Mathematical and Biological Scientists Assess the State of the Art in RNA Science at an IMA Workshop, RNA in Biology, Bioengineering, and Biotechnology

Schlick, Tamar

doi:10.1615/intjmultcompeng.v8.i4.20

Cited by 6 publications

(5 citation statements)

References 40 publications

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“…However, with the increasing interest and creation of a community devoted to RNA bioinformatics [39], we can anticipate many exciting developments in automated RNA structure prediction approaches in the coming decade. The growing appreciation for the biological importance of RNA makes such efforts more essential than ever.…”

Section: Discussionmentioning

confidence: 99%

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Computational approaches to 3D modeling of RNA

Laing

Schlick²

2010

J. Phys.: Condens. Matter

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108

126

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Many exciting discoveries have recently revealed the versatility of RNA and its importance in a variety of functions within the cell. Since the structural features of RNA are of major importance to their biological function, there is much interest in predicting RNA structure, either in free form or in interaction with various ligands, including proteins, metabolites and other molecules. In recent years, an increasing number of researchers have developed novel RNA algorithms for predicting RNA secondary and tertiary structures. In this review, we describe current experimental and computational advances and discuss recent ideas that are transforming the traditional view of RNA folding. To evaluate the performance of the most recent RNA 3D folding algorithms, we provide a comparative study in order to test the performance of available 3D structure prediction algorithms for an RNA data set of 43 structures of various lengths and motifs. We find that the algorithms vary widely in terms of prediction quality across different RNA lengths and topologies; most predictions have very large root mean square deviations from the experimental structure. We conclude by outlining some suggestions for future RNA folding research.

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Section: Discussionmentioning

confidence: 99%

“…Recent structural or topological representations using graph representation (trees and dual graphs) of RNA secondary structures like RAG [38] constitute innovative methods from an allied field that can help RNA science [39]. …”

Section: Rna Structurementioning

confidence: 99%

Computational approaches to 3D modeling of RNA

Laing

Schlick²

2010

J. Phys.: Condens. Matter

Self Cite

108

126

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“…The computational design of switchable and catalytic ribonucleic acids (RNA) becomes a major challenge for synthetic biology [ 1 ]. So far, available models and simulation tools to design and analyze functionally complex RNA based devices are very limited [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

“…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%

StreAM- $$T_g$$ T g : algorithms for analyzing coarse grained RNA dynamics based on Markov models of connectivity-graphs

Jäger¹,

Schiller²,

Babel³

et al. 2017

Algorithms Mol Biol

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BackgroundIn 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-, a stream-based algorithm for generating such Markov models of k-vertex adjacency matrices representing the RNA.ResultsWe benchmark StreAM- (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- on six long term molecular dynamics simulation of a synthetic tetracycline dependent riboswitch (500 ns) in combination with five different antibiotics.ConclusionsThe proposed algorithm performs well on large simulated as well as real world dynamic graphs. Additionally, StreAM- 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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“…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%

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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Mathematical and Biological Scientists Assess the State of the Art in RNA Science at an IMA Workshop, RNA in Biology, Bioengineering, and Biotechnology

Cited by 6 publications

References 40 publications

Computational approaches to 3D modeling of RNA

Computational approaches to 3D modeling of RNA

StreAM- $$T_g$$ 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

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