Computational approaches to 3D modeling of RNA

Laing, Christian; Schlick, Tamar

doi:10.1088/0953-8984/22/28/283101

Cited by 108 publications

(130 citation statements)

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“…Programs MC-Sym (2), FARNA (3), and NAST (1) produce all-atom models from 2D structures based on fragment libraries (MC-Sym and FARNA) or one-bead models (NAST). Though these tools predict small structures (<40 nt) reasonably, errors increase as RNA lengths increase (6). Here, we translate predicted all-atom models from these programs to 3D graphs and compute graph rmsds between these graphs and our predictions in Table 1 and SI Appendix, Table S2.…”

Section: Assessment Of Mc/sa Results Without Knowledge Of Referencementioning

confidence: 99%

“…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).…”

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confidence: 99%

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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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Section: Assessment Of Mc/sa Results Without Knowledge Of Referencementioning

confidence: 99%

mentioning

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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“…One limitation of this study is the use of an in silico prediction of the secondary structures of the 3 -UTR. Sfold is unable to predict pseudoknots and the accuracy of the prediction also diminishes as the size of the RNA increases (Laing & Schlick, 2010). A significant amount of research has nevertheless been conducted to accurately determine the energies that stabilize helices (SantaLucia & Turner, 1997;Xia et al, 1998;Mathews et al, 1999) and secondary structure prediction remains a powerful tool.…”

Section: Discussionmentioning

confidence: 99%

Polymorphisms within the COL5A1 3′‐UTR That Alters mRNA Structure and the MIR608 Gene are Associated with Achilles Tendinopathy

Abrahams

Laguette

Prince

et al. 2013

Annals of Human Genetics

119

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SummaryCOL5A1 encodes for the α1 chain of type V collagen, an important regulator of fibril assembly in tendons, ligaments and other connective tissues. A polymorphism (rs12722) within the functional COL5A1 3 -untranslated region (UTR) has been shown to associate with chronic Achilles tendinopathy and other exercise-related phenotypes. The COL5A1 3 -UTR contains several putative cis-acting elements including a functional Hsa-miR-608 binding site. The aim of this study was to determine whether previously uncharacterized polymorphisms within a functional region of the COL5A1 3 -UTR or the MIR608 gene are associated with chronic Achilles tendinopathy. The effect of these COL5A1 3 -UTR polymorphisms on the 3 -UTR predicted mRNA secondary structure was also investigated. One hundred and sixty Caucasian chronic Achilles tendinopathic and 342 control participants were genotyped for the COL5A1 3 -UTR markers rs71746744, rs16399 and rs1134170, as well as marker rs4919510 within MIR608. All four genetic markers were independently associated with chronic Achilles tendinopathy. The COL5A1 polymorphisms appear to alter the predicted secondary structure of the 3 -UTR. We propose that the secondary structure plays a role in the regulation of the COL5A1 mRNA stability and by implication type V collagen production.

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“…Currently, there are multiple tools and computational approaches that can describe RNA structure at various levels of detail. [14][15][16] In an important series of works [17][18][19][20][21][22][23] the thermodynamics of RNA secondary structure (i.e., the list of base pairs in the folded state of RNA) was characterized in terms of a nearest-neighbor model for calculating the free energies of various secondary-structure motifs. The nearest-neighbor model is the basis of various tools for the prediction of the secondary structure.…”

Section: Introductionmentioning

confidence: 99%

A nucleotide-level coarse-grained model of RNA

Šulc

Romano

Ouldridge

et al. 2014

The Journal of Chemical Physics

145

132

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We present a new, nucleotide-level model for RNA, oxRNA, based on the coarse-graining methodology recently developed for the oxDNA model of DNA. The model is designed to reproduce structural, mechanical, and thermodynamic properties of RNA, and the coarse-graining level aims to retain the relevant physics for RNA hybridization and the structure of single-and double-stranded RNA. In order to explore its strengths and weaknesses, we test the model in a range of nanotechnological and biological settings. Applications explored include the folding thermodynamics of a pseudoknot, the formation of a kissing loop complex, the structure of a hexagonal RNA nanoring, and the unzipping of a hairpin motif. We argue that the model can be used for efficient simulations of the structure of systems with thousands of base pairs, and for the assembly of systems of up to hundreds of base pairs. The source code implementing the model is released for public use. © 2014 AIP Publishing LLC. [http://dx

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Computational approaches to 3D modeling of RNA

Cited by 108 publications

References 144 publications

Graph-based sampling for approximating global helical topologies of RNA

Graph-based sampling for approximating global helical topologies of RNA

Polymorphisms within the COL5A1 3′‐UTR That Alters mRNA Structure and the MIR608 Gene are Associated with Achilles Tendinopathy

A nucleotide-level coarse-grained model of RNA

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