Coarse-grained models are simplified representations of systems in terms of fewer particles or sites with respect to more accurate reference descriptions, e. g., atomically-detailed. The definition of the effective interaction sites is commonly based on chemical intuition, and no systematic analysis is usually performed over the many different possible representations to assess the quality of this choice using quantitative metrics . We here present a  theoretical framework to gauge the effectiveness of a given coarse-grained description by measuring the model's information content. The quantity employed, namely mapping entropy, is defined in terms of multi-dimensional integrals of probability distributions over the system's configurational space, and normally is exceedingly difficult to calculate with conventional methods. In this work we illustrate how controllable approximations can be made that enable the computationally efficient calculation of mapping entropy. We employ this method to identify the representations of proteinsthat entail the largest amount of information from the original systems. It is shown that representations with low values of mapping entropy 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.
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.
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Coarse-grained models are simplified representations of systems in terms of fewer particles or sites with respect to more accurate reference descriptions, e. g., atomically-detailed. The definition of the effective interaction sites is commonly based on chemical intuition, and no systematic analysis is usually performed over the many different possible representations to assess the quality of this choice using quantitative metrics
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
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thermodynamics-based
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coarse-grained description by measuring the model's
simplified representation by measuring its
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The quantity employed, namely mapping entropy, is defined in terms of multi-dimensional integrals of probability distributions over the system's configurational space, and normally is exceedingly difficult to calculate with conventional methods. In this work we illustrate how controllable approximations can be made that enable the computationally efficient calculation of mapping entropy.
R
the representations of proteinsthat entail
those reduced descriptions of proteins, in terms of a subset of their atoms, that retain
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systems. It is shown that representations with low values of mapping entropy
model; we show that these highly informative representations