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G protein–coupled receptor modulation with pepducins: moving closer to the clinic

2011· article· en· W1727217767 on OpenAlexaff
Patricia Fitzpatrick Dimond, Kenneth E. Carlson, Michel Bouvier, Craig Gérard, Lei Xu, Lidija Covic, Anika Agarwal, Oliver P. Ernst, Jay M. Janz, Thue W. Schwartz, Thomas J. Gardella, Graeme Milligan, Athan Kuliopulos, Thomas P. Sakmar, S. Hunt

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReceptor Mechanisms and Signaling
Canadian institutionsUniversity of TorontoInstitute for Research in Immunology and Cancer
FundersTufts Medical Center
KeywordsModulation (music)ChemistryBiophysicsPhysicsBiologyAcoustics

Abstract

fetched live from OpenAlex

At the 2nd Pepducin Science Symposium held in Cambridge, Massachusetts, on November 4-5, 2010, investigators working in G protein-coupled receptor (GPCR) research convened to discuss progress since last year's inaugural conference. This year's symposium focused on increasing knowledge of the structure and function of this ubiquitous superfamily of membrane receptors and their potential modulation for disease treatment. Presentations also focused on how GPCR mechanisms might be exploited to treat diseases with pepducins, novel synthetic lipopeptide pharmacophores that modulate heptahelical GPCR activity. While the multiple roles of GPCRs in physiological and pathophysiological processes offer significant opportunities for novel drug development, the global nature of their activity challenges drug-specific and validated target identification. This year's conference highlighted advances in understanding of GPCR agonist and antagonist ligand-binding motifs, their ligand-independent functions, structure-activity relationships (SARs), and evolving unique methods to probe GPCR structure and function. Study results summarized at the meeting also provided evidence for evolving views of how signaling mechanisms work through these receptors.

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 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 score0.004
Threshold uncertainty score0.206

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.090
GPT teacher head0.313
Teacher spread0.223 · 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
Published2011
Admission routes1
Has abstractyes

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