StreAM-\(T_g\): Algorithms for Analyzing Coarse Grained RNA Dynamics Based on Markov Models of Connectivity-Graphs
In this work, we present a new coarse grained representation of RNA dynamics. It is based on cliques and their patterns within adjacency matrices 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. Each adjacency matrix represents the interactions of k nucleotides. We then define transitions between states as changes in the adjacency matrices which form a Markovian dynamics. The intense computational demand for deriving the transition probability matrices prompted us to develop StreAM-\(T_g\), a stream-based algorithm for generating such Markov models of k-vertex adjacency matrices representing the RNA. Here, we benchmark StreAM-\(T_g\) (a) for random and RNA unit sphere dynamic graphs. (b) we apply our method on a long term molecular dynamics simulation of a synthetic riboswitch (1,000 ns). In the light of experimental data our results show important design opportunities for the riboswitch.
KeywordsRNA Markovian dynamics Dynamic graphs Molecular dynamics Coarse graining Synthetic biology
The Authors gratefully acknowledge financial support by the LOEWE project CompuGene of the Hessen State Ministry of Higher Education, Research and the Arts. Parts of this work have also been supported by the DFG, through the Cluster of Excellence cfaed as well as the CRC HAEC.
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