scholarly journals Musashi binding elements in Zika and related Flavivirus 3’UTRs: A comparative studyin silico

2018 ◽  
Author(s):  
Adriano de Bernardi Schneider ◽  
Michael T. Wolfinger
Keyword(s):  
Thermodynamic Model ◽  
Rna Structure ◽  
Rna Binding ◽  
Congenital Infection ◽  
Biophysical Model ◽  
Fetal Demise ◽  
Structural Context ◽  
Neurotropic Viruses ◽  
Experimental Findings ◽  
Neurotropic Effects

ABSTRACTZika virus (ZIKV) belongs to a class of neurotropic viruses that have the ability to cause congenital infection, which can result in microcephaly or fetal demise. Recently, the RNA-binding protein Musashi-1 (Msi1), which mediates the maintenance and self-renewal of stem cells and acts as a translational regulator, has been associated with promoting ZIKV replication, neurotropism, and pathology. Msi1 predominantly binds to single-stranded motifs in the 3’ untranslated region (UTR) of RNA that contain aUAGtrinucleotide in their core. We systematically analyzed the properties of Musashi binding elements (MBEs) in the 3’UTR of flaviviruses with a thermodynamic model for RNA folding. Our results indicate that MBEs in ZIKV 3’UTRs occur predominantly in unpaired, single-stranded structural context, thus corroborating experimental observations by a biophysical model of RNA structure formation. Statistical analysis and comparison with related viruses show that ZIKV MBEs are maximally accessible among mosquito-borne flaviviruses. Our study addresses the broader question of whether other emerging arboviruses can cause similar neurotropic effects through the same mechanism in the developing fetus by establishing a link between the biophysical properties of viral RNA and teratogenicity. Moreover, our thermodynamic model can explain recent experimental findings and predict the Msi1-related neurotropic potential of other viruses.

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 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.

scholarly journals RNA Binding Properties of the Ty1 LTR-Retrotransposon Gag Protein

2021 ◽  
Vol 22 (16) ◽  
pp. 9103
Author(s):  
Julita Gumna ◽  
Angelika Andrzejewska-Romanowska ◽  
David J. Garfinkel ◽  
Katarzyna Pachulska-Wieczorek
Keyword(s):  
Rna Structure ◽  
Rna Binding ◽  
Rna Packaging ◽  
Packaging Signal ◽  
Mobility Shift ◽  
Gag Protein ◽  
Rna Dimerization ◽  
Electrophoretic Mobility Shift Assays

A universal feature of retroelement propagation is the formation of distinct nucleoprotein complexes mediated by the Gag capsid protein. The Ty1 retrotransposon Gag protein from Saccharomyces cerevisiae lacks sequence homology with retroviral Gag, but is functionally related. In addition to capsid assembly functions, Ty1 Gag promotes Ty1 RNA dimerization and cyclization and initiation of reverse transcription. Direct interactions between Gag and retrotransposon genomic RNA (gRNA) are needed for Ty1 replication, and mutations in the RNA-binding domain disrupt nucleation of retrosomes and assembly of functional virus-like particles (VLPs). Unlike retroviral Gag, the specificity of Ty1 Gag-RNA interactions remain poorly understood. Here we use microscale thermophoresis (MST) and electrophoretic mobility shift assays (EMSA) to analyze interactions of immature and mature Ty1 Gag with RNAs. The salt-dependent experiments showed that Ty1 Gag binds with high and similar affinity to different RNAs. However, we observed a preferential interaction between Ty1 Gag and Ty1 RNA containing a packaging signal (Psi) in RNA competition analyses. We also uncover a relationship between Ty1 RNA structure and Gag binding involving the pseudoknot present on Ty1 gRNA. In all likelihood, the differences in Gag binding affinity detected in vitro only partially explain selective Ty1 RNA packaging into VLPs in vivo.

scholarly journals Accurate Detection of RNA Stem-Loops in Structurome Data Reveals Widespread Association with Protein Binding Sites

2021 ◽  
Author(s):  
Pierce Radecki ◽  
Rahul Uppuluri ◽  
Kaustubh Deshpande ◽  
Sharon Aviran
Keyword(s):  
Rna Structure ◽  
Nearest Neighbor ◽  
Rna Binding ◽  
Learning Algorithm ◽  
Accurate Method ◽  
Stem Loop ◽  
Rna Molecules ◽  
Computational Overhead ◽  
Local Sequence ◽  
Functional Relevance

ABSTRACTRNA molecules are known to fold into specific structures which often play a central role in their functions and regulation. In silico folding of RNA transcripts, especially when assisted with structure profiling (SP) data, is capable of accurately elucidating relevant structural conformations. However, such methods scale poorly to the swaths of SP data generated by transcriptome-wide experiments, which are becoming more commonplace and advancing our understanding of RNA structure and its regulation at global and local levels. This has created a need for tools capable of rapidly deriving structural assessments from SP data in a scalable manner. One such tool we previously introduced that aims to process such data is patteRNA, a statistical learning algorithm capable of rapidly mining big SP datasets for structural elements. Here, we present a reformulation of patteRNA’s pattern recognition scheme that sees significantly improved precision without major compromises to computational overhead. Specifically, we developed a data-driven logistic classifier which interprets patteRNA’s statistical characterizations of SP data in addition to local sequence properties as measured with a nearest neighbor thermodynamic model. Application of the classifier to human structurome data reveals a marked association between detected stem-loops and RNA binding protein (RBP) footprints. The results of our application demonstrate that upwards of 30% of RBP footprints occur within loops of stable stem-loop elements. Overall, our work arrives at a rapid and accurate method for automatically detecting families of RNA structure motifs and demonstrates the functional relevance of identifying them transcriptome-wide.

scholarly journals ssHMM: Extracting intuitive sequence-structure motifs from high-throughput RNA-binding protein data

2016 ◽  
Author(s):  
David Heller ◽  
Martin Vingron ◽  
Ralf Krestel ◽  
Uwe Ohler ◽  
Annalisa Marsico
Keyword(s):  
High Throughput ◽  
Rna Structure ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Synthetic Data ◽  
Sequence Motif ◽  
Sequence Motifs ◽  
Sequence Structure ◽  
Rna Motif ◽  
Post Transcriptional Regulation

AbstractRNA-binding proteins (RBPs) play important roles in RNA post-transcriptional regulation and recognize target RNAs via sequence-structure motifs. To which extent RNA structure influences protein binding in the presence or absence of a sequence motif is still poorly understood. Existing RNA motif finders which produce informative motifs and simultaneously capture the relationship between primary sequence and different RNA secondary structures are missing. We developed ssHMM, an RNA motif finder that combines a hidden Markov model (HMM) with Gibbs sampling to learn the joint sequence and structure binding preferences of RBPs from high-throughput data, such as CLIP-Seq sequences, and visualizes them as a graph. Evaluations on synthetic data showed that ssHMM reliably recovers fuzzy sequence motifs in 80 to 100% of the cases. It produces motifs with higher information content than existing tools and is faster than other methods on large datasets. Examples of new sequence-structure motifs identified by ssHMM for uncharacterized RBPs are also discussed. ssHMM is freely available on Github at https://github.molgen.mpg.de/heller/ssHMM.

scholarly journals RNA-binding proteins PCBP1 and PCBP2 are critical determinants of murine erythropoiesis

2021 ◽  
Author(s):  
Xinjun Ji ◽  
Anupama Jha ◽  
Jesse Humenik ◽  
Louis R. Ghanem ◽  
Kromer Andrew ◽  
...  
Keyword(s):  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Ex Vivo ◽  
Erythroid Progenitors ◽  
Mouse Development ◽  
Fetal Demise ◽  
Exon Splicing ◽  
Genes Encoding ◽  
Blood Formation

We have previously demonstrated that the two paralogous RNA binding protein, PCBP1 and PCBP2, are individually essential for mouse development: Pcbp1 -null embryos are peri-implantation lethal while Pcbp2 -null embryos lose viability at mid-gestation. Mid-gestation Pcbp2 −/− embryos revealed a complex phenotype that included loss of certain hematopoietic determinants. Whether PCBP2 directly contributes to erythropoietic differentiation and whether PCBP1 has a role in this process remained undetermined. Here we selectively inactivate the genes encoding these two RNA-binding proteins during differentiation of the erythroid lineage in the developing mouse embryo. Individual inactivation of either locus fails to impact viability or blood formation. However, combined inactivation of the two loci results in mid-gestational repression of erythroid/hematopoietic gene expression, loss of blood formation, and fetal demise. Orthogonal ex-vivo analyses of primary erythroid progenitors selectively depleted of these two RNA binding proteins revealed that they mediate a combination of overlapping and isoform-specific impacts on hematopoietic lineage transcriptome, impacting both mRNA representation and exon splicing. These data lead us to conclude that PCBP1 and PCBP2 mediate functions critical to differentiation of the erythroid lineage.

Sign in / Sign up

Export Citation Format

Share Document