ArticleBMC structural biology2019
QRNAS: software tool for refinement of nucleic acid structures.
Article in BMC structural biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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Who cites it
51 citing papers in PubMed.
- Hierarchical breakdown of RNA structure prediction in CASP16: from reliable local helices to speculative multimer assembly.Bioinformatics (Oxford, England) · 2026Article
- Computational Analysis of Sequence Editability in the Theophylline RNA Aptamer as a Functional RNA Module.International journal of molecular sciences · 2026Article
- Programmable Edge-to-Edge Assembly of RNA Nanostructures.ACS nano · 2026Article
- Conformational analysis of nucleic acids for optimizing DNA and RNA topological models.Nucleic acids research · 2026Article
- Rational design of mechanically active RNAs: de novo engineering of functional exoribonuclease-resistant RNAs.Nucleic acids research · 2026Article
- ChironRNA: Steric Clashes Resolution in RNA Structures via E(3)-Equivariant Diffusion.bioRxiv : the preprint server for biology · 2026Article
- Structural investigation of human U6 snRNA recognition by spliceosomal recycling factor SART3 RNA recognition motifs.The FEBS journal · 2026Article
- Computational approaches for RNA structure prediction and design.Cell reports. Physical science · 2026Article
- TiRNA: a coarse-grained method with temperature and ion effects for RNA structure folding and prediction.Nucleic acids research · 2026Article
- Enhancing RNA 3D Structure Prediction in CASP16: Integrating Physics-Based Modeling With Machine Learning for Improved Predictions.Proteins · 2026Article
- Structure Modeling Protocols for Protein Multimer and RNA in CASP16 With Enhanced MSAs, Model Ranking, and Deep Learning.Proteins · 2026Article
- Generative AI for computational chemistry: A roadmap to predicting emergent phenomena.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- 3D structure and stability prediction of DNA with multi-way junctions in ionic solutions.PLoS computational biology · 2025Article
- Determining the effects of pseudouridine incorporation on human tRNAs.The EMBO journal · 2025Article
- RNA sample optimization for cryo-EM analysis.Nature protocols · 2025Review
- RNA-Puzzles Round V: blind predictions of 23 RNA structures.Nature methods · 2025Article
- NuFold: end-to-end approach for RNA tertiary structure prediction with flexible nucleobase center representation.Nature communications · 2025Article
- NAIRDB: a database of Fourier transform infrared (FTIR) data for nucleic acids.Nucleic acids research · 2025Article
- Connecting the dots: deep learning-based automated model building methods in cryo-EM.Frontiers in molecular biosciences · 2025Review
- IsRNAcirc: 3D structure prediction of circular RNAs based on coarse-grained molecular dynamics simulation.PLoS computational biology · 2024Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundComputational models of RNA 3D structure often present various inaccuracies caused by simplifications used in structure prediction methods, such as template-based modeling or coarse-grained simulations. To obtain a high-quality model, the preliminary RNA structural model needs to be refined, taking into account atomic interactions. The goal of the refinement is not only to improve the local quality of the model but to bring it globally closer to the true structure.
resultsWe present QRNAS, a software tool for fine-grained refinement of nucleic acid structures, which is an extension of the AMBER simulation method with additional restraints. QRNAS is capable of handling RNA, DNA, chimeras, and hybrids thereof, and enables modeling of nucleic acids containing modified residues.
conclusionsWe demonstrate the ability of QRNAS to improve the quality of models generated with different methods. QRNAS was able to improve MolProbity scores of NMR structures, as well as of computational models generated in the course of the RNA-Puzzles experiment. The overall geometry improvement may be associated with increased model accuracy, especially on the level of correctly modeled base-pairs, but the systematic improvement of root mean square deviation to the reference structure should not be expected. The method has been integrated into a computational modeling workflow, enabling improved RNA 3D structure prediction.
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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.