  In the theoretical modelling of a physical system a crucial step consists in
the identification of those degrees of freedom that enable a synthetic, yet
informative representation of it. While in some cases this selection can be
carried out on the basis of intuition and experience, a straightforward
discrimination of the important features from the negligible ones is difficult
for many complex systems, most notably heteropolymers and large biomolecules.
We here present a thermodynamics-based theoretical framework to gauge the
effectiveness of a given simplified representation by measuring its information
content. We employ this method to identify those reduced descriptions of
proteins, in terms of a subset of their atoms, that retain the largest amount
of information from the original model; we show that these highly informative
representations share common features that are intrinsically related to the
biological properties of the proteins under examination, thereby establishing a
bridge between protein structure, energetics, and function.
