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Record W2022205645 · doi:10.1002/bip.21283

Sequence‐dependent folding and unfolding of ligand‐bound purine riboswitches

2009· article· en· W2022205645 on OpenAlexaff
Oksana Prychyna, Michael S. Dahabieh, Melanie A. O’Neill

Bibliographic record

VenueBiopolymers · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsRiboswitchChemistryLigand (biochemistry)Folding (DSP implementation)RNAAptamerComputational biologyStereochemistryBiophysicsGeneBiochemistryNon-coding RNABiologyGeneticsReceptor

Abstract

fetched live from OpenAlex

Abstract Riboswitch regulation of gene expression requires ligand‐mediated RNA folding. From the fluorescence lifetime distribution of bound 2‐aminopurine ligand, we resolve three RNA conformers (Co, Ci, Cc) of the liganded G‐ and A‐sensing riboswitches from Bacillus subtilis. The ligand binding affinities, and sensitivity to Mg2+, together with results from mutagenesis, suggest that Co and Ci are partially unfolded species compromised in key loop‐loop interactions present in the fully folded Cc. These data verify that the ligand‐bound riboswitches may dynamically fold and unfold in solution, and reveal differences in the distribution of folded states between two structurally homologous purine riboswitches: Ligand‐mediated folding of the G‐sensing riboswitch is more effective, less dependent on Mg2+, and less debilitated by mutation, than the A‐sensing riboswitch, which remains more unfolded in its liganded state. We propose that these sequence‐dependent RNA dynamics, which adjust the balance of ligand‐mediated folding and unfolding, enable different degrees of kinetic discrimination in ligand binding, and fine‐tuning of gene regulatory mechanisms. © 2009 Wiley Periodicals, Inc. Biopolymers 91: 953–965, 2009. This article was originally published online as an accepted preprint. The “Published Online” date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com

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 score0.001
Threshold uncertainty score0.002

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.258
Teacher spread0.240 · 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
Published2009
Admission routes1
Has abstractyes

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