Biophysics of RNA Folding by Rick Russell (auth.), Rick Russell (eds.)

By Rick Russell (auth.), Rick Russell (eds.)

This quantity, written through specialists within the box, discusses the present knowing of the biophysical ideas that govern RNA folding, with featured RNAs together with the ribosomal RNAs, viral RNAs, and self-splicing introns. as well as the basic beneficial properties of RNA folding, the principal experimental and computational methods within the box are awarded with an emphasis on their person strengths and barriers, and the way they are often mixed to be extra strong than any approach on my own; those techniques contain NMR, unmarried molecule fluorescence, site-directed spin labeling, constitution mapping, comparative series research, graph idea, course-grained 3D modeling, and extra. This quantity may be of curiosity to expert researchers and complicated scholars coming into the sector of RNA folding.

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In this representation, a bulge motif is considered to be an internal symmetric or asymmetric loop with more than one unmatched nucleotide or one unstable base pair. Tree graphs offer an intuitive description of non-pseudoknot topologies, but dual graphs are needed to represent all RNA topologies including pseudoknots. The transformation from dual to tree graph can be accomplished by exchanging the vertices and edges for opposite motifs (Gan et al. 2003, 2004; Fera et al. 2004; Izzo et al. 2011).

1 Features and Utilities of RAG RAG organizes RNAs by graph ID, Laplacian eigenvalues, and classification (existing, RNA-like, and non-RNA-like). RAG archives tree graphs having up to 10 vertices and dual graphs up to 9 vertices to cover RNA topologies up to about 200 nt. Tree and dual graphs are organized by their topological characteristics measured by the vertex number V and the second smallest Laplacian eigenvalue. In addition, RAG contains the RNAMatrix program to assist structural and functional identification of RNA motifs.

RAG representations have also been used in pseudoknot analysis. Enumeration based on dual graph generation and edge-cut theory suggests that more than 90%of the potential RNA structure universe is dominated by pseudoknots (Kim et al. 2004). This fact stimulated further research in pseudoknot analysis, resulting in Rodland’s modified tree graphs, which represent and enumerate H-type pseudoknots (Rodland 2006). Schlick and coworkers used RAG dual graphs to reveal modular RNA architectures by computational analysis of existing pseudoknots, and ribosomal RNAs using dual graph isomorphism (Pasquali et al.

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