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StreAM-[Formula: see text] : algorithms for analyzing coarse grained RNA dynamics based on Markov models of connectivity-graphs

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 mod...

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Autores principales: Jager, Sven, Schiller, Benjamin, Babel, Philipp, Blumenroth, Malte, Strufe, Thorsten, Hamacher, Kay
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5450175/
https://www.ncbi.nlm.nih.gov/pubmed/28572834
http://dx.doi.org/10.1186/s13015-017-0105-0
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author Jager, Sven
Schiller, Benjamin
Babel, Philipp
Blumenroth, Malte
Strufe, Thorsten
Hamacher, Kay
author_facet Jager, Sven
Schiller, Benjamin
Babel, Philipp
Blumenroth, Malte
Strufe, Thorsten
Hamacher, Kay
author_sort Jager, Sven
collection PubMed
description 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-[Formula: see text] , a stream-based algorithm for generating such Markov models of k-vertex adjacency matrices representing the RNA. RESULTS: We benchmark StreAM-[Formula: see text] (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-[Formula: see text] 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-[Formula: see text] 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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spelling pubmed-54501752017-06-01 StreAM-[Formula: see text] : algorithms for analyzing coarse grained RNA dynamics based on Markov models of connectivity-graphs Jager, Sven Schiller, Benjamin Babel, Philipp Blumenroth, Malte Strufe, Thorsten Hamacher, Kay Algorithms Mol Biol Research 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-[Formula: see text] , a stream-based algorithm for generating such Markov models of k-vertex adjacency matrices representing the RNA. RESULTS: We benchmark StreAM-[Formula: see text] (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-[Formula: see text] 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-[Formula: see text] 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. BioMed Central 2017-05-30 /pmc/articles/PMC5450175/ /pubmed/28572834 http://dx.doi.org/10.1186/s13015-017-0105-0 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Jager, Sven
Schiller, Benjamin
Babel, Philipp
Blumenroth, Malte
Strufe, Thorsten
Hamacher, Kay
StreAM-[Formula: see text] : algorithms for analyzing coarse grained RNA dynamics based on Markov models of connectivity-graphs
title StreAM-[Formula: see text] : algorithms for analyzing coarse grained RNA dynamics based on Markov models of connectivity-graphs
title_full StreAM-[Formula: see text] : algorithms for analyzing coarse grained RNA dynamics based on Markov models of connectivity-graphs
title_fullStr StreAM-[Formula: see text] : algorithms for analyzing coarse grained RNA dynamics based on Markov models of connectivity-graphs
title_full_unstemmed StreAM-[Formula: see text] : algorithms for analyzing coarse grained RNA dynamics based on Markov models of connectivity-graphs
title_short StreAM-[Formula: see text] : algorithms for analyzing coarse grained RNA dynamics based on Markov models of connectivity-graphs
title_sort stream-[formula: see text] : algorithms for analyzing coarse grained rna dynamics based on markov models of connectivity-graphs
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5450175/
https://www.ncbi.nlm.nih.gov/pubmed/28572834
http://dx.doi.org/10.1186/s13015-017-0105-0
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