RNA structure: the long and the short of it.

The database of RNA structure has grown tremendously since the crystal structure analyses of ribosomal subunits in 2000-2001. During the past year, the trend toward determining the structure of large, complex biological RNAs has accelerated, with the analysis of three intact group I introns, A- and B-type ribonuclease P RNAs, a riboswitch-substrate complex and other structures. The growing database of RNA structures, coupled with efforts directed at the standardization of nomenclature and classification of motifs, has resulted in the identification and characterization of numerous RNA secondary and tertiary structure motifs. Because a large proportion of RNA structure can now be shown to be composed of these recurring structural motifs, a view of RNA as a modular structure built from a combination of these building blocks and tertiary linkers is beginning to emerge. At the same time, however, more detailed analysis of water, metal, ligand and protein binding to RNA is revealing the effect of these moieties on folding and structure formation. The balance between the views of RNA structure either as strictly a construct of preformed building blocks linked in a limited number of ways or as a flexible polymer assuming a global fold influenced by its environment will be the focus of current and future RNA structural biology.

MeRNA: a database of metal ion binding sites in RNA structures.

Metal ions are essential for the folding of RNA into stable tertiary structures and for the catalytic activity of some RNA enzymes. To aid in the study of the roles of metal ions in RNA structural biology, we have created MeRNA (Metals in RNA), a comprehensive compilation of all metal binding sites identified in RNA 3D structures available from the PDB and Nucleic Acid Database. Currently, our database contains information relating to binding of 9764 metal ions corresponding to 23 distinct elements, in 256 RNA structures. The metal ion locations were confirmed and ligands characterized using original literature references. MeRNA includes eight manually identified metal-ion binding motifs, which are described in the literature. MeRNA is searchable by PDB identifier, metal ion, method of structure determination, resolution and R-values for X-ray structure and distance from metal to any RNA atom or to water. New structures with their respective binding motifs will be added to the database as they become available. The MeRNA database will further our understanding of the roles of metal ions in RNA folding and catalysis and have applications in structural and functional analysis, RNA design and engineering. The MeRNA database is accessible at http://merna.lbl.gov.

PSoL: a positive sample only learning algorithm for finding non-coding RNA genes.

MOTIVATION: Small non-coding RNA (ncRNA) genes play important regulatory roles in a variety of cellular processes. However, detection of ncRNA genes is a great challenge to both experimental and computational approaches. In this study, we describe a new approach called positive sample only learning (PSoL) to predict ncRNA genes in the Escherichia coli genome. Although PSoL is a machine learning method for classification, it requires no negative training data, which, in general, is hard to define properly and affects the performance of machine learning dramatically. In addition, using the support vector machine (SVM) as the core learning algorithm, PSoL can integrate many different kinds of information to improve the accuracy of prediction. Besides the application of PSoL for predicting ncRNAs, PSoL is applicable to many other bioinformatics problems as well. RESULTS: The PSoL method is assessed by 5-fold cross-validation experiments which show that PSoL can achieve about 80% accuracy in recovery of known ncRNAs. We compared PSoL predictions with five previously published results. The PSoL method has the highest percentage of predictions overlapping with those from other methods.

MAO: a Multiple Alignment Ontology for nucleic acid and protein sequences.

The application of high-throughput techniques such as genomics, proteomics or transcriptomics means that vast amounts of heterogeneous data are now available in the public databases. Bioinformatics is responding to the challenge with new integrated management systems for data collection, validation and analysis. Multiple alignments of genomic and protein sequences provide an ideal environment for the integration of this mass of information. In the context of the sequence family, structural and functional data can be evaluated and propagated from known to unknown sequences. However, effective integration is being hindered by syntactic and semantic differences between the different data resources and the alignment techniques employed. One solution to this problem is the development of an ontology that systematically defines the terms used in a specific domain. Ontologies are used to share data from different resources, to automatically analyse information and to represent domain knowledge for non-experts. Here, we present MAO, a new ontology for multiple alignments of nucleic and protein sequences. MAO is designed to improve interoperation and data sharing between different alignment protocols for the construction of a high quality, reliable multiple alignment in order to facilitate knowledge extraction and the presentation of the most pertinent information to the biologist.

SCOR: a Structural Classification of RNA database.

The Structural Classification of RNA (SCOR) database provides a survey of the three-dimensional motifs contained in 259 NMR and X-ray RNA structures. In one classification, the structures are grouped according to function. The RNA motifs, including internal and external loops, are also organized in a hierarchical classification. The 259 database entries contain 223 internal and 203 external loops; 52 entries consist of fully complementary duplexes. A classification of the well-characterized tertiary interactions found in the larger RNA structures is also included along with examples. The SCOR database is accessible at http://scor.lbl.gov.

Three-dimensional motifs from the SCOR, structural classification of RNA database: extruded strands, base triples, tetraloops and U-turns.

Release 2.0.1 of the Structural Classification of RNA (SCOR) database, http://scor.lbl.gov, contains a classification of the internal and hairpin loops in a comprehensive collection of 497 NMR and X-ray RNA structures. This report discusses findings of the classification that have not been reported previously. The SCOR database contains multiple examples of a newly described RNA motif, the extruded helical single strand. Internal loop base triples are classified in SCOR according to their three-dimensional context. These internal loop triples contain several examples of a frequently found motif, the minor groove AGC triple. SCOR also presents the predominant and alternate conformations of hairpin loops, as shown in the most well represented tetraloops, with consensus sequences GNRA, UNCG and ANYA. The ubiquity of the GNRA hairpin turn motif is illustrated by its presence in complex internal loops.

SCOR: Structural Classification of RNA, version 2.0.

SCOR, the Structural Classification of RNA (http://scor.lbl.gov), is a database designed to provide a comprehensive perspective and understanding of RNA motif three-dimensional structure, function, tertiary interactions and their relationships. SCOR 2.0 represents a major expansion and introduces a new classification organization. The new version represents the classification as a Directed Acyclic Graph (DAG), which allows a classification node to have multiple parents, in contrast to the strictly hierarchical classification used in SCOR 1.2. SCOR 2.0 supports three types of query terms in the updated search engine: PDB or NDB identifier, nucleotide sequence and keyword. We also provide parseable XML files for all information. This new release contains 511 RNA entries from the PDB as of 15 May 2003. A total of 5880 secondary structural elements are classified: 2104 hairpin loops and 3776 internal loops. RNA motifs reported in the literature, such as 'Kink turn' and 'GNRA loops', are now incorporated into the structural classification along with definitions and descriptions.

Comparative mapping of sequence-based and structure-based protein domains.

BACKGROUND: Protein domains have long been an ill-defined concept in biology. They are generally described as autonomous folding units with evolutionary and functional independence. Both structure-based and sequence-based domain definitions have been widely used. But whether these types of models alone can capture all essential features of domains is still an open question. METHODS: Here we provide insight on domain definitions through comparative mapping of two domain classification databases, one sequence-based (Pfam) and the other structure-based (SCOP). A mapping score is defined to indicate the significance of the mapping, and the properties of the mapping matrices are studied. RESULTS: The mapping results show a general agreement between the two databases, as well as many interesting areas of disagreement. In the cases of disagreement, the functional and evolutionary characteristics of the domains are examined to determine which domain definition is biologically more informative.

A generalized affine gap model significantly improves protein sequence alignment accuracy.

Sequence alignment underpins common tasks in molecular biology, including genome annotation, molecular phylogenetics, and homology modeling. Fundamental to sequence alignment is the placement of gaps, which represent character insertions or deletions. We assessed the ability of a generalized affine gap cost model to reliably detect remote protein homology and to produce high-quality alignments. Generalized affine gap alignment with optimal gap parameters performed as well as the traditional affine gap model in remote homology detection. Evaluation of alignment quality showed that the generalized affine model aligns fewer residue pairs than the traditional affine model but achieves significantly higher per-residue accuracy. We conclude that generalized affine gap costs should be used when alignment accuracy carries more importance than aligned sequence length.

Sequence and structural conservation in RNA ribose zippers.

The "ribose zipper", an important element of RNA tertiary structure, is characterized by consecutive hydrogen-bonding interactions between ribose 2'-hydroxyls from different regions of an RNA chain or between RNA chains. These tertiary contacts have previously been observed to also involve base-backbone and base-base interactions (A-minor type). We searched for ribose zipper tertiary interactions in the crystal structures of the large ribosomal subunit RNAs of Haloarcula marismortui and Deinococcus radiodurans, and the small ribosomal subunit RNA of Thermus thermophilus and identified a total of 97 ribose zippers. Of these, 20 were found in T. thermophilus 16 S rRNA, 44 in H. marismortui 23 S rRNA (plus 2 bridging 5 S and 23 S rRNAs) and 30 in D. radiodurans 23 S rRNA (plus 1 bridging 5 S and 23 S rRNAs). These were analyzed in terms of sequence conservation, structural conservation and stability, location in secondary structure, and phylogenetic conservation. Eleven types of ribose zippers were defined based on ribose-base interactions. Of these 11, seven were observed in the ribosomal RNAs. The most common of these is the canonical ribose zipper, originally observed in the P4-P6 group I intron fragment. All ribose zippers were formed by antiparallel chain interactions and only a single example extended beyond two residues, forming an overlapping ribose zipper of three consecutive residues near the small subunit A-site. Almost all ribose zippers link stem (Watson-Crick duplex) or stem-like (base-paired), with loop (external, internal, or junction) chain segments. About two-thirds of the observed ribose zippers interact with ribosomal proteins. Most of these ribosomal proteins bridge the ribose zipper chain segments with basic amino acid residues hydrogen bonding to the RNA backbone. Proteins involved in crucial ribosome function and in early stages of ribosomal assembly also stabilize ribose zipper interactions. All ribose zippers show strong sequence conservation both within these three ribosomal RNA structures and in a large database of aligned prokaryotic sequences. The physical basis of the sequence conservation is stacked base triples formed between consecutive base-pairs on the stem or stem-like segment with bases (often adenines) from the loop-side segment. These triples have previously been characterized as Type I and Type II A-minor motifs and are stabilized by base-base and base-ribose hydrogen bonds. The sequence and structure conservation of ribose zippers can be directly used in tertiary structure prediction and may have applications in molecular modeling and design.

Structural studies of the Nudix hydrolase DR1025 from Deinococcus radiodurans and its ligand complexes.

We have determined the crystal structure, at 1.4A, of the Nudix hydrolase DR1025 from the extremely radiation resistant bacterium Deinococcus radiodurans. The protein forms an intertwined homodimer by exchanging N-terminal segments between chains. We have identified additional conserved elements of the Nudix fold, including the metal-binding motif, a kinked beta-strand characterized by a proline two positions upstream of the Nudix consensus sequence, and participation of the N-terminal extension in the formation of the substrate-binding pocket. Crystal structures were also solved of DR1025 crystallized in the presence of magnesium and either a GTP analog or Ap(4)A (both at 1.6A resolution). In the Ap(4)A co-crystal, the electron density indicated that the product of asymmetric hydrolysis, ATP, was bound to the enzyme. The GTP analog bound structure showed that GTP was bound almost identically as ATP. Neither nucleoside triphosphate was further cleaved.

Solution structure of hypothetical Nudix hydrolase DR0079 from extremely radiation-resistant Deinococcus radiodurans bacterium.

Using nuclear magnetic resonance (NMR) based methods, including residual dipolar coupling restraints, we have determined the solution structure of the hypothetical Deinococcus radiodurans Nudix protein DR0079 (171 residues, MW = 19.3 kDa). The protein contains eight beta-strands and three alpha-helices organized into three subdomains: an N-terminal beta-sheet (1-34), a central Nudix core (35-140), and a C-terminal helix-turn-helix (141-171). The Nudix core and the C-terminal helix-turn-helix form the fundamental fold common to the Nudix family, a large mixed beta-sheet sandwiched between alpha-helices. The residues that compose the signature Nudix sequence, GX5EX7REUXEEXGU (where U = I, L, or V and X = any amino acid), are contained in a turn-helix-turn motif on the face of the mixed beta-sheet. Chemical shift mapping experiments suggest that DR0079 binds Mg2+. Experiments designed to determine the biological function of the protein indicate that it is not a type I isopentenyl-diphosphate delta-isomerase and that it does not bind alpha,beta-methyleneadenosine 5'-triphosphate (AMPCPP) or guanosine 5'-[beta,gamma-imido]triphosphate (GMPPNP). In this article, the structure of DR0079 is compared to other known Nudix protein structures, a potential substrate-binding surface is proposed, and its possible biological function is discussed.

A unified representation of multiprotein complex data for modeling interaction networks.

The protein interaction network presents one perspective for understanding cellular processes. Recent experiments employing high-throughput mass spectrometric characterizations have resulted in large data sets of physiologically relevant multiprotein complexes. We present a unified representation of such data sets based on an underlying bipartite graph model that is an advance over existing models of the network. Our unified representation allows for weighting of connections between proteins shared in more than one complex, as well as addressing the higher level organization that occurs when the network is viewed as consisting of protein complexes that share components. This representation also allows for the application of the rigorous MinMaxCut graph clustering algorithm for the determination of relevant protein modules in the networks. Statistically significant annotations of clusters in the protein-protein and complex-complex networks using terms from the Gene Ontology indicate that this method will be useful for posing hypotheses about uncharacterized components of protein complexes or uncharacterized relationships between protein complexes.

Structural principles from large RNAs.

Since the year 2000 a number of large RNA three-dimensional structures have been determined by X-ray crystallography. Structures composed of more than 100 nucleotide residues include the signal recognition particle RNA, group I intron, the GlmS ribozyme, RNAseP RNA, and ribosomal RNAs from Haloarcula morismortui, Escherichia coli, Thermus thermophilus, and Deinococcus radiodurans. These large RNAs are constructed from the same secondary and tertiary structural motifs identified in smaller RNAs but appear to have a larger organizational architecture. They are dominated by long continuous interhelical base stacking, tend to segregate into domains, and are planar in overall shape as opposed to their globular protein counterparts. These findings have consequences in RNA folding, intermolecular interaction, and packing, in addition to studies of design and engineering and structure prediction.

Estimating support for protein-protein interaction data with applications to function prediction.

Almost every cellular process requires the interactions of pairs or larger complexes of proteins. High throughput protein-protein interaction (PPI) data have been generated using techniques such as the yeast two-hybrid systems, mass spectrometry method, and many more. Such data provide us with a new perspective to predict protein functions and to generate protein-protein interaction networks, and many recent algorithms have been developed for this purpose. However, PPI data generated using high throughput techniques contain a large number of false positives. In this paper, we have proposed a novel method to evaluate the support for PPI data based on gene ontology information. If the semantic similarity between genes is computed using gene ontology information and using Resnik's formula, then our results show that we can model the PPI data as a mixture model predicated on the assumption that true protein-protein interactions will have higher support than the false positives in the data. Thus semantic similarity between genes serves as a metric of support for PPI data. Taking it one step further, new function prediction approaches are also being proposed with the help of the proposed metric of the support for the PPI data. These new function prediction approaches outperform their conventional counterparts. New evaluation methods are also proposed.

Consistent dissection of the protein interaction network by combining global and local metrics.

We propose a new network decomposition method to systematically identify protein interaction modules in the protein interaction network. Our method incorporates both a global metric and a local metric for balance and consistency. We have compared the performance of our method with several earlier approaches on both simulated and real datasets using different criteria, and show that our method is more robust to network alterations and more effective at discovering functional protein modules.

Transitive closure and metric inequality of weighted graphs: detecting protein interaction modules using cliques.

We study transitivity properties of edge weights in complex networks. We show that enforcing transitivity leads to a transitivity inequality which is equivalent to ultra-metric inequality. This can be used to define transitive closure on weighted undirected graphs, which can be computed using a modified Floyd-Warshall algorithm. These new concepts are extended to dissimilarity graphs and triangle inequalities. From this, we extend the clique concept from unweighted graph to weighted graph. We outline several applications and present results of detecting protein functional modules in a protein interaction network.

Novel crystal form of the ColE1 Rom protein.

The RNA I modulator protein (Rom) acts as a co-regulator of ColE1 plasmid copy number by binding to RNA kissing hairpins and stabilizing their interaction. The structure of Rom has been determined in a new crystal form from X-ray diffraction data to 2.5 A resolution. In this structure, a dimer of the 57-amino-acid protein is found in the asymmetric unit. Each subunit consists almost entirely of two antiparallel alpha-helices joined by a short hairpin bend. The dimer contains a non-crystallographic twofold axis and forms a highly regular four-alpha-helical bundle. The structural packing in this novel crystal form is different from previously known Rom structures. The asymmetric unit contains one dimer, giving a crystal volume per protein weight (V(M)) of 1.83 A(3) Da(-1) and a low solvent content of 30%. Strong packing interactions and low solvation are characteristic of the structure. The Rom protein was cocrystallized with the Tar-Tar* kissing hairpin RNA. Although the electron-density maps do not show bound RNA, altered conformations in the side chains of Rom that are known to be involved in RNA binding have been identified. These results provide additional information about Rom protein conformational flexibility and suggest that the presence of a highly charged polymer such as RNA can promote tight packing of an RNA-binding protein, even when the RNA itself is not observed in the crystal.

The crystal structure at 1.5 angstroms resolution of an RNA octamer duplex containing tandem G.U basepairs.

The crystal structure of the RNA octamer, 5'-GGCGUGCC-3' has been determined from x-ray diffraction data to 1.5 angstroms resolution. In the crystal, this oligonucleotide forms five self-complementary double-helices in the asymmetric unit. Tandem 5'GU/3'UG basepairs comprise an internal loop in the middle of each duplex. The NMR structure of this octameric RNA sequence is also known, allowing comparison of the variation among the five crystallographic duplexes and the solution structure. The G.U pairs in the five duplexes of the crystal form two direct hydrogen bonds and are stabilized by water molecules that bridge between the base of guanine (N2) and the sugar (O2') of uracil. This contrasts with the NMR structure in which only one direct hydrogen bond is observed for the G.U pairs. The reduced stability of the r(CGUG)2 motif relative to the r(GGUC)2 motif may be explained by the lack of stacking of the uracil bases between the Watson-Crick and G.U pairs as observed in the crystal structure.

The RNA Ontology Consortium: an open invitation to the RNA community.

The aim of the RNA Ontology Consortium (ROC) is to create an integrated conceptual framework-an RNA Ontology (RO)-with a common, dynamic, controlled, and structured vocabulary to describe and characterize RNA sequences, secondary structures, three-dimensional structures, and dynamics pertaining to RNA function. The RO should produce tools for clear communication about RNA structure and function for multiple uses, including the integration of RNA electronic resources into the Semantic Web. These tools should allow the accurate description in computer-interpretable form of the coupling between RNA architecture, function, and evolution. The purposes for creating the RO are, therefore, (1) to integrate sequence and structural databases; (2) to allow different computational tools to interoperate; (3) to create powerful software tools that bring advanced computational methods to the bench scientist; and (4) to facilitate precise searches for all relevant information pertaining to RNA. For example, one initial objective of the ROC is to define, identify, and classify RNA structural motifs described in the literature or appearing in databases and to agree on a computer-interpretable definition for each of these motifs. To achieve these aims, the ROC will foster communication and promote collaboration among RNA scientists by coordinating frequent face-to-face workshops to discuss, debate, and resolve difficult conceptual issues. These meeting opportunities will create new directions at various levels of RNA research. The ROC will work closely with the PDB/NDB structural databases and the Gene, Sequence, and Open Biomedical Ontology Consortia to integrate the RO with existing biological ontologies to extend existing content while maintaining interoperability.