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Record W2265682428 · doi:10.1017/s0952523801186074

cDNA cloning and characterization of a novel squid rhodopsin kinase encoding multiple modular domains

2001· article· en· W2265682428 on OpenAlexaff
Linnia H. Mayeenuddin, Jane Mitchell

Bibliographic record

VenueVisual Neuroscience · 2001
Typearticle
Languageen
FieldNeuroscience
TopicPhotoreceptor and optogenetics research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRhodopsinBiologyComplementary DNAKinasePhosphorylationSignal transductionBiochemistrycDNA libraryCell biologyProtein kinase AMolecular biologyGene

Abstract

fetched live from OpenAlex

Rhodopsin phosphorylation is one of the key mechanisms of inactivation in vertebrate and invertebrate visual signal transduction. Here we report the cDNA cloning and protein characterization of a 70-kDa squid rhodopsin kinase, SQRK. The cDNA encoding the 70-kDa protein demonstrates high sequence identity with octopus rhodopsin kinase (92%) and mammalian beta-adrenergic receptor kinases (63-65%), but only 33% similarity with bovine rhodopsin kinase, suggesting that invertebrate rhodopsin kinases may be structurally similar to beta-adrenergic receptor kinases. This cDNA encodes three distinct modular domains: RGS, S/TKc, and PH domains. The native SQRK is an eye-specific protein that is only expressed in photoreceptor cells and the optic ganglion as determined by immunoblotting. Purified SQRK is able to phosphorylate both squid and bovine rhodopsin. Squid rhodopsin phosphorylation by purified SQRK was sensitive to both Mg2+ and GTPgammaS but was insensitive to Ca2+/CaM regulation. The ability of SQRK to phosphorylate rhodopsin was totally lost in the presence of SQRK-specific antibodies. Our results suggest that SQRK plays an important role in squid visual signal termination.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.079
GPT teacher head0.347
Teacher spread0.268 · 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

Citations13
Published2001
Admission routes1
Has abstractyes

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