  A steadily growing computational power is employed to perform molecular
dynamics simulations of biological macromolecules, which represents at the same
time an immense opportunity and a formidable challenge. In fact, large amounts
of data are produced, from which useful, synthetic, and intelligible
information has to be extracted to make the crucial step from knowing to
understanding. Here we tackled the problem of coarsening the conformational
space sampled by proteins in the course of molecular dynamics simulations. We
applied different schemes to cluster the frames of a dataset of protein
simulations; we then employed an information-theoretical framework, based on
the notion of resolution and relevance, to gauge how well the various
clustering methods accomplish this simplification of the configurational space.
Our approach allowed us to identify the level of resolution that optimally
balances simplicity and informativeness; furthermore, we found that the most
physically accurate clustering procedures are those that induce an ultrametric
structure of the low-resolution space, consistently with the hypothesis that
the protein conformational landscape has a self-similar organisation. The
proposed strategy is general and its applicability extends beyond that of
computational biophysics, making it a valuable tool to extract useful
information from large datasets.
