SimRNA: a coarse-grained method for RNA folding simulations and 3D structure prediction

Boniecki, Michał; Łach, Grzegorz; Dawson, Wayne; Tomala, Konrad; Łukasz, Paweł; Sołtysiński, Tomasz; Rother, Kristian; Bujnicki, Janusz

doi:10.1093/nar/gkv1479

Cited by 335 publications

(413 citation statements)

References 60 publications

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“…The computational calculation often has length limitations and bias toward to a close-end structure, 66,67 in which the lowest energy structure often places the ends of the RNA folding window in close proximity. Additionally, the biochemical approach is limited to a few number of candidates and is best established in vitro without taking the dynamic in vivo environment into account.…”

Section: Resultsmentioning

confidence: 99%

RNA structure in splicing: An evolutionary perspective

2016

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Pre-mRNA splicing is a key post-transcriptional regulation process in which introns are excised and exons are ligated together. A novel class of structured intron was recently discovered in fish. Simple expansions of complementary AC and GT dimers at opposite boundaries of an intron were found to form a bridging structure, thereby enforcing correct splice site pairing across the intron. In some fish introns, the RNA structures are strong enough to bypass the need of regulatory protein factors for splicing. Here, we discuss the prevalence and potential functions of highly structured introns. In humans, structured introns usually arise through the co-occurrence of C and G-rich repeats at intron boundaries. We explore the potentially instructive example of the HLA receptor genes. In HLA pre-mRNA, structured introns flank the exons that encode the highly polymorphic b sheet cleft, making the processing of the transcript robust to variants that disrupt splicing factor binding. While selective forces that have shaped HLA receptor are fairly atypical, numerous other highly polymorphic genes that encode receptors contain structured introns. Finally, we discuss how the elevated mutation rate associated with the simple repeats that often compose structured intron can make structured introns themselves rapidly evolving elements.

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Section: Resultsmentioning

confidence: 99%

RNA structure in splicing: An evolutionary perspective

2016

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“…For CG models, based on fewer degrees of freedoms, the force field can be both distance- and orientation-dependent and is often model-dependent. CG energy functions describe base stacking and base pairing interactions by incorporating simple geometric constraint (68), pairwise (140) and many-body distance-dependent (46, 125) potentials, and specific distance- and orientation-dependent functions (34, 44, 65), or more sophisticated multi-atom potentials in the form of multidimensional grids (12). CG models based on highly simplified low-resolution representation (e.g., less than three interaction sites per nucleotide) cannot provide sufficient details for noncanonical base pairing (85) and base-phosphate interactions (148).…”

Section: Three-dimensional (3d) Structure Predictionmentioning

confidence: 99%

Theory and Modeling of RNA Structure and Interactions with Metal Ions and Small Molecules

Sun

Zhang²,

Chen³

2017

Annu. Rev. Biophys.

112

110

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In addition to continuous rapid progress in RNA structure determination, probing, and biophysical studies, the past decade has seen remarkable advances in the development of a new generation of RNA folding theories and models. Here, we review RNA structure prediction models and models for ion-RNA and ligand-RNA interactions. These new models are becoming increasingly important for mechanistic understanding of RNA function and quantitative design of RNA nanotechnology. We focus on new methods for physics-based, knowledge-based, and experimental data-directed modeling for RNA structures, and explore the new theories for the predictions of metal ion and ligand binding sites and metal ion-dependent RNA stabilities. The integration of these new methods with theories for the cellular environment effects, such as molecular crowding and cotranscriptional folding, may ultimately lead to an all-encompassing RNA folding model.

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“…HiRE-RNA848586 depicts six-seven pseudoatoms per nucleotide with five pseudoatoms along the backbone. SimRNA8788, Bernauer et al 89,. as well as the previous generation and current RACER model studied909192, all represent RNA with five pseudoatoms per nucleotide.…”

Section: -D Structure Prediction Modelsmentioning

confidence: 99%

Capturing RNA Folding Free Energy with Coarse-Grained Molecular Dynamics Simulations

Bell

Cheng

Salazar

et al. 2017

Sci Rep

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We introduce a coarse-grained RNA model for molecular dynamics simulations, RACER (RnA CoarsE-gRained). RACER achieves accurate native structure prediction for a number of RNAs (average RMSD of 2.93 Å) and the sequence-specific variation of free energy is in excellent agreement with experimentally measured stabilities (R2 = 0.93). Using RACER, we identified hydrogen-bonding (or base pairing), base stacking, and electrostatic interactions as essential driving forces for RNA folding. Also, we found that separating pairing vs. stacking interactions allowed RACER to distinguish folded vs. unfolded states. In RACER, base pairing and stacking interactions each provide an approximate stability of 3–4 kcal/mol for an A-form helix. RACER was developed based on PDB structural statistics and experimental thermodynamic data. In contrast with previous work, RACER implements a novel effective vdW potential energy function, which led us to re-parameterize hydrogen bond and electrostatic potential energy functions. Further, RACER is validated and optimized using a simulated annealing protocol to generate potential energy vs. RMSD landscapes. Finally, RACER is tested using extensive equilibrium pulling simulations (0.86 ms total) on eleven RNA sequences (hairpins and duplexes).

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SimRNA: a coarse-grained method for RNA folding simulations and 3D structure prediction

Cited by 335 publications

References 60 publications

RNA structure in splicing: An evolutionary perspective

RNA structure in splicing: An evolutionary perspective

Theory and Modeling of RNA Structure and Interactions with Metal Ions and Small Molecules

Capturing RNA Folding Free Energy with Coarse-Grained Molecular Dynamics Simulations

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