Analysis of Riboswitch Structure and Function by an Energy Landscape Framework

Quarta, Giulio; Kim, Namhee; Izzo, Joseph A.; Schlick, Tamar

doi:10.1016/j.jmb.2009.08.062

Cited by 27 publications

(43 citation statements)

References 58 publications

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“…The chemical and enzymatic probes used in most of these studies, however, do not provide quantitative measures of riboswitch conformer populations (though in favorable circumstances NMR or fluorescence spectroscopy may provide such estimates) (Rieder et al 2010;Wilson et al 2011). Predicted secondary structures, base-pairing probability predictions at minimal free energy, and comparative sequence analysis can provide a basis for interpreting riboswitch-folding data (Huynen et al 1996;Quarta et al 2009;Halvorsen et al 2010;Kim et al 2011). While predictions of a single lowest energy structure (the so-called ''MFE'') are often used for this purpose, the secondary structure of any given RNA in solution is determined by the distribution of the RNA's secondary structures at thermodynamic equilibrium (Schmitz and Steger 1992;Dirks and Pierce 2004).…”

Section: Discussionmentioning

confidence: 99%

Basis for ligand discrimination between ON and OFF state riboswitch conformations: The case of the SAM-I riboswitch

Boyapati¹,

Huang²,

Spedale³

et al. 2012

RNA

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Riboswitches are RNA elements that bind to effector ligands and control gene expression. Most consist of two domains. S-Adenosyl Methionine (SAM) binds the aptamer domain of the SAM-I riboswitch and induces conformational changes in the expression domain to form an intrinsic terminator (transcription OFF state). Without SAM the riboswitch forms the transcription ON state, allowing read-through transcription. The mechanistic link between the SAM/aptamer recognition event and subsequent secondary structure rearrangement by the riboswitch is unclear. We probed for those structural features of the Bacillus subtilis yitJ SAM-I riboswitch responsible for discrimination between the ON and OFF states by SAM. We designed SAM-I riboswitch RNA segments forming ''hybrid'' structures of the ON and OFF states. The choice of segment constrains the formation of a partial P1 helix, characteristic of the OFF state, together with a partial antiterminator (AT) helix, characteristic of the ON state. For most choices of P1 vs. AT helix lengths, SAM binds with micromolar affinity according to equilibrium dialysis. Mutational analysis and in-line probing confirm that the mode of SAM binding by hybrid structures is similar to that of the aptamer. Altogether, binding measurements and in-line probing are consistent with the hypothesis that when SAM is present, stacking interactions with the AT helix stabilize a partially formed P1 helix in the hybrids. Molecular modeling indicates that continuous stacking between the P1 and the AT helices is plausible with SAM bound. Our findings raise the possibility that conformational intermediates may play a role in ligand-induced aptamer folding.

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Section: Discussionmentioning

confidence: 99%

Basis for ligand discrimination between ON and OFF state riboswitch conformations: The case of the SAM-I riboswitch

Boyapati¹,

Huang²,

Spedale³

et al. 2012

RNA

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“…Our RNA-As-Graphs (RAG) resource represents RNA 2D structures as planar tree or dual graphs to assist the cataloging, analyzing, and designing of RNA structures (16,17). Interesting applications, such as prediction of RNA-like topologies (18,19), in silico modeling of in vitro selection (20), large viral RNA analysis (21), and riboswitch analysis and design (22), have been reported by various groups. The main advantage of graphs is the drastic reduction of the RNA conformational space (i.e., topology or motif space vs. Cartesian space).…”

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confidence: 99%

Graph-based sampling for approximating global helical topologies of RNA

Kim

Laing

Elmetwaly

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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A current challenge in RNA structure prediction is the description of global helical arrangements compatible with a given secondary structure. Here we address this problem by developing a hierarchical graph sampling/data mining approach to reduce conformational space and accelerate global sampling of candidate topologies. Starting from a 2D structure, we construct an initial graph from size measures deduced from solved RNAs and junction topologies predicted by our data-mining algorithm RNAJAG trained on known RNAs. We sample these graphs in 3D space guided by knowledge-based statistical potentials derived from bending and torsion measures of internal loops as well as radii of gyration for known RNAs. Graph sampling results for 30 representative RNAs are analyzed and compared with reference graphs from both solved structures and predicted structures by available programs. This comparison indicates promise for our graph-based sampling approach for characterizing global helical arrangements in large RNAs: graph rmsds range from 2.52 to 28.24 Å for RNAs of size 25-158 nucleotides, and more than half of our graph predictions improve upon other programs. The efficiency in graph sampling, however, implies an additional step of translating candidate graphs into atomic models. Such models can be built with the same idea of graph partitioning and build-up procedures we used for RNA design.RNA 3D graph | Monte Carlo simulated annealing | RNA 3D prediction T he heightened interest in RNA biology with demonstrated successful applications to medicine and technology has presented new challenges to computational scientists in RNA structure prediction. Though general automated prediction of RNA tertiary (3D) structure from the primary sequence remains elusive, many effective approaches exist for analyzing and describing 3D RNA structures as well as predicting reasonably 3D aspects of small RNAs, ranging from coarse-grained modeling (1) to various structure assembly (2), energy minimization (3), molecular dynamics (4), and other conformational sampling approaches (5, 6).Interest in RNA structure prediction and its modular architecture has also led to many analyses of RNA local structure (7-12). In particular, several studies have focused on the helical arrangements formed by internal loops, important points of flexibility that can affect the overall 3D shape of RNAs. Indeed, the bending and torsion of helical arms connected by internal loops define unique helical conformations, as analyzed by AlHashimi and coworkers (7), Tang and Draper (8), Hagerman and coworkers (9), and Olson and coworkers (10). Recently, Pyle and coworkers (11) reported a pseudotorsional angle database from local RNA backbone geometry, and Sim and Levitt (12) cataloged preferred helical arrangements among nucleotide fragment assemblies given a secondary (2D) conformation. However, extensive topological and geometrical analyses over a large diverse set of RNAs do not exist.To such endeavors, mathematical and computational tools have been applied, including gra...

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“…Quarta et al 34 favoured a scatter plot of folding energy versus base pair distance from the ground state. RNA2Dfold 35 considers an abstracted energy surface with two anchor points.…”

Section: Notationmentioning

confidence: 99%

Computational design of RNAs with complex energy landscapes

et al. 2013

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RNA has become an integral building material in synthetic biology. Dominated by their secondary structures, which can be computed efficiently, RNA molecules are amenable not only to in vitro and in vivo selection, but also to rational, computation-based design. While the inverse folding problem of constructing an RNA sequence with a prescribed ground-state structure has received considerable attention for nearly two decades, there have been few efforts to design RNAs that can switch between distinct prescribed conformations. We introduce a user-friendly tool for designing RNA sequences that fold into multiple target structures. The underlying algorithm makes use of a combination of graph coloring and heuristic local optimization to find sequences whose energy landscapes are dominated by the prescribed conformations. A flexible interface allows the specification of a wide range of design goals. We demonstrate that bi- and tri-stable "switches" can be designed easily with moderate computational effort for the vast majority of compatible combinations of desired target structures. RNAdesign is freely available under the GPL-v3 license.

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Analysis of Riboswitch Structure and Function by an Energy Landscape Framework

Cited by 27 publications

References 58 publications

Basis for ligand discrimination between ON and OFF state riboswitch conformations: The case of the SAM-I riboswitch

Basis for ligand discrimination between ON and OFF state riboswitch conformations: The case of the SAM-I riboswitch

Graph-based sampling for approximating global helical topologies of RNA

Computational design of RNAs with complex energy landscapes

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