Optimization of a novel biophysical model using large scale in vivo antisense hybridization data displays improved prediction capabilities of structurally accessible RNA regions.
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.
McKetta Department of Chemical Engineering, The University of Texas at Austin, 200 E. Dean Keeton St., Stop C0400, Austin, TX 78712, USA.
Jorge
J
Vazquez-Anderson
McKetta Department of Chemical Engineering, The University of Texas at Austin, 200 E. Dean Keeton St., Stop C0400, Austin, TX 78712, USA.
Mia K
MK
Mihailovic
McKetta Department of Chemical Engineering, The University of Texas at Austin, 200 E. Dean Keeton St., Stop C0400, Austin, TX 78712, USA.
Kevin C
KC
Baldridge
Department of Operations Research and Financial Engineering, Princeton University, Sherrerd Hall, Charlton St., Princeton, NJ 08544, USA.
Kristofer G
KG
Reyes
McKetta Department of Chemical Engineering, The University of Texas at Austin, 200 E. Dean Keeton St., Stop C0400, Austin, TX 78712, USA.
Katie
K
Haning
Institute for Cellular & Molecular Biology, The University of Texas at Austin, 2500 Speedway, Stop A4800, Austin, TX 78712, USA.
Seung Hee
SH
Cho
Institute for Cellular & Molecular Biology, The University of Texas at Austin, 2500 Speedway, Stop A4800, Austin, TX 78712, USA.
Paul
P
Amador
Department of Operations Research and Financial Engineering, Princeton University, Sherrerd Hall, Charlton St., Princeton, NJ 08544, USA.
Warren B
WB
Powell
McKetta Department of Chemical Engineering, The University of Texas at Austin, 200 E. Dean Keeton St., Stop C0400, Austin, TX 78712, USA.
Lydia M
LM
Contreras
D001483:Base Sequence; D055592:Biophysical Phenomena; D019295:Computational Biology / Q000379:methods*; D008954:Models, Biological; D009690:Nucleic Acid Conformation; D009693:Nucleic Acid Hybridization; D016372:RNA, Antisense / Q000737:chemistry*; D012329:RNA, Bacterial / Q000737:chemistry*; D012333:RNA, Messenger / Q000378:metabolism; D012044:Regression Analysis; D013816:Thermodynamics
D016372:RNA, Antisense; D012329:RNA, Bacterial; D012333:RNA, Messenger