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In Vivo Modeling of the Morbid Human Genome using Danio rerio
Published on: August 24, 2013
Optimization of a novel biophysical model using large scale in vivo antisense hybridization data displays improved
Jorge Vazquez-Anderson1, Mia K Mihailovic1, Kevin C Baldridge1
1McKetta Department of Chemical Engineering, The University of Texas at Austin, 200 E. Dean Keeton St., Stop C0400, Austin, TX 78712, USA.
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.
