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Record W3036464240 · doi:10.1126/sciadv.abb1250

Allosteric pluripotency as revealed by protein kinase A

2020· article· en· W3036464240 on OpenAlexafffund
Jung Ah Byun, Madoka Akimoto, Bryan VanSchouwen, Tomas S. Lazarou, Susan S. Taylor, Giuseppe Melacini

Bibliographic record

VenueScience Advances · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReceptor Mechanisms and Signaling
Canadian institutionsMcMaster University
FundersNational Institute of General Medical SciencesNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsAllosteric regulationComputational biologyKinaseProtein kinase AAllosteric enzymeCell biologyBiologyEnzymeChemistryBiochemistry

Abstract

fetched live from OpenAlex

The functional response of a signaling system to an allosteric stimulus often depends on subcellular conditions, a phenomenon known as pluripotent allostery. For example, a single allosteric modulator, Rp-cAMPS, of the prototypical protein kinase A (PKA) switches from antagonist to agonist depending on MgATP levels. However, the mechanism underlying such pluripotent allostery has remained elusive for decades. Using nuclear magnetic resonance spectroscopy, ensemble models, kinase assays, and molecular dynamics simulations, we show that allosteric pluripotency arises from surprisingly divergent responses of highly homologous tandem domains. The differential responses perturb domain-domain interactions and remodel the free-energy landscape of inhibitory excited states sampled by the regulatory subunit of PKA. The resulting activation threshold values are comparable to the effective free energy of regulatory and catalytic subunit binding, which depends on metabolites, substrates, and mutations, explaining pluripotent allostery and warranting a general redefinition of allosteric targets to include specific subcellular environments.

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 score0.066
Threshold uncertainty score0.344

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 head0.008
GPT teacher head0.249
Teacher spread0.241 · 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

Citations42
Published2020
Admission routes2
Has abstractyes

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