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Record W2413993846 · doi:10.3390/cells5020026

Structural Mechanisms and Drug Discovery Prospects of Rho GTPases

2016· review· en· W2413993846 on OpenAlexaff
Cameron C. Smithers, Michael Overduin

Bibliographic record

VenueCells · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Kinase Regulation and GTPase Signaling
Canadian institutionsUniversity of Alberta
FundersBiotechnology and Biological Sciences Research CouncilWellcome Trust
KeywordsDruggabilityGTPaseCDC42RHOARas superfamilyRAC1Drug discoveryBiologyPrenylationDrug developmentComputational biologyCell biologyDrugSignal transductionBioinformaticsBiochemistryEnzymePharmacologyGene

Abstract

fetched live from OpenAlex

Rho GTPases regulate cellular morphology and dynamics, and some are key drivers of cancer progression. This superfamily offers attractive potential targets for therapeutic intervention, with RhoA, Rac1 and Cdc42 being prime examples. The challenges in developing agents that act on these signaling enzymes include the lack of obvious druggable pockets and their membrane-bound activities. However, progress in targeting the similar Ras protein is illuminating new strategies for specifically inhibiting oncogenic GTPases. The structures of multiple signaling and regulatory states of Rho proteins have been determined, and the post-translational modifications including acylation and phosphorylation points have been mapped and their functional effects examined. The development of inhibitors to probe the significance of overexpression and mutational hyperactivation of these GTPases underscores their importance in cancer progression. The ability to integrate in silico, in vitro, and in vivo investigations of drug-like molecules indicates the growing tractability of GTPase systems for lead optimization. Although no Rho-targeted drug molecules have yet been clinically approved, this family is clearly showing increasing promise for the development of precision medicine and combination cancer therapies.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.803
Threshold uncertainty score0.684

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.013
GPT teacher head0.262
Teacher spread0.249 · 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 designOther design
Domainnot available
GenreReview

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

Citations38
Published2016
Admission routes1
Has abstractyes

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