scholarly journals Computational Discovery of Evolutionary Conserved Enterovirus 71 RNA Secondary Structures

2018 ◽  
Author(s):  
Nathan Fridlyand ◽  
Alexander N Lukashev ◽  
Andrey Chursov ◽  
Franco M Venanzi ◽  
Jonathan W Yewdell ◽  
...  
Keyword(s):  
Rna Structure ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Enterovirus 71 ◽  
Secondary Structures ◽  
Rna Structures ◽  
Alternative Structures ◽  
Evolutionarily Conserved ◽  
Computational Discovery ◽  
Rna Switch

Viral RNAs store information in their sequence and structure. Little, however is known about the contribution of RNA structure to viral replication and evolution. We previously described RNA ISRAEU to computationally identify structured regions in viral RNA based on evolutionary conservation of predicted structures. Here, we apply RNA GUESS (Generator of Uniformly Evolved Secondary Structures) to better understand enterovirus 71, a highly pathogenic human picornavirus. RNA GUESS identified previously known evolutionarily conserved picornavirus RNA structures, and uniquely predicted RNA structures with significant structural conservation despite the lack of an obvious sequence conservation. One structure consists of a stretch of obligatorily unpaired nucleotides that can potentially interact with RNA-binding proteins and siRNAs. Another, forms a potential RNA switch that can form two alternative structures while the third and fourth constitute hairpin-like structures. These findings provide a launching point for physical and genetic studies regarding the function and structures of these RNA elements.

scholarly journals nearBynding: A flexible pipeline characterizing protein binding to local RNA structure

2020 ◽  
Author(s):  
Veronica F. Busa ◽  
Alexander V. Favorov ◽  
Elana J. Fertig ◽  
Anthony K. L. Leung
Keyword(s):  
Structure Data ◽  
Binding Sites ◽  
Rna Structure ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Peak Calling ◽  
Rna Structures ◽  
Data Types ◽  
Structure Information ◽  
Mrna Metabolism

AbstractThe etiology of diseases driven by dysregulated mRNA metabolism can be elucidated by characterizing the responsible RNA-binding proteins (RBPs). Although characterizations of RBPs have been mainly focused on their binding sequences, not much has been investigated about their preferences for RNA structures. We present nearBynding, an R/Bioconductor pipeline that incorporates RBP binding sites and RNA structure information to discern structural binding preferences for an RBP. nearBynding visualizes RNA structure at and proximal to sites of RBP binding transcriptome-wide, analyzes CLIP-seq data without peak-calling, and provides a flexible scaffold to study RBP binding preferences relative to diverse RNA structure data types.

scholarly journals Predicting dynamic cellular protein–RNA interactions by deep learning using in vivo RNA structures

Cell Research ◽  
2021 ◽  
Author(s):  
Lei Sun ◽  
Kui Xu ◽  
Wenze Huang ◽  
Yucheng T. Yang ◽  
Pan Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Rna Structure ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Genetic Diseases ◽  
Cell Types ◽  
Cellular Protein ◽  
Rna Structures ◽  
Additional Cell

AbstractInteractions with RNA-binding proteins (RBPs) are integral to RNA function and cellular regulation, and dynamically reflect specific cellular conditions. However, presently available tools for predicting RBP–RNA interactions employ RNA sequence and/or predicted RNA structures, and therefore do not capture their condition-dependent nature. Here, after profiling transcriptome-wide in vivo RNA secondary structures in seven cell types, we developed PrismNet, a deep learning tool that integrates experimental in vivo RNA structure data and RBP binding data for matched cells to accurately predict dynamic RBP binding in various cellular conditions. PrismNet results for 168 RBPs support its utility for both understanding CLIP-seq results and largely extending such interaction data to accurately analyze additional cell types. Further, PrismNet employs an “attention” strategy to computationally identify exact RBP-binding nucleotides, and we discovered enrichment among dynamic RBP-binding sites for structure-changing variants (riboSNitches), which can link genetic diseases with dysregulated RBP bindings. Our rich profiling data and deep learning-based prediction tool provide access to a previously inaccessible layer of cell-type-specific RBP–RNA interactions, with clear utility for understanding and treating human diseases.

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 Two Yeast La Motif-containing Proteins Are RNA-binding Proteins that Associate with Polyribosomes

1999 ◽  
Vol 10 (11) ◽  
pp. 3849-3862 ◽  
Author(s):  
Suzanne G. Sobel ◽  
Sandra L. Wolin
Keyword(s):  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Rna Polymerase Iii ◽  
Mrna Translation ◽  
Protein Synthesis Inhibitors ◽  
La Protein ◽  
Evolutionarily Conserved ◽  
Synthetically Lethal ◽  
Type Strains

We have characterized two Saccharomyces cerevisiaeproteins, Sro9p and Slf1p, which contain a highly conserved motif found in all known La proteins. Originally described as an autoantigen in patients with rheumatic disease, the La protein binds to newly synthesized RNA polymerase III transcripts. In yeast, the La protein homologue Lhp1p is required for the normal pathway of tRNA maturation and also stabilizes newly synthesized U6 RNA. We show that deletions in both SRO9 and SLF1 are not synthetically lethal with a deletion in LHP1, indicating that the three proteins do not function in a single essential process. Indirect immunofluorescence microscopy reveals that although Lhp1p is primarily localized to the nucleus, Sro9p is cytoplasmic. We demonstrate that Sro9p and Slf1p are RNA-binding proteins that associate preferentially with translating ribosomes. Consistent with a role in translation, strains lacking either Sro9p or Slf1p are less sensitive than wild-type strains to certain protein synthesis inhibitors. Thus, Sro9p and Slf1p define a new and possibly evolutionarily conserved class of La motif-containing proteins that may function in the cytoplasm to modulate mRNA translation.

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 Core spliceosomal Sm proteins as constituents of cytoplasmic mRNPs in plants

2019 ◽  
Author(s):  
Malwina Hyjek-Składanowska ◽  
Mateusz Bajczyk ◽  
Marcin Gołębiewski ◽  
Przemysław Nuc ◽  
Agnieszka Kołowerzo-Lubnau ◽  
...  
Keyword(s):  
Posttranscriptional Regulation ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Regulation Of Gene Expression ◽  
Mrna Translation ◽  
Mrna Splicing ◽  
Sm Proteins ◽  
Cytoplasmic Bodies ◽  
Evolutionarily Conserved ◽  
Processing Steps

ABSTRACTIn light of recent studies, many of the cytoplasmic posttranscriptional mRNA processing steps take place in highly specialized microdomains referred to as cytoplasmic bodies. These evolutionarily conserved microdomains are sites of regulation for both mRNA translation and degradation. It has been shown that in the larch microsporocyte cytoplasm, there is a significant pool of Sm proteins not related to snRNP complexes. These Sm proteins accumulate within distinct cytoplasmic bodies (S-bodies) that also contain mRNA. Sm proteins constitute an evolutionarily ancient family of small RNA-binding proteins. In eukaryotic cells, these molecules are involved in pre-mRNA splicing. The latest research indicates that in addition to this well-known function, Sm proteins could also have an impact on mRNA at subsequent stages of its life cycle. The aim of this work was to verify the hypothesis that canonical Sm proteins are part of the cytoplasmic mRNP complex and thus function in the posttranscriptional regulation of gene expression in plants.

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 Circular RNAs as potential biomarkers and therapeutics for cardiovascular disease

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6831 ◽  
Author(s):  
Weitie Wang ◽  
Yong Wang ◽  
Hulin Piao ◽  
Bo Li ◽  
Maoxun Huang ◽  
...  
Keyword(s):  
Rna Binding ◽  
Rna Binding Proteins ◽  
Circular Rnas ◽  
Covalent Bonds ◽  
Rna Molecules ◽  
Evolutionarily Conserved ◽  
Artery Disease ◽  
Covalently Linked ◽  
The Impact ◽  
Microrna Sponges

Circular RNAs (circRNAs) are genetic regulators that were earlier considered as “junk”. In contrast to linear RNAs, they have covalently linked ends with no polyadenylated tails. CircRNAs can act as RNA-binding proteins, sequestering agents, transcriptional regulators, as well as microRNA sponges. In addition, it is reported that some selected circRNAs are transformed into functional proteins. These RNA molecules always circularize through covalent bonds, and their presence has been demonstrated across species. They are usually abundant and stable as well as evolutionarily conserved in tissues (liver, lung, stomach), saliva, exosomes, and blood. Therefore, they have been proposed as the “next big thing” in molecular biomarkers for several diseases, particularly in cancer. Recently, circRNAs have been investigated in cardiovascular diseases (CVD) and reported to play important roles in heart failure, coronary artery disease, and myocardial infarction. Here, we review the recent literature and discuss the impact and the diagnostic and prognostic values of circRNAs in CVD.

scholarly journals The SARS-CoV-2 RNA interactome

2020 ◽  
Author(s):  
Sungyul Lee ◽  
Young-suk Lee ◽  
Yeon Choi ◽  
Ahyeon Son ◽  
Youngran Park ◽  
...  
Keyword(s):  
Life Cycle ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Rna Virus ◽  
Host Factors ◽  
Therapeutic Interventions ◽  
Host Proteins ◽  
Viral Rnas ◽  
Comprehensive List ◽  
Evolutionarily Conserved

AbstractSARS-CoV-2 is an RNA virus whose success as a pathogen relies on its ability to repurpose host RNA-binding proteins (RBPs) to form its own RNA interactome. Here, we developed and applied a robust ribonucleoprotein capture protocol to uncover the SARS-CoV-2 RNA interactome. We report 109 host factors that directly bind to SARS-CoV-2 RNAs including general antiviral factors such as ZC3HAV1, TRIM25, and PARP12. Applying RNP capture on another coronavirus HCoV-OC43 revealed evolutionarily conserved interactions between viral RNAs and host proteins. Network and transcriptome analyses delineated antiviral RBPs stimulated by JAK-STAT signaling and proviral RBPs responsible for hijacking multiple steps of the mRNA life cycle. By knockdown experiments, we further found that these viral-RNA-interacting RBPs act against or in favor of SARS-CoV-2. Overall, this study provides a comprehensive list of RBPs regulating coronaviral replication and opens new avenues for therapeutic interventions.

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