  Elastic network models, simple structure-based representations of
biomolecules where atoms interact via short-range harmonic potentials, provide
great insight into a molecule's internal dynamics and mechanical properties at
extremely low computational cost. Their efficiency and effectiveness have made
them a pivotal instrument in the computer-aided study of proteins and, since a
few years, also of nucleic acids. In general, the coarse-grained sites, i.e.
those effective force centres onto which the all-atom structure is mapped, are
constructed based on intuitive rules: a typical choice for proteins is to
retain only the C$_\alpha$ atoms of each amino acid. However, a mapping
strategy relying only on the atom type and not the local properties of its
embedding can be suboptimal compared to a more careful selection. Here we
present a strategy in which the subset of atoms, each of which is mapped onto a
unique coarse-grained site of the model, is selected in a stochastic search
aimed at optimising a cost function. The latter is taken to be a simple measure
of the consistency between the harmonic approximation of an elastic network
model and the harmonic model obtained through exact integration of the
discarded degrees of freedom. The method is applied to two representatives of
structurally very different types of biomolecules: the protein Adenylate kinase
and the RNA molecule adenine riboswitch. Our analysis quantifies the
substantial impact that an algorithm-driven selection of coarse-grained sites
can have on a model's properties.
