MétaCan
Menu
Back to cohort
Record W4403040863 · doi:10.1093/nar/gkae824

Protein–RNA interactions mediated by silvestrol—insight into a unique molecular clamp

2024· article· en· W4403040863 on OpenAlexafffund
Sai Kiran Naineni, Garvit Bhatt, Ekkanat Jiramongkolsiri, Françis Robert, Regina Cencic, Sidong Huang, Bhushan Nagar, Jerry Pelletier

Bibliographic record

VenueNucleic Acids Research · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhytochemical compounds biological activities
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsBiologyRNAHelicaseRibosomeBiochemistryRNA Helicase ATranslation (biology)Computational biologyCell biologyMessenger RNAGene

Abstract

fetched live from OpenAlex

Molecular staples or interfacial inhibitors are small molecules that exert their activity through co-association with macromolecules leading to various effects on target functions. Some molecules inhibit target activity, while others generate gain-of-function complexes. We and others have previously identified two structurally distinct classes of molecular staples, pateamine A and rocaglates. These molecules inhibit eukaryotic initiation factor (eIF) 4A, a critical RNA helicase required for translation initiation, by simultaneously interacting with both RNA and protein components. Structural insights from members of these two families indicate that they wedge themselves between RNA bases during engagement. To extend our understanding of rocaglates, we investigated the RNA-binding properties of silvestrol, a natural rocaglate distinguished by the presence of a unique dioxanyloxy ring. Our study demonstrates that silvestrol expands the RNA-binding repertoire of rocaglates due to this structural characteristic, providing a rationale for improving synthetic molecular staples targeting eIF4A.

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 score0.199
Threshold uncertainty score0.783

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.026
GPT teacher head0.334
Teacher spread0.308 · 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

Citations8
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueNucleic Acids ResearchSame topicPhytochemical compounds biological activitiesFrench-language works237,207