text: 317,216,1248156986191024128,794145137068830720,Potestio Lab,"There are many ways to construct a coarse-grained protein, here we tell you how to find out the best one! At the X-ing of stat physics, biology, and information theory - a great collaboration between @r_potestio lab @UniTrento and Scott Shell @UCSBChE! <LINK> <LINK>",https://arxiv.org/abs/2004.03988,"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. ","An information theory-based approach for optimal model reduction of