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Computational biology of RNA interactions

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Abstract The biodiversity of the RNA world has been underestimated for decades. RNA molecules are key building blocks, sensors, and regulators of modern cells. The biological function of RNA molecules cannot be separated from their ability to bind to and interact with a wide space of chemical species, including small molecules, nucleic acids, and proteins. Computational chemists, physicists, and biologists have developed a rich tool set for modeling and predicting RNA interactions. These interactions are to some extent determined by the binding conformation of the RNA molecule. RNA binding conformations are approximated with often acceptable accuracy by sequence and secondary structure motifs. Secondary structure ensembles of a given RNA molecule can be efficiently computed in many relevant situations by employing a standard energy model for base pair interactions and dynamic programming techniques. The case of bi‐molecular RNA–RNA interactions can be seen as an extension of this approach. However, unbiased transcriptome‐wide scans for local RNA–RNA interactions are computationally challenging yet become efficient if the binding motif/mode is known and other external information can be used to confine the search space. Computational methods are less developed for proteins and small molecules, which bind to RNA with very high specificity. Binding descriptors of proteins are usually determined by in vitro high‐throughput assays (e.g., microarrays or sequencing). Intriguingly, recent experimental advances, which are mostly based on light‐induced cross‐linking of binding partners, render in vivo binding patterns accessible yet require new computational methods for careful data interpretation. The grand challenge is to model the in vivo situation where a complex interplay of RNA binders competes for the same target RNA molecule. Evidently, bioinformaticians are just catching up with the impressive pace of these developments. WIREs RNA 2013, 4:107–120. doi: 10.1002/wrna.1147 This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA Interactions with Proteins and Other Molecules > Protein–RNA Recognition

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Types of interaction structures: (a) concatenation‐like interaction structures have intermolecular base pairs only in the as external bases of their internal structures. Equivalently, the structure on the concatenated sequences is pseudoknot‐free. (b) The structures computed by RNAup and intaRNA have their interactions concentrated in a single interval, but there is no restriction on the enclosing intra‐molecular structures. (c) The structure of the RIP model only exclude the tangle motif (d).

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Apparent dissociation constants of HuR–mRNA complexes at 23.5°C for natural AU‐rich elements (AREs) and UTR sequences (°) and four designed mutants of the TNFα ARE (♦). The dashed line is corresponds to the best fit Kd = 0.118. The inset compares predicted and observed values of for the designed TNFα mutants. The probabilities p(Ξ) that the HuR binding sites are accessible was computed with RNAfold from the ViennaRNA Package (data from Ref 79).

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Basic principle of in vivo cross‐linking experiments with photoactivatable ribonucleosides. Base analogs like 4‐thiouridine are taken up by cells or whole organisms. They are then incorporated into newly transcribed RNA molecules (labeling step). Protein–RNA contacts can be fixed with UV light and are stably maintained in downstream processing. Researches can then select for a protein of interest by an antibody or a specific RNA population by hybridization probes (e.g., poly‐dT beads). PAR‐CLIP experiments facilitate transcriptome‐wide binding site mapping. Quantitative proteomics experiments identify proteins that bind to RNA. Protein occupancy profiling experiments chart the landscape of protein‐bound RNA and the CLASH method70 detects RNA–RNA chimera, which are formed by RNA ligation in cross‐linked RNA–protein complexes.

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Detailed interaction map of the SodB‐RhyB interaction. The panel on the left shows a conventional drawing of the interaction structure emphasizing the dominating binding site reported in Ref 54. Additional contact sites are highlighted, with numbers in italics indicating the fraction equilibrium structures that have intermolecular base pairs in the indicated regions. The r.h.s. panel shows that probabilities of interactions between sequence intervals of both structures. In the middle, the correlation between binding probabilities at the major interaction regions is shown. Only sites 4 and 5 act cooperatively, while the interaction sites at the 3′ and 5′ ends of SodB (marked 3 and 4) are weakly anticorrelated.30

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RNA Interactions with Proteins and Other Molecules > Protein–RNA Recognition
RNA Evolution and Genomics > Computational Analyses of RNA

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