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  • Title: Optimization of a novel biophysical model using large scale in vivo antisense hybridization data displays improved prediction capabilities of structurally accessible RNA regions.
    Author: Vazquez-Anderson J, Mihailovic MK, Baldridge KC, Reyes KG, Haning K, Cho SH, Amador P, Powell WB, Contreras LM.
    Journal: Nucleic Acids Res; 2017 May 19; 45(9):5523-5538. PubMed ID: 28334800.
    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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