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Record W1974628404 · doi:10.4161/cib.24298

Ras GTPases’ interaction with effector domains

2013· article· en· W1974628404 on OpenAlexafffund
Manishha Patel, Jean‐François Côté

Bibliographic record

VenueCommunicative & Integrative Biology · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Kinase Regulation and GTPase Signaling
Canadian institutionsMcGill UniversityMontreal Clinical Research Institute
FundersCanadian Institutes of Health Research
KeywordsGTPaseRabBiologyEffectorComputational biologyCDC42Cell biology

Abstract

fetched live from OpenAlex

THE RAS SUPERFAMILY OF PROTEINS CONSISTS OF FIVE BRANCHES: Ras, Rho, Arf, Rab and Ran subfamilies. These proteins are involved in a plethora of biological functions spanning cytoskeletal organization, cell proliferation, transcription and intracellular trafficking. Ras-Binding Domains (RBDs) have classically been identified as autonomous ubiquitin-like folded regions that bind certain activated Ras GTPases of the Ras subfamily. In general, RBDs in many proteins have been tagged with membrane-targeting functions as in the case of the well-characterized c-Raf-RBD/Ras interaction. However, it is becoming apparent that the definition and functions of RBDs need to be revamped in order to reflect the new discoveries associated with this domain. Here, we discuss in more detail the recent advances associated with these RBDs. We highlight research identifying RBDs in formins, ELMOs and the RhoGEF, Syx and discuss the emerging role for RBDs in controlling autoinhibition relief and the newly recognized versatility of RBDs to interact with Rho and Arf family GTPases. In addition, these recent findings raise the exciting hypothesis that functional RBDs remain hidden in the proteome and are ready to be uncovered.

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 score0.004
Threshold uncertainty score0.014

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 communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.005

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 head0.013
GPT teacher head0.286
Teacher spread0.273 · 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

Citations15
Published2013
Admission routes2
Has abstractyes

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