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.     

Exploiting structural and topological information to improve prediction of RNA-protein binding sites

Background  

RNA-protein interactions are important for a wide range of biological processes. Current computational methods to predict interacting residues in RNA-protein interfaces predominately rely on sequence data. It is, however, known that interface residue propensity is closely correlated with structural properties. In this paper we systematically study information obtained from sequences and structures and compare their contributions in this prediction problem. Particularly, different geometrical and network topological properties of protein structures are evaluated to improve interface residue prediction accuracy.     

RIP-Chip: the isolation and identification of mRNAs, microRNAs and protein components of ribonucleoprotein complexes from cell extracts

RNA targets of multitargeted RNA-binding proteins (RBPs) can be studied by various methods including mobility shift assays, iterative in vitro selection techniques and computational approaches. These techniques, however, cannot be used to identify the cellular context within which mRNAs associate, nor can they be used to elucidate the dynamic composition of RNAs in ribonucleoprotein (RNP) complexes in response to physiological stimuli. But by combining biochemical and genomics procedures to isolate and identify RNAs associated with RNA-binding proteins, information regarding RNA-protein and RNA-RNA interactions can be examined more directly within a cellular context. Several protocols--including the yeast three-hybrid system and immunoprecipitations that use physical or chemical cross-linking--have been developed to address this issue. Cross-linking procedures in general, however, are limited by inefficiency and sequence biases. The approach outlined here, termed RNP immunoprecipitation-microarray (RIP-Chip), allows the identification of discrete subsets of RNAs associated with multi-targeted RNA-binding proteins and provides information regarding changes in the intracellular composition of mRNPs in response to physical, chemical or developmental inducements of living systems. Thus, RIP-Chip can be used to identify subsets of RNAs that have related functions and are potentially co-regulated, as well as proteins that are associated with them in RNP complexes. Using RIP-Chip, the identification and/or quantification of RNAs in RNP complexes can be accomplished within a few hours or days depending on the RNA detection method used.     

Global importance analysis: An interpretability method to quantify importance of genomic features in deep neural networks

Deep neural networks have demonstrated improved performance at predicting the sequence specificities of DNA- and RNA-binding proteins compared to previous methods that rely on k-mers and position weight matrices. To gain insights into why a DNN makes a given prediction, model interpretability methods, such as attribution methods, can be employed to identify motif-like representations along a given sequence. Because explanations are given on an individual sequence basis and can vary substantially across sequences, deducing generalizable trends across the dataset and quantifying their effect size remains a challenge. Here we introduce global importance analysis (GIA), a model interpretability method that quantifies the population-level effect size that putative patterns have on model predictions. GIA provides an avenue to quantitatively test hypotheses of putative patterns and their interactions with other patterns, as well as map out specific functions the network has learned. As a case study, we demonstrate the utility of GIA on the computational task of predicting RNA-protein interactions from sequence. We first introduce a convolutional network, we call ResidualBind, and benchmark its performance against previous methods on RNAcompete data. Using GIA, we then demonstrate that in addition to sequence motifs, ResidualBind learns a model that considers the number of motifs, their spacing, and sequence context, such as RNA secondary structure and GC-bias.     

Ribonucleoprotein remodeling during RNA localization

Cytoplasmic RNA localization is a means to create polarity by restricting protein expression to a discrete subcellular location. RNA localization is a multistep process that begins with the recognition of cis-acting sequences within the RNA by specific trans-factors, and RNAs are localized in ribonucleoprotein (RNP) complexes that contain both the RNA and numerous protein components. Components of the localization machinery transport the RNP complex, usually in a translationally repressed state, to a distinct subcellular region, resulting in spatially restricted gene expression. Recent efforts to identify both the cis- and trans-factors required for RNA localization have elucidated RNA-protein interactions that are remodeled during localization.     

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.     

Three helical domains form a protein binding site in the 5S RNA-protein complex from eukaryotic ribosomes.

A ribosomal protein binding site in the eukaryotic 5S rRNA has been delineated by examining the effect of sequence variation and nucleotide modification on the RNA's ability to exchange into the EDTA-released, yeast ribosomal 5S RNA-protein complex. 5S RNAs of divergent sequence from a variety of eukaryotic origins could be readily exchanged into the yeast complex but RNA from bacterial origins was rejected. Nucleotide modifications in any of three analogous helical regions in eukaryotic 5S RNAs of differing origin reduced the ability of this RNA molecule to form homologous or heterologous RNA-protein complexes. Because sequence comparisons did not indicate common nucleotide sequences in the interacting helical regions, a model is suggested in which the eukaryotic 5S RNA binding protein does not simply recognize specific nucleotide sequences but interacts with three strategically oriented helical domains or functional groups within these domains. Two of the domains bear a limited sequence homology with each other and contain an unpaired nucleotide or "bulge" similar to that recently reported for one of the 5S RNA binding proteins in Escherichia coli (Peattie, D.A., Douthwaite, S., Garrett, R.A. and Noller, H.F. (1981) Proc. Natl. Acad. Sci. 78, 7331-7335). The results further indicate that the single ribosomal protein of eukaryotic 5S RNA-protein complexes interacts with the same region of the 5S rRNA molecule as do the multiple protein components in complexes of prokaryotic origin.     

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.     

Linker scanning mutagenesis of the internal ribosome entry site of poliovirus RNA.

The initiation of cap-independent translation of poliovirus mRNA occurs as a result of ribosome entry at an internal site(s) within the 5' noncoding region. A series of linker scanning mutations was constructed to define the genetic determinants of RNA-protein interactions that lead to high-fidelity translation of this unusual viral mRNA. The mutations are located within two distinct stem-loop structures in the 5' noncoding region of poliovirus RNA that constitute a major portion of a putative internal ribosome entry site. On the basis of our data derived from genetic and biochemical assays, the stability of one of the stem-loop structures appears to be essential for translation initiation via internal binding of ribosomes. However, the second stem-loop structure may function in a manner that requires base pairing and proper spacing between specific nucleotide sequences. By employing RNA electrophoretic mobility shift assays, an RNA-protein interaction was detected for this latter stem-loop structure that does not occur in RNAs containing mutations which perturb the predicted hairpin structure. Analysis of in vivo-selected virus revertants, in combination with mobility shift assays, suggests that extensive genetic rearrangement can lead to restoration of 5' noncoding region functions, possibly by the repositioning of specific RNA sequence or structure motifs.     

RNA secondary structure prediction based on free energy and phylogenetic analysis.

We describe a computational method for the prediction of RNA secondary structure that uses a combination of free energy and comparative sequence analysis strategies. Using a homology-based sequence alignment as a starting point, all favorable pairings with respect to the Turner energy function are identified. Each potentially paired region within a multiple sequence alignment is scored using a function that combines both predicted free energy and sequence covariation with optimized weightings. High scoring regions are ranked and sequentially incorporated to define a growing secondary structure. Using a single set of optimized parameters, it is possible to accurately predict the foldings of several test RNAs defined previously by extensive phylogenetic and experimental data (including tRNA, 5 S rRNA, SRP RNA, tmRNA, and 16 S rRNA). The algorithm correctly predicts approximately 80% of the secondary structure. A range of parameters have been tested to define the minimal sequence information content required to accurately predict secondary structure and to assess the importance of individual terms in the prediction scheme. This analysis indicates that prediction accuracy most strongly depends upon covariational information and only weakly on the energetic terms. However, relatively few sequences prove sufficient to provide the covariational information required for an accurate prediction. Secondary structures can be accurately defined by alignments with as few as five sequences and predictions improve only moderately with the inclusion of additional sequences.     

Analysing humanly generated random number sequences: a pattern-based approach

In a random number generation task, participants are asked to generate a random sequence of numbers, most typically the digits 1 to 9. Such number sequences are not mathematically random, and both extent and type of bias allow one to characterize the brain's "internal random number generator". We assume that certain patterns and their variations will frequently occur in humanly generated random number sequences. Thus, we introduce a pattern-based analysis of random number sequences. Twenty healthy subjects randomly generated two sequences of 300 numbers each. Sequences were analysed to identify the patterns of numbers predominantly used by the subjects and to calculate the frequency of a specific pattern and its variations within the number sequence. This pattern analysis is based on the Damerau-Levenshtein distance, which counts the number of edit operations that are needed to convert one string into another. We built a model that predicts not only the next item in a humanly generated random number sequence based on the item's immediate history, but also the deployment of patterns in another sequence generated by the same subject. When a history of seven items was computed, the mean correct prediction rate rose up to 27% (with an individual maximum of 46%, chance performance of 11%). Furthermore, we assumed that when predicting one subject's sequence, predictions based on statistical information from the same subject should yield a higher success rate than predictions based on statistical information from a different subject. When provided with two sequences from the same subject and one from a different subject, an algorithm identifies the foreign sequence in up to 88% of the cases. In conclusion, the pattern-based analysis using the Levenshtein-Damarau distance is both able to predict humanly generated random number sequences and to identify person-specific information within a humanly generated random number sequence.     

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.).     

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.     

Base-pairing potential identified by in vitro selection predicts the kinked RNA backbone observed in the crystal structure of the alfalfa mosaic virus RNA-coat protein complex

The three-dimensional structure of the 3' terminus of alfalfa mosaic virus RNA in complex with an amino-terminal coat protein peptide revealed an unusual RNA fold with inter-AUGC basepairing stabilized by key arginine residues (Guogas, et al., 2004). To probe viral RNA interactions with the full-length coat protein, we have used in vitro genetic selection to characterize potential folding patterns among RNAs isolated from a complex randomized pool. Nitrocellulose filter retention, electrophoretic mobility bandshift analysis, and hydroxyl radical footprinting techniques were used to define binding affinities and to localize the potential RNA-protein interaction sites. Minimized binding sites were identified that included both the randomized domain and a portion of the constant regions of the selected RNAs. The selected RNAs, identified by their ability to bind full-length coat protein, have the potential to form the same unusual inter-AUGC Watson-Crick base pairs observed in the crystal structure, although the primary sequences diverge from the wild-type RNA. A constant feature of both the wild-type RNA and the selected RNAs is a G ribonucleotide in the third position of an AUGC-like repeat. Competitive binding assays showed that substituting adenosine for the constant guanosine in either the wild-type or selected RNAs impaired coat protein binding. These data suggest that the interactions observed in the RNA-peptide structure are likely recapitulated when the full-length protein binds. Further, the results underscore the power of in vitro genetic selection for probing RNA-protein structure and function.     

The P gene product of hepatitis B virus is required as a structural component for genomic RNA encapsidation.

Encapsidation of the pregenomic RNA into nucleocapsids is a selective process which depends on specific RNA-protein interactions. The signal involved in the packaging of the hepatitis B virus (HBV) RNA pregenome was recently defined as a short sequence located near the 5' end of that molecule (Junker-Niepmann et al., EMBO J., in press), but it remained an open question which viral proteins are required. Using a genetic approach, we analyzed whether proteins derived from the HBV P gene play an important role in pregenome encapsidation. The results obtained with point mutations, deletions, and insertions scattered throughout the P gene clearly demonstrate that (i) a P gene product containing all functional domains is required both for the encapsidation of HBV pregenomic RNA and for packaging of nonviral RNAs fused to the HBV encapsidation signal, (ii) known enzymatic activities are not involved in the packaging reaction, suggesting that P protein is required as a structural component, and (iii) P protein acts primarily in cis, i.e., pregenomic RNAs from which P protein is synthesized are preferentially encapsidated.     

RNA folding affects the recruitment of SR proteins by mouse and human polypurinic enhancer elements in the fibronectin EDA exon

In humans, inclusion or exclusion of the fibronectin EDA exon is mainly regulated by a polypurinic enhancer element (exonic splicing enhancer [ESE]) and a nearby silencer element (exonic splicing silencer [ESS]). While human and mouse ESEs behave identically, mutations introduced into the homologous mouse ESS sequence result either in no change in splicing efficiency or in complete exclusion of the exon. Here, we show that this apparently contradictory behavior cannot be simply accounted for by a localized sequence variation between the two species. Rather, the nucleotide differences as a whole determine several changes in the respective RNA secondary structures. By comparing how the two different structures respond to homologous deletions in their putative ESS sequences, we show that changes in splicing behavior can be accounted for by a differential ESE display in the two RNAs. This is confirmed by RNA-protein interaction analysis of levels of SR protein binding to each exon. The immunoprecipitation patterns show the presence of complex multi-SR protein-RNA interactions that are lost with secondary-structure variations after the introduction of ESE and ESS variations. Taken together, our results demonstrate that the sequence context, in addition to the primary sequence identity, can heavily contribute to the making of functional units capable of influencing pre-mRNA splicing.