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

Abstract

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.

StreaMD: Advanced analysis of molecular dynamics using R

“…One of its algorithms is StreaM k , an extension to count the occurrences of k ‐vertex motifs in dynamic graphs. Furthermore, streAM and streAM‐ T g are included for the construction of motif‐based Markov models from dynamic graphs …”

“…It turned out that four‐vertex motif counts can be used to annotate secondary structures and determine essential dynamics for protein simulations . Furthermore, the approach is applicable to other biomolecular systems as well, e.g., nucleic acid‐based graphs in RNAs . In this context, we developed StreaM k , an efficient algorithm that counts motifs as well as StreAM which creates motif‐based Markov state models from dynamic graphs modeled from MD simulations.…”