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Finding 3D motifs in ribosomal RNA structures.

Apostolico A, Ciriello G, Guerra C, Heitsch CE, Hsiao C, Williams LD - Nucleic Acids Res. (2009)

Bottom Line: Furthermore, it provides a new way of characterizing complex 3D motifs, notably junctions, that have been defined and identified in the secondary structure but have not been analyzed and classified in three dimensions.We demonstrate the relevance and utility of our approach by applying it to the Haloarcula marismortui large ribosomal unit.Pending the implementation of a dedicated web server, the code accompanying this article, written in JAVA, is available upon request from the contact author.

View Article: PubMed Central - PubMed

Affiliation: College of Computing, Georgia Institute of Technology, Atlanta, GA 30332-0280, USA.

ABSTRACT
The identification of small structural motifs and their organization into larger subassemblies is of fundamental interest in the analysis, prediction and design of 3D structures of large RNAs. This problem has been studied only sparsely, as most of the existing work is limited to the characterization and discovery of motifs in RNA secondary structures. We present a novel geometric method for the characterization and identification of structural motifs in 3D rRNA molecules. This method enables the efficient recognition of known 3D motifs, such as tetraloops, E-loops, kink-turns and others. Furthermore, it provides a new way of characterizing complex 3D motifs, notably junctions, that have been defined and identified in the secondary structure but have not been analyzed and classified in three dimensions. We demonstrate the relevance and utility of our approach by applying it to the Haloarcula marismortui large ribosomal unit. Pending the implementation of a dedicated web server, the code accompanying this article, written in JAVA, is available upon request from the contact author.

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Given a RNA fragment (a), we measure the distances of all the backbone atoms from the centroid computed with respect to the phosphate atoms (b), the set of distances are then quantized (c) and the frequency of each quantized value is plotted in a 2D histogram, i.e. the shape histogram (d).
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Figure 2: Given a RNA fragment (a), we measure the distances of all the backbone atoms from the centroid computed with respect to the phosphate atoms (b), the set of distances are then quantized (c) and the frequency of each quantized value is plotted in a 2D histogram, i.e. the shape histogram (d).

Mentions: A shape histogram is computed for a fragment, which we define as a sequence of consecutive nucleotides. The shape histogram is the distribution of Euclidean distances of the atoms of the fragment from a particular point, such as the centroid of the atoms. We show now how to compute the shape histogram as a simple geometric descriptor. First we compute the centroid C with respect to the phosphate (P) atoms of the fragment. Then for each nucleotide of the fragment we compute the distances of all its backbone atoms from the centroid. Since a nucleotide is composed of 12 backbone atoms, i.e. the P atom and 11 atoms of the ribose group, a fragment of k nucleotides will generate 12 × k distances. In Figure 2a and b, we illustrate this computation for a tetraloop producing 4 × 12=48 distance values. Once this set of distances has been computed, we quantize them, with a step size equal to 1 Å (Figure 2c). Finally, we count the occurrences of each quantized value, i.e. its frequency. This results in a histogram vector h [h1,hellip; ,hn], where the component hi is the frequency of the distance value di, denoted hi = f(di), i.e. the number of points/atoms at distance di from the centroid C. Histograms have a natural two dimensional plot such as displayed in Figure 2d. Shape histograms are invariant under rigid geometric transformations, so fragments in arbitrary orientations can be matched without explicitly taking rotations into account.Figure 2.


Finding 3D motifs in ribosomal RNA structures.

Apostolico A, Ciriello G, Guerra C, Heitsch CE, Hsiao C, Williams LD - Nucleic Acids Res. (2009)

Given a RNA fragment (a), we measure the distances of all the backbone atoms from the centroid computed with respect to the phosphate atoms (b), the set of distances are then quantized (c) and the frequency of each quantized value is plotted in a 2D histogram, i.e. the shape histogram (d).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2651809&req=5

Figure 2: Given a RNA fragment (a), we measure the distances of all the backbone atoms from the centroid computed with respect to the phosphate atoms (b), the set of distances are then quantized (c) and the frequency of each quantized value is plotted in a 2D histogram, i.e. the shape histogram (d).
Mentions: A shape histogram is computed for a fragment, which we define as a sequence of consecutive nucleotides. The shape histogram is the distribution of Euclidean distances of the atoms of the fragment from a particular point, such as the centroid of the atoms. We show now how to compute the shape histogram as a simple geometric descriptor. First we compute the centroid C with respect to the phosphate (P) atoms of the fragment. Then for each nucleotide of the fragment we compute the distances of all its backbone atoms from the centroid. Since a nucleotide is composed of 12 backbone atoms, i.e. the P atom and 11 atoms of the ribose group, a fragment of k nucleotides will generate 12 × k distances. In Figure 2a and b, we illustrate this computation for a tetraloop producing 4 × 12=48 distance values. Once this set of distances has been computed, we quantize them, with a step size equal to 1 Å (Figure 2c). Finally, we count the occurrences of each quantized value, i.e. its frequency. This results in a histogram vector h [h1,hellip; ,hn], where the component hi is the frequency of the distance value di, denoted hi = f(di), i.e. the number of points/atoms at distance di from the centroid C. Histograms have a natural two dimensional plot such as displayed in Figure 2d. Shape histograms are invariant under rigid geometric transformations, so fragments in arbitrary orientations can be matched without explicitly taking rotations into account.Figure 2.

Bottom Line: Furthermore, it provides a new way of characterizing complex 3D motifs, notably junctions, that have been defined and identified in the secondary structure but have not been analyzed and classified in three dimensions.We demonstrate the relevance and utility of our approach by applying it to the Haloarcula marismortui large ribosomal unit.Pending the implementation of a dedicated web server, the code accompanying this article, written in JAVA, is available upon request from the contact author.

View Article: PubMed Central - PubMed

Affiliation: College of Computing, Georgia Institute of Technology, Atlanta, GA 30332-0280, USA.

ABSTRACT
The identification of small structural motifs and their organization into larger subassemblies is of fundamental interest in the analysis, prediction and design of 3D structures of large RNAs. This problem has been studied only sparsely, as most of the existing work is limited to the characterization and discovery of motifs in RNA secondary structures. We present a novel geometric method for the characterization and identification of structural motifs in 3D rRNA molecules. This method enables the efficient recognition of known 3D motifs, such as tetraloops, E-loops, kink-turns and others. Furthermore, it provides a new way of characterizing complex 3D motifs, notably junctions, that have been defined and identified in the secondary structure but have not been analyzed and classified in three dimensions. We demonstrate the relevance and utility of our approach by applying it to the Haloarcula marismortui large ribosomal unit. Pending the implementation of a dedicated web server, the code accompanying this article, written in JAVA, is available upon request from the contact author.

Show MeSH