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 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 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.

scholarly journals RNANetMotif: identifying sequence-structure RNA network motifs in RNA-protein binding sites

2021 ◽  
Author(s):  
Hongli Ma ◽  
Han Wen ◽  
Zhiyuan Xue ◽  
Guojun Li ◽  
Zhaolei Zhang
Keyword(s):  
Binding Sites ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Solvent Accessibility ◽  
Protein Docking ◽  
Theory Approach ◽  
Sequence Structure ◽  
Structure Information

RNA molecules can adopt stable secondary and tertiary structures, which is essential in mediating physical interactions with other partners such as RNA binding proteins (RBPs) and in carrying out their cellular functions. In vivo and in vitro experiments such as RNAcompete and eCLIP have revealed in vitro binding preferences of RBPs to RNA oligomers and in vivo binding sites in cells. Analysis of these binding data showed that the structure properties of the RNAs in these binding sites are important determinants of the binding events; however, it has been a challenge to incorporate the structure information into an interpretable model. Here we describe a new approach, RNANetMotif, which takes predicted secondary structure of thousands of RNA sequences bound by an RBP as input and uses a graph theory approach to recognize enriched subgraphs. These enriched subgraphs are in essence shared sequence-structure elements that are important in RBP-RNA binding. To validate our approach, we performed RNA structure modeling via discrete molecular dynamics folding simulations for selected 4 RBPs, and RNA-protein docking for LIN28. The simulation results, e.g., solvent accessibility and energetics, further support the biological relevance of the discovered network subgraphs.

scholarly journals RADAR: annotation and prioritization of variants in the post-transcriptional regulome of RNA-binding proteins

2018 ◽  
Author(s):  
Jing Zhang ◽  
Jason Liu ◽  
Donghoon Lee ◽  
Jo-Jo Feng ◽  
Lucas Lochovsky ◽  
...  
Keyword(s):  
Transcriptional Regulation ◽  
Binding Sites ◽  
Rna Structure ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Network Centrality ◽  
Impact Score ◽  
Variant Prioritization ◽  
Post Transcriptional Regulation

AbstractRNA-binding proteins (RBPs) play key roles in post-transcriptional regulation and disease. Their binding sites cover more of the genome than coding exons; nevertheless, most noncoding variant-prioritization methods only focus on transcriptional regulation. Here, we integrate the portfolio of ENCODE-RBP experiments to develop RADAR, a variant-scoring framework. RADAR uses conservation, RNA structure, network centrality, and motifs to provide an overall impact score. Then it further incorporates tissue-specific inputs to highlight disease-specific variants. Our results demonstrate RADAR can successfully pinpoint variants, both somatic and germline, associated with RBP-function dysregulation, that cannot be found by most current prioritization methods, for example variants affecting splicing.

CSBPI_Site：Multi-Information Sources of Features to RNA Binding Sites Prediction

2021 ◽  
Vol 15 ◽  
Author(s):  
Lichao Zhang ◽  
Zihong Huang ◽  
Liang Kong
Keyword(s):  
Computational Model ◽  
Binding Sites ◽  
Noncoding Rna ◽  
Computational Models ◽  
Information Sources ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Gradient Boosting ◽  
Position Information ◽  
Rna Binding Sites

Background: RNA-binding proteins establish posttranscriptional gene regulation by coordinating the maturation, editing, transport, stability, and translation of cellular RNAs. The immunoprecipitation experiments could identify interaction between RNA and proteins, but they are limited due to the experimental environment and material. Therefore, it is essential to construct computational models to identify the function sites. Objective: Although some computational methods have been proposed to predict RNA binding sites, the accuracy could be further improved. Moreover, it is necessary to construct a dataset with more samples to design a reliable model. Here we present a computational model based on multi-information sources to identify RNA binding sites. Method: We construct an accurate computational model named CSBPI_Site, based on xtreme gradient boosting. The specifically designed 15-dimensional feature vector captures four types of information (chemical shift, chemical bond, chemical properties and position information). Results: The satisfied accuracy of 0.86 and AUC of 0.89 were obtained by leave-one-out cross validation. Meanwhile, the accuracies were slightly different (range from 0.83 to 0.85) among three classifiers algorithm, which showed the novel features are stable and fit to multiple classifiers. These results showed that the proposed method is effective and robust for noncoding RNA binding sites identification. Conclusion: Our method based on multi-information sources is effective to represent the binding sites information among ncRNAs. The satisfied prediction results of Diels-Alder riboz-yme based on CSBPI_Site indicates that our model is valuable to identify the function site.

scholarly journals SSMART: Sequence-structure motif identification for RNA-binding proteins

2018 ◽  
Author(s):  
Alina Munteanu ◽  
Neelanjan Mukherjee ◽  
Uwe Ohler
Keyword(s):  
Binding Sites ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Sequence Motif ◽  
Primary Sequence ◽  
Sequence Structure ◽  
Rna Targets

AbstractMotivationRNA-binding proteins (RBPs) regulate every aspect of RNA metabolism and function. There are hundreds of RBPs encoded in the eukaryotic genomes, and each recognize its RNA targets through a specific mixture of RNA sequence and structure properties. For most RBPs, however, only a primary sequence motif has been determined, while the structure of the binding sites is uncharacterized.ResultsWe developed SSMART, an RNA motif finder that simultaneously models the primary sequence and the structural properties of the RNA targets sites. The sequence-structure motifs are represented as consensus strings over a degenerate alphabet, extending the IUPAC codes for nucleotides to account for secondary structure preferences. Evaluation on synthetic data showed that SSMART is able to recover both sequence and structure motifs implanted into 3‘UTR-like sequences, for various degrees of structured/unstructured binding sites. In addition, we successfully used SSMART on high-throughput in vivo and in vitro data, showing that we not only recover the known sequence motif, but also gain insight into the structural preferences of the RBP.AvailabilitySSMART is freely available at https://ohlerlab.mdc-berlin.de/software/SSMART_137/[email protected]

scholarly journals Genomic and cDNA selection-amplification identifies transcriptome-wide binding sites for the Drosophila protein sex-lethal

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0250592
Author(s):  
Hiren Banerjee ◽  
Ravinder Singh
Keyword(s):  
Binding Sites ◽  
Binding Proteins ◽  
Rna Binding ◽  
Developmental Stages ◽  
Rna Binding Proteins ◽  
Systematic Analysis ◽  
Downstream Targets ◽  
Drosophila Protein ◽  
Sex Lethal ◽  
Female Specific

Background Downstream targets for a large number of RNA-binding proteins remain to be identified. The Drosophila master sex-switch protein Sex-lethal (SXL) is an RNA-binding protein that controls splicing, polyadenylation, or translation of certain mRNAs to mediate female-specific sexual differentiation. Whereas some targets of SXL are known, previous studies indicate that additional targets of SXL have escaped genetic screens. Methodology/Principal findings Here, we have used an alternative molecular approach of GEnomic Selective Enrichment of Ligands by Exponential enrichment (GESELEX) using both the genomic DNA and cDNA pools from several Drosophila developmental stages to identify new potential targets of SXL. Our systematic analysis provides a comprehensive view of the Drosophila transcriptome for potential SXL-binding sites. Conclusion/Significance We have successfully identified new SXL-binding sites in the Drosophila transcriptome. We discuss the significance of our analysis and that the newly identified binding sites and sequences could serve as a useful resource for the research community. This approach should also be applicable to other RNA-binding proteins for which downstream targets are unknown.

scholarly journals Investigating the Roles of RNA Binding Protein Combinations in Neuronal Function and Organismal Behavior

2019 ◽  
Vol 4 (Spring 2019) ◽  
Author(s):  
Alexa Vandenburg
Keyword(s):  
Binding Sites ◽  
Neuronal Development ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Binding Protein ◽  
Rna Binding Protein ◽  
Wild Type ◽  
C Elegans ◽  
Neuronal Gene ◽  
Touch Response

The Norris lab recently identified two RNA binding proteins required for proper neuron-specific splicing. The lab conducted touch- response behavioral assays to assess the function of these proteins in touch-sensing neurons. After isolating C. elegans worms with specific phenotypes, the lab used automated computer tracking and video analysis to record the worms’ behavior. The behavior of mutant worms differed from that of wild-type worms. The Norris lab also discovered two possible RNA binding protein sites in SAD-1, a neuronal gene implicated in the neuronal development of C. elegans1. These two binding sites may control the splicing of SAD-1. The lab transferred mutated DNA into the genome of wild-type worms by injecting a mutated plasmid. The newly transformed worms fluoresced green, indicating that the two binding sites control SAD-1 splicing.

scholarly journals Identification of cis Elements Directing Termination of Yeast Nonpolyadenylated snoRNA Transcripts

2004 ◽  
Vol 24 (14) ◽  
pp. 6241-6252 ◽  
Author(s):  
Kristina L. Carroll ◽  
Dennis A. Pradhan ◽  
Josh A. Granek ◽  
Neil D. Clarke ◽  
Jeffry L. Corden
Keyword(s):  
Rna Polymerase Ii ◽  
Binding Sites ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Large Degree ◽  
Sequence Motifs ◽  
Mobility Shift ◽  
Nascent Transcript ◽  
Pol Ii ◽  
Gel Mobility Shift

ABSTRACT RNA polymerase II (Pol II) termination is triggered by sequences present in the nascent transcript. Termination of pre-mRNA transcription is coupled to recognition of cis-acting sequences that direct cleavage and polyadenylation of the pre-mRNA. Termination of nonpolyadenylated [non-poly(A)] Pol II transcripts in Saccharomyces cerevisiae requires the RNA-binding proteins Nrd1 and Nab3. We have used a mutational strategy to characterize non-poly(A) termination elements downstream of the SNR13 and SNR47 snoRNA genes. This approach detected two common RNA sequence motifs, GUA[AG] and UCUU. The first motif corresponds to the known Nrd1-binding site, which we have verified here by gel mobility shift assays. We also show that Nab3 protein binds specifically to RNA containing the UCUU motif. Taken together, our data suggest that Nrd1 and Nab3 binding sites play a significant role in defining non-poly(A) terminators. As is the case with poly(A) terminators, there is no strong consensus for non-poly(A) terminators, and the arrangement of Nrd1p and Nab3p binding sites varies considerably. In addition, the organization of these sequences is not strongly conserved among even closely related yeasts. This indicates a large degree of genetic variability. Despite this variability, we were able to use a computational model to show that the binding sites for Nrd1 and Nab3 can identify genes for which transcription termination is mediated by these proteins.

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.

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