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StreAM-Tg : 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 (2017)
StreAM-Tg : algorithms for analyzing coarse grained RNA dynamics based on Markov models of connectivity-graphs.
In: Algorithms for Molecular Biology, 2017, 12 (1)
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: StreAM-Tg : algorithms for analyzing coarse grained RNA dynamics based on Markov models of connectivity-graphs
Language: English
Date: 12 September 2017
Place of Publication: Darmstadt
Year of primary publication: 2017
Publisher: Biomed Central
Journal or Publication Title: Algorithms for Molecular Biology
Volume of the journal: 12
Issue Number: 1
Corresponding Links:
Origin: Secondary publication via sponsored Golden Open Access
Abstract:

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-Tg, a stream-based algorithm for generating such Markov models of k-vertex adjacency matrices representing the RNA.

Results We benchmark StreAM-Tg(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-Tg 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-Tg 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.

Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-67942
Classification DDC: 500 Science and mathematics > 570 Life sciences, biology
Divisions: 10 Department of Biology > Computational Biology and Simulation
Date Deposited: 12 Sep 2017 13:38
Last Modified: 08 Dec 2023 07:20
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/6794
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