The building blocks and motifs of RNA architecture
Introduction
No single definition exists for RNA motifs, as they can be proposed and analyzed at different levels of RNA structure. As discussed in a previous review, RNA motifs can be broadly defined as recurrent structural elements, subject to constraints [1]. This review is complementary to a recent review of new high-resolution RNA structures that exhaustively catalogued new and recurrent motifs [2••]. Therefore, we do not attempt to comprehensively discuss each newly reported motif, but rather aim to critically review evolving notions of recurrent RNA motifs in the context of RNA function and evolution, and how to identify, find and classify them.
Section snippets
Types of RNA motifs
We can distinguish two main classes of motifs — those that operate at the level of RNA sequence and those that entail a specific three-dimensional (3D) structure, characterized by a set of 3D coordinates. An example of a sequence motif is the Shine–Dalgarno sequence of bacterial mRNAs or the Sm-binding sites of some eukaryotic non-coding RNAs [3]. At an intermediate level of analysis, the secondary structure (2D) of an RNA is prominent because it can be calculated quite accurately from sequence
Secondary structure motifs
How much information can we retrieve from analysis of secondary (2D) structures? Zorn et al. [5] calculate and compare frequency distributions of Watson–Crick (WC) paired nucleotides in helical stems and of nominally unpaired nucleotides in hairpin, internal and junction loops, as they appear in the secondary structures of the 16S and 23S rRNAs. Thus, they treat all bases in ‘loops’ as unpaired, even though a large fraction of them form non-WC base pairs, as is evident from high-resolution 3D
Representations of RNA three-dimensional structure
Different representations can be used to describe molecular structure information [29]. The most basic representation, used by the 3D structure databases, is the Cartesian coordinates of individual atoms, from which other representations can be derived. However, the large number of variables makes the Cartesian representation awkward when comparing structures or searching for recurrent motifs. Internal coordinates (torsion angles) significantly reduce the number of variables and remove the need
Conclusions
Biological data are fundamentally sequence data. Given the ease of obtaining sequence data and the difficulty of determining 3D structures at high resolution, there will always be more sequence data than structural data. The key challenge for RNA structural and computational biologists and bioinformaticians is to fully integrate these two types of data with a common ontology [63]. The fact that structured RNA molecules are mosaics of recurrent modular motifs means that high-resolution 3D
References and recommended reading
Papers of particular interest, published within the annual period of review, have been highlighted as:
• of special interest
•• of outstanding interest
Acknowledgements
NBL acknowledges grant support from the National Institutes of Health (2 R15 GM055898-03) and the American Chemical Society (PRF# 42357 -AC 4).
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