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 : AMB, 12
doi: 10.1186/s13015-017-0105-0
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (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-[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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2017 |
| Autor(en): | Jager, Sven ; Schiller, Benjamin ; Babel, Philipp ; Blumenroth, Malte ; Strufe, Thorsten ; Hamacher, Kay |
| Art des Eintrags: | Bibliographie |
| Titel: | StreAM-Tg: algorithms for analyzing coarse grained RNA dynamics based on Markov models of connectivity-graphs. |
| Sprache: | Englisch |
| Publikationsjahr: | 2017 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Algorithms for molecular biology : AMB |
| Jahrgang/Volume einer Zeitschrift: | 12 |
| DOI: | 10.1186/s13015-017-0105-0 |
| Zugehörige Links: | |
| Kurzbeschreibung (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-[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. |
| ID-Nummer: | pmid:28572834 |
| Fachbereich(e)/-gebiet(e): | 10 Fachbereich Biologie 10 Fachbereich Biologie > Computational Biology and Simulation |
| Hinterlegungsdatum: | 14 Jun 2017 12:28 |
| Letzte Änderung: | 01 Mär 2024 10:51 |
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| Suche nach Titel in: | TUfind oder in Google |
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StreAM-Tg : algorithms for analyzing coarse grained RNA dynamics based on Markov models of connectivity-graphs. (deposited 17 Sep 2017 19:55)
- StreAM-Tg: algorithms for analyzing coarse grained RNA dynamics based on Markov models of connectivity-graphs. (deposited 14 Jun 2017 12:28) [Gegenwärtig angezeigt]
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