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Record W2032552210 · doi:10.1042/bst20130257

Allosteric linkers in cAMP signalling

2014· review· en· W2032552210 on OpenAlexafffund
Madoka Akimoto, Kody Moleschi, Stephen Boulton, Bryan VanSchouwen, Rajeevan Selvaratnam, Susan S. Taylor, Giuseppe Melacini

Bibliographic record

VenueBiochemical Society Transactions · 2014
Typereview
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsMcMaster University
FundersNational Institute of General Medical SciencesCanadian Institutes of Health Research
KeywordsAllosteric regulationSignallingProtein subunitAllosteric enzymeChemistryLinkerKinaseComputational biologyBiophysicsCell biologyBiologyBiochemistryEnzymeComputer scienceGene

Abstract

fetched live from OpenAlex

Weak interactions mediated by dynamic linkers are key determinants of allosteric regulation in multidomain signalling proteins. However, the mechanisms of linker-dependent control have remained largely elusive. In the present article, we review an allosteric model introduced recently to explain how signalling proteins effectively sense and respond to weak interactions, such as those elicited by flexible linkers flanking globular domains. Central to this model is the idea that near degeneracy within the free energy landscape of conformational selection maximally amplifies the response to weak (~2RT), but conformation-selective interactions. The model was tested as proof of principle using the prototypical regulatory subunit (R) of protein kinase A and led to the unanticipated finding that dynamic linkers control kinase activation and inhibition by tuning the inhibitory pre-equilibrium of a minimally populated intermediate (apo R). A practical implication of the proposed model is a new strategy to design kinase inhibitors with enhanced potency through frustration-relieving mutations.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.993
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.047
GPT teacher head0.343
Teacher spread0.297 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations17
Published2014
Admission routes2
Has abstractyes

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