Annotation of tertiary interactions in RNA structures reveals variations and correlations

RNA tertiary motifs play an important role in RNA folding and biochemical functions. To help interpret the complex organization of RNA tertiary interactions, we comprehensively analyze a data set of 54 high-resolution RNA crystal structures for motif occurrence and correlations. Specifically, we search seven recognized categories of RNA tertiary motifs (coaxial helix, A-minor, ribose zipper, pseudoknot, kissing hairpin, tRNA D-loop/T-loop, and tetraloop-tetraloop receptor) by various computer programs. For the nonredundant RNA data set, we find 613 RNA tertiary interactions, most of which occur in the 16S and 23S rRNAs. An analysis of these motifs reveals the diversity and variety of A-minor motif interactions and the various possible loop-loop receptor interactions that expand upon the tetraloop-tetraloop receptor. Correlations between motifs, such as pseudoknot or coaxial helix with A-minor, reveal higher-order patterns. These findings may ultimately help define tertiary structure restraints for RNA tertiary structure prediction. A complete annotation of the RNA diagrams for our data set is available at http://www.biomath.nyu.edu/motifs/.     

Automated extraction and classification of RNA tertiary structure cyclic motifs

A minimum cycle basis of the tertiary structure of a large ribosomal subunit (LSU) X-ray crystal structure was analyzed. Most cycles are small, as they are composed of 3- to 5 nt, and repeated across the LSU tertiary structure. We used hierarchical clustering to quantify and classify the 4 nt cycles. One class is defined by the GNRA tetraloop motif. The inspection of the GNRA class revealed peculiar instances in sequence. First is the presence of UA, CA, UC and CC base pairs that substitute the usual sheared GA base pair. Second is the revelation of GNR(X(n))A tetraloops, where X(n) is bulged out of the classical GNRA structure, and of GN/RA formed by the two strands of interior-loops. We were able to unambiguously characterize the cycle classes using base stacking and base pairing annotations. The cycles identified correspond to small and cyclic motifs that compose most of the LSU RNA tertiary structure and contribute to its thermodynamic stability. Consequently, the RNA minimum cycles could well be used as the basic elements of RNA tertiary structure prediction methods.     

Automated classification of RNA 3D motifs and the RNA 3D Motif Atlas

The analysis of atomic-resolution RNA three-dimensional (3D) structures reveals that many internal and hairpin loops are modular, recurrent, and structured by conserved non-Watson–Crick base pairs. Structurally similar loops define RNA 3D motifs that are conserved in homologous RNA molecules, but can also occur at nonhomologous sites in diverse RNAs, and which often vary in sequence. To further our understanding of RNA motif structure and sequence variability and to provide a useful resource for structure modeling and prediction, we present a new method for automated classification of internal and hairpin loop RNA 3D motifs and a new online database called the RNA 3D Motif Atlas. To classify the motif instances, a representative set of internal and hairpin loops is automatically extracted from a nonredundant list of RNA-containing PDB files. Their structures are compared geometrically, all-against-all, using the FR3D program suite. The loops are clustered into motif groups, taking into account geometric similarity and structural annotations and making allowance for a variable number of bulged bases. The automated procedure that we have implemented identifies all hairpin and internal loop motifs previously described in the literature. All motif instances and motif groups are assigned unique and stable identifiers and are made available in the RNA 3D Motif Atlas (http://rna.bgsu.edu/motifs), which is automatically updated every four weeks. The RNA 3D Motif Atlas provides an interactive user interface for exploring motif diversity and tools for programmatic data access.     

Prediction of geometrically feasible three-dimensional structures of pseudoknotted RNA through free energy estimation

Accurate free energy estimation is essential for RNA structure prediction. The widely used Turner''s energy model works well for nested structures. For pseudoknotted RNAs, however, there is no effective rule for estimation of loop entropy and free energy. In this work we present a new free energy estimation method, termed the pseudoknot predictor in three-dimensional space (pk3D), which goes beyond Turner''s model. Our approach treats nested and pseudoknotted structures alike in one unifying physical framework, regardless of how complex the RNA structures are. We first test the ability of pk3D in selecting native structures from a large number of decoys for a set of 43 pseudoknotted RNA molecules, with lengths ranging from 23 to 113. We find that pk3D performs slightly better than the Dirks and Pierce extension of Turner''s rule. We then test pk3D for blind secondary structure prediction, and find that pk3D gives the best sensitivity and comparable positive predictive value (related to specificity) in predicting pseudoknotted RNA secondary structures, when compared with other methods. A unique strength of pk3D is that it also generates spatial arrangement of structural elements of the RNA molecule. Comparison of three-dimensional structures predicted by pk3D with the native structure measured by nuclear magnetic resonance or X-ray experiments shows that the predicted spatial arrangement of stems and loops is often similar to that found in the native structure. These close-to-native structures can be used as starting points for further refinement to derive accurate three-dimensional structures of RNA molecules, including those with pseudoknots.     

A compact RNA tertiary structure contains a buried backbone-K+ complex

The structure of a 58 nucleotide ribosomal RNA fragment buries several phosphate groups of a hairpin loop within a large tertiary core. During refinement of an X-ray crystal structure containing this RNA, a potassium ion was found to be contacted by six oxygen atoms from the buried phosphate groups; the ion is contained completely within the solvent-accessible surface of the RNA. The electrostatic potential at the ion chelation site is unusually large, and more than compensates for the substantial energetic penalties associated with partial dehydration of the ion and displacement of delocalized ions. The very large predicted binding free energy, approximately -30 kcal/mol, implies that the site must be occupied for the RNA to fold. These findings agree with previous studies of the ion-dependent folding of tertiary structure in this RNA, which concluded that a monovalent ion was bound in a partially dehydrated environment where Mg2+ could not easily compete for binding. By compensating the unfavorable free energy of buried phosphate groups with a chelated ion, the RNA is able to create a larger and more complex tertiary fold than would be possible otherwise.     

Computing tertiary structures of proteins

Using only data on sequence, a method of computing a low-resolution tertiary structure of a protein is described. The steps are: (a) Estimate the distances of individual residues from the centroid of the molecule, using data on hydrophobicity and additional geometrical constraints. (b) Using these distances, construct a two-valued matrix whose elements, the distances between residues, are greater or less thanR, the radius of the molecule. (c) Optimize to obtain a three-dimensional structure. This procedure requires modest computing facilities and is applicable to proteins with 164 residues and presumably more. It produces structures withr (correlation between inter-residue distances in the computed and native structures) between 0.5 and 0.7. Furthermore, correct inference of two or three long-range contacts suffices to yield structures withr values of 0.8–0.9. Because segments forming parallel or antiparallel folding structures intersect the radius vector at similar angles, from centroidal point distances it is possible to infer some of these long-range contacts by an elaboration of the procedure used to construct the input matrix. A criterion is also described which can be used to determine the quality of a proposed input matrix even when the native structure is not known.     

Improved prediction of RNA tertiary structure with insights into native state dynamics

The importance of RNA tertiary structure is evident from the growing number of published high resolution NMR and X-ray crystallographic structures of RNA molecules. These structures provide insights into function and create a knowledge base that is leveraged by programs such as Assemble, ModeRNA, RNABuilder, NAST, FARNA, Mc-Sym, RNA2D3D, and iFoldRNA for tertiary structure prediction and design. While these methods sample native-like RNA structures during simulations, all struggle to capture the native RNA conformation after scoring. We propose RSIM, an improved RNA fragment assembly method that preserves RNA global secondary structure while sampling conformations. This approach enhances the quality of predicted RNA tertiary structure, provides insights into the native state dynamics, and generates a powerful visualization of the RNA conformational space. RSIM is available for download from http://www.github.com/jpbida/rsim.     

The UAA/GAN internal loop motif: a new RNA structural element that forms a cross-strand AAA stack and long-range tertiary interactions

Analysis of aligned RNA sequences and high-resolution crystal structures has revealed a new RNA structural element, termed the UAA/GAN motif. Found in internal loops of the 23 S rRNA, as well as in RNase P RNA and group I and II introns, this six-nucleotide motif adopts a distinctive local structure that includes two base-pairs with non-canonical conformations and three conserved adenine bases, which form a cross-strand AAA stack in the minor groove. Most importantly, the motif invariably forms long-range tertiary contacts, as the AAA stack typically forms A-minor interactions and the flipped-out N nucleotide forms additional contacts that are specific to the structural context of each loop. The widespread presence of this motif and its propensity to form long-range contacts suggest that it plays a critical role in defining the architectures of structured RNAs.     

The K-turn motif in riboswitches and other RNA species

The kink turn is a widespread structure motif that introduces a tight bend into the axis of duplex RNA. This generally functions to mediate tertiary interactions, and to serve as a specific protein binding site. K-turns or closely related structures are found in at least seven different riboswitch structures, where they function as key architectural elements that help generate the ligand binding pocket. This article is part of a Special Issue entitled: Riboswitches.     

Characterization of an RNA receptor motif that recognizes a GCGA tetraloop

Tertiary interactions between a new RNA motif and RNA tetraloops were analyzed to determine whether this new motif shows preference for a GCGA tetraloop. In the structural context of a ligase ribozyme, this motif discriminated GCGA loop from 3 other tetraloops. The affinity between the GCGA loop and its receptor is strong enough to carry out the ribozyme activity.     

A new model for the tertiary structure of 5S ribonucleic acid in plants

Chemical, enzymatic and physicochemical methods of a structural analysis of 5S rRNAs in lupine, wheat germ, and other plants led us to propose a new three-dimensional model of these molecules The main features of the model are tertiary interactions between the β- and γ-domains of the molecule, specifically nucleotides (34)CCCA(37) in loop C and nucleotides (85)GGGU(88) in loop D. In addition we propose tertiary base-pairing in A100-U53 between loops B and E. We have confirmed this model by NMR spectroscopy and by chemical modification with diethylpyrocarbonate. Our results are consistent with the proposed model and are also applicable to all eukaryotic 5S rRNAs. Our model is clearly differentiated from others by intramolecular tertiary hydrogen bonds between the two domains.     

构建适用于蛋白质家族分类的相似性网络

图聚类用于蛋白质分类问题可以获得较好结果,其前提是将蛋白质之间复杂的相互关系转化为适当的相似性网络作为图聚类分类的输入数据。本文提出一种基于BLAST检索的相似性网络构建方法,从目标蛋白质序列出发,通过若干轮次的BLAST检索逐步从数据库中提取与目标蛋白质直接或间接相关的序列,构成关联集。关联集中序列之间的相似性关系即相似性网络,可作为图聚类算法的分类依据。对Pfam数据库中依直接相似关系难以正确分类的蛋白质的计算表明,按本文方法构建的相似性网络取得了比较满意的结果。     

The adenosine wedge: A new structural motif in ribosomal RNA

Here, we present a new recurrent RNA arrangement, the so-called adenosine wedge (A-wedge), which is found in three places of the ribosomal RNA in both ribosomal subunits. The arrangement has a hierarchical structure, consisting of elements previously described as recurrent motifs, namely, the along-groove packing motif, the A-minor and the hook-turn. Within the A-wedge, these elements are involved in different types of cause–effect relationships, providing together for the particular tertiary structure of the motif.     

Thioesterases: A new perspective based on their primary and tertiary structures

Thioesterases (TEs) are classified into EC 3.1.2.1 through EC 3.1.2.27 based on their activities on different substrates, with many remaining unclassified (EC 3.1.2.–). Analysis of primary and tertiary structures of known TEs casts a new light on this enzyme group. We used strong primary sequence conservation based on experimentally proved proteins as the main criterion, followed by verification with tertiary structure superpositions, mechanisms, and catalytic residue positions, to accurately define TE families. At present, TEs fall into 23 families almost completely unrelated to each other by primary structure. It is assumed that all members of the same family have essentially the same tertiary structure; however, TEs in different families can have markedly different folds and mechanisms. Conversely, the latter sometimes have very similar tertiary structures and catalytic mechanisms despite being only slightly or not at all related by primary structure, indicating that they have common distant ancestors and can be grouped into clans. At present, four clans encompass 12 TE families. The new constantly updated ThYme (Thioester‐active enzYmes) database contains TE primary and tertiary structures, classified into families and clans that are different from those currently found in the literature or in other databases. We review all types of TEs, including those cleaving CoA, ACP, glutathione, and other protein molecules, and we discuss their structures, functions, and mechanisms.