RNAmap2D - calculation, visualization and analysis of contact and distance maps for RNA and protein-RNA complex structures

1D2DSimScore: A novel method for comparing contacts in biomacromolecules and their complexes

The biologically relevant structures of proteins and nucleic acids and their complexes are dynamic. They include a combination of regions ranging from rigid structural segments to structural switches to regions that are almost always disordered, which interact with each other in various ways. Comparing conformational changes and variation in contacts between different conformational states is essential to understand the biological functions of proteins, nucleic acids, and their complexes. Here, we describe a new computational tool, 1D2DSimScore, for comparing contacts and contact interfaces in all kinds of macromolecules and macromolecular complexes, including proteins, nucleic acids, and other molecules. 1D2DSimScore can be used to compare structural features of macromolecular models between alternative structures obtained in a particular experiment or to score various predictions against a defined "ideal" reference structure. Comparisons at the level of contacts are particularly useful for flexible molecules, for which comparisons in 3D that require rigid-body superpositions are difficult, and in biological systems where the formation of specific inter-residue contacts is more relevant for the biological function than the maintenance of a specific global 3D structure. Similarity/dissimilarity scores calculated by 1D2DSimScore can be used to complement scores describing 3D structural similarity measures calculated by the existing tools.

YAP Circular RNA, circYap, Attenuates Cardiac Fibrosis via Binding with Tropomyosin-4 and Gamma-Actin Decreasing Actin Polymerization

Cardiac fibrosis is a common pathological feature of cardiac hypertrophy. This study was designed to investigate a novel function of Yes-associated protein (YAP) circular RNA, circYap, in modulating cardiac fibrosis and the underlying mechanisms. By circular RNA sequencing, we found that three out of fifteen reported circYap isoforms were expressed in nine human heart tissues, with the isoform hsa_circ_0002320 being the highest. The levels of this isoform in the hearts of patients with cardiac hypertrophy were found to be significantly decreased. In the pressure overload mouse model, the levels of circYap were reduced in mouse hearts with transverse aortic constriction (TAC). Upon circYap plasmid injection, the cardiac fibrosis was attenuated, and the heart function was improved along with the elevation of cardiac circYap levels in TAC mice. Tropomyosin-4 (TMP4) and gamma-actin (ACTG) were identified to bind with circYap in cardiac cells and mouse heart tissues. Such bindings led to an increased TPM4 interaction with ACTG, resulting in the inhibition of actin polymerization and the following fibrosis. Collectively, our study uncovered a novel molecule that could regulate cardiac remodeling during cardiac fibrosis and implicated a new function of circular RNA. This process may be targeted for future cardio-therapy.

Evaluating DCA-based method performances for RNA contact prediction by a well-curated data set

RNA molecules play many pivotal roles in a cell that are still not fully understood. Any detailed understanding of RNA function requires knowledge of its three-dimensional structure, yet experimental RNA structure resolution remains demanding. Recent advances in sequencing provide unprecedented amounts of sequence data that can be statistically analyzed by methods such as direct coupling analysis (DCA) to determine spatial proximity or contacts of specific nucleic acid pairs, which improve the quality of structure prediction. To quantify this structure prediction improvement, we here present a well curated data set of about 70 RNA structures of high resolution and compare different nucleotide-nucleotide contact prediction methods available in the literature. We observe only minor differences between the performances of the different methods. Moreover, we discuss how robust these predictions are for different contact definitions and how strongly they depend on procedures used to curate and align the families of homologous RNA sequences.

The circular RNA circ-Ccnb1 dissociates Ccnb1/Cdk1 complex suppressing cell invasion and tumorigenesis

Circular RNAs represent a large class of non-coding RNAs that are extensively expressed in mammals. However, the functions of circular RNAs are largely unknown. We recently reported that the circular RNA circ-Ccnb1 could bind with H2AX in p53 mutant cells and suppressed mutant p53 in tumor progression. Here we found that circ-Ccnb1 could interact with both Ccnb1 and Cdk1 proteins. Normally, Ccnb1 and Cdk1 proteins form a complex, allowing Ccnb1 to function as an all-or-none switch for cell mitosis. The interaction of circ-Ccnb1 with Ccnb1 and Cdk1 proteins dissociated the formation of Ccnb1-Cdk1 complex, by forming a large complex containing circ-Ccnb1, Ccnb1 and Cdk1. Formation of this large complex may occur in cytosol and nuclei, and Ccnb1 loses its roles in enhancing cell migration, invasion, proliferation and survival. In vivo, ectopic delivery of circ-Ccnb1 inhibited tumor growth and extended mouse viability. These results have added another layer of mechanisms for circ-Ccnb1 to regulate tumor progression in vitro and in vivo.

Enhanced breast cancer progression by mutant p53 is inhibited by the circular RNA circ-Ccnb1

TP53 mutations occur in many different types of cancers that produce mutant p53 proteins. The mutant p53 proteins have lost wild-type p53 activity and gained new functions that contribute to malignant tumor progression. Different p53 mutations create distinct profiles in loss of wild-type p53 activity and gain of functions. Targeting the consequences generated by the great number of p53 mutations would be extremely complex. Therefore, in this study we used a workaround and took advantage of the fact that mutant p53 cannot bind H2AX. Using this, we developed a new approach to repress the acquisition of mutant p53 functions. We show here that the delivery of a circular RNA circ-Ccnb1 inhibited the function of three p53 mutations. By microarray analysis and real-time PCR, we detected decreased circ-Ccnb1 expression levels in patients bearing breast carcinoma. Ectopic delivery of circ-Ccnb1 inhibited tumor growth and extended mouse viability. Using proteomics, we found that circ-Ccnb1 precipitated p53 in p53 wild-type cells, but instead precipitated Bclaf1 in p53 mutant cells. Further experiments showed that H2AX serves as a bridge, linking the interaction of circ-Ccnb1 and wild-type p53, thus allowing Bclaf1 to bind Bcl2 resulting in cell survival. In the p53 mutant cells, circ-Ccnb1 formed a complex with H2AX and Bclaf1, resulting in the induction of cell death. We found that this occurred in three p53 mutations. These results shed light on the possible development of new approaches to inhibit the malignancy of p53 mutations.

Systematic Analysis of the Binding Surfaces between tRNAs and Their Respective Aminoacyl tRNA Synthetase Based on Structural and Evolutionary Data

To determine the mechanism underlying the flow of genetic information, it is important to understand the relationship between a tRNA and its binding enzyme, a member of the aminoacyl-tRNA synthetase (aaRS) family. We have developed a novel method to project the interacting regions of tRNA-aaRS complexes, obtained from their three-dimensional structures, onto two-dimensional space. The interacting surface between each tRNA and its aaRS was successfully identified by determining these interactions with an atomic distance threshold of 3.3 Å. We analyzed their interactions, using 60 mainly bacterial and eukaryotic tRNA-aaRS complexes, and showed that the tRNA sequence regions that interacted most strongly with each aaRS are the anticodon loop and the CCA terminal region, followed by the D-stem. A sequence conservation analysis of the canonical tRNAs was conducted in 83 bacterial, 182 archaeal, and 150 eukaryotic species. Our results show that the three tRNA regions that interact with the aaRS and two additional loop regions (D-loop and TΨC-loop) known to be important for formation of the tRNA L-shaped structure are broadly conserved. We also found sequence conservations near the tRNA discriminator in the Bacteria and Archaea, and an enormous number of noncanonical tRNAs in the Eukaryotes. This is the first global view of tRNA evolution based on its structure and an unprecedented number of sequence data.

Induction of tumor apoptosis through a circular RNA enhancing Foxo3 activity

Circular RNAs are a class of non-coding RNAs that are receiving extensive attention. Despite reports showing circular RNAs acting as microRNA sponges, the biological functions of circular RNAs remain largely unknown. We show that in patient tumor samples and in a panel of cancer cells, circ-Foxo3 was minimally expressed. Interestingly, during cancer cell apoptosis, the expression of circ-Foxo3 was found to be significantly increased. We found that silencing endogenous circ-Foxo3 enhanced cell viability, whereas ectopic expression of circ-Foxo3 triggered stress-induced apoptosis and inhibited the growth of tumor xenografts. Also, expression of circ-Foxo3 increased Foxo3 protein levels but repressed p53 levels. By binding to both, circ-Foxo3 promoted MDM2-induced p53 ubiquitination and subsequent degradation, resulting in an overall decrease of p53. With low binding affinity to Foxo3 protein, circ-Foxo3 prevented MDM2 from inducing Foxo3 ubiquitination and degradation, resulting in increased levels of Foxo3 protein. As a result, cell apoptosis was induced by upregulation of the Foxo3 downstream target PUMA.

Exploring Alternative RNA Structure Sets Using MC-Flashfold and db2cm

We created an accelerated version of MC-Fold called MC-Flashfold that allows us to compute large numbers of competing secondary structures including noncanonical base pairs. We visualize the base pairs in these sets using high quality intuitive dot plots and arc plots. Our new tools allow us to explore RNA dynamics by visualizing the competing structures in free energy bands. Here we describe how to use these tools to generate dot plots that reveal the postulated anti-terminator stem in the E. coli trp operon leader sequence. These plots show the anti-terminator hairpin loop during transcription and as a minor population of the full-length leader sequence. This is a case of switching RNA structure that had been originally postulated based on short dyad inverted repeats. Other switching RNA sequences can be analyzed by using our method.

Assembly and analysis of eukaryotic Argonaute-RNA complexes in microRNA-target recognition

Experimental studies have uncovered a variety of microRNA (miRNA)–target duplex structures that include perfect, imperfect and seedless duplexes. However, non-canonical binding modes from imperfect/seedless duplexes are not well predicted by computational approaches, which rely primarily on sequence and secondary structural features, nor have their tertiary structures been characterized because solved structures to date are limited to near perfect, straight duplexes in Argonautes (Agos). Here, we use structural modeling to examine the role of Ago dynamics in assembling viable eukaryotic miRNA-induced silencing complexes (miRISCs). We show that combinations of low-frequency, global modes of motion of Ago domains are required to accommodate RNA duplexes in model human and C. elegans Ago structures. Models of viable miRISCs imply that Ago adopts variable conformations at distinct target sites that generate distorted, imperfect miRNA-target duplexes. Ago's ability to accommodate a duplex is dependent on the region where structural distortions occur: distortions in solvent-exposed seed and 3′-end regions are less likely to produce steric clashes than those in the central duplex region. Energetic analyses of assembled miRISCs indicate that target recognition is also driven by favorable Ago-duplex interactions. Such structural insights into Ago loading and target recognition mechanisms may provide a more accurate assessment of miRNA function.

MINT: software to identify motifs and short-range interactions in trajectories of nucleic acids

Structural biology experiments and structure prediction tools have provided many high-resolution three-dimensional structures of nucleic acids. Also, molecular dynamics force field parameters have been adapted to simulating charged and flexible nucleic acid structures on microsecond time scales. Therefore, we can generate the dynamics of DNA or RNA molecules, but we still lack adequate tools for the analysis of the resulting huge amounts of data. We present MINT (Motif Identifier for Nucleic acids Trajectory) — an automatic tool for analyzing three-dimensional structures of RNA and DNA, and their full-atom molecular dynamics trajectories or other conformation sets (e.g. X-ray or nuclear magnetic resonance-derived structures). For each RNA or DNA conformation MINT determines the hydrogen bonding network resolving the base pairing patterns, identifies secondary structure motifs (helices, junctions, loops, etc.) and pseudoknots. MINT also estimates the energy of stacking and phosphate anion-base interactions. For many conformations, as in a molecular dynamics trajectory, MINT provides averages of the above structural and energetic features and their evolution. We show MINT functionality based on all-atom explicit solvent molecular dynamics trajectory of the 30S ribosomal subunit.

Structure based approach for understanding organism specific recognition of protein-RNA complexes

Background

Protein-RNA interactions perform diverse functions within the cell. Understanding the recognition mechanism of protein-RNA complexes has been a challenging task in molecular and computational biology. In earlier works, the recognition mechanisms have been studied for a specific complex or using a set of non–redundant complexes. In this work, we have constructed 18 sets of same protein-RNA complexes belonging to different organisms from Protein Data Bank (PDB). The similarities and differences in each set of complexes have been revealed in terms of various sequence and structure based features such as root mean square deviation, sequence homology, propensity of binding site residues, variance, conservation at binding sites, binding segments, binding motifs of amino acid residues and nucleotides, preferred amino acid-nucleotide pairs and influence of neighboring residues for binding.

Results

We found that the proteins of mesophilic organisms have more number of binding sites than thermophiles and the binding propensities of amino acid residues are distinct in E. coli, H. sapiens, S. cerevisiae, thermophiles and archaea. Proteins prefer to bind with RNA using a single residue segment in all the organisms while RNA prefers to use a stretch of up to six nucleotides for binding with proteins. We have developed amino acid residue-nucleotide pair potentials for different organisms, which could be used for predicting the binding specificity. Further, molecular dynamics simulation studies on aspartyl tRNA synthetase complexed with aspartyl tRNA showed specific modes of recognition in E. coli, T. thermophilus and S. cerevisiae.

Conclusion

Based on structural analysis and molecular dynamics simulations we suggest that the mode of recognition depends on the type of the organism in a protein-RNA complex.

Reviewers

This article was reviewed by Sandor Pongor, Gajendra Raghava and Narayanaswamy Srinivasan.

Electronic supplementary material

The online version of this article (doi:10.1186/s13062-015-0039-8) contains supplementary material, which is available to authorized users.

Computational modeling of protein-RNA complex structures

Protein-RNA interactions play fundamental roles in many biological processes, such as regulation of gene expression, RNA splicing, and protein synthesis. The understanding of these processes improves as new structures of protein-RNA complexes are solved and the molecular details of interactions analyzed. However, experimental determination of protein-RNA complex structures by high-resolution methods is tedious and difficult. Therefore, studies on protein-RNA recognition and complex formation present major technical challenges for macromolecular structural biology. Alternatively, protein-RNA interactions can be predicted by computational methods. Although less accurate than experimental measurements, theoretical models of macromolecular structures can be sufficiently accurate to prompt functional hypotheses and guide e.g. identification of important amino acid or nucleotide residues. In this article we present an overview of strategies and methods for computational modeling of protein-RNA complexes, including software developed in our laboratory, and illustrate it with practical examples of structural predictions.