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Record W1970046535 · doi:10.3109/10799893.2012.759591

G protein-coupled receptor dimers: look like their parents, but act like teenagers!

2013· article· en· W1970046535 on OpenAlexafffund
Jaime Wertman, Denis J. Dupré

Bibliographic record

VenueJournal of Receptors and Signal Transduction · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReceptor Mechanisms and Signaling
Canadian institutionsDalhousie University
FundersCanadian Institutes of Health Research
KeywordsG protein-coupled receptorReceptorRhodopsin-like receptorsEffectorBiologySignal transductionFunction (biology)Cell biologyG proteinComputational biologyGeneticsMetabotropic receptorAgonist

Abstract

fetched live from OpenAlex

G protein-coupled receptors (GPCRs) represent the largest group of cell surface receptors and an important pharmacological target. Though originally thought to act in a one receptor-one effector fashion, it is now known that these receptors are capable of oligomerization and can function as dimers or higher order oligomers in native tissue. They do not only assemble with identical receptors as homodimers, but also associate with different GPCRs to form heterodimers. We discuss here how heterodimeric GPCRs can assemble, traffic and signal in a manner distinct from their individual receptor components or from homodimers. These receptor pairs are also demonstrated to be regulated by different chaperones, Rabs and scaffolding proteins, further emphasizing their potential as unique targets. We believe in the importance of investigating each GPCR heterodimer as an individual signaling complex, as they appear to act differently from each monomer constituting them. Just as teenagers may resemble their parents and share their genetic makeup, they can still act in a manner that is entirely unique!

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)0.002
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0030.003
Scholarly communication0.0040.004
Open science0.0000.002
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0100.008

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 head0.009
GPT teacher head0.207
Teacher spread0.199 · 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

Citations7
Published2013
Admission routes2
Has abstractyes

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