The role of RNA sequence and structure in RNA--protein interactions

We investigate the sequence and structural properties of RNA-protein interaction sites in 211 RNA-protein chain pairs, the largest set of RNA-protein complexes analyzed to date. Statistical analysis confirms and extends earlier analyses made on smaller data sets. There are 24.6% of hydrogen bonds between RNA and protein that are nucleobase specific, indicating the importance of both nucleobase-specific and -nonspecific interactions. While there is no significant difference between RNA base frequencies in protein-binding and non-binding regions, distinct preferences for RNA bases, RNA structural states, protein residues, and protein secondary structure emerge when nucleobase-specific and -nonspecific interactions are considered separately. Guanine nucleobase and unpaired RNA structural states are significantly preferred in nucleobase-specific interactions; however, nonspecific interactions disfavor guanine, while still favoring unpaired RNA structural states. The opposite preferences of nucleobase-specific and -nonspecific interactions for guanine may explain discrepancies between earlier studies with regard to base preferences in RNA-protein interaction regions. Preferences for amino acid residues differ significantly between nucleobase-specific and -nonspecific interactions, with nonspecific interactions showing the expected bias towards positively charged residues. Irregular protein structures are strongly favored in interactions with the protein backbone, whereas there is little preference for specific protein secondary structure in either nucleobase-specific interaction or -nonspecific interaction. Overall, this study shows strong preferences for both RNA bases and RNA structural states in protein-RNA interactions, indicating their mutual importance in protein recognition.     

The interaction networks of structured RNAs

All pairwise interactions occurring between bases which could be detected in three-dimensional structures of crystallized RNA molecules are annotated on new planar diagrams. The diagrams attempt to map the underlying complex networks of base–base interactions and, especially, they aim at conveying key relationships between helical domains: co-axial stacking, bending and all Watson–Crick as well as non-Watson–Crick base pairs. Although such wiring diagrams cannot replace full stereographic images for correct spatial understanding and representation, they reveal structural similarities as well as the conserved patterns and distances between motifs which are present within the interaction networks of folded RNAs of similar or unrelated functions. Finally, the diagrams could help devising methods for meaningfully transforming RNA structures into graphs amenable to network analysis.     

Revealing unique properties of the ribosome using a network based analysis

The ribosome is a complex molecular machine that offers many potential sites for functional interference, therefore representing a major target for antibacterial drugs. The growing number of high-resolution structures of ribosomes from different organisms, in free form and in complex with various ligands, provides unique data for structural and comparative analyses of RNA structures. We model the ribosome structure as a network, where nucleotides are represented as nodes and intermolecular interactions as edges. As shown previously for proteins, we found that the major functional sites of the ribosome exhibit significantly high centrality measures. Specifically, we demonstrate that mutations that strongly affect ribosome function and assembly can be distinguished from mild mutations based on their network properties. Furthermore, we observed that closeness centrality of the rRNA nucleotides is highly conserved in the bacteria, suggesting the network representation as a comparative tool for the ribosome analysis. Finally, we suggest a global topology perspective to characterize functional sites and to reveal the unique properties of the ribosome.     

New metrics for comparing and assessing discrepancies between RNA 3D structures and models

To benchmark progress made in RNA three-dimensional modeling and assess newly developed techniques, reliable and meaningful comparison metrics and associated tools are necessary. Generally, the average root-mean-square deviations (RMSDs) are quoted. However, RMSD can be misleading since errors are spread over the whole molecule and do not account for the specificity of RNA base interactions. Here, we introduce two new metrics that are particularly suitable to RNAs: the deformation index and deformation profile. The deformation index is calibrated by the interaction network fidelity, which considers base–base-stacking and base–base-pairing interactions within the target structure. The deformation profile highlights dissimilarities between structures at the nucleotide scale for both intradomain and interdomain interactions. Our results show that there is little correlation between RMSD and interaction network fidelity. The deformation profile is a tool that allows for rapid assessment of the origins of discrepancies.     

Structure-based analysis of protein-RNA interactions using the program ENTANGLE

Until recently, drawing general conclusions about RNA recognition by proteins has been hindered by the paucity of high-resolution structures. We have analyzed 45 PDB entries of protein-RNA complexes to explore the underlying chemical principles governing both specific and non-sequence specific binding. To facilitate the analysis, we have constructed a database of interactions using ENTANGLE, a JAVA-based program that uses available structural models in their PDB format and searches for appropriate hydrogen bonding, stacking, electrostatic, hydrophobic and van der Waals interactions. The resulting database of interactions reveals correlations that suggest the basis for the discrimination of RNA from DNA and for base-specific recognition. The data illustrate both major and minor interaction strategies employed by families of proteins such as tRNA synthetases, ribosomal proteins, or RNA recognition motifs with their RNA targets. Perhaps most surprisingly, specific RNA recognition appears to be mediated largely by interactions of amide and carbonyl groups in the protein backbone with the edge of the RNA base. In cases where a base accepts a proton, the dominant amino acid donor is arginine, whereas in cases where the base donates a proton, the predominant acceptor is the backbone carbonyl group, not a side-chain group. This is in marked contrast to DNA-protein interactions, which are governed predominantly by amino acid side-chain interactions with functional groups that are presented in the accessible major groove. RNA recognition often proceeds through loops, bulges, kinks and other irregular structures that permit use of all the RNA functional groups and this is seen throughout the protein-RNA interaction database.     

A contact-waiting-time metric and RNA folding rates

Metrics for indirectly predicting the folding rates of RNA sequences are of interest. In this letter, we introduce a simple metric of RNA structural complexity, which accounts for differences in the energetic contributions of RNA base contacts toward RNA structure formation. We apply the metric to RNA sequences whose folding rates were previously determined experimentally. We find that the metric has good correlation (correlation coefficient: −0.95, p?0.01) with the logarithmically transformed folding rates of those RNA sequences. This suggests that the metric can be useful for predicting RNA folding rates. We use the metric to predict the folding rates of bacterial and eukaryotic group II introns. Future applications of the metric (e.g., to predict structural RNAs) could prove fruitful.     

RNA base-amino acid interaction strengths derived from structures and sequences.

We investigate RNA base-amino acid interactions by counting their contacts in structures and their implicit contacts in various functional sequences where the structures can be assumed to be preserved. These frequencies are cast into equations to extract relative interaction energetics. Previously we used this approach in considering the major groove interactions of DNA, and here we apply it to the more diverse interactions observed in RNA. Structures considered are the three different tRNA synthetase complexes, the U1A spliceosomal protein with an RNA hairpin and the BIV TAR-Tat complex. We use binding data for the base frequencies for the seryl, aspartyl and glutaminyl tRNA-synthetase and U1 RNA-protein complexes. We compare with the previously reported DNA major groove peptide contacts the results for atoms of RNA bases, usually in the major groove. There are strong similarities between the rank orders of interacting bases in the DNA and the RNA cases. The apparent strongest RNA interaction observed is between arginine and guanine which was also one of the strongest DNA interactions. The similar data for base atomic interactions, whether base paired or not, support the importance of strong atomic interactions over local structure considerations, such as groove width and alpha-helicity.     

Quantitative analysis of nucleic acid three-dimensional structures

A new computer program to annotate DNA and RNA three-dimensional structures, MC-Annotate, is introduced. The goals of annotation are to efficiently extract and manipulate structural information, to simplify further structural analyses and searches, and to objectively represent structural knowledge. The input of MC-Annotate is a PDB formatted DNA or RNA three-dimensional structure. The output of MC-Annotate is composed of a structural graph that contains the annotations, and a series of HTML documents, one for each nucleotide conformation and base-base interaction present in the input structure. The atomic coordinates of all nucleotides and the homogeneous transformation matrices of all base-base interactions are stored in the structural graph. Symbolic classifications of nucleotide conformations, using sugar puckering modes and nitrogen base orientations around the glycosyl bond, and base-base interactions, using stacking and hydrogen bonding information, are introduced. Peculiarity factors of nucleotide conformations and base-base interactions are defined to indicate their marginalities with all other examples. The peculiarity factors allow us to identify irregular regions and possible stereochemical errors in 3-D structures without interactive visualization. The annotations attached to each nucleotide conformation include its class, its torsion angles, a distribution of the root-mean-square deviations with examples of the same class, the list of examples of the same class, and its peculiarity value. The annotations attached to each base-base interaction include its class, a distribution of distances with examples of the same class, the list of examples of the same class, and its peculiarity value. The distance between two homogeneous transformation matrices is evaluated using a new metric that distinguishes between the rotation and the translation of a transformation matrix in the context of nitrogen bases. MC-Annotate was used to build databases of nucleotide conformations and base-base interactions. It was applied to the ribosomal RNA fragment that binds to protein L11, which annotations revealed peculiar nucleotide conformations and base-base interactions in the regions where the RNA contacts the protein. The question of whether the current database of RNA three-dimensional structures is complete is addressed.     

Analysis of the conformational energy landscape of human snRNA with a metric based on tree representation of RNA structures

It is an outstanding problem to clarify how the RNA sequence is related to its structure and biological functions. We developed a simplified definition of a metric for tree representation of RNA secondary structures and analyzed the conformational energy landscapes of human spliceosomal snRNAs. We discuss the structural properties of the biological sequence by calculating the conformational energy landscapes based on the structural distance between each of the pairs in the set of suboptimal structures. The new index value is introduced for estimating the shapes of distribution patterns in conformational energy landscapes. We apply our method to the five human snRNAs and show that U1 snRNA has a multi-valley profile of the landscape, whereas the landscapes of the other four snRNAs have one steep valley. This result reflects different biological functions of these snRNAs in the pre-mRNA splicing process. The results of analyzing tRNAs and rRNAs show that the conformational energy landscapes of these sequences have multi-valley profiles.     

Fuzzy kernel clustering of RNA secondary structure ensemble using a novel similarity metric

Measuring the (dis)similarity between RNA secondary structures is critical for the study of RNA secondary structures and has implications to RNA functional characterization. Although a number of methods have been developed for comparing RNA structural similarities, their applications have been limited by the complexity of the required computation. In this paper, we present a novel method for comparing the similarity of RNA secondary structures generated from the same RNA sequence, i.e., a secondary structure ensemble, using a matrix representation of the RNA structures. Relevant features of the RNA secondary structures can be easily extracted through singular value decomposition (SVD) of the representing matrices. We have mapped the feature vectors of the singular values to a kernel space, where (dis)similarities among the mapped feature vectors become more evident, making clustering of RNA secondary structures easier to handle. The pair-wise comparison of RNA structures is achieved through computing the distance between the singular value vectors in the kernel space. We have applied a fuzzy kernel clustering method, using this similarity metric, to cluster the RNA secondary structure ensembles. Our application results suggest that our fuzzy kernel clustering method is highly promising for classifications of RNA structure ensembles, because of its low computational complexity and high clustering accuracy.     

Prediction of protein--protein interaction sites in heterocomplexes with neural networks.

In this paper we address the problem of extracting features relevant for predicting protein--protein interaction sites from the three-dimensional structures of protein complexes. Our approach is based on information about evolutionary conservation and surface disposition. We implement a neural network based system, which uses a cross validation procedure and allows the correct detection of 73% of the residues involved in protein interactions in a selected database comprising 226 heterodimers. Our analysis confirms that the chemico-physical properties of interacting surfaces are difficult to distinguish from those of the whole protein surface. However neural networks trained with a reduced representation of the interacting patch and sequence profile are sufficient to generalize over the different features of the contact patches and to predict whether a residue in the protein surface is or is not in contact. By using a blind test, we report the prediction of the surface interacting sites of three structural components of the Dnak molecular chaperone system, and find close agreement with previously published experimental results. We propose that the predictor can significantly complement results from structural and functional proteomics.     

Topological classification of RNA structures

We present a novel topological classification of RNA secondary structures with pseudoknots. It is based on the topological genus of the circular diagram associated to the RNA base-pair structure. The genus is a positive integer number whose value quantifies the topological complexity of the folded RNA structure. In such a representation, planar diagrams correspond to pure RNA secondary structures and have zero genus, whereas non-planar diagrams correspond to pseudoknotted structures and have higher genus. The topological genus allows for the definition of topological folding motifs, similar in spirit to those introduced and commonly used in protein folding. We analyze real RNA structures from the databases Worldwide Protein Data Bank and Pseudobase and classify them according to their topological genus. For simplicity, we limit our analysis by considering only Watson-Crick complementary base pairs and G-U wobble base pairs. We compare the results of our statistical survey with existing theoretical and numerical models. We also discuss possible applications of this classification and show how it can be used for identifying new RNA structural motifs.     

Fuzzy Kernel Clustering of RNA Secondary Structure Ensemble Using a Novel Similarity Metric

Abstract

Measuring the (dis)similarity between RNA secondary structures is critical for the study of RNA secondary structures and has implications to RNA functional characterization. Although a number of methods have been developed for comparing RNA structural similarities, their applications have been limited by the complexity of the required computation. In this paper, we present a novel method for comparing the similarity of RNA secondary structures generated from the same RNA sequence, i.e., a secondary structure ensemble, using a matrix representation of the RNA structures. Relevant features of the RNA secondary structures can be easily extracted through singular value decomposition (SVD) of the representing matrices. We have mapped the feature vectors of the singular values to a kernel space, where (dis)similarities among the mapped feature vectors become more evident, making clustering of RNA secondary structures easier to handle. The pair-wise comparison of RNA structures is achieved through computing the distance between the singular value vectors in the kernel space. We have applied a fuzzy kernel clustering method, using this similarity metric, to cluster the RNA secondary structure ensembles. Our application results suggest that our fuzzy kernel clustering method is highly promising for classifications of RNA structure ensembles, because of its low computational complexity and high clustering accuracy.     

Extracting three-way gene interactions from microarray data

MOTIVATION: It is an important and difficult task to extract gene network information from high-throughput genomic data. A common approach is to cluster genes using pairwise correlation as a distance metric. However, pairwise correlation is clearly too simplistic to describe the complex relationships among real genes since co-expression relationships are often restricted to a specific set of biological conditions/processes. In this study, we described a three-way gene interaction model that captures the dynamic nature of co-expression relationship between a gene pair through the introduction of a controller gene. RESULTS: We surveyed 0.4 billion possible three-way interactions among 1000 genes in a microarray dataset containing 678 human cancer samples. To test the reproducibility and statistical significance of our results, we randomly split the samples into a training set and a testing set. We found that the gene triplets with the strongest interactions (i.e. with the smallest P-values from appropriate statistical tests) in the training set also had the strongest interactions in the testing set. A distinctive pattern of three-way interaction emerged from these gene triplets: depending on the third gene being expressed or not, the remaining two genes can be either co-expressed or mutually exclusive (i.e. expression of either one of them would repress the other). Such three-way interactions can exist without apparent pairwise correlations. The identified three-way interactions may constitute candidates for further experimentation using techniques such as RNA interference, so that novel gene network or pathways could be identified.     

An innate twist between Crick’s wobble and Watson-Crick base pairs

Non-Watson-Crick pairs like the G·U wobble are frequent in RNA duplexes. Their geometric dissimilarity (nonisostericity) with the Watson-Crick base pairs and among themselves imparts structural variations decisive for biological functions. Through a novel circular representation of base pairs, a simple and general metric scheme for quantification of base-pair nonisostericity, in terms of residual twist and radial difference that can also envisage its mechanistic effect, is proposed. The scheme is exemplified by G·U and U·G wobble pairs, and their predicable local effects on helical twist angle are validated by MD simulations. New insights into a possible rationale for contextual occurrence of G·U and other non-WC pairs, as well as the influence of a G·U pair on other non-Watson-Crick pair neighborhood and RNA-protein interactions are obtained from analysis of crystal structure data. A few instances of RNA-protein interactions along the major groove are documented in addition to the well-recognized interaction of the G·U pair along the minor groove. The nonisostericity-mediated influence of wobble pairs for facilitating helical packing through long-range interactions in ribosomal RNAs is also reviewed.     

RNAMotifScan: automatic identification of RNA structural motifs using secondary structural alignment

Recent studies have shown that RNA structural motifs play essential roles in RNA folding and interaction with other molecules. Computational identification and analysis of RNA structural motifs remains a challenging task. Existing motif identification methods based on 3D structure may not properly compare motifs with high structural variations. Other structural motif identification methods consider only nested canonical base-pairing structures and cannot be used to identify complex RNA structural motifs that often consist of various non-canonical base pairs due to uncommon hydrogen bond interactions. In this article, we present a novel RNA structural alignment method for RNA structural motif identification, RNAMotifScan, which takes into consideration the isosteric (both canonical and non-canonical) base pairs and multi-pairings in RNA structural motifs. The utility and accuracy of RNAMotifScan is demonstrated by searching for kink-turn, C-loop, sarcin-ricin, reverse kink-turn and E-loop motifs against a 23S rRNA (PDBid: 1S72), which is well characterized for the occurrences of these motifs. Finally, we search these motifs against the RNA structures in the entire Protein Data Bank and the abundances of them are estimated. RNAMotifScan is freely available at our supplementary website (http://genome.ucf.edu/RNAMotifScan).     

Effect of single-residue bulges on RNA double-helical structures: crystallographic database analysis and molecular dynamics simulation studies

Asymmetric bulge loop motifs are widely dispersed in all types of functional RNAs. They are frequently occurring structural motifs in folded RNA structures and appear commonly in pre-microRNA and ribosomes, where they are involved in specific RNA–RNA and RNA–protein interactions. It is therefore necessary to understand such motifs from a structural point of view. We analyzed all available RNA structures and identified quite a few fragments of double helices that contain bulges. We found that these discontinuities often introduce kinks into the double helices, which also affects the stacking overlap between the base pairs across the irregularity. In order to understand the influence of these bulges on stability and flexibility, we carried out molecular dynamics simulations of three different single-residue bulge-containing RNA helices using the CHARMM36 force field. The structural variability at the junctions of RNA bulges is expected to differ from that in continuous double-helical stretches. The structural features of the junction region were observed to vary noticeably depending on the orientation of the bulge residue. When the base of the bulge residue is looped out, the RNA stretch behaves like a standard long A-form RNA double helix, whereas the entire RNA behaves differently when the base of the bulge residue is intercalated between base pairs inside the RNA stem. Such single-base intercalation was found to introduce a permanent kink into the composite double helix, which could be a recognition element for Dicer during the maturation of miRNA.     

Protein-RNA interactions: exploring binding patterns with a three-dimensional superposition analysis of high resolution structures

MOTIVATION: The recognition of specific RNA sequences and structures by proteins is critical to our understanding of RNA processing, gene expression and viral replication. The diversity of RNA structures suggests that RNA recognition is substantially different than that of DNA. RESULTS: The atomic coordinates of 41 protein-RNA complexes have been used to probe composite nucleoside binding pockets that form the structural and chemical underpinnings of base recognition. Composite nucleoside binding pockets were constructed using three-dimensional superpositions of each RNA nucleoside. Unlike protein-DNA interactions which are dominated by accessibility, RNA recognition frequently occurs in non-canonical and single-strand-like structures that allow interactions to occur from a much wider set of geometries and make fuller use of unique base shapes and hydrogen-bonding ability. By constructing composites that include all van der Waals, hydrogen-bonding, stacking and general non-polar interactions made to a particular nucleoside, the strategies employed are made readily visible. Protein-RNA interactions can result in the formation of a glove-like tight binding pocket around RNA bases, but the size, shape and non-polar binding patterns differ between specific RNA bases. We show that adenine can be distinguished from guanine based on the size and shape of the binding pocket and steric exclusion of the guanine N2 exocyclic amino group. The unique shape and hydrogen-bonding pattern for each RNA base allow proteins to make specific interactions through a very small number of contacts, as few as two in some cases. AVAILABILITY: The program ENTANGLE is available from http://www.bioc.rice.edu/~shamoo     

Rotavirus Architecture at Subnanometer Resolution

Rotavirus, a nonturreted member of the Reoviridae, is the causative agent of severe infantile diarrhea. The double-stranded RNA genome encodes six structural proteins that make up the triple-layer particle. X-ray crystallography has elucidated the structure of one of these capsid proteins, VP6, and two domains from VP4, the spike protein. Complementing this work, electron cryomicroscopy (cryoEM) has provided relatively low-resolution structures for the triple-layer capsid in several biochemical states. However, a complete, high-resolution structural model of rotavirus remains unresolved. Combining new structural analysis techniques with the subnanometer-resolution cryoEM structure of rotavirus, we now provide a more detailed structural model for the major capsid proteins and their interactions within the triple-layer particle. Through a series of intersubunit interactions, the spike protein (VP4) adopts a dimeric appearance above the capsid surface, while forming a trimeric base anchored inside one of the three types of aqueous channels between VP7 and VP6 capsid layers. While the trimeric base suggests the presence of three VP4 molecules in one spike, only hints of the third molecule are observed above the capsid surface. Beyond their interactions with VP4, the interactions between VP6 and VP7 subunits could also be readily identified. In the innermost T=1 layer composed of VP2, visualization of the secondary structure elements allowed us to identify the polypeptide fold for VP2 and examine the complex network of interactions between this layer and the T=13 VP6 layer. This integrated structural approach has resulted in a relatively high-resolution structural model for the complete, infectious structure of rotavirus, as well as revealing the subtle nuances required for maintaining interactions in such a large macromolecular assembly.