RNA secondary structure modelling following the IPANEMAP workflow

International audienceRNA secondary structure modelling has been a challenge since the early days of molecular biology. Although algorithms for RNA structure modelling are more and more efficient and accurate, they significantly benefit from the integration of experimental structure probing data. RNA structure probing consists in submitting an RNA to enzymes or small molecules that specifically react with individual nucleotides according to their pairing status. Most enzymes used are single strand specific RNAses (RNAses T1, U2, nuclease S1 …) with the notable exception of the double strand specific RNAse V1. Although they are low molecular weight proteins, they are too bulky to access some nucleotides of a folded RNA. Small molecules can essentially reach any nucleotide and most of them are also single-strand specific although psoralen has recently been successfully used a double strand probe (Lu et al., 2016). For the longest time, RNA probing experiments remained tedious and rather qualitative than quantitative. RNA structure probing recently reached the medium, and then high, throughput. Pioneered and mostly developed within the Weeks lab, the SHAPE technology uses small molecules that react with flexible ribose, thus essentially reporting single-stranded nucleotides with some subtleties (Frezza et al., 2019; Steen et al., 2012). A medium throughput version of the SHAPE protocol was first developed based on capillary electrophoresis, later to be transformed into a high throughput method using next generation sequencing. The same workflows can be applied to more traditional probes such as DiMethyl Sulfate (DMS) and N-Cyclohexyl-N′-(2-morpholinoethyl)carbodiimide metho-p-toluenesulfonate (CMCT) that reveal unpaired A,C and G,U respectively. It appeared that different probes provide complementary information that further improves RNA structure prediction. We therefore developed IPANEMAP, an experimental and computational workflow that models RNA secondary structure from different sets of RNA structure probing performed with different probes, and/or in different conditions and/or on mutants (Saaidi et al. Submitted). This workflow relies on medium or high throughput structure probing, and combines statistical sampling, clustering (Ding and Lawrence, 2003) and pseudo-potentials (Deigan et al, 2009). The method was shown to produce more accurate and stable predictions than other workflows developed to date, even when a single reactivity profile is available, while the availability of multiple reactivities was shown to increase robustness and, to a lesser extent, accuracy of the modeling (Saaidi et al. Submitted). Below, we detail a whole IPANEMAP workflow, starting with experimental probing with DMS and/or CMCT and/or SHAPE reagent. Such probing can be carried out in various relevant conditions (varying température, Mg2+ concentration, introducing point mutations in the RNA to be modeled etc) (Saaidi et al. Submitted). Two versions of the experimental procedure (medium throughput and high throughput) are proposed, DMS and CMCT probing were adapted from Ehresmann et al. and Brunel et al. while the SHAPE probing is described in K. Weeks team publications (Karabiber et al., 2013; Low and Weeks, 2010a; Mortimer and Weeks, 2007; Smola et al., 2015a; Wilkinson et al., 2006, 2008). We then detail instructions for executing the IPANEMAP algorithm to obtain the RNA secondary structure model

Progress Toward SHAPE Constrained Computational Prediction of Tertiary Interactions in RNA Structure

International audienceAs more sequencing data accumulate and novel puzzling genetic regulations are discovered, the need for accurate automated modeling of RNA structure increases. RNA structure modeling from chemical probing experiments has made tremendous progress, however accurately predicting large RNA structures is still challenging for several reasons: RNA are inherently flexible and often adopt many energetically similar structures, which are not reliably distinguished by the available, incomplete thermodynamic model. Moreover, computationally, the problem is aggravated by the relevance of pseudoknots and non-canonical base pairs, which are hardly predicted efficiently. To identify nucleotides involved in pseudoknots and non-canonical interactions, we scrutinized the SHAPE reactivity of each nucleotide of the 188 nt long lariat-capping ribozyme under multiple conditions. Reactivities analyzed in the light of the X-Ray structure were shown to report accurately the nucleotide status. Those that seemed paradoxical were rationalized by the nucleotide behavior along molecular dynamic simulations. We show that valuable information on intricate interactions can be deduced from probing with different reagents, and in the presence or absence of Mg 2+. Furthermore, probing at increasing temperature was remarkably efficient at pointing to non-canonical interactions and pseudoknot pairings. The possibilities of following such strategies to inform structure modeling software are discussed

HIV-1 gRNA, a biological substrate, uncovers the potency of DDX3X biochemical activity

International audienceDEAD-box helicases play central roles in the metabolism of many RNAs and ribonucleoproteins by assisting their synthesis, folding, function and even their degradation or disassembly. They have been implicated in various phenomena, and it is often difficult to rationalize their molecular roles from in vivo studies. Once purified in vitro, most of them only exhibit a marginal activity and poor specificity. The current model is that they gain specificity and activity through interaction of their intrinsically disordered domains with specific RNA or proteins. DDX3 is a DEAD-box cellular helicase that has been involved in several steps of the HIV viral cycle, including transcription, RNA export to the cytoplasm and translation. In this study, we investigated DDX3 biochemical properties in the context of a biological substrate. DDX3 was overexpressed, purified and its enzymatic activities as well as its RNA binding properties were characterized using both model substrates and a biological substrate, HIV-1 gRNA. Biochemical characterization of DDX3 in the context of a biological substrate identifies HIV-1 gRNA as a rare example of specific substrate and unravels the extent of DDX3 ATPase activity. Analysis of DDX3 binding capacity indicates an unexpected dissociation between its binding capacity and its biochemical activity. We further demonstrate that interaction of DDX3 with HIV-1 gRNA relies both on specific RNA determinants and on the disordered N-and C-terminal regions of the protein. These findings shed a new light regarding the potentiality of DDX3 biochemical activity supporting its multiple cellular functions