ArticleThe journal of physical chemistry. B2021
Sieving RNA 3D Structures with SHAPE and Evaluating Mechanisms Driving Sequence-Dependent Reactivity Bias.
Article in The journal of physical chemistry. B, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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Who cites it
6 citing papers in PubMed.
- Markov models of SHAPE data improve secondary structure prediction.bioRxiv : the preprint server for biology · 2026Article
- A Guide to Computational Cotranscriptional Folding Featuring the SRP RNA.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Molecular Dynamics Simulations with Grand-Canonical Reweighting Suggest Cooperativity Effects in RNA Structure Probing Experiments.Journal of chemical theory and computation · 2023Article
- Role of RNA Domain Structure and Orientation in the Coxsackievirus B3 Virulence Phenotype.Journal of virology · 2023Article
- Biomotors, viral assembly, and RNA nanobiotechnology: Current achievements and future directions.Computational and structural biotechnology journal · 2022Review
- SHAPER: A Web Server for Fast and Accurate SHAPE Reactivity Prediction.Frontiers in molecular biosciences · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) chemical probing provides local RNA flexibility information at single-nucleotide resolution. In general, SHAPE is thought of as a secondary structure (2D) technology, but we find evidence that robust tertiary structure (3D) information is contained in SHAPE data. Here, we report a new model that achieves a higher correlation between SHAPE data and native RNA 3D structures than the previous 3D structure-SHAPE relationship model. Furthermore, we demonstrate that the new model improves our ability to discern between SHAPE-compatible and -incompatible structures on model decoys. After identifying sequence-dependent bias in SHAPE experiments, we propose a mechanism driving sequence-dependent bias in SHAPE experiments, using replica-exchange umbrella sampling simulations to confirm that the SHAPE sequence bias is largely explained by the stability of the unreacted SHAPE reagent in the binding pocket. Taken together, this work represents multiple practical advances in our mechanistic and predictive understanding of SHAPE technology.
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Registered trials
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