Coarse-Grained HiRE-RNA Model for ab Initio RNA Folding beyond Simple Molecules, Including Noncanonical and Multiple Base Pairings

Cragnolini, Tristan; Laurin, Yoann; Derreumaux, Philippe; Pasquali, Samuela

doi:10.1021/acs.jctc.5b00200

Cited by 68 publications

(111 citation statements)

References 72 publications

(123 reference statements)

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“…TOPRNA captures effects of secondary structure constraints on loop conformations and free energies. HiRE-RNA848586 depicts six-seven pseudoatoms per nucleotide with five pseudoatoms along the backbone. SimRNA8788, Bernauer et al 89,.…”

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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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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“…High-resolution RNA (HiRE-RNA) coarse-grain modeling incorporates seven pseudoatoms per nucleotide rather than four. 143,144 This extra information allows for stacking and base-pairing terms, which are ignored in TOPRNA, but has a slower processing time.…”

Section: Biophysical Characterization Of Lncrna:protein Complexesmentioning

confidence: 99%

Biochemical Methods To Investigate lncRNA and the Influence of lncRNA:Protein Complexes on Chromatin

McFadden

Hargrove

2016

Biochemistry

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Long noncoding RNAs (lncRNAs), defined as non-translated transcripts greater than 200 nucleotides in length, are often differentially expressed throughout developmental stages, tissue types, and disease states. The identification, visualization, and suppression/overexpression of these sequences have revealed impacts on a wide range of biological processes, including epigenetic regulation. Biochemical investigations on select systems have revealed striking insight into the biological roles of lncRNAs and lncRNA:protein complexes, which in turn prompt even more unanswered questions. To begin, multiple protein-and RNA-centric technologies have been employed to isolate lncRNA:protein and lncRNA:chromatin complexes. LncRNA interactions with the multi-subunit protein complex PRC2, which acts as a transcriptional silencer, represent some of the few cases where the binding affinity, selectivity, and activity of a lncRNA:protein complex have been investigated. At the same time, recent reports of full-length lncRNA secondary structures suggest the formation of complex structures with multiple independent folding domains and pave the way for more detailed structural investigations and predictions of lncRNA threedimensional structure. This review will provide an overview of the methods and progress made to date as well as highlight new methods that promise to further inform the molecular recognition, specificity, and function of lncRNAs. Graphical Abstract *Corresponding Author: amanda.hargrove@duke.edu. Notes The authors declare no competing financial interest. HHS Public Access Author Manuscript Author ManuscriptAuthor Manuscript Author ManuscriptThe "noncoding RNA revolution" (Cech, Stetiz) has revealed myriad functional RNA molecules with roles extending far beyond that of a messenger between DNA and protein. 1 The world of noncoding RNAs (ncRNAs), or RNAs that are not usually translated to proteins, came to light in large part as a result of the Encyclopedia of DNA Elements (ENCODE) project. 2 This consortium found that while up to 90% of the genome was transcribed only 1.2% was translated to protein. Furthermore, this large pool of untranslated transcripts demonstrated biochemical indices of function traditionally ascribed solely to proteins. 3 Research exploring the biological activity of these ncRNA transcripts promptly grew. Among the many newly discovered functions of noncoding RNAs, several classes are now known to play critical roles in the regulation of gene expression 1 as well as disease progression. 4 NcRNAs are classified based on size, with small ncRNAs less than 200 n.t. and long noncoding RNAs (lncRNAs) greater than 200 n.t. Several small ncRNA classes, including microRNAs (miRNAs) and small-interfering RNAs (siRNA), regulate gene expression by forming partially complementary duplexes with mRNAs, which in turn promote mRNA degradation or inhibit mRNA translation into peptides. [5][6][7][8] LncRNAs, on the other hand, have been found to exhibit a wide range of regulatory roles, including traffick...

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“…The past decade has seen remarkable progress in RNA structure prediction, especially for small size RNAs (33, 34, 41, 79, 130). However, the prediction accuracy for medium to large (longer than 100 nucleotides) structures has been inconsistent for different RNA structures and sequences, even with the input from experimental data (101).…”

Section: Introductionmentioning

confidence: 99%

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

Sun

Zhang²,

Chen³

2017

Annu. Rev. Biophys.

111

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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Coarse-Grained HiRE-RNA Model for ab Initio RNA Folding beyond Simple Molecules, Including Noncanonical and Multiple Base Pairings

Cited by 68 publications

References 72 publications

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

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

Biochemical Methods To Investigate lncRNA and the Influence of lncRNA:Protein Complexes on Chromatin

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

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