Consensus Structure Research Articles

Energy landscape analysis for regulatory RNA finding using scalable distributed cyberinfrastructure

SUMMARYWe investigate the folding energy landscape for a given RNA sequence through Boltzmann ensemble (BE) sampling of RNA secondary structures. The ensemble of sampled structures is used to derive distributions of energies and base‐pair distances between two configurations. We identify structural features that can be utilized for RNA gene finding. Characterization of the EL through BE sampling of secondary structures is computationally demanding and has multiple heterogeneous stages. We develop the Distributed Adaptive Runtime Environment to effectively address the computational requirements. Distributed Adaptive Runtime Environment is built upon an extensible and interoperable pilot‐job and supports the concurrent execution of a broad range of task sizes across a range of infrastructure. It is used to investigate two RNA systems of different sizes, S‐adenosyl methionine (SAM) binding RNA sequences known as SAM‐I riboswitches, and the S gene of the bovine corona virus RNA genome. We demonstrate how the implementation lowers the total time to solution for increases in RNA length, the number of sequences investigated, and the number of sampled structures. The distributions of energies and base‐pair distances reveal variations in folding dynamics and pathways among the SAM riboswitch sequences. Our results for BCoV RNA genome sequences also indicate sensitivity of folding to coding‐neutral variations in sequence. We search for a characteristic motif from within the SAM‐I consensus structure – a four‐way junction, among BE sampled structures for all 2910 SAM‐I sequences identified from Rfam (the curated ncRNA family database). We find that BE sampling provides insight into the variations in conformational distribution among sequences of the same ncRNA family. Therefore, BE sampling of secondary structures is a viable pre‐processing or post‐processing tool to complement comparative sequence analysis. The understanding gained shows how appropriately designed cyberinfrastructure can provide new insight into RNA folding and structure formation. Copyright © 2011 John Wiley & Sons, Ltd.

The R2 retrotransposon RNA families

Analysis of the R2 retrotransposons from multiple silkmoth and fruitfly species have revealed three segments that contain conserved RNA secondary structures. These conserved structures play important roles in the propagation of the R2 element, including R2 RNA processing and transposon integration into the host genome as well as a likely role in translation. Two of the structured regions comprise protein binding sites: one is located in the 3' UTR and the other is in the 5' UTR close to the putative start of the R2 open reading frame (ORF). The 3' structure was deduced from chemical mapping and sequence comparison. The 5' structure was determined using a combination of chemical mapping, oligonucleotide binding, NMR and sequence analysis and contains an unusual pseudoknot structure. The third structure occurs at the 5' end of the R2 RNA and is responsible for self-cleavage of the 5' end of the element from a 28S ribosomal RNA co-transcript. A structure for this fragment was proposed based on motif searching and sequence comparison. There is remarkable similarity in sequence and structure to the hepatitis delta virus (HDV) ribozyme. Seed alignments for the 5' structure and the R2 ribozyme, containing representative sequences and consensus structures, have been submitted to the Rfam database.

A Vast Machine: Computer Models, Climate Data and The Politics of Global Warming – By Paul N. Edwards

A Vast Machine: Computer Models, Climate Data and The Politics of Global Warming – By Paul N. Edwards

Multitarget Structure–Activity Relationships Characterized by Activity-Difference Maps and Consensus Similarity Measure

Dual and triple activity-difference (DAD/TAD) maps are tools for the systematic characterization of structure-activity relationships (SAR) of compound data sets screened against two or three targets. DAD and TAD maps are two- and three- dimensional representations of the pairwise activity differences of compound data sets, respectively. Adding pairwise structural similarity information into these maps readily reveals activity cliff regions in the SAR for one, two, or three targets. In addition, pairs of compounds in the smooth regions of the SAR and scaffold hops are also easily identified in these maps. Herein, DAD and TAD maps are employed for the systematic characterization of the SAR of a benchmark set of 299 compounds screened against dopamine, norepinephrine, and serotonin transporters. To reduce the well-known dependence of the activity landscape on the structural representation, five selected 2D and 3D structure representations were used to characterize the SAR. Systematic analysis of the DAD and TAD maps reveals regions in the landscape with similar SAR for two or the three targets as well as regions with inverse SAR, i.e., changes in structure that increase activity for one target, but decrease activity for the other target. Focusing the analysis on pairs of compounds with high structure similarity revealed the presence of single-, dual-, and triple-target activity cliffs, i.e., small changes in structure with high changes in potency for one, two, or the three targets, respectively. Triple-target scaffold hops are also discussed. Activity cliffs and scaffold hops were also quantified and represented using two recently proposed approaches namely, mean Structure Activity Landscape Index (mean SALI) and Consensus Structure-Activity Similarity (SAS) maps.

Characterization of pathogenic germline mutations in human Protein Kinases

BackgroundProtein Kinases are a superfamily of proteins involved in crucial cellular processes such as cell cycle regulation and signal transduction. Accordingly, they play an important role in cancer biology. To contribute to the study of the relation between kinases and disease we compared pathogenic mutations to neutral mutations as an extension to our previous analysis of cancer somatic mutations. First, we analyzed native and mutant proteins in terms of amino acid composition. Secondly, mutations were characterized according to their potential structural effects and finally, we assessed the location of the different classes of polymorphisms with respect to kinase-relevant positions in terms of subfamily specificity, conservation, accessibility and functional sites.ResultsPathogenic Protein Kinase mutations perturb essential aspects of protein function, including disruption of substrate binding and/or effector recognition at family-specific positions. Interestingly these mutations in Protein Kinases display a tendency to avoid structurally relevant positions, what represents a significant difference with respect to the average distribution of pathogenic mutations in other protein families.ConclusionsDisease-associated mutations display sound differences with respect to neutral mutations: several amino acids are specific of each mutation type, different structural properties characterize each class and the distribution of pathogenic mutations within the consensus structure of the Protein Kinase domain is substantially different to that for non-pathogenic mutations. This preferential distribution confirms previous observations about the functional and structural distribution of the controversial cancer driver and passenger somatic mutations and their use as a proxy for the study of the involvement of somatic mutations in cancer development.

Conserved RNA structures in the non-canonical Hac1/Xbp1 intron

The unconventional splicing of Hac1 by the ribonuclease Ire1 is a key event in the activation of the unfolded protein response (UPR) in Saccharomyces cerevisiae. This splicing is independent of the spliceosome and is mediated by a secondary structure at the intron-exon boundaries of the mRNA. Similar unconventional splicing was also described for the gene Xbp1 in human, mouse, C. elegans and D. melanogaster, and for Hac1 in five other fungi. We used reported RNA structures to build a multiple sequence alignment and the Infernal package to search for homologous structures. We identified homologous non-canonical intron structures in 128 out of 156 searched eukaryotic genomes. Our results show that the sequence of the Hac1/Xbp1 intron is highly conserved only around the splice sites recognized by Ire1. The consensus structure of the Hac1/Xbp1 mRNA is well conserved in Fungi and Metazoa and resembles structures previously described. We show that a typical Hac1/Xbp1 intron is very short, only 20-26 bases, whereas yeast species have a long intron (>100 bases). We identified six species with unambiguous Hac1/Xbp1 homologs that have lost the non-canonical intron structure. We propose that these species use a different mechanism to regulate the UPR.

Binding and cleavage of unstructured RNA by nuclear RNase P.

Ribonuclease P (RNase P) is an essential endoribonuclease for which the best-characterized function is processing the 5' leader of pre-tRNAs. Compared to bacterial RNase P, which contains a single small protein subunit and a large catalytic RNA subunit, eukaryotic nuclear RNase P is more complex, containing nine proteins and an RNA subunit in Saccharomyces cerevisiae. Consistent with this, nuclear RNase P has been shown to possess unique RNA binding capabilities. To understand the unique molecular recognition of nuclear RNase P, the interaction of S. cerevisiae RNase P with single-stranded RNA was characterized. Unstructured, single-stranded RNA inhibits RNase P in a size-dependent manner, suggesting that multiple interactions are required for high affinity binding. Mixed-sequence RNAs from protein-coding regions also bind strongly to the RNase P holoenzyme. However, in contrast to poly(U) homopolymer RNA that is not cleaved, a variety of mixed-sequence RNAs have multiple preferential cleavage sites that do not correspond to identifiable consensus structures or sequences. In addition, pre-tRNA(Tyr), poly(U)(50) RNA, and mixed-sequence RNA cross-link with purified RNase P in the RNA subunit Rpr1 near the active site in "Conserved Region I," although the exact positions vary. Additional contacts between poly(U)(50) and the RNase P proteins Rpr2p and Pop4p were identified. We conclude that unstructured RNAs interact with multiple protein and RNA contacts near the RNase P RNA active site, but that cleavage depends on the nature of interaction with the active site.

Consensus Models of Activity Landscapes with Multiple Chemical, Conformer, and Property Representations

We report consensus Structure-Activity Similarity (SAS) maps that address the dependence of activity landscapes on molecular representation. As a case study, we characterized the activity landscape of 54 compounds with activities against human cathepsin B (hCatB), human cathepsin L (hCatL), and Trypanosoma brucei cathepsin B (TbCatB). Starting from an initial set of 28 descriptors we selected ten representations that capture different aspects of the chemical structures. These included four 2D (MACCS keys, GpiDAPH3, pairwise, and radial fingerprints) and six 3D (4p and piDAPH4 fingerprints with each including three conformers) representations. Multiple conformers are used for the first time in consensus activity landscape modeling. The results emphasize the feasibility of identifying consensus data points that are consistently formed in different reference spaces generated with several fingerprint models, including multiple 3D conformers. Consensus data points are not meant to eliminate data, disregarding, for example, "true" activity cliffs that are not identified by some molecular representations. Instead, consensus models are designed to prioritize the SAR analysis of activity cliffs and other consistent regions in the activity landscape that are captured by several molecular representations. Systematic description of the SARs of two targets give rise to the identification of pairs of compounds located in the same region of the activity landscape of hCatL and TbCatB suggesting similar mechanisms of action for the pairs involved. We also explored the relationship between property similarity and activity similarity and found that property similarities are suitable to characterize SARs. We also introduce the concept of structure-property-activity (SPA) similarity in SAR studies.

NcRNA CONSENSUS SECONDARY STRUCTURE DERIVATION USING GRAMMAR STRINGS

Many noncoding RNAs (ncRNAs) function through both their sequences and secondary structures. Thus, secondary structure derivation is an important issue in today's RNA research. The state-of-the-art structure annotation tools are based on comparative analysis, which derives consensus structure of homologous ncRNAs. Despite promising results from existing ncRNA aligning and consensus structure derivation tools, there is a need for more efficient and accurate ncRNA secondary structure modeling and alignment methods. In this work, we introduce a consensus structure derivation approach based on grammar string, a novel ncRNA secondary structure representation that encodes an ncRNA's sequence and secondary structure in the parameter space of a context-free grammar (CFG) and a full RNA grammar including pseudoknots. Being a string defined on a special alphabet constructed from a grammar, grammar string converts ncRNA alignment into sequence alignment. We derive consensus secondary structures from hundreds of ncRNA families from BraliBase 2.1 and 25 families containing pseudoknots using grammar string alignment. Our experiments have shown that grammar string-based structure derivation competes favorably in consensus structure quality with Murlet and RNASampler. Source code and experimental data are available at http://www.cse.msu.edu/~yannisun/grammar-string.

Molecular modeling and in silico characterization of Mycobacterium tuberculosis TlyA: Possible misannotation of this tubercle bacilli-hemolysin

BackgroundThe TlyA protein has a controversial function as a virulence factor in Mycobacterium tuberculosis (M. tuberculosis). At present, its dual activity as hemolysin and RNA methyltransferase in M. tuberculosis has been indirectly proposed based on in vitro results. There is no evidence however for TlyA relevance in the survival of tubercle bacilli inside host cells or whether both activities are functionally linked. A thorough analysis of structure prediction for this mycobacterial protein in this study shows the need for reevaluating TlyA's function in virulence.ResultsBioinformatics analysis of TlyA identified a ribosomal protein binding domain (S4 domain), located between residues 5 and 68 as well as an FtsJ-like methyltranferase domain encompassing residues 62 and 247, all of which have been previously described in translation machinery-associated proteins. Subcellular localization prediction showed that TlyA lacks a signal peptide and its hydrophobicity profile showed no evidence of transmembrane helices. These findings suggested that it may not be attached to the membrane, which is consistent with a cytoplasmic localization. Three-dimensional modeling of TlyA showed a consensus structure, having a common core formed by a six-stranded β-sheet between two α-helix layers, which is consistent with an RNA methyltransferase structure. Phylogenetic analyses showed high conservation of the tlyA gene among Mycobacterium species. Additionally, the nucleotide substitution rates suggested purifying selection during tlyA gene evolution and the absence of a common ancestor between TlyA proteins and bacterial pore-forming proteins.ConclusionAltogether, our manual in silico curation suggested that TlyA is involved in ribosomal biogenesis and that there is a functional annotation error regarding this protein family in several microbial and plant genomes, including the M. tuberculosis genome.

An expanded collection and refined consensus model of glmS ribozymes.

Self-cleaving glmS ribozymes selectively bind glucosamine-6-phosphate (GlcN6P) and use this metabolite as a cofactor to promote self-cleavage by internal phosphoester transfer. Representatives of the glmS ribozyme class are found in Gram-positive bacteria where they reside in the 5' untranslated regions (UTRs) of glmS messenger RNAs that code for the essential enzyme L-glutamine:D-fructose-6-phosphate aminotransferase. By using comparative sequence analyses, we have expanded the number of glmS ribozyme representatives from 160 to 463. All but two glmS ribozymes are present in glmS mRNAs and most exhibit striking uniformity in sequence and structure, which are features that make representatives attractive targets for antibacterial drug development. However, our discovery of rare variants broadens the consensus sequence and structure model. For example, in the Deinococcus-Thermus phylum, several structural variants exist that carry additional stems within the catalytic core and changes to the architecture of core-supporting substructures. These findings reveal that glmS ribozymes have a broader phylogenetic distribution than previously known and suggest that additional rare structural variants may remain to be discovered.

Semantics and Ambiguity of Stochastic RNA Family Models

Stochastic models, such as hidden Markov models or stochastic context-free grammars (SCFGs) can fail to return the correct, maximum likelihood solution in the case of semantic ambiguity. This problem arises when the algorithm implementing the model inspects the same solution in different guises. It is a difficult problem in the sense that proving semantic nonambiguity has been shown to be algorithmically undecidable, while compensating for it (by coalescing scores of equivalent solutions) has been shown to be NP-hard. For stochastic context-free grammars modeling RNA secondary structure, it has been shown that the distortion of results can be quite severe. Much less is known about the case when stochastic context-free grammars model the matching of a query sequence to an implicit consensus structure for an RNA family. We find that three different, meaningful semantics can be associated with the matching of a query against the model--a structural, an alignment, and a trace semantics. Rfam models correctly implement the alignment semantics, and are ambiguous with respect to the other two semantics, which are more abstract. We show how provably correct models can be generated for the trace semantics. For approaches, where such a proof is not possible, we present an automated pipeline to check post factum for ambiguity of the generated models. We propose that both the structure and the trace semantics are worth-while concepts for further study, possibly better suited to capture remotely related family members.

Selection patterns on restorer-like genes reveal a conflict between nuclear and mitochondrial genomes throughout angiosperm evolution

Eukaryotic cells have harbored mitochondria for at least 1.5 billion years in an apparently mutually beneficial symbiosis. Studies on the agronomically important crop trait cytoplasmic male sterility (CMS) have suggested the semblance of a host-parasite relationship between the nuclear and mitochondrial genomes, but molecular evidence for this is lacking. Key players in CMS systems are the fertility restorer (Rf) genes required for the development of a functional male gametophyte in plants carrying a mitochondrial CMS gene. In the majority of cases, Rf genes encode pentatricopeptide repeat (PPR) proteins. We show that most angiosperms for which extensive genomic sequence data exist contain multiple PPR genes related to Rf genes. These Rf-like genes show a number of characteristic features compared with other PPR genes, including chromosomal clustering and unique patterns of evolution, notably high rates of nonsynonymous to synonymous substitutions, suggesting diversifying selection. The highest probabilities of diversifying selection were seen for amino acid residues 1, 3, and 6 within the PPR motif. PPR proteins are involved in RNA processing, and mapping the selection data to a predicted consensus structure of an array of PPR motifs suggests that these residues are likely to form base-specific contacts to the RNA ligand. We suggest that the selection patterns on Rf-like genes reveal a molecular "arms-race" between the nuclear and mitochondrial genomes that has persisted throughout most of the evolutionary history of angiosperms.

The suboptimal structures find the optimal RNAs: homology search for bacterial non-coding RNAs using suboptimal RNA structures

Non-coding RNAs (ncRNAs) are regulatory molecules encoded in the intergenic or intragenic regions of the genome. In prokaryotes, biocomputational identification of homologs of known ncRNAs in other species often fails due to weakly evolutionarily conserved sequences, structures, synteny and genome localization, except in the case of evolutionarily closely related species. To eliminate results from weak conservation, we focused on RNA structure, which is the most conserved ncRNA property. Analysis of the structure of one of the few well-studied bacterial ncRNAs, 6S RNA, demonstrated that unlike optimal and consensus structures, suboptimal structures are capable of capturing RNA homology even in divergent bacterial species. A computational procedure for the identification of homologous ncRNAs using suboptimal structures was created. The suggested procedure was applied to strongly divergent bacterial species and was capable of identifying homologous ncRNAs.

ON COMPARING TWO STRUCTURED RNA MULTIPLE ALIGNMENTS

We present a method, called BlockMatch, for aligning two blocks, where a block is an RNA multiple sequence alignment with the consensus secondary structure of the alignment in Stockholm format. The method employs a quadratic-time dynamic programming algorithm for aligning columns and column pairs of the multiple alignments in the blocks. Unlike many other tools that can perform pairwise alignment of either single sequences or structures only, BlockMatch takes into account the characteristics of all the sequences in the blocks along with their consensus structures during the alignment process, thus being able to achieve a high-quality alignment result. We apply BlockMatch to phylogeny reconstruction on a set of 5S rRNA sequences taken from fifteen bacteria species. Experimental results showed that the phylogenetic tree generated by our method is more accurate than the tree constructed based on the widely used ClustalW tool. The BlockMatch algorithm is implemented into a web server, accessible at http://bioinformatics.njit.edu/blockmatch. A jar file of the program is also available for download from the web server.

Consensus structure of Pf1 filamentous bacteriophage from X-ray fibre diffraction and solid-state NMR.

Filamentous bacteriophages (filamentous bacterial viruses or Inovirus) are simple and well-characterised macromolecular assemblies that are widely used in molecular biology and biophysics, both as paradigms for studying basic biological questions and as practical tools in areas as diverse as immunology and solid-state physics. The strains fd, M13 and f1 are virtually identical filamentous phages that infect bacteria expressing F-pili, and are sometimes grouped as the Ff phages. For historical reasons fd has often been used for structural studies, but M13 and f1 are more often used for biological experiments. Many other strains have been identified that are genetically quite distinct from Ff and yet have a similar molecular structure and life cycle. One of these, Pf1, gives the highest resolution X-ray fibre diffraction patterns known for filamentous bacteriophage. These diffraction patterns have been used in the past to derive a molecular model for the structure of the phage. Solid-state NMR experiments have been used in separate studies to derive a significantly different model of Pf1. Here we combine previously published X-ray fibre diffraction data and solid-state NMR data to give a consensus structure model for Pf1 filamentous bacteriophage, and we discuss the implications of this model for assembly of the phage at the bacterial membrane.

Fast RNA structure alignment for crossing input structures

The complexity of pairwise RNA structure alignment depends on the structural restrictions assumed for both the input structures and the computed consensus structure. For arbitrarily crossing input and consensus structures, the problem is NP-hard. For non-crossing consensus structures, Jiang et al.'s (2002) [9] algorithm computes the alignment in O ( n 2 m 2 ) time where n and m denote the lengths of the two input sequences. If the input structures are also non-crossing, the problem corresponds to tree editing which can be solved in O ( m 2 n ( 1 + log n m ) ) time (Demaine et al., 2007) [3]. We present a new algorithm that solves the problem for d-crossing structures in O ( d m 2 n log n ) time, where d is a parameter that is one for non-crossing structures, bounded by n for crossing structures, and much smaller than n on many practical examples. Crossing input structures allow for applications where the input is not a fixed structure but is given as base-pair probability matrices.

Fine-tuning structural RNA alignments in the twilight zone

BackgroundA widely used method to find conserved secondary structure in RNA is to first construct a multiple sequence alignment, and then fold the alignment, optimizing a score based on thermodynamics and covariance. This method works best around 75% sequence similarity. However, in a "twilight zone" below 55% similarity, the sequence alignment tends to obscure the covariance signal used in the second phase. Therefore, while the overall shape of the consensus structure may still be found, the degree of conservation cannot be estimated reliably.ResultsBased on a combination of available methods, we present a method named planACstar for improving structure conservation in structural alignments in the twilight zone. After constructing a consensus structure by alignment folding, planACstar abandons the original sequence alignment, refolds the sequences individually, but consistent with the consensus, aligns the structures, irrespective of sequence, by a pure structure alignment method, and derives an improved sequence alignment from the alignment of structures, to be re-submitted to alignment folding, etc.. This circle may be iterated as long as structural conservation improves, but normally, one step suffices.ConclusionsEmploying the tools ClustalW, RNAalifold, and RNAforester, we find that for sequences with 30-55% sequence identity, structural conservation can be improved by 10% on average, with a large variation, measured in terms of RNAalifold's own criterion, the structure conservation index.

A family of non-classical pseudoknots in influenza A and B viruses

A very conserved pseudoknot structure has been shown to fold in influenza virus RNA. The pseudoknot encompasses the 3’ splice site of segment 8 RNA in both influenza A and B viruses. By sequence comparison of influenza virus strains, we derive a consensus motif that defines a novel RNA pseudoknot family. The orientation of the coaxially stacked stems in the influenza pseudoknot differs from that in classical H-pseudoknots. Apart from the size of the loops, the topology of the influenza pseudoknot resembles that of some long-range pseudoknotted conformations. A seed alignment of the influenza pseudoknot family, containing representative strain sequences together with a consensus structure description, has been submitted to the RNA families (Rfam) database.

Multiple structure alignment and consensus identification for proteins

BackgroundAn algorithm is presented to compute a multiple structure alignment for a set of proteins and to generate a consensus (pseudo) protein which captures common substructures present in the given proteins. The algorithm represents each protein as a sequence of triples of coordinates of the alpha-carbon atoms along the backbone. It then computes iteratively a sequence of transformation matrices (i.e., translations and rotations) to align the proteins in space and generate the consensus. The algorithm is a heuristic in that it computes an approximation to the optimal alignment that minimizes the sum of the pairwise distances between the consensus and the transformed proteins.ResultsExperimental results show that the algorithm converges quite rapidly and generates consensus structures that are visually similar to the input proteins. A comparison with other coordinate-based alignment algorithms (MAMMOTH and MATT) shows that the proposed algorithm is competitive in terms of speed and the sizes of the conserved regions discovered in an extensive benchmark dataset derived from the HOMSTRAD and SABmark databases.The algorithm has been implemented in C++ and can be downloaded from the project's web page. Alternatively, the algorithm can be used via a web server which makes it possible to align protein structures by uploading files from local disk or by downloading protein data from the RCSB Protein Data Bank.ConclusionsAn algorithm is presented to compute a multiple structure alignment for a set of proteins, together with their consensus structure. Experimental results show its effectiveness in terms of the quality of the alignment and computational cost.