By Rick Russell (auth.), Rick Russell (eds.)
This quantity, written by way of specialists within the box, discusses the present realizing 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 elemental positive aspects of RNA folding, the valuable experimental and computational ways within the box are offered with an emphasis on their person strengths and barriers, and the way they are often mixed to be extra robust than any approach by myself; those methods comprise NMR, unmarried molecule fluorescence, site-directed spin labeling, constitution mapping, comparative series research, graph conception, course-grained 3D modeling, and extra. This quantity could be of curiosity to expert researchers and complicated scholars coming into the sphere of RNA folding.
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Additional info for Biophysics of RNA Folding
2004). Since RNA molecules are hierarchical, maintaining independently stable and conserved secondary folds, RNA’s function depends on its secondary fold or topology. Thus, analysis of the secondary structure provides an excellent starting point for investigating RNA structures. In particular, 2D graphical representations can be used to enumerate, construct, and analyze two-dimensional (2D) RNA secondary structures, and thus explore the theoretical RNA secondary-structure repertoire and its classiﬁcation.
Important factors involving tertiary, thermodynamic, and kinetic parameters should be taken into account in current RNA graphs and design/analysis strategies. Ideally, graphs must be extended into 3D objects to convey geometrical aspects in addition to topological features, and the transition from graphs to atomic-level models is challenging. Combined computational and experimental approaches are also required to produce the most successful results. While graph theory and topology offer one route to approach RNA structure analysis and design, other mathematical and computational tools can also be envisioned for such problems.
They also Directed labeled Graph Favorable structure with topological descriptors Graph merge of tree graphs from RAG Asai (Hamada et al. 2006) Ng (Ng and Mishra 2007) Knisley (Haynes et al. 2006; Koessler et al. 2010) Pseudoknot structure analysis Characterization of motifs, comparison of structures, and exploration of topology in RNA pools for in vitro selection Motif classiﬁcations Noncoding RNA classiﬁcation based on biophysical parameters Noncoding RNA classiﬁcation by families Support vector machine, Gaussian Measurement of similar hairpins in radial basis function kernel, novel precursor microRNAs graphical invariants Multi-layer perception network, Prediction of RNA-like tree graphs graphical invariants based on based on mathematical domination numbers descriptors and their comparison algorithms Free-energy minimization Secondary structure prediction for large RNAs Marginalized kernel function, graphical invariants Graph-mining algorithm Rooted plane tree graph Heitsch (Bakhtin and Heitsch 2008, 2009; Hower and Heitsch 2011) Graphs in which stem-loop, loops, or stems Support vector machine, iterative Wang (Shu et al.