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Record W2901524433 · doi:10.1097/fjc.0000000000000535

Nucleoligands-repurposing G Protein–coupled Receptor Ligands to Modulate Nuclear-localized G Protein–coupled Receptors in the Cardiovascular System

2017· review· en· W2901524433 on OpenAlexafffund
Nicolas Audet, Rabah Dabouz, Bruce G. Allen, Terence E. Hébert

Bibliographic record

VenueJournal of Cardiovascular Pharmacology · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReceptor Mechanisms and Signaling
Canadian institutionsUniversité de MontréalMontreal Heart InstituteMcGill University
FundersCanadian Institutes of Health Research
KeywordsG protein-coupled receptorReceptorPharmacophoreRhodopsin-like receptorsCell biologyBiologySignal transductionIntracellularCell signalingNuclear receptorComputational biologyBioinformaticsBiochemistryMetabotropic receptor

Abstract

fetched live from OpenAlex

There is significant evidence that internal pools of G protein-coupled receptors (GPCRs) exist and may be affected by both endogenous signaling molecules and hydrophobic pharmaceutical ligands, once assumed to only affect cell surface versions of these receptors. Here, we discuss evidence that the biology of nuclear GPCRs in particular is complex, rich, and highly interactive with GPCR signaling from the cell surface. Caging existing GPCR ligands may be an excellent means of further stratifying the phenotypic effects of known pharmacophores such as β-adrenergic, angiotensin II, and type B endothelin receptor ligands in the cardiovascular system. We describe some synthetic strategies we have used to design ligands to go from in cellulo to in vivo experiments. We also consider how surface and intracellular GPCR signaling might be integrated and ways to dissect this. If they could be selectively targeted, nuclear GPCRs and their associated nucleoligands would represent a completely novel area for exploration by Pharma.

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)0.009
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.971
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.010
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0030.000
Research integrity0.0010.002
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 head0.033
GPT teacher head0.306
Teacher spread0.273 · 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 designNot applicable
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

Citations13
Published2017
Admission routes2
Has abstractyes

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