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Record W2902049135 · doi:10.1126/scisignal.aat1631

Functional selectivity profiling of the angiotensin II type 1 receptor using pathway-wide BRET signaling sensors

2018· article· en· W2902049135 on OpenAlexafffund
Yoon Namkung, Christian LeGouill, Sahil Kumar, Yubo Cao, Larissa B. Teixeira, Viktoriya Lukasheva, Jenna Giubilaro, Sarah C. Simões, Jean‐Michel Longpré, Dominic Devost, Terence E. Hébert, Graciela Piñeyro, Richard Leduc, Cláudio M. Costa-Neto, Michel Bouvier, Stéphane A. Laporte

Bibliographic record

VenueScience Signaling · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReceptor Mechanisms and Signaling
Canadian institutionsMcGill UniversityInstitute for Research in Immunology and CancerCentre Hospitalier Universitaire Sainte-JustineUniversité de SherbrookeUniversité de MontréalMcGill University Health Centre
FundersCanadian Institutes of Health Research
KeywordsG protein-coupled receptorFunctional selectivitySignal transductionReceptorEffectorCell biologyArrestinBiologyCell signalingChemistryBiochemistry

Abstract

fetched live from OpenAlex

and β-arrestin, there are also biases among G protein subtypes. We also demonstrated that biases observed at the receptor and G protein levels propagated to downstream signaling pathways and that these biases could occur through the engagement of different G proteins to activate a common effector. We also used these tools to determine how naturally occurring AT1R variants affected signaling bias. This suite of BRET biosensors provides a useful resource for fingerprinting biased ligands and mutant receptors and for dissecting functional selectivity at various levels of GPCR signaling.

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)0.001
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.248
Teacher spread0.227 · 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

Citations156
Published2018
Admission routes2
Has abstractyes

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