By Rick Russell (auth.), Rick Russell (eds.)
This quantity, written by way of specialists within the box, discusses the present realizing of the biophysical rules that govern RNA folding, with featured RNAs together with the ribosomal RNAs, viral RNAs, and self-splicing introns. as well as the elemental gains of RNA folding, the critical experimental and computational techniques within the box are awarded with an emphasis on their person strengths and barriers, and the way they are often mixed to be extra robust than any technique by myself; those techniques comprise NMR, unmarried molecule fluorescence, site-directed spin labeling, constitution mapping, comparative series research, graph concept, course-grained 3D modeling, and extra. This quantity may be of curiosity to specialist researchers and complicated scholars getting into the sector of RNA folding.
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Extra resources for Biophysics of RNA Folding
For example, RNA-based drugs (Lee et al. 2005), antibacterial drug targets (Breaker 2009), and delivery of anticancer agents into infected cells (Sullenger and Gilboa 2002) have been pursued. Another recent development is design of new RNAs with ﬂuorescent properties (Paige et al. 2011). More generally, the identiﬁcation and design of new structural and functional classes of RNA have led to exciting research in theoretical and experimental areas. Modularity and hierarchy of RNA structure. Experimental approaches such as chemical probing (Weeks 2010), X-ray crystallography (Burley 2000), and NMR spectroscopy (Al-Hashimi et al.
Comput Appl Biosci 6(5):309–318 Shapiro BA et al (2008) Protocols for the in silico design of RNA nanostructures. Methods Mol Biol 474:93–115 Sharma S et al (2008) iFoldRNA: three-dimensional RNA structure prediction and folding. Bioinformatics 24(17):1951–1952 Sharp PA (2009) The centrality of RNA. Cell 136(4):577–580 Shu W et al (2008) A novel representation of RNA secondary structure based on element-contact graphs. BMC Bioinformatics 9:188 Soukup GA, Breaker RR (1999) Engineering precision RNA molecular switches.
They also merged two small graphs and predicted larger RNA-like structures by applying neural network analysis. Ng et al. used RAG’s topological descriptors, including Laplacian eigevalues, to facilitate measurement of the similarity of hairpins in novel precursor microRNAs (Ng and Mishra 2007). Heitsch and coworkers used a tree graphical representation to analyze the branching degree of entire RNA viral genomes like Hepatitis C (9,400 bases). They also Directed labeled Graph Favorable structure with topological descriptors Graph merge of tree graphs from RAG Asai (Hamada et al.