Combinatorics of RNA–RNA interaction

RNA–RNA binding is an important phenomenon observed for many classes of non-coding RNAs and plays a crucial role in a number of regulatory processes. Recently several MFE folding algorithms for predicting the joint structure of two interacting RNA molecules have been proposed. Here joint structure means that in a diagram representation the intramolecular bonds of each partner are pseudoknot-free, that the intermolecular binding pairs are noncrossing, and that there is no so-called “zigzag” configuration. This paper presents the combinatorics of RNA interaction structures including their generating function, singularity analysis as well as explicit recurrence relations. In particular, our results imply simple asymptotic formulas for the number of joint structures.     

Hydration of protein–RNA recognition sites

We investigate the role of water molecules in 89 protein–RNA complexes taken from the Protein Data Bank. Those with tRNA and single-stranded RNA are less hydrated than with duplex or ribosomal proteins. Protein–RNA interfaces are hydrated less than protein–DNA interfaces, but more than protein–protein interfaces. Majority of the waters at protein–RNA interfaces makes multiple H-bonds; however, a fraction do not make any. Those making H-bonds have preferences for the polar groups of RNA than its partner protein. The spatial distribution of waters makes interfaces with ribosomal proteins and single-stranded RNA relatively ‘dry’ than interfaces with tRNA and duplex RNA. In contrast to protein–DNA interfaces, mainly due to the presence of the 2′OH, the ribose in protein–RNA interfaces is hydrated more than the phosphate or the bases. The minor groove in protein–RNA interfaces is hydrated more than the major groove, while in protein–DNA interfaces it is reverse. The strands make the highest number of water-mediated H-bonds per unit interface area followed by the helices and the non-regular structures. The preserved waters at protein–RNA interfaces make higher number of H-bonds than the other waters. Preserved waters contribute toward the affinity in protein–RNA recognition and should be carefully treated while engineering protein–RNA interfaces.     

Recognition of viral RNA stem–loops by the tandem double-stranded RNA binding domains of PKR

In humans, the double-stranded RNA (dsRNA)-activated protein kinase (PKR) is expressed in late stages of the innate immune response to viral infection by the interferon pathway. PKR consists of tandem dsRNA binding motifs (dsRBMs) connected via a flexible linker to a Ser/Thr kinase domain. Upon interaction with viral dsRNA, PKR is converted into a catalytically active enzyme capable of phosphorylating a number of target proteins that often results in host cell translational repression. A number of high-resolution structural studies involving individual dsRBMs from proteins other than PKR have highlighted the key features required for interaction with perfectly duplexed RNA substrates. However, viral dsRNA molecules are highly structured and often contain deviations from perfect A-form RNA helices. By use of small-angle X-ray scattering (SAXS), we present solution conformations of the tandem dsRBMs of PKR in complex with two imperfectly base-paired viral dsRNA stem–loops; HIV-1 TAR and adenovirus VA_I-AS. Both individual components and complexes were purified by size exclusion chromatography and characterized by dynamic light scattering at multiple concentrations to ensure monodispersity. SAXS ab initio solution conformations of the individual components and RNA–protein complexes were determined and highlight the potential of PKR to interact with both stem and loop regions of the RNA. Excellent agreement between experimental and model-based hydrodynamic parameter determination heightens our confidence in the obtained models. Taken together, these data support and provide a framework for the existing biochemical data regarding the tolerance of imperfectly base-paired viral dsRNA by PKR.     

Recognition of HIV-TAR RNA using neomycin–benzimidazole conjugates

Synthesis of a novel class of compounds and their biophysical studies with TAR-RNA are presented. The synthesis of these compounds was achieved by conjugating neomycin, an aminoglycoside, with benzimidazoles modeled from a B-DNA minor groove binder, Hoechst 33258. The neomycin–benzimidazole conjugates have varying linkers that connect the benzimidazole and neomycin units. The linkers of varying length (5–23 atoms) in these conjugates contain one to three triazole units. The UV thermal denaturation experiments showed that the conjugates resulted in greater stabilization of the TAR-RNA than either neomycin or benzimidazole used in the synthesis of conjugates. These results were corroborated by the FID displacement and tat-TAR inhibition assays. The binding of ligands to the TAR-RNA is affected by the length and composition of the linker. Our results show that increasing the number of triazole groups and the linker length in these compounds have diminishing effect on the binding to TAR-RNA. Compounds that have shorter linker length and fewer triazole units in the linker displayed increased affinity towards the TAR RNA.     

LigandRNA: computational predictor of RNA–ligand interactions

RNA molecules have recently become attractive as potential drug targets due to the increased awareness of their importance in key biological processes. The increase of the number of experimentally determined RNA 3D structures enabled structure-based searches for small molecules that can specifically bind to defined sites in RNA molecules, thereby blocking or otherwise modulating their function. However, as of yet, computational methods for structure-based docking of small molecule ligands to RNA molecules are not as well established as analogous methods for protein-ligand docking. This motivated us to create LigandRNA, a scoring function for the prediction of RNA–small molecule interactions. Our method employs a grid-based algorithm and a knowledge-based potential derived from ligand-binding sites in the experimentally solved RNA–ligand complexes. As an input, LigandRNA takes an RNA receptor file and a file with ligand poses. As an output, it returns a ranking of the poses according to their score. The predictive power of LigandRNA favorably compares to five other publicly available methods. We found that the combination of LigandRNA and Dock6 into a “meta-predictor” leads to further improvement in the identification of near-native ligand poses. The LigandRNA program is available free of charge as a web server at http://ligandrna.genesilico.pl.     

Identification of antisense RNA stem–loops that inhibit RNA–protein interactions using a bacterial reporter system

Many well-characterized examples of antisense RNAs from prokaryotic systems involve hybridization of the looped regions of stem–loop RNAs, presumably due to the high thermodynamic stability of the resulting loop–loop and loop–linear interactions. In this study, the identification of RNA stem–loops that inhibit U1A protein binding to the hpII RNA through RNA–RNA interactions was attempted using a bacterial reporter system based on phage λ N-mediated antitermination. As a result, loop sequences possessing 7–8 base complementarity to the 5′ region of the boxA element important for functional antitermination complex formation, but not the U1 hpII loop, were identified. In vitro and in vivo mutational analysis strongly suggested that the selected loop sequences were binding to the boxA region, and that the structure of the antisense stem–loop was important for optimal inhibitory activity. Next, in an attempt to demonstrate the ability to inhibit the interaction between the U1A protein and the hpII RNA, the rational design of an RNA stem–loop that inhibits U1A-binding to a modified hpII was carried out. Moderate inhibitory activity was observed, showing that it is possible to design and select antisense RNA stem–loops that disrupt various types of RNA–protein interactions.     

Quantifying sequence and structural features of protein–RNA interactions

Increasing awareness of the importance of protein–RNA interactions has motivated many approaches to predict residue-level RNA binding sites in proteins based on sequence or structural characteristics. Sequence-based predictors are usually high in sensitivity but low in specificity; conversely structure-based predictors tend to have high specificity, but lower sensitivity. Here we quantified the contribution of both sequence- and structure-based features as indicators of RNA-binding propensity using a machine-learning approach. In order to capture structural information for proteins without a known structure, we used homology modeling to extract the relevant structural features. Several novel and modified features enhanced the accuracy of residue-level RNA-binding propensity beyond what has been reported previously, including by meta-prediction servers. These features include: hidden Markov model-based evolutionary conservation, surface deformations based on the Laplacian norm formalism, and relative solvent accessibility partitioned into backbone and side chain contributions. We constructed a web server called aaRNA that implements the proposed method and demonstrate its use in identifying putative RNA binding sites.     

Recognition of RNA duplex by a neomycin–Hoechst 33258 conjugate

DNA minor groove binding drugs such as Hoechst 33258 have been shown to bind to a number of RNA structures. Similarly, RNA binding ligands such as neomycin have been shown by us to bind to a number of A-form DNA structures. A neomycin–Hoechst 33258 conjugate was recently shown to bind B-DNA, where Hoechst exhibits high affinity for the minor groove of A/T tract DNA and neomycin docks into the major groove. Further studies now indicate that the Hoechst moiety of the conjugate can be driven to bind RNA duplex as a consequence of neomycin binding in the RNA major groove. This is the first example of Hoechst 33258 binding to RNA duplex not containing bulges or loop motifs.     

Structural dynamics of a single-stranded RNA–helix junction using NMR

Many regulatory RNAs contain long single strands (ssRNA) that adjoin secondary structural elements. Here, we use NMR spectroscopy to study the dynamic properties of a 12-nucleotide (nt) ssRNA tail derived from the prequeuosine riboswitch linked to the 3′ end of a 48-nt hairpin. Analysis of chemical shifts, NOE connectivity, ¹³C spin relaxation, and residual dipolar coupling data suggests that the first two residues (A25 and U26) in the ssRNA tail stack onto the adjacent helix and assume an ordered conformation. The following U26-A27 step marks the beginning of an A₆-tract and forms an acute pivot point for substantial motions within the tail, which increase toward the terminal end. Despite substantial internal motions, the ssRNA tail adopts, on average, an A-form helical conformation that is coaxial with the helix. Our results reveal a surprising degree of structural and dynamic complexity at the ssRNA–helix junction, which involves a fine balance between order and disorder that may facilitate efficient pseudoknot formation on ligand recognition.     

A structural perspective of the protein–RNA interactions involved in virus-induced RNA silencing and its suppression

Small RNAs, including small interfering RNAs (siRNAs), microRNAs (miRNAs) and Piwi-associated interfering RNAs (piRNAs), are powerful gene expression regulators. This RNA-mediated regulation results in sequence-specific inhibition of gene expression by translational repression and/or mRNA degradation. siRNAs and miRNAs are generated by RNase III enzymes and subsequently loaded into Argonaute protein, a key component of the RNA induced silencing complex (RISC), to form the core of the RNA silencing machinery. RNA silencing acts as an ancient cell defense system against molecular parasites, such as transgenes, viruses and transposons. RNA silencing also plays an important role in the control of development. In plants, RNA silencing serves as a potent antiviral defense system. In response, many viruses have developed strategies to suppress RNA silencing. The striking sequence diversity among viral suppressors suggests that different viral suppressors could target different components of the RNA silencing machinery at different steps in different suppressing modes. Significant progresses have been made in this field for the past 5 years on the basis of structural information derived from RNase III family proteins, Dicer fragments and homologs, Argonaute homologs and viral suppressors. In this paper, we will review the current progress on the understanding of molecular mechanisms of RNA silencing; highlight the structural principles determining the protein–RNA recognition events along the RNA silencing pathways and the suppression mechanisms displayed by viral suppressors.     

Sequence-specific cleavage of the RNA strand in DNA–RNA hybrids by the fusion of ribonuclease H with a zinc finger

Ribonucleases (RNases) are valuable tools applied in the analysis of RNA sequence, structure and function. Their substrate specificity is limited to recognition of single bases or distinct secondary structures in the substrate. Currently, there are no RNases available for purely sequence-dependent fragmentation of RNA. Here, we report the development of a new enzyme that cleaves the RNA strand in DNA–RNA hybrids 5 nt from a nonanucleotide recognition sequence. The enzyme was constructed by fusing two functionally independent domains, a RNase HI, that hydrolyzes RNA in DNA–RNA hybrids in processive and sequence-independent manner, and a zinc finger that recognizes a sequence in DNA–RNA hybrids. The optimization of the fusion enzyme’s specificity was guided by a structural model of the protein-substrate complex and involved a number of steps, including site-directed mutagenesis of the RNase moiety and optimization of the interdomain linker length. Methods for engineering zinc finger domains with new sequence specificities are readily available, making it feasible to acquire a library of RNases that recognize and cleave a variety of sequences, much like the commercially available assortment of restriction enzymes. Potentially, zinc finger-RNase HI fusions may, in addition to in vitro applications, be used in vivo for targeted RNA degradation.