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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 impossible for most complex systems, most notably large biomolecules. The larger the system, however, the greater the necessity of describing it in terms of few parameters, e.g. a subset of its atoms' coordinates. In this talk, I will present a thermodynamics-based theoretical framework to gauge the effectiveness of a given simplified model by measuring its information content. This method was employed to identify those representations of proteins that retain the largest amount of information from the original all-atom structure, and show that they 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.
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