ArticleNucleic acids research2017
Optimization of a novel biophysical model using large scale in vivo antisense hybridization data displays improved prediction capabilities of structurally accessible RNA regions.
Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 22 citations in OpenAlex.
- A high-throughput search for intracellular factors that affect RNA folding identifiesRNA biology · 2024Article
- Engineering Toehold-Mediated Switches for Native RNA Detection and Regulation in Bacteria.Journal of molecular biology · 2022Review
- The International Society of RNA Nanotechnology and Nanomedicine (ISRNN): The Present and Future of the Burgeoning Field.ACS nano · 2021Article
- RNA oxidation in chromatin modification and DNA-damage response following exposure to formaldehyde.Scientific reports · 2020Article
- Multiple Small RNAs Interact to Co-regulate Ethanol Tolerance inFrontiers in bioengineering and biotechnology · 2020Article
- High-throughput in vivo mapping of RNA accessible interfaces to identify functional sRNA binding sites.Nature communications · 2018Article
- A Canonical Biophysical Model of the CsrA Global Regulator Suggests Flexible Regulator-Target Interactions.Scientific reports · 2018Article
- Synthetic Biology of Small RNAs and Riboswitches.Microbiology spectrum · 2018Review
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Current approaches to design efficient antisense RNAs (asRNAs) rely primarily on a thermodynamic understanding of RNA-RNA interactions. However, these approaches depend on structure predictions and have limited accuracy, arguably due to overlooking important cellular environment factors. In this work, we develop a biophysical model to describe asRNA-RNA hybridization that incorporates in vivo factors using large-scale experimental hybridization data for three model RNAs: a group I intron, CsrB and a tRNA. A unique element of our model is the estimation of the availability of the target region to interact with a given asRNA using a differential entropic consideration of suboptimal structures. We showcase the utility of this model by evaluating its prediction capabilities in four additional RNAs: a group II intron, Spinach II, 2-MS2 binding domain and glgC 5΄ UTR. Additionally, we demonstrate the applicability of this approach to other bacterial species by predicting sRNA-mRNA binding regions in two newly discovered, though uncharacterized, regulatory RNAs.
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