scholarly journals Stability of local secondary structure determines selectivity of viral RNA chaperones

2018 ◽  
Author(s):  
Jack P.K. Bravo ◽  
Alexander Borodavka ◽  
Anders Barth ◽  
Antonio N. Calabrese ◽  
Peter Mojzes ◽  
...  
Keyword(s):  
Rna Structure ◽  
Rna Binding ◽  
High Fidelity ◽  
Multiple Sequence ◽  
Genomic Rnas ◽  
Local Secondary Structure ◽  
Complete Set ◽  
Rna Chaperones ◽  
Duplex Formation ◽  
Segmented Genome

AbstractTo maintain genome integrity, segmented double-stranded RNA viruses of theReoviridaefamily must accurately select and package a complete set of up to a dozen distinct genomic RNAs. It is thought that the high fidelity segmented genome assembly involves multiple sequence-specific RNA-RNA interactions between single-stranded RNA segment precursors. These are mediated by virus-encoded non-structural proteins with RNA chaperone-like activities, such as rotavirus NSP2 and avian reovirus σNS. Here, we compared the abilities of NSP2 and σNS to mediate sequence-specific interactions between rotavirus genomic segment precursors. Despite their similar activities, NSP2 successfully promotes inter-segment association, while σNS fails to do so. To understand the mechanisms underlying such selectivity in promoting inter-molecular duplex formation, we compared RNA-binding and helix-unwinding activities of both proteins. We demonstrate that octameric NSP2 binds structured RNAs with high affinity, resulting in efficient intramolecular RNA helix disruption. Hexameric σNS oligomerises into an octamer that binds two RNAs, yet it exhibits only limited RNA-unwinding activity compared to NSP2. Thus, the formation of intersegment RNA-RNA interactions is governed by both helix-unwinding capacity of the chaperones and stability of RNA structure. We propose that this protein-mediated RNA selection mechanism may underpin the high fidelity assembly of multi-segmented RNA genomes inReoviridae.

scholarly journals CoBold: a method for identifying different functional classes of transient RNA structure features that can impact RNA structure formation in vivo

2020 ◽  
Author(s):  
Adrián López Martín ◽  
Mohamed Mounir ◽  
Irmtraud M Meyer
Keyword(s):  
Structure Formation ◽  
Rna Structure ◽  
Rna Secondary Structure ◽  
Computational Method ◽  
Data Visualisation ◽  
Multiple Sequence ◽  
Functional Roles ◽  
Functional Classes ◽  
The Given

Abstract RNA structure formation in vivo happens co-transcriptionally while the transcript is being made. The corresponding co-transcriptional folding pathway typically involves transient RNA structure features that are not part of the final, functional RNA structure. These transient features can play important functional roles of their own and also influence the formation of the final RNA structure in vivo. We here present CoBold, a computational method for identifying different functional classes of transient RNA structure features that can either aid or hinder the formation of a known reference RNA structure. Our method takes as input either a single RNA or a corresponding multiple-sequence alignment as well as a known reference RNA secondary structure and identifies different classes of transient RNA structure features that could aid or prevent the formation of the given RNA structure. We make CoBold available via a web-server which includes dedicated data visualisation.

scholarly journals Double-stranded RNA drives SARS-CoV-2 nucleocapsid protein to undergo phase separation at specific temperatures

2021 ◽  
Author(s):  
Christine Roden ◽  
Yifan Dai ◽  
Ian Seim ◽  
Myungwoon Lee ◽  
Rachel Sealfon ◽  
...  
Keyword(s):  
Phase Separation ◽  
Rna Structure ◽  
Nucleocapsid Protein ◽  
Rna Binding ◽  
Genome Packaging ◽  
Rna Motifs ◽  
Double Stranded Rna ◽  
N Protein ◽  
Binding Domains

Betacoronavirus SARS-CoV-2 infections caused the global Covid-19 pandemic. The nucleocapsid protein (N-protein) is required for multiple steps in the betacoronavirus replication cycle. SARS-CoV-2-N-protein is known to undergo liquid-liquid phase separation (LLPS) with specific RNAs at particular temperatures to form condensates. We show that N-protein recognizes at least two separate and distinct RNA motifs, both of which require double-stranded RNA (dsRNA) for LLPS. These motifs are separately recognized by N-protein's two RNA binding domains (RBDs). Addition of dsRNA accelerates and modifies N-protein LLPS in vitro and in cells and controls the temperature condensates form. The abundance of dsRNA tunes N-protein-mediated translational repression and may confer a switch from translation to genome packaging. Thus, N-protein's two RBDs interact with separate dsRNA motifs, and these interactions impart distinct droplet properties that can support multiple viral functions. These experiments demonstrate a paradigm of how RNA structure can control the properties of biomolecular condensates.

Development of an RNA-protein crosslinker to capture protein interactions with diverse RNA structures in cells

RNA ◽  
2021 ◽  
pp. rna.078896.121
Author(s):  
Yan Han ◽  
Xuzhen Guo ◽  
Tiancai Zhang ◽  
Jiangyun Wang ◽  
Keqiong Ye
Keyword(s):  
Protein Interactions ◽  
Rna Structure ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
High Specificity ◽  
Ester Group ◽  
Yeast Cells ◽  
Primary Amines ◽  
Low Efficiency ◽  
In Cells

Characterization of RNA-protein interaction is fundamental for understanding metabolism and function of RNA. UV crosslinking has been widely used to map the targets of RNA-binding proteins, but is limited by low efficiency, requirement for zero-distance contact and biases for single-stranded RNA structure and certain residues of RNA and protein. Here, we report the development of an RNA-protein crosslinker (AMT-NHS) composed of a psoralen derivative and an N-hydroxysuccinimide ester group, which react with RNA bases and primary amines of protein, respectively. We show that AMT-NHS can penetrate into living yeast cells and crosslink Cbf5 to H/ACA snoRNAs with high specificity. The crosslinker induced different crosslinking patterns than UV and targeted both single- and double-stranded regions of RNA. The crosslinker provides a new tool to capture diverse RNA-protein interactions in cells.

scholarly journals Augmented base pairing networks encode RNA-small molecule binding preferences

2020 ◽  
Vol 48 (14) ◽  
pp. 7690-7699
Author(s):  
Carlos Oliver ◽  
Vincent Mallet ◽  
Roman Sarrazin Gendron ◽  
Vladimir Reinharz ◽  
William L Hamilton ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Structure Function ◽  
Small Molecule ◽  
Rna Structure ◽  
Rna Binding ◽  
Binding Specificity ◽  
Representation Learning ◽  
Learning Task ◽  
Graph Representation ◽  
Base Pairing

Abstract RNA-small molecule binding is a key regulatory mechanism which can stabilize 3D structures and activate molecular functions. The discovery of RNA-targeting compounds is thus a current topic of interest for novel therapies. Our work is a first attempt at bringing the scalability and generalization abilities of machine learning methods to the problem of RNA drug discovery, as well as a step towards understanding the interactions which drive binding specificity. Our tool, RNAmigos, builds and encodes a network representation of RNA structures to predict likely ligands for novel binding sites. We subject ligand predictions to virtual screening and show that we are able to place the true ligand in the 71st–73rd percentile in two decoy libraries, showing a significant improvement over several baselines, and a state of the art method. Furthermore, we observe that augmenting structural networks with non-canonical base pairing data is the only representation able to uncover a significant signal, suggesting that such interactions are a necessary source of binding specificity. We also find that pre-training with an auxiliary graph representation learning task significantly boosts performance of ligand prediction. This finding can serve as a general principle for RNA structure-function prediction when data is scarce. RNAmigos shows that RNA binding data contains structural patterns with potential for drug discovery, and provides methodological insights for possible applications to other structure-function learning tasks. The source code, data and a Web server are freely available at http://rnamigos.cs.mcgill.ca.

scholarly journals Unravelling the Stability and Capsid Dynamics of the Three Virions of Brome Mosaic Virus Assembled Autonomously In Vivo

2019 ◽  
Author(s):  
Antara Chakravarty ◽  
Christian Beren ◽  
Rees Garmann ◽  
A.L.N. Rao
Keyword(s):  
Mosaic Virus ◽  
Transient Expression ◽  
Rna Binding ◽  
Conformational Dynamics ◽  
Brome Mosaic Virus ◽  
Crystallographic Structure ◽  
Binding Motif ◽  
Genomic Rnas ◽  
Viral Capsids

ABSTRACTViral capsids are dynamic assemblies that undergo controlled conformational transitions to perform various biological functions. The replicated three-molecule RNA progeny of Brome mosaic virus (BMV) are packaged by a single capsid protein (CP) into three types of morphologically indistinguishable icosahedral virions with T=3 quasi-symmetry. Type 1 (B1v) and type 2 (B2v) virions respectively package genomic RNA1 or RNA2, while type 3 (B3+4v) co-packages genomic RNA3 (B3) and its sub-genomic RNA4 (B4). In this study, the application of a robust Agrobacterium-mediated transient expression system allowed us to assemble each virion type separately in planta. Physical and biochemical approaches analyzing the morphology, size, and electrophoretic mobility failed to distinguish between the virion types, so protease-based mapping experiments were used to analyze the conformational dynamics of the individual virions. The crystallographic structure of the BMV capsid shows four trypsin-cleavage sites (K65, R103, K111 and K165 on the A, B and C subunits) exposed on the exterior of the capsid. Irrespective of the digestion time, while retaining their capsid structural integrity, B1v and B2v released only two peptides involving amino acids 2-8 and 16-22 from the N-proximal arginine-rich RNA binding motif. In contrast, B3+4v capsids are unstable to trypsin, releasing several peptides in addition to the four sites predicted to be exposed on the capsid exterior. These results, demonstrating qualitatively different dynamics for the three types of BMV virions, suggest that the different RNA genes they contain may have different translational timing and efficiency and may even impart different structures to their capsids.IMPORTANCEThe majority of viruses contain RNA genomes protected by a shell of capsid proteins. Although crystallographic studies show that viral capsids are static structures, accumulating evidence suggests that in solution virions are highly dynamic assemblies. The three genomic RNAs (RNAs 1, 2 and 3) and a single subgenomic RNA (RNA4) of Brome mosaic virus (BMV), an RNA virus pathogenic to plants, are distributed among three physically homogeneous virions. This study examines the capsid dynamics by MALDI-TOF analyses following trypsin digestion of the three virions assembled separately in vivo using the Agrobacterium-mediated transient expression approach. The results provide compelling evidence that virions packaging genomic RNAs1 and 2 are more stable and dynamically distinct from those co-packaging RNA3 and 4, suggesting that RNA-dependent capsid dynamics play an important biological role in the viral life cycle.

scholarly journals Inferring Sequence-Structure Preferences of RNA-Binding Proteins with Convolutional Residual Networks

2018 ◽  
Author(s):  
Peter K. Koo ◽  
Praveen Anand ◽  
Steffan B. Paul ◽  
Sean R. Eddy
Keyword(s):  
Rna Structure ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Sequence Data ◽  
Synthetic Data ◽  
Sequence Motifs ◽  
Negative Effects ◽  
Interpretable Model ◽  
Saliency Analysis

AbstractTo infer the sequence and RNA structure specificities of RNA-binding proteins (RBPs) from experiments that enrich for bound sequences, we introduce a convolutional residual network which we call ResidualBind. ResidualBind significantly outperforms previous methods on experimental data from many RBP families. We interrogate ResidualBind to identify what features it has learned from high-affinity sequences with saliency analysis along with 1st-order and 2nd-orderin silicomutagenesis. We show that in addition to sequence motifs, ResidualBind learns a model that includes the number of motifs, their spacing, and both positive and negative effects of RNA structure context. Strikingly, ResidualBind learns RNA structure context, including detailed base-pairing relationships, directly from sequence data, which we confirm on synthetic data. ResidualBind is a powerful, flexible, and interpretable model that can uncovercis-recognition preferences across a broad spectrum of RBPs.

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