymePhysics

Abstract

fetched live from OpenAlex

Hsp90 is a dimeric molecular chaperone responsible for the folding, maturation, and activation of hundreds of substrate proteins called 'clients'. Numerous co-chaperone proteins regulate progression through the ATP-dependent client activation cycle. The most potent stimulator of the Hsp90 ATPase activity is the co-chaperone Aha1p. Only one molecule of Aha1p is required to fully stimulate the Hsp90 dimer despite the existence of two, presumably identical, binding sites for this regulator. Using ATPase assays with Hsp90 heterodimers, we find that Aha1p stimulates ATPase activity by a three-step mechanism via the catalytic loop in the middle domain of Hsp90. Binding of the Aha1p N domain to the Hsp90 middle domain exerts a small stimulatory effect but also drives a separate conformational rearrangement in the Hsp90 N domains. This second event drives a rearrangement in the N domain of the opposite subunit and is required for the stimulatory action of the Aha1p C domain. Furthermore, the second event can be blocked by a mutation in one subunit of the Hsp90 dimer but not the other. This work provides a foundation for understanding how post-translational modifications regulate co-chaperone engagement with the Hsp90 dimer.

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)0.001
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 score0.107
Threshold uncertainty score0.167

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.012
GPT teacher head0.280
Teacher spread0.267 · 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

Citations73
Published2016
Admission routes2
Has abstractyes

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