ArticleNucleic acids research2019
Limits in accuracy and a strategy of RNA structure prediction using experimental information.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- ShapeRNA: an integrated web server for RNA secondary structure, ensemble, and functional analysis.Nucleic acids research · 2026Article
- Amplification-Free Dual-Blocking Autocatalytic CRISPR-Cascade for Atto-Molar DNA Detection with Low Nonspecific Signal.bioRxiv : the preprint server for biology · 2025Article
- RNA-Puzzles Round V: blind predictions of 23 RNA structures.Nature methods · 2025Article
- Autonomous Nucleic Acid and Protein Nanocomputing Agents Engineered to Operate in Living Cells.ACS nano · 2025Review
- Turn-on RNA Mango Beacons forRNA (New York, N.Y.) · 2024Article
- Break to Build: Isothermal Assembly of Nucleic Acid Nanoparticles (NANPs)Bioconjugate chemistry · 2023Article
- Combining Experimental Restraints and RNA 3D Structure Prediction in RNA Nanotechnology.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Improved gRNA secondary structures allow editing of target sites resistant to CRISPR-Cas9 cleavage.Nature communications · 2022Article
- Whole proteome mapping of compound-protein interactions.Current research in chemical biology · 2022Article
- Detection ofLife (Basel, Switzerland) · 2021Article
- An NMR-based approach reveals the core structure of the functional domain of SINEUP lncRNAs.Nucleic acids research · 2020Article
- Molecular Structure, Binding Affinity, and Biological Activity in the Epigenome.International journal of molecular sciences · 2020Review
- Advances in RNA 3D Structure Modeling Using Experimental Data.Frontiers in genetics · 2020Review
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Authors and funding
6 authors.
Funding
Abstract
RNA structural complexity and flexibility present a challenge for computational modeling efforts. Experimental information and bioinformatics data can be used as restraints to improve the accuracy of RNA tertiary structure prediction. Regarding utilization of restraints, the fundamental questions are: (i) What is the limit in prediction accuracy that one can achieve with arbitrary number of restraints? (ii) Is there a strategy for selection of the minimal number of restraints that would result in the best structural model? We address the first question by testing the limits in prediction accuracy using native contacts as restraints. To address the second question, we develop an algorithm based on the distance variation allowed by secondary structure (DVASS), which ranks restraints according to their importance to RNA tertiary structure prediction. We find that due to kinetic traps, the greatest improvement in the structure prediction accuracy is achieved when we utilize only 40-60% of the total number of native contacts as restraints. When the restraints are sorted by DVASS algorithm, using only the first 20% ranked restraints can greatly improve the prediction accuracy. Our findings suggest that only a limited number of strategically selected distance restraints can significantly assist in RNA structure modeling.
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