RNA structural motif recognition based on least-squares distance

RNA structural motifs are recurrent structural elements occurring in RNA molecules. RNA structural motif recognition aims to find RNA substructures that are similar to a query motif, and it is important for RNA structure analysis and RNA function prediction. In view of this, we propose a new method known as RNA Structural Motif Recognition based on Least-Squares distance (LS-RSMR) to effectively recognize RNA structural motifs. A test set consisting of five types of RNA structural motifs occurring in Escherichia coli ribosomal RNA is compiled by us. Experiments are conducted for recognizing these five types of motifs. The experimental results fully reveal the superiority of the proposed LS-RSMR compared with four other state-of-the-art methods.     

Crystal structure of a conserved hypothetical protein from Escherichia coli

The crystal structure of a conserved hypothetical protein from Escherichia coli has been determined using X-ray crystallography. The protein belongs to the Cluster of Orthologous Group COG1553 (National Center for Biotechnology Information database, NLM, NIH), for which there was no structural information available until now. Structural homology search with DALI algorism indicated that this protein has a new fold with no obvious similarity to those of other proteins with known three-dimensional structures. The protein quaternary structure consists of a dimer of trimers, which makes a characteristic cylinder shape. There is a large closed cavity with approximate dimensions of 16 Å × 16 Å × 20 Å in the center of the hexameric structure. Six putative active sites are positioned along the equatorial surface of the hexamer. There are several highly conserved residues including two possible functional cysteines in the putative active site. The possible molecular function of the protein is discussed.     

Prediction of functional sites in proteins using conserved functional group analysis

A detailed knowledge of a protein's functional site is an absolute prerequisite for understanding its mode of action at the molecular level. However, the rapid pace at which sequence and structural information is being accumulated for proteins greatly exceeds our ability to determine their biochemical roles experimentally. As a result, computational methods are required which allow for the efficient processing of the evolutionary information contained in this wealth of data, in particular that related to the nature and location of functionally important sites and residues. The method presented here, referred to as conserved functional group (CFG) analysis, relies on a simplified representation of the chemical groups found in amino acid side-chains to identify functional sites from a single protein structure and a number of its sequence homologues. We show that CFG analysis can fully or partially predict the location of functional sites in approximately 96% of the 470 cases tested and that, unlike other methods available, it is able to tolerate wide variations in sequence identity. In addition, we discuss its potential in a structural genomics context, where automation, scalability and efficiency are critical, and an increasing number of protein structures are determined with no prior knowledge of function. This is exemplified by our analysis of the hypothetical protein Ydde_Ecoli, whose structure was recently solved by members of the North East Structural Genomics consortium. Although the proposed active site for this protein needs to be validated experimentally, this example illustrates the scope of CFG analysis as a general tool for the identification of residues likely to play an important role in a protein's biochemical function. Thus, our method offers a convenient solution to rapidly and automatically process the vast amounts of data that are beginning to emerge from structural genomics projects.     

Progress of structural genomics initiatives: an analysis of solved target structures

The explosion in gene sequence data and technological breakthroughs in protein structure determination inspired the launch of structural genomics (SG) initiatives. An often stated goal of structural genomics is the high-throughput structural characterisation of all protein sequence families, with the long-term hope of significantly impacting on the life sciences, biotechnology and drug discovery. Here, we present a comprehensive analysis of solved SG targets to assess progress of these initiatives. Eleven consortia have contributed 316 non-redundant entries and 323 protein chains to the Protein Data Bank (PDB), and 459 and 393 domains to the CATH and SCOP structure classifications, respectively. The quality and size of these proteins are comparable to those solved in traditional structural biology and, despite huge scope for duplicated efforts, only 14% of targets have a close homologue (>/=30% sequence identity) solved by another consortium. Analysis of CATH and SCOP revealed the significant contribution that structural genomics is making to the coverage of superfamilies and folds. A total of 67% of SG domains in CATH are unique, lacking an already characterised close homologue in the PDB, whereas only 21% of non-SG domains are unique. For 29% of domains, structure determination revealed a remote evolutionary relationship not apparent from sequence, and 19% and 11% contributed new superfamilies and folds. The secondary structure class, fold and superfamily distributions of this dataset reflect those of the genomes. The domains fall into 172 different folds and 259 superfamilies in CATH but the distribution is highly skewed. The most populous of these are those that recur most frequently in the genomes. Whilst 11% of superfamilies are bacteria-specific, most are common to all three superkingdoms of life and together the 316 PDB entries have provided new and reliable homology models for 9287 non-redundant gene sequences in 206 completely sequenced genomes. From the perspective of this analysis, it appears that structural genomics is on track to be a success, and it is hoped that this work will inform future directions of the field.     

Structure-based functional identification of a novel heme-binding protein from <Emphasis Type=Italic>Thermus thermophilus</Emphasis> HB8

The TT1485 gene from Thermus thermophilus HB8 encodes a hypothetical protein of unknown function with about 20 sequence homologs of bacterial or archaeal origin. Together they form a family of uncharacterized proteins, the cluster of orthologous group COG3253. Using a combination of amino acid sequence analysis, three-dimensional structural studies and biochemical assays, we identified TT1485 as a novel heme-binding protein. The crystal structure reveals that this protein is a pentamer and each monomer exhibits a β-barrel fold. TT1485 is structurally similar to muconolactone isomerase, but this provided no functional clues. Amino acid sequence analysis revealed remote homology to a heme enzyme, chlorite dismutase. Strikingly, amino acid residues that are highly conserved in the homologous hypothetical proteins and chlorite dismutase cluster around a deep cavity on the surface of each monomer. Molecular modeling shows that the cavity can accommodate a heme group with a strictly conserved His as a heme ligand. TT1485 reconstituted with iron protoporphyrin IX chloride gave a low chlorite dismutase activity, indicating that TT1485 catalyzes a reaction other than chlorite degradation. The presence of a possible Fe–His–Asp triad in the heme proximal site suggests that TT1485 functions as a novel heme peroxidase to detoxify hydrogen peroxide within the cell.     

cDNA cloning and characterization of Drb1, a new member of RRM-type neural RNA-binding protein

Neural RNA recognition motif (RRM)-type RNA-binding proteins play essential roles in neural development. To search for a new member of neural RRM-type RNA-binding protein, we screened rat cerebral expression library with polyclonal antibody against consensus RRM sequences. We have cloned and characterized a rat cDNA that belongs to RRM-type RNA-binding protein family, which we designate as drb1. Orthologs of drb1 exist in human and mouse. The predicted amino acid sequence reveals an open reading frame of 476 residues with a corresponding molecular mass of 53kDa and consists of four RNA-binding domains. drb1 gene is specifically expressed in fetal (E12, E16) rat brain and gradually reduced during development. In situ hybridization demonstrated neuron-specific signals in fetal rat brain. RNA-binding assay indicated that human Drb1 protein possesses binding preference on poly(C)RNA. These results indicate that Drb1 is a new member of neural RNA-binding proteins, which expresses under spatiotemporal control.     

Alternative conformations at the RNA-binding surface of the N-terminal U2AF(65) RNA recognition motif

The essential pre-mRNA splicing factor, U2 auxiliary factor 65KD (U2AF(65)) recognizes the polypyrimidine tract (Py-tract) consensus sequence of the pre-mRNA using two RNA recognition motifs (RRMs), the most prevalent class of eukaryotic RNA-binding domain. The Py-tracts of higher eukaryotic pre-mRNAs are often interrupted with purines, yet U2AF(65) must identify these degenerate Py-tracts for accurate pre-mRNA splicing. Previously, the structure of a U2AF(65) variant in complex with poly(U) RNA suggested that rearrangement of flexible side-chains or bound water molecules may contribute to degenerate Py-tract recognition by U2AF(65). Here, the X-ray structure of the N-terminal RRM domain of U2AF(65) (RRM1) is described at 1.47 A resolution in the absence of RNA. Notably, RNA-binding by U2AF(65) selectively stabilizes pre-existing alternative conformations of three side-chains located at the RNA interface (Arg150, Lys225, and Arg227). Additionally, a flexible loop connecting the beta2/beta3 strands undergoes a conformational change to interact with the RNA. These pre-existing alternative conformations may contribute to the ability of U2AF(65) to recognize a variety of Py-tract sequences. This rare, high-resolution view of an important member of the RRM class of RNA-binding domains highlights the role of alternative side-chain conformations in RNA recognition.