The building blocks and motifs of RNA architecture

RNA motifs can be defined broadly as recurrent structural elements containing multiple intramolecular RNA-RNA interactions, as observed in atomic-resolution RNA structures. They constitute the modular building blocks of RNA architecture, which is organized hierarchically. Recent work has focused on analyzing RNA backbone conformations to identify, define and search for new instances of recurrent motifs in X-ray structures. One current view asserts that recurrent RNA strand segments with characteristic backbone configurations qualify as independent motifs. Other considerations indicate that, to characterize modular motifs, one must take into account the larger structural context of such strand segments. This follows the biologically relevant motivation, which is to identify RNA structural characteristics that are subject to sequence constraints and that thus relate RNA architectures to sequences.     

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).     

A structural database for k-turn motifs in RNA

The kink-turn (k-turn) is a common structural motif in RNA that introduces a tight kink into the helical axis. k-turns play an important architectural role in RNA structures and serve as binding sites for a number of proteins. We have created a database of known and postulated k-turn sequences and three-dimensional (3D) structures, available via the internet. This site provides (1) a database of sequence and structure, as a resource for the RNA community, and (2) a tool to enable the manipulation and comparison of 3D structures where known.     

Clustering RNA structural motifs in ribosomal RNAs using secondary structural alignment

RNA structural motifs are the building blocks of the complex RNA architecture. Identification of non-coding RNA structural motifs is a critical step towards understanding of their structures and functionalities. In this article, we present a clustering approach for de novo RNA structural motif identification. We applied our approach on a data set containing 5S, 16S and 23S rRNAs and rediscovered many known motifs including GNRA tetraloop, kink-turn, C-loop, sarcin-ricin, reverse kink-turn, hook-turn, E-loop and tandem-sheared motifs, with higher accuracy than the state-of-the-art clustering method. We also identified a number of potential novel instances of GNRA tetraloop, kink-turn, sarcin-ricin and tandem-sheared motifs. More importantly, several novel structural motif families have been revealed by our clustering analysis. We identified a highly asymmetric bulge loop motif that resembles the rope sling. We also found an internal loop motif that can significantly increase the twist of the helix. Finally, we discovered a subfamily of hexaloop motif, which has significantly different geometry comparing to the currently known hexaloop motif. Our discoveries presented in this article have largely increased current knowledge of RNA structural motifs.     

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/.     

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.     

Colored motifs reveal computational building blocks in the C. elegans brain

Background

Complex networks can often be decomposed into less complex sub-networks whose structures can give hints about the functional organization of the network as a whole. However, these structural motifs can only tell one part of the functional story because in this analysis each node and edge is treated on an equal footing. In real networks, two motifs that are topologically identical but whose nodes perform very different functions will play very different roles in the network.

Methodology/Principal Findings

Here, we combine structural information derived from the topology of the neuronal network of the nematode C. elegans with information about the biological function of these nodes, thus coloring nodes by function. We discover that particular colorations of motifs are significantly more abundant in the worm brain than expected by chance, and have particular computational functions that emphasize the feed-forward structure of information processing in the network, while evading feedback loops. Interneurons are strongly over-represented among the common motifs, supporting the notion that these motifs process and transduce the information from the sensor neurons towards the muscles. Some of the most common motifs identified in the search for significant colored motifs play a crucial role in the system of neurons controlling the worm''s locomotion.

Conclusions/Significance

The analysis of complex networks in terms of colored motifs combines two independent data sets to generate insight about these networks that cannot be obtained with either data set alone. The method is general and should allow a decomposition of any complex networks into its functional (rather than topological) motifs as long as both wiring and functional information is available.     

The identification of novel RNA structural motifs using COMPADRES: an automated approach to structural discovery

Recurring RNA structural motifs are important sites of tertiary interaction and as such, are integral to RNA macromolecular structure. Although numerous RNA motifs have been classified and characterized, the identification of new motifs is of great interest. In this study, we discovered four new conformationally recurring motifs: the pi-turn, the Omega-turn, the alpha-loop and the C2'-endo mediated flipped adenosine motif. Not only do they have complex and interesting structures, but they participate in contacts of high biological significance. In a first for the RNA field, new motifs were discovered by a fully automated algorithm. This algorithm, COMPADRES, utilized a reduced representation of the RNA backbone and was highly successful at discerning unique structural relationships. This study also shows that recurring RNA substructures are not necessarily accompanied by consistent primary or secondary structure.     

Recurrent structural RNA motifs, Isostericity Matrices and sequence alignments

The occurrences of two recurrent motifs in ribosomal RNA sequences, the Kink-turn and the C-loop, are examined in crystal structures and systematically compared with sequence alignments of rRNAs from the three kingdoms of life in order to identify the range of the structural and sequence variations. Isostericity Matrices are used to analyze structurally the sequence variations of the characteristic non-Watson–Crick base pairs for each motif. We show that Isostericity Matrices for non-Watson–Crick base pairs provide important tools for deriving the sequence signatures of recurrent motifs, for scoring and refining sequence alignments, and for determining whether motifs are conserved throughout evolution. The systematic use of Isostericity Matrices identifies the positions of the insertion or deletion of one or more nucleotides relative to the structurally characterized examples of motifs and, most importantly, specifies whether these changes result in new motifs. Thus, comparative analysis coupled with Isostericity Matrices allows one to produce and refine structural sequence alignments. The analysis, based on both sequence and structure, permits therefore the evaluation of the conservation of motifs across phylogeny and the derivation of rules of equivalence between structural motifs. The conservations observed in Isostericity Matrices form a predictive basis for identifying motifs in sequences.     

Tertiary motifs as building blocks for the design of protein‐binding peptides

Despite advances in protein engineering, the de novo design of small proteins or peptides that bind to a desired target remains a difficult task. Most computational methods search for binder structures in a library of candidate scaffolds, which can lead to designs with poor target complementarity and low success rates. Instead of choosing from pre‐defined scaffolds, we propose that custom peptide structures can be constructed to complement a target surface. Our method mines tertiary motifs (TERMs) from known structures to identify surface‐complementing fragments or “seeds.” We combine seeds that satisfy geometric overlap criteria to generate peptide backbones and score the backbones to identify the most likely binding structures. We found that TERM‐based seeds can describe known binding structures with high resolution: the vast majority of peptide binders from 486 peptide‐protein complexes can be covered by seeds generated from single‐chain structures. Furthermore, we demonstrate that known peptide structures can be reconstructed with high accuracy from peptide‐covering seeds. As a proof of concept, we used our method to design 100 peptide binders of TRAF6, seven of which were predicted by Rosetta to form higher‐quality interfaces than a native binder. The designed peptides interact with distinct sites on TRAF6, including the native peptide‐binding site. These results demonstrate that known peptide‐binding structures can be constructed from TERMs in single‐chain structures and suggest that TERM information can be applied to efficiently design novel target‐complementing binders.     

TollML: a database of toll-like receptor structural motifs

Toll-like receptors (TLRs) play a key role in the innate immune system. TLRs recognize pathogen-associated molecular patterns and initiate an intracellular kinase cascade to induce an immediate defensive response. During recent years TLRs have become the focus of tremendous research interest. A central repository for the growing amount of relevant TLR sequence information has been created. Nevertheless, structural motifs of most sequenced TLR proteins, such as leucine-rich repeats (LRRs), are poorly annotated in the established databases. A database that organizes the structural motifs of TLRs could be useful for developing pattern recognition programs, structural modeling and understanding functional mechanisms of TLRs. We describe TollML, a database that integrates all of the TLR sequencing data from the NCBI protein database. Entries were first divided into TLR families (TLR1-23) and then semi-automatically subdivided into three levels of structural motif categories: (1) signal peptide (SP), ectodomain (ECD), transmembrane domain (TD) and Toll/IL-1 receptor (TIR) domain of each TLR; (2) LRRs of each ECD; (3) highly conserved segment (HCS), variable segment (VS) and insertions of each LRR. These categories can be searched quickly using an easy-to-use web interface and dynamically displayed by graphics. Additionally, all entries have hyperlinks to various sources including NCBI, Swiss-Prot, PDB, LRRML and PubMed in order to provide broad external information for users. The TollML database is available at .     

Clustering of protein structural fragments reveals modular building block approach of nature

Structures of peptide fragments drawn from a protein can potentially occupy a vast conformational continuum. We co-ordinatize this conformational space with the help of geometric invariants and demonstrate that the peptide conformations of the currently available protein structures are heavily biased in favor of a finite number of conformational types or structural building blocks. This is achieved by representing a peptides' backbone structure with geometric invariants and then clustering peptides based on closeness of the geometric invariants. This results in 12,903 clusters, of which 2207 are made up of peptides drawn from functionally and/or structurally related proteins. These are termed "functional" clusters and provide clues about potential functional sites. The rest of the clusters, including the largest few, are made up of peptides drawn from unrelated proteins and are termed "structural" clusters. The largest clusters are of regular secondary structures such as helices and beta strands as well as of beta hairpins. Several categories of helices and strands are discovered based on geometric differences. In addition to the known classes of loops, we discover several new classes, which will be useful in protein structure modeling. Our algorithm does not require assignment of secondary structure and, therefore, overcomes the limitations in loop classification due to ambiguity in secondary structure assignment at loop boundaries.     

Towards building the silicon cell: a modular approach

Systems Biology aims to understand quantitatively how properties of biological systems can be understood as functions of the characteristics of, and interactions between their macromolecular components. Whereas, traditional biochemistry focused on isolation and characterization of cellular components, the challenge for Systems Biology lies in integration of this knowledge and the knowledge about molecular interactions. Computer models play an important role in this integration. We here discuss an approach with which we aim to link kinetic models on small parts of metabolism together, so as to form detailed kinetic models of larger chunks of metabolism, and ultimately of the entire living cell. Specifically, we will discuss techniques that can be used to model a sub-network in isolation of a larger network of which it is a part, while still maintaining the dynamics of the larger complete network. We will start by outlining the JWS online system, the silicon cell project, and the type of models we propose. JWS online is a model repository, which can be used for the storage, simulation and analysis of kinetic models. We advocate to integrate a top-down approach, where measurements on the complete system are used to derive fluxes in a detailed structural model, with a bottom-up approach, consisting of the integration of molecular mechanism-based detailed kinetic models into the structural model.     

SMotif: a server for structural motifs in proteins

SMotif is a server that identifies important structural segments or motifs for a given protein structure(s) based on conservation of both sequential as well as important structural features such as solvent inaccessibility, secondary structural content, hydrogen bonding pattern and residue packing. This server also provides three-dimensional orientation patterns of the identified motifs in terms of inter-motif distances and torsion angles. These motifs may form the common core and therefore, can also be employed to design and rationalize protein engineering and folding experiments. AVAILABILITY: SMotif server is available via the URL http://caps.ncbs.res.in/SMotif/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Discovering structural motifs using a structural alphabet: Application to magnesium-binding sites

Background  

For many metalloproteins, sequence motifs characteristic of metal-binding sites have not been found or are so short that they would not be expected to be metal-specific. Striking examples of such metalloproteins are those containing Mg²⁺, one of the most versatile metal cofactors in cellular biochemistry. Even when Mg²⁺-proteins share insufficient sequence homology to identify Mg²⁺-specific sequence motifs, they may still share similarity in the Mg²⁺-binding site structure. However, no structural motifs characteristic of Mg²⁺-binding sites have been reported. Thus, our aims are (i) to develop a general method for discovering structural patterns/motifs characteristic of ligand-binding sites, given the 3D protein structures, and (ii) to apply it to Mg²⁺-proteins sharing <30% sequence identity. Our motif discovery method employs structural alphabet encoding to convert 3D structures to the corresponding 1D structural letter sequences, where the Mg²⁺-structural motifs are identified as recurring structural patterns.     

SMoS: a database of structural motifs of protein superfamilies

The Structural Motifs of Superfamilies (SMoS) database provides information about the structural motifs of aligned protein domain superfamilies. Such motifs among structurally aligned multiple members of protein superfamilies are recognized by the conservation of amino acid preference and solvent inaccessibility and are examined for the conservation of other features like secondary structural content, hydrogen bonding, non-polar interaction and residue packing. These motifs, along with their sequence and spatial orientation, represent the conserved core structure of each superfamily and also provide the minimal requirement of sequence and structural information to retain each superfamily fold.     

Elastic properties of ribosomal RNA building blocks: molecular dynamics of the GTPase-associated center rRNA

Explicit solvent molecular dynamics (MD) was used to describe the intrinsic flexibility of the helix 42–44 portion of the 23S rRNA (abbreviated as Kt-42+rGAC; kink-turn 42 and GTPase-associated center rRNA). The bottom part of this molecule consists of alternating rigid and flexible segments. The first flexible segment (Hinge1) is the highly anharmonic kink of Kt-42. The second one (Hinge2) is localized at the junction between helix 42 and helices 43/44. The rigid segments are the two arms of helix 42 flanking the kink. The whole molecule ends up with compact helices 43/44 (Head) which appear to be modestly compressed towards the subunit in the Haloarcula marismortui X-ray structure. Overall, the helix 42–44rRNA is constructed as a sophisticated intrinsically flexible anisotropic molecular limb. The leading flexibility modes include bending at the hinges and twisting. The Head shows visible internal conformational plasticity, stemming from an intricate set of base pairing patterns including dynamical triads and tetrads. In summary, we demonstrate how rRNA building blocks with contrasting intrinsic flexibilities can form larger architectures with highly specific patterns of preferred low-energy motions and geometries.     

Architecture of basic building blocks in protein and domain structural interaction networks

MOTIVATION: The structural interaction of proteins and their domains in networks is one of the most basic molecular mechanisms for biological cells. Topological analysis of such networks can provide an understanding of and solutions for predicting properties of proteins and their evolution in terms of domains. A single paradigm for the analysis of interactions at different layers, such as domain and protein layers, is needed. RESULTS: Applying a colored vertex graph model, we integrated two basic interaction layers under a unified model: (1) structural domains and (2) their protein/complex networks. We identified four basic and distinct elements in the model that explains protein interactions at the domain level. We searched for motifs in the networks to detect their topological characteristics using a pruning strategy and a hash table for rapid detection. We obtained the following results: first, compared with a random distribution, a substantial part of the protein interactions could be explained by domain-level structural interaction information. Second, there were distinct kinds of protein interaction patterns classified by specific and distinguishable numbers of domains. The intermolecular domain interaction was the most dominant protein interaction pattern. Third, despite the coverage of the protein interaction information differing among species, the similarity of their networks indicated shared architectures of protein interaction network in living organisms. Remarkably, there were only a few basic architectures in the model (>10 for a 4-node network topology), and we propose that most biological combinations of domains into proteins and complexes can be explained by a small number of key topological motifs. CONTACT: doheon@kaist.ac.kr.