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Record W4410850949 · doi:10.1126/science.adp2742

BRAF oncogenic mutants evade autoinhibition through a common mechanism

2025· article· en· W4410850949 on OpenAlexaff
Hugo Lavoie, Ting Jin, Driss Lajoie, Marion Décossas, Patrick Gendron, Bing Wang, František Filandr, Malha Sahmi, Chang Hwa Jo, Sandra Weber, Geneviève Arseneault, Sasmita Tripathy, Pierre Beaulieu, Doris A. Schuetz, David C. Schriemer, Anne Marinier, William J. Rice, Pierre Maisonneuve, Marc Therrien

Bibliographic record

VenueScience · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMelanoma and MAPK Pathways
Canadian institutionsUniversité de MontréalUniversity of CalgaryInstitute for Research in Immunology and Cancer
FundersNational Cancer Institute
KeywordsMutantKinaseProtein kinase domainMutationCell biologyHelix (gastropod)MAPK/ERK pathwayV600EHEK 293 cellsExtracellularBiologyProtein kinase ASignal transductionPoint mutationChemistryBiochemistryReceptorGene

Abstract

fetched live from OpenAlex

Uncontrolled activation of the rat sarcoma (RAS)-extracellular signal-regulated kinase (ERK) pathway drives tumor growth, often because of oncogenic BRAF mutations. BRAF regulation, involving monomeric autoinhibition and activation by dimerization, has been intensely scrutinized, but mechanisms enabling oncogenic mutants to evade regulation remain unclear. By using cryo-electron microscopy, we solved the three-dimensional structures of the three oncogenic BRAF mutant classes, including the common V600E variant. These mutations disrupted wild-type BRAF's autoinhibited state, mediated by interactions between the cysteine-rich domain and kinase domain, thereby shifting the kinase domain into a preactivated conformation. This structural change likely results from helix αC displacement. PLX8394, a BRAF inhibitor that stabilizes helix αC in an inactive conformation, restored the autoinhibited conformation of oncogenic BRAF, explaining the properties of this class of compounds.

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 score0.021
Threshold uncertainty score0.317

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 head0.009
GPT teacher head0.286
Teacher spread0.277 · 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

Citations20
Published2025
Admission routes1
Has abstractyes

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