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37
Elimination Methods
, 2000
"... As pointed out by Duarte and Pyle (1), the two-dimensional (2D) η-θ plot is a Ramachandranlike diagram that can provide us a graphic representation of quantitatively distinct structural features for analyzing and modeling RNA three-dimensional (3D) structures. Particularly, they showed that on this ..."
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Cited by 67 (4 self)
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As pointed out by Duarte and Pyle (1), the two-dimensional (2D) η-θ plot is a Ramachandranlike diagram that can provide us a graphic representation of quantitatively distinct structural features for analyzing and modeling RNA three-dimensional (3D) structures. Particularly, they showed that on this η-θ plot, clusters of nucleotides with similar η and θ pseudo-torsional angles have similar conformational properties and vice versa. To depict this η-θ plot, we prepared a dataset that includes non-redundant crystal structures with minimum resolution of 3.0 ˚A from the PDB database (2). This dataset finally contains 117 crystal RNA structures, particularly including 74 structures used by Wadley et al. (3), with 9,527 nucleotides in total. We then used AMIGOS that was developed by Duarte and Pyle (1) to calculate the η and θ pseudo-torsion angles for all non-terminal nucleotides (9,267 nt in total) from all RNA molecules in the above dataset and plotted these calculated pseudo-torsion angles on the axes of a 2D plot as illustrated in Figure 1. Instead of using the vector quantization (VQ) approach as done in our previous work (4), we here applied the so-called affinity propagation (AP) clustering algorithm, introduced by Frey and Dueck recently (5), to classify all the non-terminal nucleotides in our prepared
RNA structural motifs: Building blocks of a modular biomolecule
- Q. Rev. Biophys
, 2005
"... Abstract. 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 f ..."
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Cited by 32 (0 self)
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Abstract. 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.
WebFR3D––a server for finding, aligning and analyzing recurrent RNA 3D motifs
- Nucleic Acids Res
, 2011
"... WebFR3D—a server for finding, aligning and analyzing recurrent RNA 3D motifs ..."
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Cited by 18 (5 self)
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WebFR3D—a server for finding, aligning and analyzing recurrent RNA 3D motifs
SARA: a server for function annotation of RNA structures
- Nucleic Acids Res
, 2009
"... Recent interest in non-coding RNA transcripts has resulted in a rapid increase of deposited RNA structures in the Protein Data Bank. However, a characterization and functional classification of the RNA structure and function space have only been partially addressed. Here, we introduce the SARA progr ..."
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Cited by 12 (1 self)
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Recent interest in non-coding RNA transcripts has resulted in a rapid increase of deposited RNA structures in the Protein Data Bank. However, a characterization and functional classification of the RNA structure and function space have only been partially addressed. Here, we introduce the SARA program for pair-wise alignment of RNA structures as a web server for structure-based RNA function assignment. The SARA server relies on the SARA program, which aligns two RNA structures based on a unit-vector root-mean-square approach. The likely accuracy of the SARA alignments is assessed by three different P-values estimating the statistical significance of the sequence, secondary structure and tertiary structure identity scores, respectively. Our benchmarks, which relied on a set of 419 RNA structures with known SCOR structural class, indicate that at a negative logarithm of mean P-value higher or equal than 2.5, SARA can assign the correct or a similar SCOR class to 81.4 % and 95.3 % of the benchmark set, respectively. The SARA server is freely accessible via the World Wide Web at
MINAS–a database of metal ions in nucleic AcidS
- Nucleic Acids Res
, 2012
"... Correctly folded into the respective native 3D struc-ture, RNA and DNA are responsible for uncountable key functions in any viable organism. In order to exert their function, metal ion cofactors are closely involved in folding, structure formation and, e.g. in ribozymes, also the catalytic mechanism ..."
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Cited by 6 (0 self)
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Correctly folded into the respective native 3D struc-ture, RNA and DNA are responsible for uncountable key functions in any viable organism. In order to exert their function, metal ion cofactors are closely involved in folding, structure formation and, e.g. in ribozymes, also the catalytic mechanism. The database MINAS, Metal Ions in Nucleic AcidS
Sequence-structure relationships in RNA loops: establishing the basis for loop homology modeling
- Nucleic Acids Res
, 2010
"... The specific function of RNA molecules frequently resides in their seemingly unstructured loop regions. We performed a systematic analysis of RNA loops extracted from experimentally determined three-dimensional structures of RNA molecules. A comprehensive loop-structure data set was created and orga ..."
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Cited by 6 (1 self)
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The specific function of RNA molecules frequently resides in their seemingly unstructured loop regions. We performed a systematic analysis of RNA loops extracted from experimentally determined three-dimensional structures of RNA molecules. A comprehensive loop-structure data set was created and organized into distinct clusters based on structural and sequence similar-ity. We detected clear evidence of the hallmark of homology present in the sequence–structure relationships in loops. Loops differing by <25 % in sequence identity fold into very similar structures. Thus, our results support the application of homology modeling for RNA loop model building. We established a threshold that may guide the sequence divergence-based selection of template structures for RNA loop homology modeling. Of all possible sequences that are, under the assumption of isosteric relationships, theoretically compatible with actual sequences observed in RNA structures, only a small fraction is contained in the Rfam database of RNA sequences and classes implying that the actual RNA loop space may consist of a limited number of unique loop structures and conserved sequences. The loop-structure data sets are made available via an online database, RLooM. RLooM also offers functionalities for the modeling of RNA loop structures in support of RNA engineering and design efforts.
Clustering RNA structural motifs in ribosomal RNAs using secondary structural alignment
- Nucleic Acids Res
, 2012
"... 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 cluster-ing approach for de novo RNA structural motif iden ..."
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Cited by 4 (0 self)
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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 cluster-ing approach for de novo RNA structural motif iden-tification. We applied our approach on a data set containing 5S, 16S and 23S rRNAs and redis-covered 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 signifi-cantly different geometry comparing to the currently known hexaloop motif. Our discoveries presented in this article have largely increased current know-ledge of RNA structural motifs.
RNA Bricks–a database of RNA 3D motifs and their interactions
- Nucleic Acids Res
, 2014
"... The RNA Bricks database ..."
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RsiteDB: a database of protein binding pockets that interact with RNA nucleotide bases
- Nucleic Acids Res
, 2009
"... nucleotide bases ..."
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SETTER: web server for RNA structure comparison
- Nucleic Acids Res
, 2012
"... The recent discoveries of regulatory non-coding RNAs changed our view of RNA as a simple infor-mation transfer molecule. Understanding the archi-tecture and function of active RNA molecules requires methods for comparing and analyzing their 3D structures. While structural alignment of short RNAs is ..."
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Cited by 3 (0 self)
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The recent discoveries of regulatory non-coding RNAs changed our view of RNA as a simple infor-mation transfer molecule. Understanding the archi-tecture and function of active RNA molecules requires methods for comparing and analyzing their 3D structures. While structural alignment of short RNAs is achievable in a reasonable amount of time, large structures represent much bigger challenge. Here, we present the SETTER web server for the RNA structure pairwise comparison utilizing the SETTER (SEcondary sTructure-based TERtiary Structure Similarity Algorithm) algorithm. The SETTER method divides an RNA structure into the set of non-overlapping structural elements called generalized secondary structure units (GSSUs). The SETTER algorithm scales as O(n2) with the size of a GSSUs and as O(n) with the number of GSSUs in the structure. This scaling gives SETTER its high speed as the average size of the GSSU remains constant irrespective of the size of the structure. However, the favorable speed of the algorithm does not compromise its accuracy. The SETTER web server together with the stand-alone implementation of the SETTER algo-rithm are freely accessible at
