Tethering of proteins to RNAs by bacteriophage proteins

Many steps in the control of gene expression are dependent on RNA-binding proteins, most of which are bi-functional, in as much as they both bind to RNA and interact with other protein partners in a functional complex. A powerful approach to study the functional properties of these proteins in vivo, independently of their RNA-binding ability, is to attach or tether them to specifically engineered reporter mRNAs whose fate can be easily followed. Two tethering systems have been mainly used in eukaryotic cells, namely the MS2 coat protein system and the lambda N-B box system. In this review, we firstly describe several studies in which these tethering systems have been used and provide an overview of these applications. We next describe the major features of these two systems, and, finally, we highlight a number of points that should be considered when designing experiments using this approach.     

Tethering of proteins to RNAs using the bovine immunodeficiency virus-Tat peptide and BIV-TAR RNA

To study the functions of RNA-binding proteins independent of their RNA-binding activity, tethering methods have been developed, based on the use of the RNA-binding domain of a well-characterized RNA-binding protein and its target RNA. Two bacteriophage proteins have mainly been used as tethers: the MS2 coat protein and the lambda N protein. Here we report an alternative system using the Tat (trans-activator) peptide from the bovine immunodeficiency virus (BIV), which binds to BIV-TAR (trans-activation response) RNA. We demonstrate the usefulness of this system by applying it to the analysis of the TNRC6B protein, a component of the microRNA-induced silencing complex.     

Design of RNA-binding proteins and ligands

The rapidly expanding database of RNA structures and protein complexes is beginning to lead to the successful design of specific RNA-binding molecules. Recent combinatorial and structure-based approaches have utilized known nucleic-acid-binding scaffolds from both proteins and small molecules to display a relatively small set of functional groups often used in protein--RNA recognition. Several studies have shown that the tethering of multiple binding modules can enhance RNA-binding affinity and specificity, a strategy also commonly used in DNA recognition.     

Prediction of RNA-binding proteins from primary sequence by a support vector machine approach

Elucidation of the interaction of proteins with different molecules is of significance in the understanding of cellular processes. Computational methods have been developed for the prediction of protein-protein interactions. But insufficient attention has been paid to the prediction of protein-RNA interactions, which play central roles in regulating gene expression and certain RNA-mediated enzymatic processes. This work explored the use of a machine learning method, support vector machines (SVM), for the prediction of RNA-binding proteins directly from their primary sequence. Based on the knowledge of known RNA-binding and non-RNA-binding proteins, an SVM system was trained to recognize RNA-binding proteins. A total of 4011 RNA-binding and 9781 non-RNA-binding proteins was used to train and test the SVM classification system, and an independent set of 447 RNA-binding and 4881 non-RNA-binding proteins was used to evaluate the classification accuracy. Testing results using this independent evaluation set show a prediction accuracy of 94.1%, 79.3%, and 94.1% for rRNA-, mRNA-, and tRNA-binding proteins, and 98.7%, 96.5%, and 99.9% for non-rRNA-, non-mRNA-, and non-tRNA-binding proteins, respectively. The SVM classification system was further tested on a small class of snRNA-binding proteins with only 60 available sequences. The prediction accuracy is 40.0% and 99.9% for snRNA-binding and non-snRNA-binding proteins, indicating a need for a sufficient number of proteins to train SVM. The SVM classification systems trained in this work were added to our Web-based protein functional classification software SVMProt, at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi. Our study suggests the potential of SVM as a useful tool for facilitating the prediction of protein-RNA interactions.     

Finding the right RNA: identification of cellular mRNA substrates for RNA-binding proteins.

Defects in RNA-binding proteins have been implicated in human genetic disorders. However, efforts in understanding the functions of these proteins have been hampered by the inability to obtain their mRNA substrates. To identify cognate cellular mRNAs associated with an RNA-binding protein, we devised a strategy termed isolation of specific nucleic acids associated with proteins (SNAAP). The SNAAP technique allows isolation and subsequent identification of these mRNAs. To assess the validity of this approach, we utilized cellular mRNA and protein from K562 cells and alphaCP1, a protein implicated in a-globin mRNA stability, as a model system. Immobilization of an RNA-binding protein with the glutathione-S-transferase (GST) domain enables isolation of mRNA within an mRNP context and the identity of the bound mRNAs is determined by the differential display assay. The specificity of protein-RNA interactions was considerably enhanced when the interactions were carried out in the presence of cellular extract rather than purified components. Two of the mRNAs specifically bound by alphaCP1 were mRNAs encoding the transmembrane receptor protein, TAPA-1, and the mitochondrial cytochrome c oxidase subunit II enzyme, coxII. A specific poly(C)-sensitive complex formed on the TAPA-1 and coxII 3' UTRs consistent with the binding of aCP1. Furthermore, direct binding of purified alphaCP proteins to these 3' UTRs was demonstrated and the binding sites determined. These results support the feasibility of the SNAAP technique and suggest a broad applicability for the approach in identifying mRNA targets for clinically relevant RNA-binding proteins that will provide insights into their possible functions.     

Identifying mRNAs bound by RNA-binding proteins using affinity purification and differential display

Many methods are available and widely used to determine specific proteins that bind to a particular RNA of interest. However, approaches to identify unknown substrate RNAs to which an RNA-binding protein binds and potentially regulates are not as common. In this article we describe a technique termed isolation of specific nucleic acids associated with proteins (SNAAP) that allows the identification of mRNAs associated with a protein. Methods are detailed for expressing and purifying fusion proteins that are used to isolate substrate mRNPs employing differential display technology. Lastly, experiments are described to confirm that the RNAs identified are indeed bonafide substrates for an RNA-binding protein. As the number of known RNA-binding proteins increases, of which many are involved in genetic disorders, it is essential that methodologies exist to identify RNA-protein interactions to better understand the manifestation of disease.     

Classifying RNA-binding proteins based on electrostatic properties

Protein structure can provide new insight into the biological function of a protein and can enable the design of better experiments to learn its biological roles. Moreover, deciphering the interactions of a protein with other molecules can contribute to the understanding of the protein's function within cellular processes. In this study, we apply a machine learning approach for classifying RNA-binding proteins based on their three-dimensional structures. The method is based on characterizing unique properties of electrostatic patches on the protein surface. Using an ensemble of general protein features and specific properties extracted from the electrostatic patches, we have trained a support vector machine (SVM) to distinguish RNA-binding proteins from other positively charged proteins that do not bind nucleic acids. Specifically, the method was applied on proteins possessing the RNA recognition motif (RRM) and successfully classified RNA-binding proteins from RRM domains involved in protein-protein interactions. Overall the method achieves 88% accuracy in classifying RNA-binding proteins, yet it cannot distinguish RNA from DNA binding proteins. Nevertheless, by applying a multiclass SVM approach we were able to classify the RNA-binding proteins based on their RNA targets, specifically, whether they bind a ribosomal RNA (rRNA), a transfer RNA (tRNA), or messenger RNA (mRNA). Finally, we present here an innovative approach that does not rely on sequence or structural homology and could be applied to identify novel RNA-binding proteins with unique folds and/or binding motifs.     

Identification of two novel proteins that interact with germ-cell-specific RNA-binding proteins DAZ and DAZL1

The human DAZ (deleted in azoospermia) gene family on the Y chromosome and an autosomal DAZ-like gene, DAZL1, encode RNA-binding proteins that are expressed exclusively in germ cells. Their role in spermatogenesis is supported by their homology with a Drosophila male infertility gene boule and sterility of Daz11 knock-out mice. While all mammals contain a DAZL1 homologue on their autosomes, DAZ homologues are present only on the Y chromosomes of great apes and Old World monkeys. The DAZ and DAZL1 proteins differ in the copy numbers of a DAZ repeat and the C-terminal sequences. We studied the interaction of DAZ and DAZL1 with other proteins as an approach to investigate functional similarity between these two proteins. Using DAZ as bait in a yeast two-hybrid system, we isolated two DAZAP (DAZ-associated protein) genes. DAZAP1 encodes a novel RNA-binding protein that is expressed most abundantly in the testis, and DAZAP2 encodes a ubiquitously expressed protein with no recognizable functional motif. DAZAP1 and DAZAP2 bind similarly to both DAZ and DAZL1 through the DAZ repeats. The DAZAP genes were mapped to chromosomal regions 19p13.3 and 2q33-q34, respectively, where no genetic diseases affecting spermatogenesis are known to map.     

Identification and characterization of proteins binding A + U-rich elements

A + U-Rich elements (AREs) have been extensively investigated as cis-acting determinants of rapid mRNA turnover. Recently, a number of RNA-binding proteins interacting with AREs have been described. This article presents strategies and techniques used by our laboratory to identify and characterize a family of ARE-binding proteins collectively termed AUF1. However, these techniques may be applied to the study of any protein displaying sequence-specific RNA binding activity. The techniques described here include the purification of native AUF1 from cultured cells as well as the preparation of recombinant AUF1 proteins using a bacterial expression system. Analyses of RNA-protein interactions are also described, including the use of gel mobility shift assays with synthetic RNA probes to monitor specific RNA binding activity in cell extracts or with recombinant proteins. Variations of this technique are also described to evaluate the RNA binding affinity of recombinant proteins and the use of specific RNA competitors to assess RNA determinants of protein binding specificity. Other techniques presented include the identification of specific proteins in RNA:protein complexes using antibody supershifts and the estimation of molecular weights of RNA-binding proteins by UV crosslinking. Results of individual experiments are presented as examples of some techniques. Throughout the article, suggestions are included to avoid commonly encountered problems and to assist in the optimization of these techniques for the study of other RNA-binding proteins.     

Tethered function assays using 3' untranslated regions

Proteins that regulate mRNA metabolism are often bipartite: an RNA binding activity confers substrate specificity, and a "functional" domain elicits the biological outcome. In some cases, these two activities reside on different polypeptides that form a complex on the mRNA. Regardless, experimental separation of RNA binding from function facilitates analysis of both properties, liberating each from the constraints of the other. "Tethered function" assays bring a protein to a reporter RNA through a designed RNA-protein interaction. The function of the tethered protein-whether that be stability, translation, localization, or transport, or otherwise-is then assessed. We refer to this approach as a "tethered function" assay, since it can be examined. The approach does not require knowledge of the natural RNA binding sites, or of the composition of the naturally occurring protein complexes. It can be useful in dissecting how proteins that act on RNAs work, and in identifying active components of multiprotein complexes. RNA-binding proteins previously have been linked to foreign RNA-binding specificities, for a wide variety of purposes. We emphasize here the particular value of tethering to the 3' untranslated region of eukaryotic mRNAs, and the opportunities it presents for the analysis of how those mRNAs are regulated. We discuss experimental design, as well as published and potential applications.     

A versatile assay for RNA-binding proteins in living cells

RNA-binding proteins (RBPs) control RNA fate from synthesis to decay. Since their cellular expression levels frequently do not reflect their in vivo activity, methods are needed to assess the steady state RNA-binding activity of RBPs as well as their responses to stimuli. While electrophoresis mobility shift assays (EMSA) have been used for such determinations, their results serve at best as proxies for the RBP activities in living cells. Here, we describe a quantitative dual fluorescence method to analyze protein–mRNA interactions in vivo. Known or candidate RBPs are fused to fluorescent proteins (eGFP, YFP), expressed in cells, cross-linked in vivo to RNA by ultraviolet light irradiation, and immunoprecipitated, after lysis, with a single chain antibody fragment directed against eGFP (GFP-binding protein, GBP). Polyadenylated RNA-binding activity of fusion proteins is assessed by hybridization with an oligo(DT) probe coupled with a red fluorophore. Since UV light is directly applied to living cells, the assay can be used to monitor dynamic changes in RNA-binding activities in response to biological or pharmacological stimuli. Notably, immunoprecipitation and hybridization can also be performed with commercially available GBP-coupled 96-well plates (GFP-multiTrap), allowing highly parallel RNA-binding measurements in a single experiment. Therefore, this method creates the possibility to conduct in vivo high-throughput RNA-binding assays. We believe that this fast and simple radioactivity-free method will find many useful applications in RNA biology.     

RNA-binding proteins in early development

RNA-binding proteins play a major part in the control of gene expression during early development. At this stage, the majority of regulation occurs at the levels of translation and RNA localization. These processes are, in general, mediated by RNA-binding proteins interacting with specific sequence motifs in the 3'-untranslated regions of their target RNAs. Although initial work concentrated on the analysis of these sequences and their trans-acting factors, we are now beginning to gain an understanding of the mechanisms by which some of these proteins function. In this review, we will describe a number of different families of RNA-binding proteins, grouping them together on the basis of common regulatory strategies, and emphasizing the recurrent themes that occur, both across different species and as a response to different biological problems.     

Proteome-wide prediction of novel DNA/RNA-binding proteins using amino acid composition and periodicity in the hyperthermophilic archaeon Pyrococcus furiosus.

Proteins play a critical role in complex biological systems, yet about half of the proteins in publicly available databases are annotated as functionally unknown. Proteome-wide functional classification using bioinformatics approaches thus is becoming an important method for revealing unknown protein functions. Using the hyperthermophilic archaeon Pyrococcus furiosus as a model species, we used the support vector machine (SVM) method to discriminate DNA/RNA-binding proteins from proteins with other functions, using amino acid composition and periodicities as feature vectors. We defined this value as the composition score (CO) and periodicity score (PD). The P. furiosus proteins were classified into three classes (I-III) on the basis of the two-dimensional correlation analysis of CO score and PD score. As a result, approximately 87% of the functionally known proteins categorized as class I proteins (CO score + PD score > 0.6) were found to be DNA/RNA-binding proteins. Applying the two-dimensional correlation analysis to the 994 hypothetical proteins in P. furiosus, a total of 151 proteins were predicted to be novel DNA/RNA-binding protein candidates. DNA/RNA-binding activities of randomly chosen hypothetical proteins were experimentally verified. Six out of seven candidate proteins in class I possessed DNA/RNA-binding activities, supporting the efficacy of our method.     

Analysis of electric moments of RNA-binding proteins: implications for mechanism and prediction

Background  

Protein-RNA interactions play important role in many biological processes such as gene regulation, replication, protein synthesis and virus assembly. Although many structures of various types of protein-RNA complexes have been determined, the mechanism of protein-RNA recognition remains elusive. We have earlier shown that the simplest electrostatic properties viz. charge, dipole and quadrupole moments, calculated from backbone atomic coordinates of proteins are biased relative to other proteins, and these quantities can be used to identify DNA-binding proteins. Closely related, RNA-binding proteins are investigated in this study. In particular, discrimination between various types of RNA-binding proteins, evolutionary conservation of these bulk electrostatic features and effect of conformational changes by complex formation are investigated. Basic binding mechanism of a putative RNA-binding protein (HI1333 from Haemophilus influenza) is suggested as a potential application of this study.     

CAG trinucleotide RNA repeats interact with RNA-binding proteins.

Genes associated with several neurological diseases are characterized by the presence of an abnormally long trinucleotide repeat sequence. By way of example, Huntington's disease (HD), is characterized by selective neuronal degeneration associated with the expansion of a polyglutamine-encoding CAG tract. Normally, this CAG tract is comprised of 11-34 repeats, but in HD it is expanded to > 37 repeats in affected individuals. The mechanism by which CAG repeats cause neuronal degeneration is unknown, but it has been speculated that the expansion primarily causes abnormal protein functioning, which in turn causes HD pathology. Other mechanisms, however, have not been ruled out. Interactions between RNA and RNA-binding proteins have previously been shown to play a role in the expression of several eukaryotic genes. Herein, we report the association of cytoplasmic proteins with normal length and extended CAG repeats, using gel shift and UV crosslinking assays. Cytoplasmic protein extracts from several rat brain regions, including the striatum and cortex, sites of neuronal degeneration in HD, contain a 63-kD RNA-binding protein that specifically interacts with these CAG-repeat sequences. These protein-RNA interactions are dependent on the length of the CAG repeat, with longer repeats binding substantially more protein. Two CAG repeat-binding proteins are present in human cortex and striatum; one comigrates with the rat protein at 63 kD, while the other migrates at 49 kD. These data suggest mechanisms by which RNA-binding proteins may be involved in the pathological course of trinucleotide repeat-associated neurological diseases.     

Molecular mechanisms of phosphorylation-regulated TTP (tristetraprolin) action and screening for further TTP-interacting proteins

TTP (tristetraprolin) is an RNA-binding protein which regulates mRNA stability or translation or both. The molecular mechanisms which are responsible and which discriminate between regulation of mRNA stability and translation are not completely understood so far, but are clearly dependent on p38 MAPK (mitogen-activated protein kinase)/MK (MAPK-activated protein kinase) 2/3-mediated phosphorylation of TTP. To learn more about these mechanisms, phosphorylation-dependent TTP-interacting proteins could be of great interest. Many interacting partners, which belong to the mRNA-processing and -regulating machinery, have been identified by hypothesis-driven co-immunoprecipitation and in the classical Y2H (yeast two-hybrid) approach, where TTP was identified as prey, and are summarized in the present paper. However, because of transactivating properties of TTP, an unbiased Y2H approach using TTP as bait was hindered. Since novel methods for the identification of phosphorylation-dependent interaction partners and of interactors of full-length auto-activating proteins in eukaryotic systems have evolved in the last few years, these methods should be applied to screen for additional phosphorylation-dependent interaction partners of TTP and could lead towards a complete understanding of TTP function at the molecular level.     

PRINTR: Prediction of RNA binding sites in proteins using SVM and profiles

Protein–RNA interactions play a key role in a number of biological processes such as protein synthesis, mRNA processing, assembly and function of ribosomes and eukaryotic spliceosomes. A reliable identification of RNA-binding sites in RNA-binding proteins is important for functional annotation and site-directed mutagenesis. We developed a novel method for the prediction of protein residues that interact with RNA using support vector machine (SVM) and position-specific scoring matrices (PSSMs). Two cases have been considered in the prediction of protein residues at RNA-binding surfaces. One is given the sequence information of a protein chain that is known to interact with RNA; the other is given the structural information. Thus, five different inputs have been tested. Coupled with PSI-BLAST profiles and predicted secondary structure, the present approach yields a Matthews correlation coefficient (MCC) of 0.432 by a 7-fold cross-validation, which is the best among all previous reported RNA-binding sites prediction methods. When given the structural information, we have obtained the MCC value of 0.457, with PSSMs, observed secondary structure and solvent accessibility information assigned by DSSP as input. A web server implementing the prediction method is available at the following URL: .