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Record W3138626194 · doi:10.7554/elife.66097

Architecture and structural dynamics of the heteromeric GluK2/K5 kainate receptor

2021· article· en· W3138626194 on OpenAlexafffund
Nandish Khanra, Patricia M.G.E. Brown, Amanda M. Perozzo, Derek Bowie, Joel R. Meyerson

Bibliographic record

VenueeLife · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsMcGill University
FundersFonds de Recherche du Québec - SantéNational Institute of General Medical SciencesNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchLeon Levy Foundation
KeywordsKainate receptorProtein subunitBiophysicsKainic acidStructural biologyIon channelAMPA receptorHomomericChemistryGlutamate receptorReceptorBiologyBiochemistry

Abstract

fetched live from OpenAlex

Kainate receptors (KARs) are L-glutamate-gated ion channels that regulate synaptic transmission and modulate neuronal circuits. KARs have strict assembly rules and primarily function as heteromeric receptors in the brain. A longstanding question is how KAR heteromer subunits organize and coordinate together to fulfill their signature physiological roles. Here we report structures of the GluK2/GluK5 heteromer in apo, antagonist-bound, and desensitized states. The receptor assembles with two copies of each subunit, ligand binding domains arranged as two heterodimers and GluK5 subunits proximal to the channel. Strikingly, during desensitization, GluK2, but not GluK5, subunits undergo major structural rearrangements to facilitate channel closure. We show how the large conformational differences between antagonist-bound and desensitized states are mediated by the linkers connecting the pore helices to the ligand binding domains. This work presents the first KAR heteromer structure, reveals how its subunits are organized, and resolves how the heteromer can accommodate functionally distinct closed channel structures.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.006
Threshold uncertainty score0.276

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.023
GPT teacher head0.308
Teacher spread0.285 · 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

Citations44
Published2021
Admission routes2
Has abstractyes

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