  Elastic network models (ENMs) are valuable and efficient tools for
characterizing the collective internal dynamics of proteins based on the
knowledge of their native structures. The increasing evidence that the
biological functionality of RNAs is often linked to their innate internal
motions, poses the question of whether ENM approaches can be successfully
extended to this class of biomolecules. This issue is tackled here by
considering various families of elastic networks of increasing complexity
applied to a representative set of RNAs. The fluctuations predicted by the
alternative ENMs are stringently validated by comparison against extensive
molecular dynamics simulations and SHAPE experiments. We find that simulations
and experimental data are systematically best reproduced by either an all-atom
or a three-beads-per-nucleotide representation (sugar-base-phosphate), with the
latter arguably providing the best balance of accuracy and computational
complexity.
