Computational approaches to RNA structure prediction, analysis, and design

Laing, Christian; Schlick, Tamar

doi:10.1016/j.sbi.2011.03.015

Cited by 141 publications

(103 citation statements)

References 77 publications

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“…3 We created plots using the AssayToolbox library for R [39,40]. We generate all random graphs using a generator for dynamic graphs 4 derived for vertex combination.…”

Section: Evaluation Setupmentioning

confidence: 99%

“…3 http://www.cbs.tu-darmstadt.de/streAM-Tg.tar.gz. 4 https://github.com/BenjaminSchiller/DNA.datasets the choice of MD integration step lengths as well as trajectory granularity.…”

Section: Runtime Evaluationmentioning

confidence: 99%

“…So far, available models and simulation tools to design and analyze functionally complex RNA based devices are very limited [2]. Although several tools are available to assess secondary as well as tertiary RNA structure [3], current capabilities to simulate dynamics are still underdeveloped [4] and rely heavily on atomistic molecular dynamics (MD) techniques [5]. RNA structure is largely modular and composed of repetitive motifs [4] that form structural elements such as hairpins and stems based on hydrogen-bonding patterns [6].…”

Section: Introductionmentioning

confidence: 99%

“…Although several tools are available to assess secondary as well as tertiary RNA structure [3], current capabilities to simulate dynamics are still underdeveloped [4] and rely heavily on atomistic molecular dynamics (MD) techniques [5]. RNA structure is largely modular and composed of repetitive motifs [4] that form structural elements such as hairpins and stems based on hydrogen-bonding patterns [6]. Such structural modules play an important role for nano design [1,7].…”

Section: Introductionmentioning

confidence: 99%

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StreAM- $$T_g$$ : Algorithms for Analyzing Coarse Grained RNA Dynamics Based on Markov Models of Connectivity-Graphs

Jäger

Schiller

Strufe

et al. 2016

Lecture Notes in Computer Science

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Background:In this work, we present a new coarse grained representation of RNA dynamics. It is based on adjacency matrices and their interactions patterns obtained from molecular dynamics simulations. RNA molecules are well-suited for this representation due to their composition which is mainly modular and assessable by the secondary structure alone. These interactions can be represented as adjacency matrices of k nucleotides. Based on those, we define transitions between states as changes in the adjacency matrices which form Markovian dynamics. The intense computational demand for deriving the transition probability matrices prompted us to develop StreAM-T g , a streambased algorithm for generating such Markov models of k-vertex adjacency matrices representing the RNA. Results:We benchmark StreAM-T g (a) for random and RNA unit sphere dynamic graphs (b) for the robustness of our method against different parameters. Moreover, we address a riboswitch design problem by applying StreAM-T g on six long term molecular dynamics simulation of a synthetic tetracycline dependent riboswitch (500 ns) in combination with five different antibiotics. Conclusions:The proposed algorithm performs well on large simulated as well as real world dynamic graphs. Additionally, StreAM-T g provides insights into nucleotide based RNA dynamics in comparison to conventional metrics like the root-mean square fluctuation. In the light of experimental data our results show important design opportunities for the riboswitch.

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“…3 We created plots using the AssayToolbox library for R [39,40]. We generate all random graphs using a generator for dynamic graphs 4 derived for vertex combination.…”

Section: Evaluation Setupmentioning

confidence: 99%

“…3 http://www.cbs.tu-darmstadt.de/streAM-Tg.tar.gz. 4 https://github.com/BenjaminSchiller/DNA.datasets the choice of MD integration step lengths as well as trajectory granularity.…”

Section: Runtime Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

StreAM- $$T_g$$ : Algorithms for Analyzing Coarse Grained RNA Dynamics Based on Markov Models of Connectivity-Graphs

Jäger

Schiller

Strufe

et al. 2016

Lecture Notes in Computer Science

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“…Liang and Schlick [47,48] systematically assessed existing RNA tertiary structure prediction methods and found that the accuracy is greater than 6.0 Å for most RNAs (50-130 nt), with a mean RMSD of 20 Å. Also, existing RNA tertiary structure prediction methods are mostly not automated, requiring human interaction for further manual adjustments to optimize the generated structures.…”

Section: Rna Secondary Structure Predictionmentioning

confidence: 99%

Large-scale study of long non-coding RNA functions based on structure and expression features

Zhao

Wang

Chen

et al. 2013

Sci. China Life Sci.

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Mammals and other complex organisms can transcribe an abundance of long non-coding RNAs (lncRNAs) that fulfill a wide variety of regulatory roles in many biological processes. These roles, including as scaffolds and as guides for protein-coding genes, mainly depend on the structure and expression level of lncRNAs. In this review, we focus on the current methods for analyzing lncRNA structure and expression, which is basic but necessary information for in-depth, large-scale analysis of lncRNA functions. The ENCODE project, which has published 30 papers to date, including a few that extensively characterize long non-coding RNAs (lncRNAs), has revealed that 76% of the human genome is transcribed to produce a range of lncRNAs [1]. The landscape of lncRNAs in mammals was unveiled by the rapid progress of deep sequencing technology [2] and computational methods to identify lncRNA [3,4]. These lncRNAs participate in a wide variety of biological processes, such as imprinting control, cell differentiation, immune responses through regulating expression, and activity and localization of protein coding genes [5,6]. However, the function and mechanisms of most lncRNAs are still unknown. Here, we combine our work and other related work to systematically illustrate the current methods to study lncRNA function through their structures and expression profiles. RNA secondary structure predictionSecondary structures of RNAs are the basis of their tertiary structures, so we will first briefly review methods for their prediction. Such methods predict standard Watson-Crick base pairs (AU and CG) and non-standard base pairs in a RNA sequence. There are many methods for RNA secondary structure prediction, which are based on different principles. Here we focus on two types of commonly used methods: the minimum free energy method [7][8][9][10][11], and the multiple sequence alignment method [1214]. The minimum free energy methods are now the most widely used methods of RNA secondary structure predic-

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Molecular Modeling of Nucleic Acid Structure

Galindo‐Murillo

Bergonzo

Cheatham

2013

CP Nucleic Acid Chemistry

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This unit is the first in a series of four units covering the analysis of nucleic acid structure by molecular modeling. This unit provides an overview of computer simulation of nucleic acids. Topics include the static structure model, computational graphics and energy models, generation of an initial model, and characterization of the overall three-dimensional structure. Subject GroupNucleic Acid Chemistry; Nucleic Acid Structure and Folding; Structural Analysis of Biomolecules; Experimental Determination of Structure; Folding and Conformational Change Molecular modeling, loosely defined, relates to the use of models to investigate the threedimensional structure, dynamics, energetics, and properties of a molecule or set of molecules. At the heart of this is specification of a molecular model, which provides a molecular structure at an appropriate level of granularity, usually in terms of threedimensional atomic coordinates. Molecular modeling can be approached on many levels, ranging from energy minimization (finding the set of coordinates that minimizes the energy) with a complete ab-initio quantum-mechanical treatment of the energetics, to sampling "reasonable" conformations with a simplified energy representation or potential, to physically manipulatable models where no implicit energy representation is included. These methods serve not only as tools to aid in the interpretation of experimental data, but to directly complement such data by providing a relationship between the macroscopic behaviors observed experimentally and the microscopic properties represented in the model or simulation.As discussed in other units, various molecular modeling tools can serve as conformational search engines for sampling conformational space subject to the restraints inferred from nuclear magnetic resonance (NMR; see UNIT 7.2) and crystallography (see UNIT 7.1 and UNIT 7.6) experiments. This is a critical step in the refinement of three-dimensional atomic structure. Inclusion of some representation of the energy, such as through the use of a specially parameterized empirical force field, can aid in this endeavor by limiting sampling to more realistic (in terms of energy) conformations.Correspondence to: Thomas E. Cheatham, III, tec3@utah.edu. NIH Public Access Author ManuscriptCurr Protoc Nucleic Acid Chem. Author manuscript; available in PMC 2014 May 21. Published in final edited form as:Curr Protoc Nucleic Acid Chem. 2001 November ; 0 7: Unit-7.5. doi:10.1002/0471142700.nc0705s06. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author ManuscriptAs mentioned above, molecular mechanics methods (described in greater detail in UNIT 7.8) can be used not only as a tool in structure refinement, but can directly complement experimental data. For instance, molecular dynamics simulations can be used to aid in the interpretation of NMR order parameters, or to estimate anisotropic rotational diffusion. In addition, computer simulation techniques have the potential to give structural and dynamic insight into the atomic ...

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Computational approaches to RNA structure prediction, analysis, and design

Cited by 141 publications

References 77 publications

StreAM- $$T_g$$ : Algorithms for Analyzing Coarse Grained RNA Dynamics Based on Markov Models of Connectivity-Graphs

StreAM- $$T_g$$ : Algorithms for Analyzing Coarse Grained RNA Dynamics Based on Markov Models of Connectivity-Graphs

Large-scale study of long non-coding RNA functions based on structure and expression features

Molecular Modeling of Nucleic Acid Structure

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