TY - JOUR
T1 - Optimization of a novel biophysical model using large scale in vivo antisense hybridization data displays improved prediction capabilities of structurally accessible RNA regions
AU - Vazquez-Anderson, Jorge
AU - Mihailovic, Mia K.
AU - Baldridge, Kevin C.
AU - Reyes, Kristofer G.
AU - Haning, Katie
AU - Cho, Seung Hee
AU - Amador, Paul
AU - Powell, Warren Buckler
AU - Contreras, Lydia M.
PY - 2017/5/19
Y1 - 2017/5/19
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85027168760&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85027168760&partnerID=8YFLogxK
U2 - 10.1093/nar/gkx115
DO - 10.1093/nar/gkx115
M3 - Article
C2 - 28334800
AN - SCOPUS:85027168760
VL - 45
SP - 5523
EP - 5538
JO - Nucleic Acids Research
JF - Nucleic Acids Research
SN - 0305-1048
IS - 9
ER -