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Record W2013926038 · doi:10.1088/1478-3975/7/3/036001

Coarse-graining RNA nanostructures for molecular dynamics simulations

2010· article· en· W2013926038 on OpenAlexaff
Maxim Paliy, Roderick Melnik, Bruce A. Shapiro

Bibliographic record

VenuePhysical Biology · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsWilfrid Laurier UniversityWestern University
FundersNational Institutes of Health
KeywordsRNAContext (archaeology)Molecular dynamicsGranularityNucleotideNanobiotechnologyBiological systemStatistical physicsPhysicsComputational biologyChemistryComputer scienceNanotechnologyBiologyMaterials scienceComputational chemistryBiochemistry

Abstract

fetched live from OpenAlex

A series of coarse-grained models have been developed for study of the molecular dynamics of RNA nanostructures. The models in the series have one to three beads per nucleotide and include different amounts of detailed structural information. Such a treatment allows us to reach, for systems of thousands of nucleotides, a time scale of microseconds (i.e. by three orders of magnitude longer than in full atomistic modeling) and thus to enable simulations of large RNA polymers in the context of bionanotechnology. We find that the three-beads-per-nucleotide models, described by a set of just a few universal parameters, are able to describe different RNA conformations and are comparable in structural precision to the models where detailed values of the backbone P-C4' dihedrals taken from a reference structure are included. These findings are discussed in the context of RNA conformation classes.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.008
GPT teacher head0.281
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 designSimulation or modeling
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

Citations37
Published2010
Admission routes1
Has abstractyes

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