ArticleJournal of molecular biology2017
F-RAG: Generating Atomic Coordinates from RNA Graphs by Fragment Assembly.
Article in Journal of molecular biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- PlanarFold: a coarse-grained molecular dynamics model of RNA in two-dimensional space.Nature communications · 2026Article
- Open questions on viral frameshifting: Exploiting the structural plasticity of the frameshifting element for therapeutic intervention.Biophysical journal · 2026Review
- Phase Space Invaders' podcast episode with Tamar Schlick: a trajectory from mathematics to biology.Biophysical reviews · 2025Article
- RNA fold prediction by Monte Carlo in graph space and the statistical mechanics of tertiary interactions.RNA (New York, N.Y.) · 2024Article
- Predicting 3D RNA structure from the nucleotide sequence using Euclidean neural networks.Biophysical journal · 2024Article
- Prediction of 3D RNA Structures from Sequence Using Energy Landscapes of RNA Dimers: Application to RNA Tetraloops.Journal of chemical theory and computation · 2024Article
- FebRNA: An automated fragment-ensemble-based model for building RNA 3D structures.Biophysical journal · 2022Article
- RNA-As-Graphs Motif Atlas-Dual Graph Library of RNA Modules and Viral Frameshifting-Element Applications.International journal of molecular sciences · 2022Article
- Length-dependent motions of SARS-CoV-2 frameshifting RNA pseudoknot and alternative conformations suggest avenues for frameshifting suppression.Nature communications · 2022Article
- Length-Dependent Deep Learning Model for RNA Secondary Structure Prediction.Molecules (Basel, Switzerland) · 2022Article
- rsRNASP: A residue-separation-based statistical potential for RNA 3D structure evaluation.Biophysical journal · 2022Article
- Length-dependent motions of SARS-CoV-2 frameshifting RNA pseudoknot and alternative conformations suggest avenues for frameshifting suppression.Research square · 2022Article
- Biomolecular Modeling and Simulation: A Prospering Multidisciplinary Field.Annual review of biophysics · 2021Article
- Structure-altering mutations of the SARS-CoV-2 frameshifting RNA element.Biophysical journal · 2021Article
- A Fiedler Vector Scoring Approach for Novel RNA Motif Selection.The journal of physical chemistry. B · 2021Article
- Structure-Altering Mutations of the SARS-CoV-2 Frame Shifting RNA Element.bioRxiv : the preprint server for biology · 2020Article
- Identification of novel RNA design candidates by clustering the extended RNA-As-Graphs library.Biochimica et biophysica acta. General subjects · 2020Article
- Inverse folding with RNA-As-Graphs produces a large pool of candidate sequences with target topologies.Journal of structural biology · 2020Article
- RAG-Web: RNA structure prediction/design using RNA-As-Graphs.Bioinformatics (Oxford, England) · 2020Article
- 3dRNA: Building RNA 3D structure with improved template library.Computational and structural biotechnology journal · 2020Article
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2 authors.
Funding
Abstract
Coarse-grained models represent attractive approaches to analyze and simulate ribonucleic acid (RNA) molecules, for example, for structure prediction and design, as they simplify the RNA structure to reduce the conformational search space. Our structure prediction protocol RAGTOP (RNA-As-Graphs Topology Prediction) represents RNA structures as tree graphs and samples graph topologies to produce candidate graphs. However, for a more detailed study and analysis, construction of atomic from coarse-grained models is required. Here we present our graph-based fragment assembly algorithm (F-RAG) to convert candidate three-dimensional (3D) tree graph models, produced by RAGTOP into atomic structures. We use our related RAG-3D utilities to partition graphs into subgraphs and search for structurally similar atomic fragments in a data set of RNA 3D structures. The fragments are edited and superimposed using common residues, full atomic models are scored using RAGTOP's knowledge-based potential, and geometries of top scoring models is optimized. To evaluate our models, we assess all-atom RMSDs and Interaction Network Fidelity (a measure of residue interactions) with respect to experimentally solved structures and compare our results to other fragment assembly programs. For a set of 50 RNA structures, we obtain atomic models with reasonable geometries and interactions, particularly good for RNAs containing junctions. Additional improvements to our protocol and databases are outlined. These results provide a good foundation for further work on RNA structure prediction and design applications.
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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.