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A steadily growing computational power is employed to perform moleculardynamics simulations of biological macromolecules, which represents at the sametime an immense opportunity and a formidable challenge. In fact, large amountsof data are produced, from which useful, synthetic, and intelligibleinformation has to be extracted to make the crucial step from knowing tounderstanding. Here we tackled the problem of coarsening the conformationalspace sampled by proteins in the course of molecular dynamics simulations. Weapplied different schemes to cluster the frames of a dataset of proteinsimulations; we then employed an information-theoretical framework, based onthe notion of resolution and relevance, to gauge how well the variousclustering methods accomplish this simplification of the configurational space.Our approach allowed us to identify the level of resolution that optimallybalances simplicity and informativeness; furthermore, we found that the mostphysically accurate clustering procedures are those that induce an ultrametricstructure of the low-resolution space, consistently with the hypothesis thatthe protein conformational landscape has a self-similar organisation. Theproposed strategy is general and its applicability extends beyond that ofcomputational biophysics, making it a valuable tool to extract usefulinformation from large datasets.

Information-theoretical measures identify accurate low-resolution representations of protein configurational space