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Record W2126217576 · doi:10.1017/s1355838202029059

Inhibition of protein synthesis by aminoglycoside???arginine conjugates

2002· article· en· W2126217576 on OpenAlexafffund
M Carrière, Veerappan Vijayabaskar, Drew J. Applefield, Isabelle Harvey, Philippe Garneau, Jon R. Lorsch, Aviva Lapidot, Jerry Pelletier

Bibliographic record

VenueRNA · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsMcGill University
FundersNational Cancer InstituteCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsRibosomeAminoglycosideBiologyBiochemistryTranslation (biology)RNAProtein biosynthesisArginineSmall moleculeBiophysicsAmino acidAntibioticsMessenger RNA

Abstract

fetched live from OpenAlex

Inhibition of translation by small molecule ligands has proven to be a useful tool for understanding this complex cellular mechanism, as well as providing drugs of significant medical importance. Many small molecule ligands inhibit translation by binding to RNA or RNA/protein components of the ribosomal subunits and usurping their function. A class of peptidomimetics [aminoglycoside-arginine conjugates (AAC)] has recently been designed to inhibit HIV TAR/tat interaction and in experiments aimed at assessing the inhibitory effects of AACs on TAR-containing transcripts, we found that AACs are general inhibitors of translation. Experiments reported herein aim at characterizing these novel properties of AACs. We find that AACs are inhibitors of eukaryotic and prokaryotic translation and exert their effects by blocking peptide chain elongation. Structure/activity relationship studies suggest that inhibition of translation by AACs is directly related to the number of arginine groups present on the aminoglycoside backbone and to the nature of the core aminoglycoside. AACs are therefore attractive tools for understanding and probing ribosome function.

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 score0.013
Threshold uncertainty score0.463

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 head0.011
GPT teacher head0.207
Teacher spread0.196 · 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

Citations19
Published2002
Admission routes2
Has abstractyes

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