ReviewBioscience reports2019
Computational modeling of RNA 3D structure based on experimental data.
Review in Bioscience reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed.
- Building RNA coarse-grained force fields: Design principles and training strategies.Biophysical journal · 2026Review
- Cleavage region organizes the structural architecture of the SINE-derived B2 repressive ribozyme.Communications biology · 2026Article
- FRET-guided selection of RNA 3D structures.Nucleic acids research · 2026Article
- Membrane Composition Modulates Vp54 Binding: A Combined Experimental and Computational Study.Pathogens (Basel, Switzerland) · 2025Article
- Review
- The bioinformatics of the finding that the hepatitis delta virus RNA editing mechanism by a conformational switch exists in genotype 7 in addition to genotype 3.Briefings in bioinformatics · 2025Article
- Comparative analysis of RNA 3D structure prediction methods: towards enhanced modeling of RNA-ligand interactions.Nucleic acids research · 2024Article
- State-of-the-RNArt: benchmarking current methods for RNA 3D structure prediction.NAR genomics and bioinformatics · 2024Article
- The Multiscale Ernwin/SPQR RNA Structure Prediction Pipeline.Methods in molecular biology (Clifton, N.J.) · 2024Article
- When will RNA get its AlphaFold moment?Nucleic acids research · 2023Article
- Biophysical characterization of the structure of a SARS-CoV-2 self-amplifying RNA (saRNA) vaccine.Biology methods & protocols · 2023Article
- Computational Pipeline for Reference-Free Comparative Analysis of RNA 3D Structures Applied to SARS-CoV-2 UTR Models.International journal of molecular sciences · 2022Article
- Cryo-EM advances in RNA structure determination.Signal transduction and targeted therapy · 2022Review
- Rapid structure-function insights via hairpin-centric analysis of big RNA structure probing datasets.NAR genomics and bioinformatics · 2021Article
- Structural 3D Domain Reconstruction of the RNA Genome from Viruses with Secondary Structure Models.Viruses · 2021Article
- Direct Mapping of Higher-Order RNA Interactions by SHAPE-JuMP.Biochemistry · 2021Article
- IsRNA1:Journal of chemical theory and computation · 2021Article
- AnnapuRNA: A scoring function for predicting RNA-small molecule binding poses.PLoS computational biology · 2021Article
- RNA 3D Structure Prediction Using Coarse-Grained Models.Frontiers in molecular biosciences · 2021Review
- Predicting RNA Scaffolds with a Hybrid Method of Vfold3D and VfoldLA.Methods in molecular biology (Clifton, N.J.) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
RNA molecules are master regulators of cells. They are involved in a variety of molecular processes: they transmit genetic information, sense cellular signals and communicate responses, and even catalyze chemical reactions. As in the case of proteins, RNA function is dictated by its structure and by its ability to adopt different conformations, which in turn is encoded in the sequence. Experimental determination of high-resolution RNA structures is both laborious and difficult, and therefore the majority of known RNAs remain structurally uncharacterized. To address this problem, predictive computational methods were developed based on the accumulated knowledge of RNA structures determined so far, the physical basis of the RNA folding, and taking into account evolutionary considerations, such as conservation of functionally important motifs. However, all theoretical methods suffer from various limitations, and they are generally unable to accurately predict structures for RNA sequences longer than 100-nt residues unless aided by additional experimental data. In this article, we review experimental methods that can generate data usable by computational methods, as well as computational approaches for RNA structure prediction that can utilize data from experimental analyses. We outline methods and data types that can be potentially useful for RNA 3D structure modeling but are not commonly used by the existing software, suggesting directions for future development.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.