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

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.

Multiple Small RNAs Interact to Co-regulate Ethanol Tolerance in Zymomonas mobilis

“…As shown in Figures 4E,F, Zms4 and Zms6 contain multiple functional sites that potentially contribute to multi-tasking in their function. Moreover, these sites are predicted to occupy regions of high and low hybridization efficacy, as identified by the InTherAcc biophysical model (Vazquez-Anderson et al, 2017), which could explain the observed differences in binding affinities. These results also imply that Zms4 and Zms6 could bind and regulate multiple targets simultaneously and efficiently.…”

“…Ultimately, electrophoretic mobility shift assays (EMSAs) and reporter gene systems can complement these approaches in testing direct binding of RNA and protein targets in vitro and in vivo (Corcoran et al, 2012;Tomasini et al, 2017). Most recent, the mapping of sRNA interfaces that could be available in vivo for interactions has also been useful to determine biologically relevant mRNA targets (Vazquez-Anderson et al, 2017;Mihailovic et al, 2018).…”

High-throughput in vivo mapping of RNA accessible interfaces to identify functional sRNA binding sites

“…For example, in vivo chemical and enzymatic probing methods gauge the level of “protection” or reactivity of individual bases/backbones within a region of interest, and have recently been adapted for high-throughput use 26 , 27 . As these local nucleotide availabilities do not always correlate with regional-level accessibility that more accurately mimics RNA:RNA interactions of regulatory interfaces 28 , efforts have also been placed on methods to quantify regional in vivo RNA hybridization. Corresponding datasets have yielded useful predictions of regions capable of establishing RNA:RNA interactions 28 .…”

“…As these local nucleotide availabilities do not always correlate with regional-level accessibility that more accurately mimics RNA:RNA interactions of regulatory interfaces 28 , efforts have also been placed on methods to quantify regional in vivo RNA hybridization. Corresponding datasets have yielded useful predictions of regions capable of establishing RNA:RNA interactions 28 . Nonetheless, acquisition of these data is typically limited by low-throughput experiments 28 , 29 and reliance on sRNA overexpression that disturbs native transcript or protein stoichiometry 30 .…”

“…Corresponding datasets have yielded useful predictions of regions capable of establishing RNA:RNA interactions 28 . Nonetheless, acquisition of these data is typically limited by low-throughput experiments 28 , 29 and reliance on sRNA overexpression that disturbs native transcript or protein stoichiometry 30 . As such, there remains a need for high-throughput methods to identify characteristic features of RNA regions that are likely to engage in regulatory interactions with respective mRNA targets.…”