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Record W4248570313 · doi:10.1002/ange.202001758

Two Distinct Structures of Membrane‐Associated Homodimers of GTP‐ and GDP‐Bound KRAS4B Revealed by Paramagnetic Relaxation Enhancement

2020· article· en· W4248570313 on OpenAlexafffund
Ki‐Young Lee, Zhenhao Fang, Masahiro Enomoto, Geneviève M. C. Gasmi-Seabrook, Le Zheng, Shohei Koide, Mitsuhiko Ikura, Christopher B. Marshall

Bibliographic record

VenueAngewandte Chemie · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Kinase Regulation and GTPase Signaling
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
FundersCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchCanada Foundation for InnovationPrincess Margaret Cancer FoundationFoundation for the National Institutes of Health
KeywordsKRASGTP'ChemistryGTPaseBiophysicsMembraneAllosteric regulationEffectorCell biologyBiochemistryBiologyMutationReceptor

Abstract

fetched live from OpenAlex

Abstract KRAS homo‐dimerization has been implicated in the activation of RAF kinases, however, the mechanism and structural basis remain elusive. We developed a system to study KRAS dimerization on nanodiscs using paramagnetic relaxation enhancement (PRE) NMR spectroscopy, and determined distinct structures of membrane‐anchored KRAS dimers in the active GTP‐ and inactive GDP‐loaded states. Both dimerize through an α4–α5 interface, but the relative orientation of the protomers and their contacts differ substantially. Dimerization of KRAS‐GTP, stabilized by electrostatic interactions between R135 and E168, favors an orientation on the membrane that promotes accessibility of the effector‐binding site. Remarkably, “cross”‐dimerization between GTP‐ and GDP‐bound KRAS molecules is unfavorable. These models provide a platform to elucidate the structural basis of RAF activation by RAS and to develop inhibitors that can disrupt the KRAS dimerization. The methodology is applicable to many other farnesylated small GTPases.

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)0.000
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 score0.003
Threshold uncertainty score0.509

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.012
GPT teacher head0.239
Teacher spread0.228 · 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

Citations15
Published2020
Admission routes2
Has abstractyes

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