Representation, searching and discovery of patterns of bases in complex RNA structures

We describe a graph theoretic method designed to perform efficient searches for substructural patterns in nucleic acid structural coordinate databases using a simplified vectorial representation. Two vectors represent each nucleic acid base and the relative positions of bases with respect to one another are described in terms of distances between the defined start and end points of the vectors on each base. These points comprise the nodes and the distances the edges of a graph, and a pattern search can then be performed using a subgraph isomorphism algorithm. The minimal representation was designed to facilitate searches for complex patterns but was first tested on simple, well-characterised arrangements of bases such as base pairs and GNRA-tetraloop receptor interactions. The method performed very well for these interaction types. A survey of side-by-side base interactions, of which the adenosine platform is the best known example, also locates examples of similar base rearrangements that we consider to be important in structural regulation. A number of examples were found, with GU platforms being particularly prevalent. A GC platform in the RNA of the Thermus thermophilus small ribosomal subunit is in an analogous position to an adenosine platform in other species. An unusual GG platform is also observed close to one of the substrate binding sites in Haloarcula marismortui large ribosomal subunit RNA

SPRITE and ASSAM: web servers for side chain 3D-motif searching in protein structures

Similarities in the 3D patterns of amino acid side chains can provide insights into their function despite the absence of any detectable sequence or fold similarities. Search for protein sites (SPRITE) and amino acid pattern search for substructures and motifs (ASSAM) are graph theoretical programs that can search for 3D amino side chain matches in protein structures, by representing the amino acid side chains as pseudo-atoms. The geometric relationship of the pseudo-atoms to each other as a pattern can be represented as a labeled graph where the pseudo-atoms are the graph's nodes while the edges are the inter-pseudo-atomic distances. Both programs require the input file to be in the PDB format. The objective of using SPRITE is to identify matches of side chains in a query structure to patterns with characterized function. In contrast, a 3D pattern of interest can be searched for existing occurrences in available PDB structures using ASSAM. Both programs are freely accessible without any login requirement. SPRITE is available at http://mfrlab.org/grafss/sprite/while ASSAM can be accessed at http://mfrlab.org/grafss/assam/

COGNAC: a web server for searching and annotating hydrogen-bonded base interactions in RNA three-dimensional structures

Hydrogen bonds are crucial factors that stabilize a complex ribonucleic acid (RNA) molecule's three-dimensional (3D) structure. Minute conformational changes can result in variations in the hydrogen bond interactions in a particular structure. Furthermore, networks of hydrogen bonds, especially those found in tight clusters, may be important elements in structure stabilization or function and can therefore be regarded as potential tertiary motifs. In this paper, we describe a graph theoretical algorithm implemented as a web server that is able to search for unbroken networks of hydrogen-bonded base interactions and thus provide an accounting of such interactions in RNA 3D structures. This server, COGNAC (COnnection tables Graphs for Nucleic ACids), is also able to compare the hydrogen bond networks between two structures and from such annotations enable the mapping of atomic level differences that may have resulted from conformational changes due to mutations or binding events. The COGNAC server can be accessed at http://mfrlab.org/grafss/cognac

NASSAM: a server to search for and annotate tertiary interactions and motifs in three-dimensional structures of complex RNA molecules

Similarities in the 3D patterns of RNA base interactions or arrangements can provide insights into their functions and roles in stabilization of the RNA 3D structure. Nucleic Acids Search for Substructures and Motifs (NASSAM) is a graph theoretical program that can search for 3D patterns of base arrangements by representing the bases as pseudo-atoms. The geometric relationship of the pseudo-atoms to each other as a pattern can be represented as a labeled graph where the pseudo-atoms are the graph's nodes while the edges are the inter-pseudo-atomic distances. The input files for NASSAM are PDB formatted 3D coordinates. This web server can be used to identify matches of base arrangement patterns in a query structure to annotated patterns that have been reported in the literature or that have possible functional and structural stabilization implications. The NASSAM program is freely accessible without any login requirement at http://mfrlab.org/grafss/nassam/

Computation of protein geometry and its applications: Packing and function prediction

This chapter discusses geometric models of biomolecules and geometric constructs, including the union of ball model, the weigthed Voronoi diagram, the weighted Delaunay triangulation, and the alpha shapes. These geometric constructs enable fast and analytical computaton of shapes of biomoleculres (including features such as voids and pockets) and metric properties (such as area and volume). The algorithms of Delaunay triangulation, computation of voids and pockets, as well volume/area computation are also described. In addition, applications in packing analysis of protein structures and protein function prediction are also discussed.Comment: 32 pages, 9 figure

Structure of the NheA Component of the Nhe Toxin from Bacillus cereus: Implications for Function

The structure of NheA, a component of the Bacillus cereus Nhe tripartite toxin, has been solved at 2.05 Å resolution using selenomethionine multiple-wavelength anomalous dispersion (MAD). The structure shows it to have a fold that is similar to the Bacillus cereus Hbl-B and E. coli ClyA toxins, and it is therefore a member of the ClyA superfamily of α-helical pore forming toxins (α-PFTs), although its head domain is significantly enlarged compared with those of ClyA or Hbl-B. The hydrophobic β-hairpin structure that is a characteristic of these toxins is replaced by an amphipathic β-hairpin connected to the main structure via a β-latch that is reminiscent of a similar structure in the β-PFT Staphylococcus aureus α-hemolysin. Taken together these results suggest that, although it is a member of an archetypal α-PFT family of toxins, NheA may be capable of forming a β rather than an α pore

Direct observation of DNA threading in flap endonuclease complexes

Maintenance of genome integrity requires that branched nucleic acid molecules are accurately processed to produce double-helical DNA. Flap endonucleases are essential enzymes that trim such branched molecules generated by Okazaki fragment synthesis during replication. Here, we report crystal structures of bacteriophage T5 flap endonuclease in complexes with intact DNA substrates, and products, at resolutions of 1.9–2.2 Å. They reveal single-stranded DNA threading through a hole in the enzyme enclosed by an inverted Vshaped helical arch straddling the active site. Residues lining the hole induce an unusual barb-like conformation in the DNA substrate juxtaposing the scissile phosphate and essential catalytic metal ions. A series of complexes and biochemical analyses show how the substrate’s single-stranded branch approaches, threads through, and finally emerges on the far side of the enzyme. Our studies suggest that substrate recognition involves an unusual “flycasting, thread, bend and barb” mechanis

A Burkholderia pseudomallei Toxin Inhibits Helicase Activity of Translation Factor eIF4A

This is the author accepted manuscript. The final version is available from American Association for the Advancement of Science via the DOI in this record.The structure of BPSL1549, a protein of unknown function from Burkholderia pseudomallei, reveals a similarity to Escherichia coli cytotoxic necrotizing factor 1. We found that BPSL1549 acted as a potent cytotoxin against eukaryotic cells and was lethal when administered to mice. Expression levels of bpsl1549 correlate with conditions expected to promote or suppress pathogenicity. BPSL1549 promotes deamidation of glutamine-339 of the translation initiation factor eIF4A, abolishing its helicase activity and inhibiting translation. We propose to name BPSL1549 Burkholderia lethal factor 1

TOPS++FATCAT: Fast flexible structural alignment using constraints derived from TOPS+ Strings Model

<p>Abstract</p> <p>Background</p> <p>Protein structure analysis and comparison are major challenges in structural bioinformatics. Despite the existence of many tools and algorithms, very few of them have managed to capture the intuitive understanding of protein structures developed in structural biology, especially in the context of rapid database searches. Such intuitions could help speed up similarity searches and make it easier to understand the results of such analyses.</p> <p>Results</p> <p>We developed a TOPS++FATCAT algorithm that uses an intuitive description of the proteins' structures as captured in the popular TOPS diagrams to limit the search space of the aligned fragment pairs (AFPs) in the flexible alignment of protein structures performed by the FATCAT algorithm. The TOPS++FATCAT algorithm is faster than FATCAT by more than an order of magnitude with a minimal cost in classification and alignment accuracy. For beta-rich proteins its accuracy is better than FATCAT, because the TOPS+ strings models contains important information of the parallel and anti-parallel hydrogen-bond patterns between the beta-strand SSEs (Secondary Structural Elements). We show that the TOPS++FATCAT errors, rare as they are, can be clearly linked to oversimplifications of the TOPS diagrams and can be corrected by the development of more precise secondary structure element definitions.</p> <p>Software Availability</p> <p>The benchmark analysis results and the compressed archive of the TOPS++FATCAT program for Linux platform can be downloaded from the following web site: <url>http://fatcat.burnham.org/TOPS/</url></p> <p>Conclusion</p> <p>TOPS++FATCAT provides FATCAT accuracy and insights into protein structural changes at a speed comparable to sequence alignments, opening up a possibility of interactive protein structure similarity searches.</p

A unified statistical model to support local sequence order independent similarity searching for ligand-binding sites and its application to genome-based drug discovery

Functional relationships between proteins that do not share global structure similarity can be established by detecting their ligand-binding-site similarity. For a large-scale comparison, it is critical to accurately and efficiently assess the statistical significance of this similarity. Here, we report an efficient statistical model that supports local sequence order independent ligand–binding-site similarity searching. Most existing statistical models only take into account the matching vertices between two sites that are defined by a fixed number of points. In reality, the boundary of the binding site is not known or is dependent on the bound ligand making these approaches limited. To address these shortcomings and to perform binding-site mapping on a genome-wide scale, we developed a sequence-order independent profile–profile alignment (SOIPPA) algorithm that is able to detect local similarity between unknown binding sites a priori. The SOIPPA scoring integrates geometric, evolutionary and physical information into a unified framework. However, this imposes a significant challenge in assessing the statistical significance of the similarity because the conventional probability model that is based on fixed-point matching cannot be applied. Here we find that scores for binding-site matching by SOIPPA follow an extreme value distribution (EVD). Benchmark studies show that the EVD model performs at least two-orders faster and is more accurate than the non-parametric statistical method in the previous SOIPPA version. Efficient statistical analysis makes it possible to apply SOIPPA to genome-based drug discovery. Consequently, we have applied the approach to the structural genome of Mycobacterium tuberculosis to construct a protein–ligand interaction network. The network reveals highly connected proteins, which represent suitable targets for promiscuous drugs