Genome-wide identification of protein binding sites on RNAs in mammalian cells

RNA-binding proteins (RBPs) are proteins that bind to the RNA and participate in forming ribonucleoprotein complexes. They have crucial roles in various biological processes such as RNA splicing, editing, transport, maintenance, degradation, intracellular localization and translation. The RBPs bind RNA with different RNA-sequence specificities and affinities, thus, identification of protein binding sites on RNAs (R-PBSs) will deeper our understanding of RNA-protein interactions. Currently, high-throughput sequencing of RNA isolated by crosslinking immunoprecipitation (HITS-CLIP, also known as CLIP-Seq) is one of the most powerful methods to map RNA-protein binding sites or RNA modification sites. However, this method is only used for identification of single known RBPs and antibodies for RBPs are required. Here we developed a novel method, called capture of protein binding sites on RNAs (RPBS-Cap) to identify genome-wide protein binding sites on RNAs without using antibodies. Double click strategy is used for the RPBS-Cap assay. Proteins and RNAs are UV-crosslinked in vivo first, then the proteins are crosslinked to the magnetic beads. The RNA elements associated with proteins are captured, reverse transcribed and sequenced. Our approach has potential applications for studying genome-wide RNA-protein interactions.     

A microbead-based system for identifying and characterizing RNA-protein interactions by flow cytometry

We present a high throughput, versatile approach to identify RNA-protein interactions and to determine nucleotides important for specific protein binding. In this approach, oligonucleotides are coupled to microbeads and hybridized to RNA-protein complexes. The presence or absence of RNA and/or protein fluorescence indicates the formation of an oligo-RNA-protein complex on each bead. The observed fluorescence is specific for both the hybridization and the RNA-protein interaction. We find that the method can discriminate noncomplementary and mismatch sequences. The observed fluorescence reflects the affinity and specificity of the RNA-protein interaction. In addition, the fluorescence patterns footprint the protein recognition site to determine nucleotides important for protein binding. The system was developed with the human protein U1A binding to RNAs derived from U1 snRNA but can also detect RNA-protein interactions in total RNA backgrounds. We propose that this strategy, in combination with emerging coded bead systems, can identify RNAs and RNA sequences important for interacting with RNA-binding proteins on genomic scales.     

Partner-specific prediction of RNA-binding residues in proteins: A critical assessment

RNA-protein interactions play essential roles in regulating gene expression. While some RNA-protein interactions are “specific”, that is, the RNA-binding proteins preferentially bind to particular RNA sequence or structural motifs, others are “non-RNA specific.” Deciphering the protein-RNA recognition code is essential for comprehending the functional implications of these interactions and for developing new therapies for many diseases. Because of the high cost of experimental determination of protein-RNA interfaces, there is a need for computational methods to identify RNA-binding residues in proteins. While most of the existing computational methods for predicting RNA-binding residues in RNA-binding proteins are oblivious to the characteristics of the partner RNA, there is growing interest in methods for partner-specific prediction of RNA binding sites in proteins. In this work, we assess the performance of two recently published partner-specific protein-RNA interface prediction tools, PS-PRIP, and PRIdictor, along with our own new tools. Specifically, we introduce a novel metric, RNA-specificity metric (RSM), for quantifying the RNA-specificity of the RNA binding residues predicted by such tools. Our results show that the RNA-binding residues predicted by previously published methods are oblivious to the characteristics of the putative RNA binding partner. Moreover, when evaluated using partner-agnostic metrics, RNA partner-specific methods are outperformed by the state-of-the-art partner-agnostic methods. We conjecture that either (a) the protein-RNA complexes in PDB are not representative of the protein-RNA interactions in nature, or (b) the current methods for partner-specific prediction of RNA-binding residues in proteins fail to account for the differences in RNA partner-specific versus partner-agnostic protein-RNA interactions, or both.     

Kinetic studies of RNA-protein interactions using surface plasmon resonance

Although structural, biochemical, and genetic studies have provided much insight into the determinants of specificity and affinity of proteins for RNA, little is currently known about the kinetics that underlie RNA-protein interactions. Protein-RNA complexes are dynamic, and the kinetics of binding and release could influence many processes, such as the ability of RNA-binding proteins to compete for binding sites, the sequential assembly of ribonucleoprotein complexes, and the ability of bound RNA to move between cellular compartments. Therefore, to attain a complete and biologically relevant understanding of RNA-protein interactions, complex formation must be studied not only in equilibrated reactions, but also as a dynamic process. BIACORE, a surface plasmon resonance-based biosensor technology, allows intermolecular interactions to be measured in real time, and can provide both equilibrium and kinetic information about complex formation. This technology is a powerful tool with which to study the dynamics of RNA-protein interactions. We have used BIACORE extensively to obtain detailed insight into the interaction between RNA and proteins carrying RNA recognition motif domains. Here we discuss the physical principles on which BIACORE is based, and the required instrumentation. We describe how to design well-controlled RNA-protein interaction experiments aimed at yielding high-quality data, and outline the steps required for data analysis. In addition, we present examples to illustrate how kinetic studies have provided us with unique insights into the interaction of the spliceosomal U1A protein and the neuronal HuD protein with their respective RNA targets.     

Prediction of RNA binding sites in proteins from amino acid sequence

RNA-protein interactions are vitally important in a wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses. We have developed a computational tool for predicting which amino acids of an RNA binding protein participate in RNA-protein interactions, using only the protein sequence as input. RNABindR was developed using machine learning on a validated nonredundant data set of interfaces from known RNA-protein complexes in the Protein Data Bank. It generates a classifier that captures primary sequence signals sufficient for predicting which amino acids in a given protein are located in the RNA-protein interface. In leave-one-out cross-validation experiments, RNABindR identifies interface residues with >85% overall accuracy. It can be calibrated by the user to obtain either high specificity or high sensitivity for interface residues. RNABindR, implementing a Naive Bayes classifier, performs as well as a more complex neural network classifier (to our knowledge, the only previously published sequence-based method for RNA binding site prediction) and offers the advantages of speed, simplicity and interpretability of results. RNABindR predictions on the human telomerase protein hTERT are in good agreement with experimental data. The availability of computational tools for predicting which residues in an RNA binding protein are likely to contact RNA should facilitate design of experiments to directly test RNA binding function and contribute to our understanding of the diversity, mechanisms, and regulation of RNA-protein complexes in biological systems. (RNABindR is available as a Web tool from http://bindr.gdcb.iastate.edu.).     

Detection of double-stranded RNA-protein interactions by methylene blue-mediated photo-crosslinking.

Double-stranded(ds) RNA-binding proteins have diverse functions in the cell. An obstacle to investigating the interactions between these proteins and dsRNA is the relative inefficiency of traditional UV-crosslinking methods for extended regions of dsRNA. We have therefore developed an alternative procedure for RNA-protein photo-crosslinking that efficiently induces RNA-protein crosslinks in double-stranded regions of RNA. We show that dsRNA-protein crosslinks can be induced by visible light in the presence of the dye methylene blue, which most likely mediates crosslinking by intercalating in the dsRNA helix. A recombinant dsRNA binding domain from the Drosophila staufen protein and human protein kinase R were crosslinked by UV or methylene blue to a series of dsRNAs. In each case, the degree of crosslinking was greater with methylene blue, particularly with RNAs with few single-stranded loops. Methylene blue-mediated crosslinking therefore complements and extends the existing repertoire of crosslinking methods for detecting RNA-protein interactions.     

Genome-wide Mapping of Cellular Protein–RNA Interactions Enabled by Chemical Crosslinking

RNA–protein interactions influence many biological processes. Identifying the binding sites of RNA-binding proteins（RBPs） remains one of the most fundamental and important challenges to the studies of such interactions. Capturing RNA and RBPs via chemical crosslinking allows stringent purification procedures that significantly remove the non-specific RNA and protein interactions. Two major types of chemical crosslinking strategies have been developed to date, i.e., UV-enabled crosslinking and enzymatic mechanism-based covalent capture. In this review, we compare such strategies and their current applications, with an emphasis on the technologies themselves rather than the biology that has been revealed. We hope such methods could benefit broader audience and also urge for the development of new methods to study RNA RBP interactions.     

Analysis of RNA-protein interactions by a microplate-based fluorescence anisotropy assay

Quantitative studies of RNA-protein interactions are quite cumbersome using traditional methods. We developed a rapid microplate-based fluorescence anisotropy (FA)/fluorescence polarization assay that works well, even with RNA probes >90 nucleotides long. We analyzed binding of RNA targets by vigilin/DDP1/SCP160p and by c-myc coding region instability determinant (CRD) binding protein, CRD-BP. Vigilin is essential for cell viability and functions in heterochromatin formation and mRNA decay. The CRD-BP stabilizes c-myc mRNA. Vigilin bound to a vitellogenin mRNA 3'-UTR probe with a two to three-fold lower affinity than to a Drosophila dodecasatellite ssDNA binding site and bound to the c-myc CRD with a two- to three-fold lower affinity than to the vitellogenin mRNA 3'-UTR. Competition between vigilin and CRD-BP for binding to the CRD may therefore play a role in regulating c-myc mRNA degradation. We analyzed suitability of the microplate-based FA assay for high-throughput screening for small-molecule regulators of RNA-protein interactions. The assay exhibits high reproducibility and precision and works well in 384-well plates and in 5 microl to 20 microl samples. To demonstrate the potential of this assay for screening libraries of small molecules to identify novel regulators of RNA-protein interactions, we identified neomycin and H33342 as inhibitors of binding of vigilin to the vitellogenin mRNA 3'-UTR.     

Quantitative mass spectrometry and PAR-CLIP to identify RNA-protein interactions

Systematic analysis of the RNA-protein interactome requires robust and scalable methods. We here show the combination of two completely orthogonal, generic techniques to identify RNA-protein interactions: PAR-CLIP reveals a collection of RNAs bound to a protein whereas SILAC-based RNA pull-downs identify a group of proteins bound to an RNA. We investigated binding sites for five different proteins (IGF2BP1-3, QKI and PUM2) exhibiting different binding patterns. We report near perfect agreement between the two approaches. Nevertheless, they are non-redundant, and ideally complement each other to map the RNA-protein interaction network.     

A Novel Strategy for Analyzing RNA-Protein Interactions by Surface Plasmon Resonance Biosensor

Surface plasmon resonance (SPR) biosensor is a promising technology for its various advantages including the real-time measurement of biomolecular interactions without labeling. A method of hybridizing RNAs on the surface of the streptavidin-coated (SA) sensor chip to study RNA-protein interactions was described in this paper. In our study, it has been shown that the nucleocapsid (N) protein of severe acute respiratory syndrome coronavirus (SARS-CoV) has a high binding affinity for the leader sequence of SARS-CoV genome. Effect of temperature on the RNA-DNA hybridization was also examined. This method can provide the affinity of interactions with high sensitivity. Therefore, it will be useful in screening binding candidates for a given RNA target motif with one chip.     

RNA和蛋白质的相互作用

RNA与蛋白质的相互作用是许多基本的细胞生理过程得以实现的决定性因素.近年来,随着技术的改进和新方法的建立,RNA和蛋白质的相互作用研究取得了长足进步.目前科研人员已经鉴定了许多RNA上的蛋白质结合位点,也发现了许多蛋白质中的RNA结合结构域,并对它们的结构特征进行了比较详细的研究.这些都为最终探明RNA和蛋白质相互作用的分子机制,从而从本质上认识相关的细胞生理过程打下了坚实的基础.     

RNANetMotif: Identifying sequence-structure RNA network motifs in RNA-protein binding sites

RNA molecules can adopt stable secondary and tertiary structures, which are 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 coarse-grained molecular dynamics folding simulations for selected 4 RBPs, and RNA-protein docking for LIN28B. The simulation results, e.g., solvent accessibility and energetics, further support the biological relevance of the discovered network subgraphs.     

RNase-mediated protein footprint sequencing reveals protein-binding sites throughout the human transcriptome

Background

Sequence specific RNA binding proteins are important regulators of gene expression. Several related crosslinking-based, high-throughput sequencing methods, including PAR-CLIP, have recently been developed to determine direct binding sites of global protein-RNA interactions. However, no studies have quantitatively addressed the contribution of background binding to datasets produced by these methods.

Results

We measured non-specific RNA background in PAR-CLIP data, demonstrating that covalently crosslinked background binding is common, reproducible and apparently universal among laboratories. We show that quantitative determination of background is essential for identifying targets of most RNA-binding proteins and can substantially improve motif analysis. We also demonstrate that by applying background correction to an RNA binding protein of unknown binding specificity, Caprin1, we can identify a previously unrecognized RNA recognition element not otherwise apparent in a PAR-CLIP study.

Conclusions

Empirical background measurements of global RNA-protein crosslinking are a necessary addendum to other experimental controls, such as performing replicates, because covalently crosslinked background signals are reproducible and otherwise unavoidable. Recognizing and quantifying the contribution of background extends the utility of PAR-CLIP and can improve mechanistic understanding of protein-RNA specificity, protein-RNA affinity and protein-RNA association dynamics.     

Analyzing mRNA-protein complexes using a yeast three-hybrid system

RNA-protein interactions are essential for the proper execution and regulation of every step in the life of a eukaryotic mRNA. Here we describe a three-hybrid system in which RNA-protein interactions can be analyzed using simple phenotypic or enzymatic assays in Saccharomyces cerevisiae. The system can be used to detect or confirm an RNA-protein interaction, to analyze RNA-protein interactions genetically, and to discover new protein or RNA partners when only one is known. Multicomponent complexes containing more than one protein can be detected, identified, and analyzed. We describe the method and how to use it, and discuss applications that bear particularly on eukaryotic mRNAs.     

RNA base-amino acid interaction strengths derived from structures and sequences.

We investigate RNA base-amino acid interactions by counting their contacts in structures and their implicit contacts in various functional sequences where the structures can be assumed to be preserved. These frequencies are cast into equations to extract relative interaction energetics. Previously we used this approach in considering the major groove interactions of DNA, and here we apply it to the more diverse interactions observed in RNA. Structures considered are the three different tRNA synthetase complexes, the U1A spliceosomal protein with an RNA hairpin and the BIV TAR-Tat complex. We use binding data for the base frequencies for the seryl, aspartyl and glutaminyl tRNA-synthetase and U1 RNA-protein complexes. We compare with the previously reported DNA major groove peptide contacts the results for atoms of RNA bases, usually in the major groove. There are strong similarities between the rank orders of interacting bases in the DNA and the RNA cases. The apparent strongest RNA interaction observed is between arginine and guanine which was also one of the strongest DNA interactions. The similar data for base atomic interactions, whether base paired or not, support the importance of strong atomic interactions over local structure considerations, such as groove width and alpha-helicity.     

Methods for comprehensive experimental identification of RNA-protein interactions

The importance of RNA-protein interactions in controlling mRNA regulation and non-coding RNA function is increasingly appreciated. A variety of methods exist to comprehensively define RNA-protein interactions. We describe these methods and the considerations required for designing and interpreting these experiments.     

Quantitative profiling of in vivo-assembled RNA-protein complexes using a novel integrated proteomic approach

Identification of proteins in RNA-protein complexes is an important step toward understanding regulation of RNA-based processes. Because of the lack of appropriate methodologies, many studies have relied on the creation of in vitro assembled RNA-protein complexes using synthetic RNA and cell extracts. Such complexes may not represent authentic RNPs as they exist in living cells as synthetic RNA may not fold properly and nonspecific RNA-protein interactions can form during cell lysis and purification processes. To circumvent limitations in current approaches, we have developed a novel integrated strategy namely MS2 in vivo biotin tagged RNA affinity purification (MS2-BioTRAP) to capture bona fide in vivo-assembled RNA-protein complexes. In this method, HB-tagged bacteriophage protein MS2 and stem-loop tagged target or control RNAs are co-expressed in cells. The tight association between MS2 and the RNA stem-loop tags allows efficient HB-tag based affinity purification of authentic RNA-protein complexes. Proteins associated with target RNAs are subsequently identified and quantified using SILAC-based quantitative mass spectrometry. Here the 1.2 kb internal ribosome entry site (IRES) from lymphoid enhancer factor-1 mRNA has been used as a proof-of-principle target RNA. An IRES target was chosen because of its importance in protein translation and our limited knowledge of proteins associated with IRES function. With a conventionally translated target RNA as control, 36 IRES binding proteins have been quantitatively identified including known IRES binding factors, novel interacting proteins, translation initiation factors (eIF4A-1, eIF-2A, and eIF3g), and ribosomal subunits with known noncanonical actions (RPS19, RPS7, and RPL26). Validation studies with the small molecule eIF4A-1 inhibitor Hippuristanol shows that translation of endogenous lymphoid enhancer factor-1 mRNA is especially sensitive to eIF4A-1 activity. Our work demonstrates that MS2 in vivo biotin tagged RNA affinity purification is an effective and versatile approach that is generally applicable for other RNA-protein complexes.