ArticleRNA (New York, N.Y.)2009
Predicting structures and stabilities for H-type pseudoknots with interhelix loops.
Article in RNA (New York, N.Y.), 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.
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Who cites it
66 citing papers in PubMed.
- Enhancing RNA 3D Structure Prediction in CASP16: Integrating Physics-Based Modeling With Machine Learning for Improved Predictions.Proteins · 2026Article
- RNA-Puzzles Round V: blind predictions of 23 RNA structures.Nature methods · 2025Article
- mRNA vaccine sequence and structure design and optimization: Advances and challenges.The Journal of biological chemistry · 2025Review
- RNA fold prediction by Monte Carlo in graph space and the statistical mechanics of tertiary interactions.RNA (New York, N.Y.) · 2024Article
- Machine learning in RNA structure prediction: Advances and challenges.Biophysical journal · 2024Review
- Kinetic pathway of HIV-1 TAR cotranscriptional folding.Nucleic acids research · 2024Article
- Knotify_V2.0: Deciphering RNA Secondary Structures with H-Type Pseudoknots and Hairpin Loops.Genes · 2024Article
- Advancing RNA 3D structure prediction: Exploring hierarchical and hybrid approaches in CASP15.Proteins · 2023Article
- RNA Folding Based on 5 Beads Model and Multiscale Simulation.Interdisciplinary sciences, computational life sciences · 2023Article
- Predicting 3D structures and stabilities for complex RNA pseudoknots in ion solutions.Biophysical journal · 2023Article
- cRNAsp12 Web Server for the Prediction of Circular RNA Secondary Structures and Stabilities.International journal of molecular sciences · 2023Article
- Knotify+: Toward the Prediction of RNA H-Type Pseudoknots, Including Bulges and Internal Loops.Biomolecules · 2023Article
- Applications of Molecular Dynamics Simulation in Protein Study.Membranes · 2022Review
- VfoldMCPX: predicting multistrand RNA complexes.RNA (New York, N.Y.) · 2022Article
- Vfold2D-MC: A Physics-Based Hybrid Model for Predicting RNA Secondary Structure Folding.The journal of physical chemistry. B · 2021Article
- Thermostability, Tunability, and Tenacity of RNA as Rubbery Anionic Polymeric Materials in Nanotechnology and Nanomedicine-Specific Cancer Targeting with Undetectable Toxicity.Chemical reviews · 2021Review
- Article
- Modeling Loop Composition and Ion Concentration Effects in RNA Hairpin Folding Stability.Biophysical journal · 2020Article
- Topological constraints of RNA pseudoknotted and loop-kissing motifs: applications to three-dimensional structure prediction.Nucleic acids research · 2020Article
- A Polymer Physics Framework for the Entropy of Arbitrary Pseudoknots.Biophysical journal · 2019Article
6 more citing papers are in PubMed but not listed here.
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2 authors.
Funding
Abstract
RNA pseudoknots play a critical role in RNA-related biology from the assembly of ribosome to the regulation of viral gene expression. A predictive model for pseudoknot structure and stability is essential for understanding and designing RNA structure and function. A previous statistical mechanical theory allows us to treat canonical H-type RNA pseudoknots that contain no intervening loop between the helices (see S. Cao and S.J. Chen [2006] in Nucleic Acids Research, Vol. 34; pp. 2634-2652). Biologically significant RNA pseudoknots often contain interhelix loops. Predicting the structure and stability for such more-general pseudoknots remains an unsolved problem. In the present study, we develop a predictive model for pseudoknots with interhelix loops. The model gives conformational entropy, stability, and the free-energy landscape from RNA sequences. The main features of this new model are the computation of the conformational entropy and folding free-energy base on the complete conformational ensemble and rigorous treatment for the excluded volume effects. Extensive tests for the structural predictions show overall good accuracy with average sensitivity and specificity equal to 0.91 and 0.91, respectively. The theory developed here may be a solid starting point for first-principles modeling of more complex, larger RNAs.
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