2026-10-14T15:14:52Zhttps://www.radar-service.eu/oai/OAIHandler

10.48606/5n82rm9sjkndqqhe2026-09-02T03:02:24Z

https://dx.doi.org/10.48606/5n82rm9sjkndqqhe Eble, Franziska Molecular Dynamics Simulation Data and Analysis Workflow to Investigate Chain Interlocking in the Guanidine-II Riboswitch Dimer University of Konstanz 2026 Chemistry dataset MD Simulations Dataset info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Franke, Leon Peter, Christine The Guanidine-II Riboswitch (Gd-II RS) is an example of structured RNA that exerts its function by dimerization. This dimerization interface is formed by two tetraloops in which two of the bases form an interlocked pair. This interlocking was first observed in Steuer et al. (2024) (https://doi.org/10.1093/nargab/lqae132). To investigate the interactions underlying this conformation and a potential base-identity dependence, we performed molecular dynamics simulations of the Gd-II RS with varied base identity of the interlocking bases. We analyzed the simulation data by adapting a hydrogen-bond based network analysis scheme for dimensionality reduction and clustering. We characterized the resulting clusters using geometric criteria for interlocking and arc diagrams for visualizing changes in hydrogen bond network topology. This dataset contains the following directories and files: (1) simulation_data: Atomistic molecular dynamics (MD) simulation trajectory data of the Guanidine-II Riboswitch Dimer bound to a C4-diguanidine ligand. (2) analysis: Jupyter notebooks, a python coding environment file, and the 2D UMAP embedding as well as the cluster assignment by datapoint that can be used to replicate the analyses and figures produced for the associated publication. (3) simulation_setup: All setup and force field files necessary to replicate or extend the provided MD simulation data. See the README file for a more in depth description of the content. RNA Riboswitch Guanidinium MD Simulation Molecular Dynamics Network Analysis Residue Interaction Network Machine Learning Clustering Dimensionality Reduction Franke, Leon Peter, Christine eng 10.48606/fTBvhsaGfoDmhOTV https://ror.org/018mejw64 application/x-tar