RNA molecular dynamics dataset captures diverse conformations
mdRNAcat: A Molecular Dynamics RNA Catalog of REST2 Conformational Ensembles
Machine Learning
Summary
Studying RNA molecules requires tools that show how RNA can change shape. The authors created a detailed dataset of simulated RNA molecules, capturing many different shapes and the forces on atoms inside them. This dataset comes from advanced simulations of 48 different RNA sequences, providing data that others can use to build or test new models of RNA behavior. It includes both raw data and information about how the simulations were run. This resource can help improve computer models that predict RNA shapes and dynamics.
What this means in practice
- •For computational biologists: Train and test RNA structure prediction models using a diverse, physically consistent dataset of RNA conformations and forces from molecular dynamics simulations.
- •For drug design teams: Develop or refine RNA-targeting molecule designs by accessing detailed RNA conformational ensembles to understand binding site flexibility.
Authors
Anton E. Dorn, Emile de Bruyn, Fabrice von der Lehr, Philipp Knechtges, Stefan Kesselheim, Alexander Schug
Abstract
Learning molecular models of RNA requires simulation data that capture conformational variability and provide consistent physical training signals. Here, we introduce mdRNAcat, a curated dataset of all-atom molecular dynamics (MD) simulations of 48 RNA systems selected to promote sequence diversity. Each system is simulated using replica-exchange with solute scaling (REST2), with 32 Hamiltonian states and production lengths of 200-300 ns per state. The dataset provides atomic coordinates, instantaneous forces, and forces reevaluated under the unscaled reference Hamiltonian, together with structural annotations and replica-exchange metadata. Coordinates and forces are available at 1 ps intervals for the reference state and 100 ps intervals across all states in a standardized HDF5 format. We characterize conformational sampling, exchange behavior, base pairing, and sugar-pucker distributions to document the properties of the simulated ensembles. mdRNAcat provides a reusable resource for developing and evaluating data-driven RNA models, including coarse-grained force fields.