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Brief Funct Genomics. Dec 2010; 9(5-6): 391–404.
Published online Dec 1, 2010. doi:  10.1093/bfgp/elq028
PMCID: PMC3080770

Deciphering the role of RNA-binding proteins in the post-transcriptional control of gene expression


Eukaryotic cells express a large variety of ribonucleic acid-(RNA)-binding proteins (RBPs) with diverse affinity and specificity towards target RNAs that play a crucial role in almost every aspect of RNA metabolism. In addition, specific domains in RBPs impart catalytic activity or mediate protein–protein interactions, making RBPs versatile regulators of gene expression. In this review, we elaborate on recent experimental and computational approaches that have increased our understanding of RNA–protein interactions and their role in cellular function. We review aspects of gene expression that are modulated post-transcriptionally by RBPs, namely the stability of polymerase II-derived mRNA transcripts and their rate of translation into proteins. We further highlight the extensive regulatory networks of RBPs that implement a combinatorial control of gene expression. Taking cues from the recent development in the field, we argue that understanding spatio-temporal RNA–protein association on a transcriptome level will provide invaluable and unexpected insights into the regulatory codes that define growth, differentiation and disease.

Keywords: RNA-binding proteins, RNA-binding domains, RBP–RNA interaction, RBP regulatory networks, RBP target identification


As they emerge from the RNA polymerase, nascent RNA transcripts are immediately covered with RNA-binding proteins (RBPs) that assist in various aspects of RNA metabolism (biogenesis, maturation, transport, cellular localization, turnover) and function [1]. The composition of ribonucleoprotein (RNP) complexes further undergoes dynamic remodeling not only as the RNA matures but also in response to changes in intra- and extra-cellular conditions. This is due to both changes in the protein composition of the different sub-cellular compartments to which an RNA molecule is targeted, as well as to post-translational modifications that modulate the ability of RBPs to bind RNA and to interact with other proteins. It is generally thought that individual RBPs have hundreds to thousands of targets [2] and that the combinatorial binding of multiple RBPs to mRNAs contributes to the specification of cell identity or state [3]. Interestingly, increasingly many interactions of RBPs with their own mRNAs or the mRNAs encoding other RBPs are found [2, 4–6] and the function of these regulatory feedback loops is insufficiently understood. A few emerging paradigms are that RBPs coordinately regulate the localization, stability and translation of many targets [2, 7, 8] and that post-transcriptional regulation by miRNA-guided RNP complexes is a subtle mechanism to stabilize gene expression levels, perhaps in contrast with the binary mode (on/off) of transcriptional regulation implemented by transcription factors (TFs) [9]. Post-transcriptional miRNA-dependent regulation also appears to provide a means for rapidly responding to changes in environmental conditions [10] or for highly sub-compartment-specific protein expression [11]. Newly developed high-throughput approaches to globally identify the targets of RBPs in a specific cell type under specific conditions have greatly improved our understanding of how RBPs can systematically and coherently affect the levels of proteins in individual pathways [12]. In this review, we present the current understanding of this continously evolving field and discuss the future impacts that the recent developments in experimental technologies may bring in our understanding of cellular function.


RBPs are defined through their ability to bind RNA targets, which they do through one or more RNA-binding domains (RBDs). In the current release (24.0) of the Pfam database [13] over 1100 human genes are annotated with a function in RNA metabolism. One may expect that the variety of functions is reflected in a variety of structures and RBDs, but this is not the case. About 40 different types of domains that are involved in RNA recognition are known to date though the numbers are constantly increasing. Some RBDs are only found in single species or in proteins with specific functions like in cap-binding or viral proteins [14], but a few RBDs implement the RNA-binding activity of a large number of proteins (for an overview about common RBDs, see [15]). The presence of multiple domains enables a more specific and higher affinity interaction of the RBP with its target. Surprisingly, only a few RBDs, among which are the RNA-recognition motif (RRM) [16], the heterogeneous nuclear (hn) RNP K homology (KH) [17], the double-stranded RBD (dsRBD) [18], Pumilio (PUF repeats) homology (PUM-H) [19], zinc finger (ZnF) [20] and Piwi/Argonaute/Zwilli (PAZ) [21] domain, are well studied (see Table 1 for details about several common RBDs).

Table 1:
Characteristics of several common RNA-binding motif/domains

Apart from RBDs, RBPs frequently contain domains that impart catalytic or protein–protein interaction activities. For instance, all adenosine deaminases (ADARs) contain two or three dsRBDs coupled with a catalytic deaminase domain that is involved in editing their double-stranded nucleic acid substrates [22]. On the other hand, the dsRBDs in Drosha and Dicer are coupled with two RNase III domains that bring about endonucleolytic cleavage of primary- and precursor-miRNA, respectively [23, 24]. Several RBPs contain domains that are involved in potein–protein interaction and participate in large RNP complexes that function in maturation, localization and stability of RNA molecules. Prominent examples are splicing factors, heterogeneous nuclear ribonucleoproteins (hnRNPs) and serine/arginine-rich (SR) proteins that assist spliceosomes in splice site selection. Finally, atypical RRMs that can bind proteins rather than RNA have been described [25, 26]. For instance, the third RRM of Drosophila ELAV protein has been suggested to serve as a bifunctional domain that can interact with both protein and RNA [27].

Other sequence elements within RBPs specify the sub-cellular localization that is essential for their function. For instance, the nucleolar localization of H/ACA box small nucleolar RNP (snoRNP) is attributed to the core protein Dyskerin that possesses a nuclear localization signal (NLS), as well as an acidic/lysine-rich domain (KKE/D motif) that is found in many other nucleolar proteins [28].

Finally, the specificity of interaction between RBP and RNA targets can lie in guide RNAs as opposed to RBDs. Increasingly many classes of guide RNA are being described, among these being the miRNAs [29] and endo-siRNAs [30–32] that associated with Argonaute proteins, piRNAs that associate with Piwi proteins [33–35] and small nuclear and small nucleolar RNAs (sn- and sno-RNAs) [36, 37] that associate with multiple proteins in a complex.

Thus, the combinatorial arrangement of various domains in RBPs imparts a tremendous structural and functional diversity to RBPs that enables them to modify and regulate virtually every step of target RNA metabolism and function.


Recent discoveries particularly in the field of miRNAs revealed novel mechanisms by which the levels of mRNA available for translation are controlled post-transcriptionally, either by modulating mRNA stability or by relocalizing mature mRNAs into translationally silent compartments such as the processing (P)-bodies. In the following section we review the expanding role of RBPs in regulating mRNA stability and translation.

Nuclear factors regulating stability, coding potential and translation rate of mRNA

The regulatory roles of nuclear factors extend beyond the processing of precursors into mature RNA forms that can be exported to the cytoplasm. Editing enzymes with nuclear localization such as ADARs that act on RNAs modulate the levels of adenosine to inosine modification in their target RNAs, which in turn may change the coding potential of exported mRNAs. A prominent example is the serotonin receptor 2C mRNA which through its five ADAR-edited sites may generate 32 mRNA isoforms encoding 24 different proteins with varied efficacy of G-protein coupling [38]. Similarly, transfer RNA (tRNA) editing by ADARs acting on tRNAs (ADATs) at or near the anticodon position modulates codon recognition during mRNA translation [39]. Non-sense mediated decay (NMD) is a mechanism by which transcripts that are incorrectly or incompletely spliced thereby carrying pre-mature termination codons are degraded by a specific cellular machinery [40]. Though this is primarily a post-transcriptional surveillance mechanism that prevents synthesis of truncated proteins that may otherwise have toxic effects in the cell, NMD has been recruited to tightly regulate bursts of protein synthesis at distinct sub-cytoplasmic locations. For instance in neurons, translationally quiescent Arc/Arg3.1 mRNAs with introns in the 3′-UTR migrate to dendrites and upon activating stimuli at the synapse, generate a single polypeptide before being degraded by NMD [41]. There is increasing evidence that alternative processing of the mRNA by 3′-end processing factors leads to differential susceptibility to post-transcriptional regulation in different cell types or phases of the cell cycle [42]. Shorter 3′-UTRs are preferentially generated in cancer cells and are associated with increased mRNA stability and translation, whereas longer 3′-UTRs are predominantly generated in non-transformed cells in which they offer more possibilities for regulation.

Cytoplasmic regulation of mRNA stability and translation

The composition of mRNPs changes as they are exported from nucleus to cytoplasm. SR proteins like ASF/SF2, that accompanied the transcript through the splicing process, along with newly assembled translation initiation factors, aid in the recruitment of the translation machinery [43, 44]. In contrast, binding of the miRNA-induced silencing complex (miRISC) brings about decapping, deadenylation and translational repression of the mRNA [45]. A few examples of miRNA-mediated translational activation have also been reported [46–48]. For example, it has been reported that miR369-3 stimulates translation of TNF-alpha mRNA by recruiting an Argonaute2-FXR1 complex in quiescent cells arrested at G0/G1 phase [46, 49, 50]. Interestingly, miR-369-3 repressed translation in actively dividing cells. AU-rich elements (AREs) located in 3′-UTRs serve as docking sites for various proteins that modulate mRNA stability [51]. ARE-binding proteins (ARE-BPs) of the Hu family (HuB, HuC, HuD and HuR) typically stabilize transcripts, while other ARE-BPs like AUF1 and TIA-1 primarily destabilize their target mRNAs [52]. The distinction between stabilizing and destabilizing factors is starting to become blurred because various atypical examples have been uncovered. For instance, binding of the usually stabilizing RBP HuR to an ARE located in the c-myc transcript recruits the let-7 miRNA to repress translation and promote subsequent degradation [53]. Likewise, a number of studies suggest that RBPs can assist in the recruitment of the RISC complex to its site of action either directly or indirectly [54, 55]. Recently, it has been shown that binding of Pumilio to the 3′-UTR of tumor suppressor p27 transcript induces a local change in the 3′-UTR secondary structure, exposing the miR-221/miR-222 target site which is ultimately responsible for p27 silencing in proliferating cells. In this study, no direct interaction between Argonaute and Pumilio was detected [55]. On the contrary, the RBP Dnd1 has been found to counteract miR-221-mediated translation repression of p27 by binding to an ARE in the 3′-UTR of p27 mRNA and prohibiting RISC assembly [56]. The effect of RBPs on mRNA stability can be coupled though not always coherently, with their effect on translation rate. An interesting example is the Cox-2 mRNA, whose stability is enhanced by both HuR and CUGBP2 proteins, though HuR enhances and CUGBP2 inhibits its translation [57]. It is noteworthy that many RBPs are involved in multiple, quite distinct processes. For example, as mentioned earlier, the splicing factor ASF/SF2 also assists in mRNA export to the cytoplasm where it promotes mRNA translation [43, 44]. Another splicing factor, TIA-1, functions in the assembly of stress granules (SGs) in the cytoplasm. SGs are formed when translation initiation is impaired due to external stress or lack of translation initiation factors. The repressed mRNAs are bound by TIA-1 through its RBD and the self-aggregates of the protein through QN-rich prion-like domains promote SG assembly [58]. Likewise, cold inducible RBP (CIRP) migrates from nucleus to cytoplasmic SGs upon oxidative stress and acts as a translational repressor, independent of TIA-1 mediated SG formation [59].


Since RBPs play crucial roles in the metabolism of a multitude of RNAs, it is important to regulate precisely their quantity and localization within cellular compartments. Hence, diverse mechanisms have evolved to ensure precise turnover and localization of RBPs. Furthermore, because RBPs orchestrate specific cellular responses under conditions such as stress, the levels of RBPs have to be rapidly adjusted under such conditions. Mechanisms for rapidly turning on or off the expresison or activity of RBPs have also ben evolved, starting at the level of transcription. Some RBPs such as Nova and SNRPN are expressed in a tissue-specific manner (in this case in neurons). Others are regulated at the level of splicing. The Staufen gene, for e.g. has multiple isoforms which have been shown to exhibit different RNA-binding activity in rat [60]. The phosphorylation state of a splicing factor is a major determinant that regulates its shuttling from nucleus to cytoplasm [61]. SR proteins contain one or more stretches of serine/arginine residues. Phosphorylation at serine residues influences the localization of SR proteins within different sub-cellular compartments and releases them from speckles where they are relatively inactive, into the nucleus where they can actively bind to pre-mRNAs [62, 63].

Complex-regulatory networks in which RBPs regulate their own and each other’s expression have been already uncovered. For example, RNA editing enzyme ADAR2 can edit its own pre-mRNA to activate a cryptic 3′-acceptor splice site, which changes the predicted reading frame of the mature ADAR2 transcript and generates a non-functional protein variant [64]. Similarly, the splicing factor Transformer2-β1 (Tra2-β1) autoregulates its expression through alternative splicing. High levels of Tra2-β1 promote binding of Tra2-β1 to its own pre-mRNA leading to the generation of a functionally inactive splice variant [4]. Quite generally, the genes encoding SR proteins contain highly conserved so-called ‘poison exons’ that harbor premature stop codons and are used to tighly regulate their protein level through a feedback mechanism that involves NMD [65]. The alternatively spliced introns in the 3′-UTR of the SR protein-encoding mRNAs serve a similar purpose [65]. In the case of the splicing factors SC-35 and Tra-2beta1 mRNA destabilization is induced in an autoregulatory manner [4, 5]. Among the many potential targets of miRNAs, splicing factors can of course trigger broad downstream effects. For example, upregulation of miR-23a/b in the post-natal development of cardiac tissue leads to tissue-specific repression of the CUGBP1 and CUGBP2 splicing factors, which in turn shifts the splicing of a conserved set of genes into an adult pattern [66]. Similarly, the neuron-specific miR-124 plays a crucial role in setting up the neuronal-splicing program by repressing the expression of polypyrimide tract-binding protein (PTB), a global repressor of alternative splicing in non-neuronal cells. PTB and its neuron-specific paralog nPTB have largely overlapping target sets, though PTB is a stronger repressor of alternatively spliced exons compared to nPTB. More importantly, PTB promotes an exon exclusion event in both its own transcript as well as that of nPTB [6]. The exon exclusion results in enhanced transcript degradation via non-sense-mediated RNA decay [67, 68]. Repression of PTB by miR-124 leads to the expression of functionally active nPTB which further defines the pattern of splicing in the neuronal tissue [69]. On the other hand, miR-133 has been shown to directly downregulate nPTB during muscle development by binding to its 3′-UTR [70]. MicroRNAs regulate many other RBPs with central cellular functions. A very interesting example is the auto-regulatory loop involving the let-7 miRNA that down-regulates the expression of the endoribonuclease Dicer, which is involved in pre-miRNA processing. Since let-7 is itself processed by Dicer, it is likely that this negative feedback loop is involved in setting the miRNA expression at appropriate levels within cells [71].

A few recent studies suggest a strong interplay between the ARE-BPs and miRNPs in modulating gene expression. Computational studies have shown that effective miRNA target sites reside within A/U-rich regions inside 3′-UTRs [72, 73]. Several proteins have been described to bind AREs and regulate mRNA stability, and one therefore expects that ARE-BPs modulate the interaction of miRNA with their targets. An example that we already mentioned is that of the ARE-BP HuR that regulates the expression of c-myc by recruiting the let-7 miRNA [53]. In contrast, binding of HuR to the ARE of cationic amino acid transporter (CAT-1) mRNA upon stress relieves miR-122-mediated translational repression of CAT-1 mRNA in liver tissue [74]. Similarly, the Deadend ARE-BP (Dnd1) has been shown to inhibit the access of a miRNA to its target site [56]. Interestingly, results from our lab show that binding sites for several RBPs on their target RNAs occur in close vicinity, suggesting extensive crosstalks between RBP complexes that probably result in precise regulation of gene expression levels in a variety of conditions. Binding sites that were experimentally identified in an mRNA transcript of the cell cycle related p27 (Kip1) gene for a number of proteins are shown in Figure 1. Similar to splicing factors, RBPs that are regulate mRNA stability are also frequently found to regulate their own and each other’s expression. For example, the AU-rich element-binding protein (ARE-BP) Tristetraproline (TTP) negatively regulates its own expression by binding to an ARE in the 3′-UTR of its own mRNA [75]. Similary, in Drosophila, another neuronal ARE-BP Elav has been suggested to regulate its own mRNA by binding to ARE elements in its 3′-UTR [76]. Cross-regulatory RNP interactions have also been identified. For example, HuR stabilizes TIA-1 mRNA and upregulates TIA-1 expression while TIAR suppresses TIA-1 expression [77].

Figure 1:
Footprints of several RBPs (CDS: coding sequence) on the p27 (Kip1) transcript are shown. Information on RBP-binding sites were obtained from 4-thiouridine CLIP performed on individual RBPs (see reference [96] and unpublished data from M.Z. lab). Overlapping ...

Experiments in which RBP-bound mRNAs were identified after RBP immunoprecipitation (RIP-Chip) indicated that RBPs typically bind a large number of mRNAs. Moreover, they revealed that the targets of an RBP are not a random subset of the transcripts that are expressed in the cell, but rather functionally related transcripts, this being the essence of the ‘regulon theory’ in eukaryotes (similar to operon model in prokaryotes) [78]. A recent example consistent with this model is that of the RBP Musashi-1, whose knockdown results in the upregulation of a significant number of apoptosis-related genes, encoding components of SGs [79].


Given the multitude of targets and functions of RBPs, various methods have been developed over the years to investigate their binding specificity. The electrophoretic mobility shift assay is a technique that is used currently to detect interactions between proteins and nucleic acids in vitro [80, 81]. It is based on the principle that an oligonucleotide migrates slower in an electric field when bound by the protein and it is of course suited only to document that a given RBP binds a given oligonucleotide sequence. Another in vitro procedure called Systematic Evolution of Ligands by Exponential Enrichment has been widely used to identify aptamers (ssDNA or RNA) that specifically bind to a protein of interest starting from a large library of randomly generated oligonucleotides [82]. Through multiple rounds of selection, purification under stringent conditions and amplification, high-affinity interactors can be selectively enriched. It has been found however that the high-affinity targets obtained in vitro are frequently not the most specific. The recently developed RNAcompete method uses a single binding reaction to determine in vitro the affinities an RBP for a complete set of k-mers (oligonucleotides) that are presented in structured and unstructured RNA contexts. This method has been applied to various RBPs and the results have been shown to be consistent with previous in vivo studies [83]. One drawback of these in vitro studies is that they employ recombinant proteins which limits their use when the proteins of interest are unstable, difficult to express or purify to homogeniety.

Genome-wide microarray analyses upon RBP knockdown or overexpression may identify in vivo targets of RBPs. However, such analyses profile global steady state levels of mRNAs which poorly correlate with the cellular protein levels [84], and it is therefore unclear how informative such analyses are. RNA immunoprecipitation followed by microarray-based identification of protein-bound RNAs (RIP-Chip) [85–89] has been widely used to identify in vivo targets of RBPs. Although this method has the advantage of identifying interactions occurring in vivo, the RNA–protein complex cannot be washed stringently, thereby having the potential of isolating many false positive targets. Also, because with this approach one isolates large RNA molecules, uncovering the sequence or structure specificity of the protein from RIP-Chip data requires complex computational analyses. To avoid artifacts arising from interactions that occur after cell lysis, methods that rely on cross-linking RNA–protein complexes in vivo by ultraviolet (UV) irradiation (at 254 nm) have been proposed [90]. Although such methods have been in use for decades, advances in technology such as high-throughput sequencing and higher computational power have dramatically increased the power of the procedure.

The cross-linking and immunoprecipitation assay (CLIP) exploits covalent protein–nucleic acid cross-linking to stringently purify complexes of RNAs with a specific RBP. Moreover, nuclease digestion of RNA sequence stretches that are not protected by the RBP enables the isolation of binding sites at very high resolution [91, 92]. The protected RNA fragments are then ligated to adapters at both 5′- and 3′–end, converted into cDNA and PCR-amplified. Initial CLIP experiments employed sequencing of cloned PCR-amplified fragments in a suitable vector. Based on the location of YCAY sequence motifs with respect to intron and exon boundaries, this method generated a genome-wide RNA map predicting the pattern of NOVA-dependent splicing in the brain [91]. A more recent version of the protocol called HITS-CLIP (high-throughput sequencing of RNA isolated by crosslinking and immunoprecipitation) has provided a more comprehensive picture of the function of this nervous system-specific RBP, revealing the role of NOVA in alternative polyadenylation of transcripts in the brain [93]. HITS-CLIP has further been used to identify functional miRNA–mRNA interaction sites [94] and, in combination with custom microarray-based analyses, to identify specific pathways targeted by RBPs [95]. Though highly successful, this method has a few limitations. UV irradiation at shorter wavelengths (254 nm) may introduce nucleic acids breaks as well as modifications in the nucleotides that may hamper accurate mapping of the cDNAs to the corresponding genomic loci. Given that the binding sites of RBPs are usually very short, <10 nt [14], the nuclease digestion has to be very precisely monitored and optimized. Overdigestion will lead to RNA fragments that are too short to be unambiguously mapped to the genome, while underdigestion to fragments that are too long to be sequenced in their entirety yielding reads that do not contain interaction sites.

Recently, another promising variant of this method called photoactivatable-ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP) has been proposed. PAR-CLIP uses photoreactive analogs of ribonucleosides to crosslink RNA–protein complexes in vivo [96]. When 4-thiouridine is used as a uridine analog, crosslinked 4-thiouridines are subsequently reverse transcribed and PCR amplified as cytosines. Loci to which many reads with thymidine to cytosine mutations are mapped, represent in all likelihood binding sites to which the protein has been crosslinked. The crosslink-diagnostic mutations enable one to pinpoint RBP-binding sites at site resolution. An even more specific mutational pattern has been observed when PAR-CLIP was applied to Argonaute proteins: the location of thymidine-to-cytosine mutation was very frequently found immediately upstream of the region of complementarity between the mRNA and the 5′-end (known as the seed region) of an abundantly expressed miRNA [96]. Using this information, one can identify with accuracy the miRNA that is involved in regulating a particular transcript at a specific site. Another advantage of PAR-CLIP is that the photoreactive analogs can be crosslinked at higher wavelengths (365 nm) which are less damaging to the nucleic acids. While use of 4-thiouridine is suitable for RBPs that have uridines in their binding motif or very close to it, it is not suitable for RBPs that bind to uridine depleted stretches. For such cases, 6-thioguanosine has been proposed as an alternative [96]. Preliminary studies suggest that 6-thioguanosine crosslinking also yields diagnostic guanosine to adenosine mutations that could be used to accurately pin-point the location of binding sites. However, this change appears to occur less frequently than the thymidine-to-cytosine transition which is observed when 4-thiouridine is used. In addition, earlier reports indicated that 6-thio guanosine is toxic to cells and prevents RNA and protein synthesis beyond certain concentrations [97], hence the incorporation frequency of 6-thioguanosine in the RNA cannot be as high as that of 4-thiouridine. Also, the crosslinking efficiency of 6-thioguanosine is several folds lower than that of 4-thiouridine [96] making its use more limited compared to that of 4-thiouridine. Finally, incorporation of photoreactive analogs in animal tissues remains a daunting task. Therefore, this technique has so far been be used in analyses of cell lines.

In yet another promising development in this field, a CLIP variant known as iCLIP exploits the propensity of reverse transcriptase to terminate at a nucleotide remains modified after the protein that was crosslinked to it was digested away [98]. Sequence reads obtained from iCLIP are thus expected to start exactly at the site of crosslink enabling identification of RBP-binding sites at nucleotide resolution. It has been argued that with previous methods, many bona fide binding sites would have caused the reverse transcriptase to stop before reaching the 5′-adaptor, yielding aborted cDNAs that would be selectively lost during the PCR cycles due to the lack of the annealing site for the sequencing primer. Further studies will be necessary to establish to what extent this occurs, because at least in PAR-CLIP, large numbers of sequence reads with crosslink diagnostic mutations inside the reads have been obtained.

Taken together, all the CLIP variants have been employed with demonstrable success in the transcriptome-wide identification of binding sites for numerous RBPs. The improvements in the technique are enabling increasingly many groups to apply it to a variety of proteins [91, 93, 96, 98–100], and we expect that in the not-too-distant future, catalogs of RBP-binding sites will be available, similar to resources that have been constructed for TFs. However, additional work will be needded to establish protocols that would allow one to obtain quantitative data on RBP–RNA interactions in vivo. It is clear that the choices made in the many steps of the CLIP protocol can lead to the identification of specific subsets of binding sites. For example, in order to identify the position of the crosslink with methods other than iCLIP, the sequence read has to cover the RBP-binding site. This is only ensured when the length of the fragments that are subjected to deep sequencing is relatively short, and thus the nuclease digestion has to be carried out to the extent that little beyond the RBP-protected fragment is left intact. The complete digestion products will likely be too short to be unambigously mapped, and almost complete digestion is very difficult to control. As alternatives, one may use incomplete digestion followed by computational analyses to identify the short-binding sites presumably located at some distance form the suquenced reads, or digestion by a nuclease that does not cleave between all dinucleotide pairs. In fact, of the nucleases that have been employed in CLIP, only RNAse I does not have a preferred nucleotide for cleavage, while the others have specific preferences. RNAse T1 cleaves 3′ of guanosine [101] and RNAse A cleaves only after cytosine and uridine [102]. Another commonly used nuclease, micrococcal nuclease cleaves preferentially 5′ of an adenosine or thymidine [103]. Thus, the choice of nuclease may influence the subset of sites that is isolated through CLIP, enriching for those that do not contain optimal nuclease cleavage sites in the immediate vicinity of the RBP binding site. It is also known that 254 nm UV irradiation preferentially crosslinks pyrimidines [104] with crosslinks normally not occuring between base paired regions [105]. Thus, one may expect that binding sites that are embedded in pyrimidine-rich regions will be preferentially enriched in the 254 nm CLIP data. Overall, a large number of techniques to decipher RNA–protein interactions is available today, each having its own set of advantages and limitations, which need to be weighted in relationship to the problem at hand.

Rapid advancements in the experimental methods have facilitated identification of RBP-binding sites at a transcriptome-wide level and lead to fascinating insights into the intricate regulatory networks involving RBPs. Although additional work will be needed to increase the accuracy of these methods, it should soon be possible to obtain comprehensive maps of RNA-protein interactome in a variety of cell types and in a variety of conditions.


Availability of large-scale data sets of binding sites for individual RBPs opens the door for detailed computational studies aiming to uncover the factors that govern the specificity of RBP–RNA interactions. Numerous methods are available for inferring binding motifs in nucleic acid sequences. Though most of the methods have been in fact developed in the context of inferring the specificity and binding sites of TFs these methods can readily be applied to RBPs. A relatively recent description of the general framework of sequence motif finding can be found in ref. [106]. Depending on the available input data, the problem of inferring sequence motifs that are bound by RBPs comes in various flavors, a typical setting being the following. Through a high-throughput experimental method (for e.g. RIP-Chip) one has isolated a large number of putative targets of the RBP of interest and the question is to find the location of the RBP-binding sites in these targets as well as the sequence motif that is recognized by the RBP. In this case, one can employ one of the available motif finding tools [107–110] to simultaneously infer the binding specificity of the protein and the location of the binding sites in the targets. The Phylogibbs algorithm [108, 109] can take into account not only individual sequences, but also multiple alignments of orthologous regions from a set of species, in order to identify sequence motifs that are both over-represented in the input sequences, and evolutionarily conserved. Motif finders have been successfully used to identify binding motifs of RBPs. For example, Phylogibbs was used to recover the known binding motifs of the Pumilio and Quaking proteins and to discover the specificity of Insulin growth factor 2-binding proteins from PAR-CLIP data [96]. It has been argued, however, that a further increase in specificity of binding-site identification can be obtained by taking into account clustering of sequence elements [96, 111]. A rationale could be the multi-domain structure of RBPs, each domain binding a specific sequence element in the RNA. Methods that would take advantage of this property remain to be developed, though for the determination of binding motifs in which deletions and insertions are allowed in the presumed binding site, one can employ for example the glam2 [112] motif finder.

A further complication for the prediction of RBP-binding sites in contrast to binding sites of TFs is that the RNAs are not double helical, but form complex structures. Many RBPs bind only to RNAs that are in a single-stranded conformation while other recognize specific structural elements. For these reasons, one expects that methods that consider both the sequence and structure of the putative targets should have increased accuracy. A simple way of taking into account RNA secondary structure is to extract from sequence-based predictions of RBP-binding sites those that are also predicted to be located in relatively accessible regions. Tafer et al. [113] and Hausser et al. [73] studied the effect of structural accessibility of target sites in the context of miRNA-binding sites and Li et al. [114] in the context of RBPs. Indeed, they found that functional miRNA- and RBP-binding sites are located in regions that form less stable secondary structures. A more sophisticated approach is to consider the secondary structure in the prediction of RBP-binding sites. For instance, in the memeris program [115] the estimated probability of a given region assuming a single-stranded conformation features as a prior probability that a sequence-specified binding site is located in that region of the sequence.

Another set of tools attempts to identify structural elements that are important for the interaction with the RBP. For instance, Lopez de Silanes et al. [116] used a stochastic context-free grammar model and inferred that the TIA-1 protein appears to recognize U-rich elements in the context of bent stem–loop structures. Rabani et al. [117] developed the rnapromo tool to identify over-represented structural motifs among the predicted secondary structures of a set of input RNA sequences assumed to have a motif in common. To what extent-specific secondary structure motifs are important for the recognition of binding sites by RBPs remains to be determined. Structural motifs that are required for the recognition by specific proteins are known [118], though Ray et al. [83] found that most of the nine proteins for which binding affinities to a large number of oligonucleotides were measured do not appear to require that their target sites are presented in a particular structural configuration. As we learn more about the dynamic associations between RBPs and RNAs and as the accuracy of secondary-structure prediction algorithms improve, predictive models of the accessibility and occupancy of individual binding sites in the RNA in specific cellular conditions will become more common. As a few examples, we mention the attempts to predict the effect of RNA secondary structures on RNA–ligand binding [119] or the impact of protein binding on the secondary structure of ssRNAs [120]. Finally, computational prediction of RBP–RNA interaction has also been approached from a computational structural biology point of view. Zheng et al. [121] designed a distance-dependent statistical potential function to predict RNA sequences that would be recognized by various RRM and KH homology domains, as well as to predict the affinities of interaction. It would be extremely interesting to study the relationship between these predictions, the affinities of RBPs for oligonucleotides that were estimated by RNAcompete [83] and in vivo binding data obtained through CLIP. The challenge now lies in integrating these diverse datasets to derive regulatory codes that can predict tissue-specific regulation of gene expression. Barash et al. [122] have illustrated the importance of combining multiple features like the occurence of motifs for various splicing factors and other regulatory sequences, to unravel the splicing code which ultimately explains tissue specific alternative splicing events [122]. Such studies are within reach and would greatly improve our understanding of the combinatorial regulation by RBPs.


In spite of the fact that RBPs have been extensively studied for several decades, it is clear that there is much left to learn about their roles and interactions in the regulation of gene expression. Several novel RBDs have been recently discovered that remain to be characterized (see for e.g. [123, 124]). Technological advances enable characterization of RBP-binding sites at nucleotide resolution opening numerous avenues of investigation. Novel functions are being identified for even well-studied RBPs. Dynamical changes in the sets of targets of individual RBPs in response to the cellular state and external stimuli are beginning to be characterized and dynamic interplays of multiple RBPs on the same target are being identified. The aim in the long run is to construct a detailed, temporally resolved map of RBP–RNA interactions that we expected will reveal novel pathogenic mechanisms and will aid in the development of more specifically tailored and effective therapeutic agents.

Key Points

  • RBPs play a crucial role in post-transcriptional control of gene expression.
  • Combinatorial arrangement of various RBDs along with other functional domains impart structural and functional diversity to RBPs that enables them to modiy and regulate virtually every step of target RNA metabolism and function.
  • The expression level and sub-cellular localization of RBPs is precisely controlled by complex regulatory and autoregulatory networks.
  • Individual RBPs have hundreds of targets and precise control of gene expression occurs due to specific spatio-temporal association of multiple RBPs with their target RNA.
  • Several computational and experimental tools that are now available to study global protein–RNA interactions will provide us with invaluable insights into principles of gene regulation.


We thank Lukasz Jaskiewicz for critical reading of the article and for useful suggestions. S.K. is supported by an EMBO LTF-fellowship and S.L. by a Swiss National Foundation grant (number CRSII3_127454/1) to M.Z.



Shivendra Kishore is an experimental postdoctoral fellow.


Sandra Luber is a computational postdoctoral researcher in the group of Mihaela Zavolan at the Biozentrum, University of Basel.


Mihaela Zavolan is an associate professor at the Biozentrum, University of Basel. She is an MD and holds a PhD degree in Computer Science. Her group combines computational and experimental techniques to study post-transcriptional gene regulation.


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