A graph theoretical approach for predicting common RNA secondary structure motifs including pseudoknots in unaligned sequences

MOTIVATION: RNA structure motifs contained in mRNAs have been found to play important roles in regulating gene expression. However, identification of novel RNA regulatory motifs using computational methods has not been widely explored. Effective tools for predicting novel RNA regulatory motifs based on genomic sequences are needed. RESULTS: We present a new method for predicting common RNA secondary structure motifs in a set of functionally or evolutionarily related RNA sequences. This method is based on comparison of stems (palindromic helices) between sequences and is implemented by applying graph-theoretical approaches. It first finds all possible stable stems in each sequence and compares stems pairwise between sequences by some defined features to find stems conserved across any two sequences. Then by applying a maximum clique finding algorithm, it finds all significant stems conserved across at least k sequences. Finally, it assembles in topological order all possible compatible conserved stems shared by at least k sequences and reports a number of the best assembled stem sets as the best candidate common structure motifs. This method does not require prior structural alignment of the sequences and is able to detect pseudoknot structures. We have tested this approach on some RNA sequences with known secondary structures, in which it is capable of detecting the real structures completely or partially correctly and outperforms other existing programs for similar purposes. AVAILABILITY: The algorithm has been implemented in C++ in a program called comRNA, which is available at http://ural.wustl.edu/softwares.html     

The annotation of RNA motifs

The recent deluge of new RNA structures, including complete atomic-resolution views of both subunits of the ribosome, has on the one hand literally overwhelmed our individual abilities to comprehend the diversity of RNA structure, and on the other hand presented us with new opportunities for comprehensive use of RNA sequences for comparative genetic, evolutionary and phylogenetic studies. Two concepts are key to understanding RNA structure: hierarchical organization of global structure and isostericity of local interactions. Global structure changes extremely slowly, as it relies on conserved long-range tertiary interactions. Tertiary RNA-RNA and quaternary RNA-protein interactions are mediated by RNA motifs, defined as recurrent and ordered arrays of non-Watson-Crick base-pairs. A single RNA motif comprises a family of sequences, all of which can fold into the same three-dimensional structure and can mediate the same interaction(s). The chemistry and geometry of base pairing constrain the evolution of motifs in such a way that random mutations that occur within motifs are accepted or rejected insofar as they can mediate a similar ordered array of interactions. The steps involved in the analysis and annotation of RNA motifs in 3D structures are: (a) decomposition of each motif into non-Watson-Crick base-pairs; (b) geometric classification of each basepair; (c) identification of isosteric substitutions for each basepair by comparison to isostericity matrices; (d) alignment of homologous sequences using the isostericity matrices to identify corresponding positions in the crystal structure; (e) acceptance or rejection of the null hypothesis that the motif is conserved.     

An algorithm for searching RNA motifs in genomic sequences

RNA molecules, which are found in all living cells, fold into characteristic structures that account for their diverse functional activities. Many of these RNA structures consist of a collection of fundamental RNA motifs. The various combinations of RNA basic components form different RNA classes and define their unique structural and functional properties. The availability of many genome sequences makes it possible to search computationally for functional RNAs. Biological experiments indicate that functional RNAs have characteristic RNA structural motifs represented by specific combinations of base pairings and conserved nucleotides in the loop regions. The searching for those well-ordered RNA structures and their homologues in genomic sequences is very helpful for the understanding of RNA-based gene regulation. In this paper, we consider the following problem: given an RNA sequence with a known secondary structure, efficiently determine candidate segments in genomic sequences that can potentially form RNA secondary structures similar to the given RNA secondary structure. Our new bottom-up approach searches all potential stem-loops similar to ones of the given RNA secondary structure first, and then based on located stem-loops, detects potential homologous structural RNAs in genomic sequences.     

RNA structural motifs: building blocks of a modular biomolecule

RNAs are modular biomolecules, composed largely of conserved structural subunits, or motifs. These structural motifs comprise the secondary structure of RNA and are knit together via tertiary interactions into a compact, functional, three-dimensional structure and are to be distinguished from motifs defined by sequence or function. A relatively small number of structural motifs are found repeatedly in RNA hairpin and internal loops, and are observed to be composed of a limited number of common 'structural elements'. In addition to secondary and tertiary structure motifs, there are functional motifs specific for certain biological roles and binding motifs that serve to complex metals or other ligands. Research is continuing into the identification and classification of RNA structural motifs and is being initiated to predict motifs from sequence, to trace their phylogenetic relationships and to use them as building blocks in RNA engineering.     

An attempt to unify the structure of polymerases

With the great availability of sequences from RNA- and DNA-dependent RNA and DNA polymerases, it has become possible to delineate a few highly conserved regions for various polymerase types. In this work a DNA polymerase sequence from bacteriophage SPO2 was found to be homologous to the polymerase domain of the Klenow fragment of polymerase I from Escherichia coli, which is known to be closely related to those from Staphylococcus pneumoniae, Thermus aquaticus and bacteriophages T7 and T5. The alignment of the SPO2 polymerase with the other five sequences considerably narrowed the conserved motifs in these proteins. Three of the motifs matched reasonably all the conserved motifs of another DNA polymerase type, characterized by human polymerase alpha. It is also possible to find these three motifs in monomeric DNA-dependent RNA polymerases and two of them in DNA polymerase beta and DNA terminal transferases. These latter two motifs also matched two of the four motifs recently identified in 84 RNA-dependent polymerases. From the known tertiary architecture of the Klenow fragment of E. coli pol I, a spatial arrangement can be implied for these motifs. In addition, numerous biochemical experiments suggesting a role for the motifs in a common function (dNTP binding) also support these inferences. This speculative hypothesis, attempting to unify polymerase structure at least locally, if not globally, under the pol I fold, should provide a useful model to direct mutagenesis experiments to probe template and substrate specificity in polymerases.     

Mining frequent stem patterns from unaligned RNA sequences

MOTIVATION: In detection of non-coding RNAs, it is often necessary to identify the secondary structure motifs from a set of putative RNA sequences. Most of the existing algorithms aim to provide the best motif or few good motifs, but biologists often need to inspect all the possible motifs thoroughly. RESULTS: Our method RNAmine employs a graph theoretic representation of RNA sequences and detects all the possible motifs exhaustively using a graph mining algorithm. The motif detection problem boils down to finding frequently appearing patterns in a set of directed and labeled graphs. In the tasks of common secondary structure prediction and local motif detection from long sequences, our method performed favorably both in accuracy and in efficiency with the state-of-the-art methods such as CMFinder. AVAILABILITY: The software is available upon request.     

Structural characterization of naturally occurring RNA single mismatches

RNA is known to be involved in several cellular processes; however, it is only active when it is folded into its correct 3D conformation. The folding, bending and twisting of an RNA molecule is dependent upon the multitude of canonical and non-canonical secondary structure motifs. These motifs contribute to the structural complexity of RNA but also serve important integral biological functions, such as serving as recognition and binding sites for other biomolecules or small ligands. One of the most prevalent types of RNA secondary structure motifs are single mismatches, which occur when two canonical pairs are separated by a single non-canonical pair. To determine sequence–structure relationships and to identify structural patterns, we have systematically located, annotated and compared all available occurrences of the 30 most frequently occurring single mismatch-nearest neighbor sequence combinations found in experimentally determined 3D structures of RNA-containing molecules deposited into the Protein Data Bank. Hydrogen bonding, stacking and interaction of nucleotide edges for the mismatched and nearest neighbor base pairs are described and compared, allowing for the identification of several structural patterns. Such a database and comparison will allow researchers to gain insight into the structural features of unstudied sequences and to quickly look-up studied sequences.     

RNAProfile: an algorithm for finding conserved secondary structure motifs in unaligned RNA sequences

The recent interest sparked due to the discovery of a variety of functions for non-coding RNA molecules has highlighted the need for suitable tools for the analysis and the comparison of RNA sequences. Many trans-acting non-coding RNA genes and cis-acting RNA regulatory elements present motifs, conserved both in structure and sequence, that can be hardly detected by primary sequence analysis alone. We present an algorithm that takes as input a set of unaligned RNA sequences expected to share a common motif, and outputs the regions that are most conserved throughout the sequences, according to a similarity measure that takes into account both the sequence of the regions and the secondary structure they can form according to base-pairing and thermodynamic rules. Only a single parameter is needed as input, which denotes the number of distinct hairpins the motif has to contain. No further constraints on the size, number and position of the single elements comprising the motif are required. The algorithm can be split into two parts: first, it extracts from each input sequence a set of candidate regions whose predicted optimal secondary structure contains the number of hairpins given as input. Then, the regions selected are compared with each other to find the groups of most similar ones, formed by a region taken from each sequence. To avoid exhaustive enumeration of the search space and to reduce the execution time, a greedy heuristic is introduced for this task. We present different experiments, which show that the algorithm is capable of characterizing and discovering known regulatory motifs in mRNA like the iron responsive element (IRE) and selenocysteine insertion sequence (SECIS) stem–loop structures. We also show how it can be applied to corrupted datasets in which a motif does not appear in all the input sequences, as well as to the discovery of more complex motifs in the non-coding RNA.     

Covariance analysis of RNA recognition motifs identifies functionally linked amino acids.

The RNA recognition motif (RRM) is one of the most common eukaryotic protein motifs. RRM sequences form a conserved globular structure known as the RNA-binding domain (RBD) or the ribonucleoprotein domain. Many proteins that contain RRM sequences bind RNA in a sequence-specific manner. To investigate the basis for the RNA-binding specificity of RRMs, we subjected 330 aligned RRM sequences to covariance analysis. The analysis revealed a single network of covariant amino acid pairs comprising the buried core of the RBD and a surface patch. Structural studies have implicated a subset of these residues in RNA binding. The covariance linkages identify a larger set of amino acid residues, including some not directly in contact with bound RNA, that may influence RNA-binding specificity.     

PSSMTS: position specific scoring matrices on tree structures

Identifying non-coding RNA regions on the genome using computational methods is currently receiving a lot of attention. In general, it is essentially more difficult than the problem of detecting protein-coding genes because non-coding RNA regions have only weak statistical signals. On the other hand, most functional RNA families have conserved sequences and secondary structures which are characteristic of their molecular function in a cell. These are known as sequence motifs and consensus structures, respectively. In this paper, we propose an improved method which extends a pairwise structural alignment method for RNA sequences to handle position specific scoring matrices and hence to incorporate motifs into structural alignment of RNA sequences. To model sequence motifs, we employ position specific scoring matrices (PSSMs). Experimental results show that PSSMs enable us to find individual RNA families efficiently, especially if we have biological knowledge such as sequence motifs. K. Sato and K. Morita contributed equally to this work.     

Prediction of consensus structural motifs in a family of coregulated RNA sequences

Given a set of homologous or functionally related RNA sequences, the consensus motifs may represent the binding sites of RNA regulatory proteins. Unlike DNA motifs, RNA motifs are more conserved in structures than in sequences. Knowing the structural motifs can help us gain a deeper insight of the regulation activities. There have been various studies of RNA secondary structure prediction, but most of them are not focused on finding motifs from sets of functionally related sequences. Although recent research shows some new approaches to RNA motif finding, they are limited to finding relatively simple structures, e.g. stem-loops. In this paper, we propose a novel genetic programming approach to RNA secondary structure prediction. It is capable of finding more complex structures than stem-loops. To demonstrate the performance of our new approach as well as to keep the consistency of our comparative study, we first tested it on the same data sets previously used to verify the current prediction systems. To show the flexibility of our new approach, we also tested it on a data set that contains pseudoknot motifs which most current systems cannot identify. A web-based user interface of the prediction system is set up at http://bioinfo. cis.nctu.edu.tw/service/gprm/.     

Cardioviral RNA structure logo analysis: entropy, correlations, and prediction

In recent years, there has been an increased number of sequenced RNAs leading to the development of new RNA databases. Thus, predicting RNA structure from multiple alignments is an important issue to understand its function. Since RNA secondary structures are often conserved in evolution, developing methods to identify covariate sites in an alignment can be essential for discovering structural elements. Structure Logo is a technique established on the basis of entropy and mutual information measured to analyze RNA sequences from an alignment. We proposed an efficient Structure Logo approach to analyze conservations and correlations in a set of Cardioviral RNA sequences. The entropy and mutual information content were measured to examine the conservations and correlations, respectively. The conserved secondary structure motifs were predicted on the basis of the conservation and correlation analyses. Our predictive motifs were similar to the ones observed in the viral RNA structure database, and the correlations between bases also corresponded to the secondary structure in the database.     

Searching RNA motifs and their intermolecular contacts with constraint networks

MOTIVATION: Searching RNA gene occurrences in genomic sequences is a task whose importance has been renewed by the recent discovery of numerous functional RNA, often interacting with other ligands. Even if several programs exist for RNA motif search, none exists that can represent and solve the problem of searching for occurrences of RNA motifs in interaction with other molecules. RESULTS: We present a constraint network formulation of this problem. RNA are represented as structured motifs that can occur on more than one sequence and which are related together by possible hybridization. The implemented tool MilPat is used to search for several sRNA families in genomic sequences. Results show that MilPat allows to efficiently search for interacting motifs in large genomic sequences and offers a simple and extensible framework to solve such problems. New and known sRNA are identified as H/ACA candidates in Methanocaldococcus jannaschii. AVAILABILITY: http://carlit.toulouse.inra.fr/MilPaT/MilPat.pl.     

Arrangement of 3D structural motifs in ribosomal RNA

Structural 3D motifs in RNA play an important role in the RNA stability and function. Previous studies have focused on the characterization and discovery of 3D motifs in RNA secondary and tertiary structures. However, statistical analyses of the distribution of 3D motifs along the RNA appear to be lacking. Herein, we present a novel strategy for evaluating the distribution of 3D motifs along the RNA chain and those motifs whose distributions are significantly non-random are identified. By applying it to the X-ray structure of the large ribosomal subunit from Haloarcula marismortui, helical motifs were found to cluster together along the chain and in the 3D structure, whereas the known tetraloops tend to be sequentially and spatially dispersed. That the distribution of key structural motifs such as tetraloops differ significantly from a random one suggests that our method could also be used to detect novel 3D motifs of any size in sufficiently long/large RNA structures. The motif distribution type can help in the prediction and design of 3D structures of large RNA molecules.     

The Signal Recognition Particle Database (SRPDB).

The SRPDB (Signal Recognition Particle Database) offers aligned SRP RNA and SRP protein sequences, phylogenetically ordered and annotated. This release adds three SRP RNA sequences (totaling 96 SRP RNA sequences) and 11 SRP protein sequences (a total of 39 protein sequences from SRP9, SRP14, SRP19, SRP21, SRP54, SRP68 or SRP72). Also downloadable are sample SRP RNA secondary structure diagrams, a three-dimensional model of the human SRP RNA, search motifs and software.     

Prediction of a new class of RNA recognition motif

The observation that activation domains (AD) of procarboxypeptidases are rather long compared to the pro-regions of other zymogens raises the possibility that they could play additional roles apart from precluding enzymatic activity within the proenzyme and helping in its folding process. In the present work, we compared the overall pro-domain tertiary structure with several proteins belonging to the same fold in the structural classification of proteins (SCOP) database by using structure and sequence comparisons. The best score obtained was between the activation domain of human procarboxypeptidase A4 (ADA4h) and the human U1A protein from the U1 snRNP. Structural alignment revealed the existence of RNP1- and RNP2-related sequences in ADA4h. After modeling ADA4h on U1A, the new structure was used to extract a new sequence pattern characteristic for important residues at key positions. The new sequence pattern allowed scanning protein sequences to predict the RNA-binding function for 32 sequences undetected by PFAM. Unspecific RNA electrophoretic mobility shift assays experimentally supported the prediction that ADA4h binds an RNA motif similar to the U1A binding-motif of stem-loop II of U1 small nuclear RNA. The experiments carried out with ADA4h in the present work suggest the sharing of a common ancestor with other RNA recognition motifs. However, the fact that key residues preventing activity within the proenzyme are also key residues for RNA binding might have induced the activation domains of procarboxypeptidases to evolve from the canonical RNP1 and RNP2 sequences.