Optimization of a novel biophysical model using large scale in vivo antisense hybridization data displays improved prediction capabilities of structurally accessible RNA regions

Jorge Vazquez-Anderson, Mia K. Mihailovic, Kevin C. Baldridge, Kristofer G. Reyes, Katie Haning, Seung Hee Cho, Paul Amador, Warren Buckler Powell, Lydia M. Contreras

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)5523-5538
Number of pages16
JournalNucleic acids research
Volume45
Issue number9
DOIs
StatePublished - May 19 2017

All Science Journal Classification (ASJC) codes

  • Genetics

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