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Record W4288443351 · doi:10.1101/2022.07.26.501653

Proteins with multiple G protein-coupled receptor domains

2022· preprint· en· W4288443351 on OpenAlexaff
Kilic Isildayancan, Amit Kessel, Ron Solan, Rachel Kolodny, Nir Ben‐Tal

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReceptor Mechanisms and Signaling
Canadian institutionsUniversity of Toronto
FundersTel Aviv UniversityIsrael Science Foundation
KeywordsG protein-coupled receptorTransmembrane domainTransmembrane proteinComputational biologyBiologyReceptorChemokine receptorSignal transductionMembrane proteinCell biologyChemokineGeneticsMembrane

Abstract

fetched live from OpenAlex

Abstract Currently known G protein-coupled receptors (GPCRs) have a single transmembrane domain. Many GPCRs form dimers that have two transmembrane domains (one per protein), and there are indications that this dimeric interaction is functionally meaningful. Here, based on sequence analysis and structure predictions, we report the existence of 57 proteins with two, three, or four GPCR domains within the same protein chain. We analyze the structures of these multi-GPCRs and show that almost all have DRY/NPxxY motifs, a strong indication of signaling activity. By homology, most of the multi-GPCRs that we identified are olfactory-related; a few are chemokine-related. Multi-GPCR candidates are found in various Chordata species including fish, camel, marmite, Chinese hamster, and new world monkeys. The discovery of receptors with multiple transmembrane domains suggests the possibility for signal regulation and amplification within an individual receptor, revealing another step in GPCR evolution and a new layer of complexity in signal transduction.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.004

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 head0.008
GPT teacher head0.201
Teacher spread0.193 · 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 designNot applicable
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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

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