ISFOLD: structure prediction of base pairs in non-helical RNA motifs from isostericity signatures in their sequence alignments

The existence and identity of non-Watson-Crick base pairs (bps) within RNA bulges, internal loops, and hairpin loops cannot reliably be predicted by existing algorithms. We have developed the Isfold (Isosteric Folding) program as a tool to examine patterns of nucleotide substitutions from sequence alignments or mutation experiments and identify plausible bp interactions. We infer these interactions based on the observation that each non-Watson-Crick bp has a signature pattern of isosteric substitutions where mutations can be made that preserve the 3D structure. Isfold produces a dynamic representation of predicted bps within defined motifs in order of their probabilities. The software was developed under Windows XP, and is capable of running on PC and MAC with Matlab 7.1 (SP3) or higher. A PC stand-alone version that does not require Matlab also is available. This software and a user manual are freely available at www.ucsf.edu/frankel/isfold.     

Fine-tuning structural RNA alignments in the twilight zone

Background  

A 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.     

Protein-protein interactions in the membrane: sequence, structural, and biological motifs

Single-span transmembrane (TM) helices have structural and functional roles well beyond serving as mere anchors to tether water-soluble domains in the vicinity of the membrane. They frequently direct the assembly of protein complexes and mediate signal transduction in ways analogous to small modular domains in water-soluble proteins. This review highlights different sequence and structural motifs that direct TM assembly and discusses their roles in diverse biological processes. We believe that TM interactions are potential therapeutic targets, as evidenced by natural proteins that modulate other TM interactions and recent developments in the design of TM-targeting peptides.     

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.     

Multiple sequence alignments

Multiple sequence alignments are very widely used in all areas of DNA and protein sequence analysis. The main methods that are still in use are based on 'progressive alignment' and date from the mid to late 1980s. Recently, some dramatic improvements have been made to the methodology with respect either to speed and capacity to deal with large numbers of sequences or to accuracy. There have also been some practical advances concerning how to combine three-dimensional structural information with primary sequences to give more accurate alignments, when structures are available.     

Detecting DNA-binding helix-turn-helix structural motifs using sequence and structure information

In this work, we analyse the potential for using structural knowledge to improve the detection of the DNA-binding helix–turn–helix (HTH) motif from sequence. Starting from a set of DNA-binding protein structures that include a functional HTH motif and have no apparent sequence similarity to each other, two different libraries of hidden Markov models (HMMs) were built. One library included sequence models of whole DNA-binding domains, which incorporate the HTH motif, the second library included shorter models of ‘partial’ domains, representing only the fraction of the domain that corresponds to the functionally relevant HTH motif itself. The libraries were scanned against a dataset of protein sequences, some containing the HTH motifs, others not. HMM predictions were compared with the results obtained from a previously published structure-based method and subsequently combined with it. The combined method proved more effective than either of the single-featured approaches, showing that information carried by motif sequences and motif structures are to some extent complementary and can successfully be used together for the detection of DNA-binding HTHs in proteins of unknown function.     

Gleaning structural and functional information from correlations in protein multiple sequence alignments

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Identifying novel sequence variants of RNA 3D motifs

Predicting RNA 3D structure from sequence is a major challenge in biophysics. An important sub-goal is accurately identifying recurrent 3D motifs from RNA internal and hairpin loop sequences extracted from secondary structure (2D) diagrams. We have developed and validated new probabilistic models for 3D motif sequences based on hybrid Stochastic Context-Free Grammars and Markov Random Fields (SCFG/MRF). The SCFG/MRF models are constructed using atomic-resolution RNA 3D structures. To parameterize each model, we use all instances of each motif found in the RNA 3D Motif Atlas and annotations of pairwise nucleotide interactions generated by the FR3D software. Isostericity relations between non-Watson–Crick basepairs are used in scoring sequence variants. SCFG techniques model nested pairs and insertions, while MRF ideas handle crossing interactions and base triples. We use test sets of randomly-generated sequences to set acceptance and rejection thresholds for each motif group and thus control the false positive rate. Validation was carried out by comparing results for four motif groups to RMDetect. The software developed for sequence scoring (JAR3D) is structured to automatically incorporate new motifs as they accumulate in the RNA 3D Motif Atlas when new structures are solved and is available free for download.     

Automated identification of RNA 3D modules with discriminative power in RNA structural alignments

Recent progress in predicting RNA structure is moving towards filling the ‘gap’ in 2D RNA structure prediction where, for example, predicted internal loops often form non-canonical base pairs. This is increasingly recognized with the steady increase of known RNA 3D modules. There is a general interest in matching structural modules known from one molecule to other molecules for which the 3D structure is not known yet. We have created a pipeline, metaRNAmodules, which completely automates extracting putative modules from the FR3D database and mapping of such modules to Rfam alignments to obtain comparative evidence. Subsequently, the modules, initially represented by a graph, are turned into models for the RMDetect program, which allows to test their discriminative power using real and randomized Rfam alignments. An initial extraction of 22 495 3D modules in all PDB files results in 977 internal loop and 17 hairpin modules with clear discriminatory power. Many of these modules describe only minor variants of each other. Indeed, mapping of the modules onto Rfam families results in 35 unique locations in 11 different families. The metaRNAmodules pipeline source for the internal loop modules is available at http://rth.dk/resources/mrm.     

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.     

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).     

Scoring profile-to-profile sequence alignments

Sequence alignment profiles have been shown to be very powerful in creating accurate sequence alignments. Profiles are often used to search a sequence database with a local alignment algorithm. More accurate and longer alignments have been obtained with profile-to-profile comparison. There are several steps that must be performed in creating profile-profile alignments, and each involves choices in parameters and algorithms. These steps include (1) what sequences to include in a multiple alignment used to build each profile, (2) how to weight similar sequences in the multiple alignment and how to determine amino acid frequencies from the weighted alignment, (3) how to score a column from one profile aligned to a column of the other profile, (4) how to score gaps in the profile-profile alignment, and (5) how to include structural information. Large-scale benchmarks consisting of pairs of homologous proteins with structurally determined sequence alignments are necessary for evaluating the efficacy of each scoring scheme. With such a benchmark, we have investigated the properties of profile-profile alignments and found that (1) with optimized gap penalties, most column-column scoring functions behave similarly to one another in alignment accuracy; (2) some functions, however, have much higher search sensitivity and specificity; (3) position-specific weighting schemes in determining amino acid counts in columns of multiple sequence alignments are better than sequence-specific schemes; (4) removing positions in the profile with gaps in the query sequence results in better alignments; and (5) adding predicted and known secondary structure information improves alignments.     

Considerations in the identification of functional RNA structural elements in genomic alignments

Background  

Accurate identification of novel, functional noncoding (nc) RNA features in genome sequence has proven more difficult than for exons. Current algorithms identify and score potential RNA secondary structures on the basis of thermodynamic stability, conservation, and/or covariance in sequence alignments. Neither the algorithms nor the information gained from the individual inputs have been independently assessed. Furthermore, due to issues in modelling background signal, it has been difficult to gauge the precision of these algorithms on a genomic scale, in which even a seemingly small false-positive rate can result in a vast excess of false discoveries.     

Fast pairwise structural RNA alignments by pruning of the dynamical programming matrix

It has become clear that noncoding RNAs (ncRNA) play important roles in cells, and emerging studies indicate that there might be a large number of unknown ncRNAs in mammalian genomes. There exist computational methods that can be used to search for ncRNAs by comparing sequences from different genomes. One main problem with these methods is their computational complexity, and heuristics are therefore employed. Two heuristics are currently very popular: pre-folding and pre-aligning. However, these heuristics are not ideal, as pre-aligning is dependent on sequence similarity that may not be present and pre-folding ignores the comparative information. Here, pruning of the dynamical programming matrix is presented as an alternative novel heuristic constraint. All subalignments that do not exceed a length-dependent minimum score are discarded as the matrix is filled out, thus giving the advantage of providing the constraints dynamically. This has been included in a new implementation of the FOLDALIGN algorithm for pairwise local or global structural alignment of RNA sequences. It is shown that time and memory requirements are dramatically lowered while overall performance is maintained. Furthermore, a new divide and conquer method is introduced to limit the memory requirement during global alignment and backtrack of local alignment. All branch points in the computed RNA structure are found and used to divide the structure into smaller unbranched segments. Each segment is then realigned and backtracked in a normal fashion. Finally, the FOLDALIGN algorithm has also been updated with a better memory implementation and an improved energy model. With these improvements in the algorithm, the FOLDALIGN software package provides the molecular biologist with an efficient and user-friendly tool for searching for new ncRNAs. The software package is available for download at http://foldalign.ku.dk.     

Recurrent RNA motifs as probes for studying RNA-protein interactions in the ribosome

To understand how the nucleotide sequence of ribosomal RNA determines its tertiary structure, we developed a new approach for identification of those features of rRNA sequence that are responsible for formation of different short- and long-range interactions. The approach is based on the co-analysis of several examples of a particular recurrent RNA motif. For different cases of the motif, we design combinatorial gene libraries in which equivalent nucleotide positions are randomized. Through in vivo expression of the designed libraries we select those variants that provide for functional ribosomes. Then, analysis of the nucleotide sequences of the selected clones would allow us to determine the sequence constraints imposed on each case of the motif. The constraints shared by all cases are interpreted as providing for the integrity of the motif, while those ones specific for individual cases would enable the motif to fit into the particular structural context. Here we demonstrate the validity of this approach for three examples of the so-called along-groove packing motif found in different parts of ribosomal RNA.     

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.