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Low-resolution coarse-grained (CG) models provide remarkable computational and conceptual advantages for simulating soft materials. In principle, bottom-up CG models can reproduce all structural and thermodynamic properties of atomically detailed models that can be observed at the resolution of the CG model. This review discusses recent progress in developing theory and computational methods for achieving this promise. We first briefly review variational approaches for parameterizing interaction potentials and their relationship to machine learning methods. We then discuss recent approaches for simultaneously improving both the transferability and thermodynamic properties of bottom-up models by rigorously addressing the density and temperature dependence of these potentials. We also briefly discuss exciting progress in modeling high-resolution observables with low-resolution CG models. More generally, we highlight the essential role of the bottom-up framework not only for fundamentally understanding the limitations of prior CG models but also for developing robust computational methods that resolve these limitations in practice. [ABSTRACT FROM AUTHOR]

Knowledge-based approaches frequently employ empirical relations to determine effective potentials for coarse-grained protein models directly from protein databank structures. Although these approaches have enjoyed considerable success and widespread popularity in computational protein science, their fundamental basis has been widely questioned. It is well established that conventional knowledge-based approaches do not correctly treat many-body correlations between amino acids. Moreover, the physical significance of potentials determined by using structural statistics from different proteins has remained obscure. In the present work, we address both of these concerns by introducing and demonstrating a theory for calculating transferable potentials directly from a databank of protein structures. This approach assumes that the databank structures correspond to representative configurations sampled from equilibrium solution ensembles for different proteins. Given this assumption, this physics-based theory exactly treats many-body structural correlations and directly determines the transferable potentials that provide a variationally optimized approximation to the free energy landscape for each protein. We illustrate this approach by first constructing a databank of protein structures using a model potential and then quantitatively recovering this potential from the structure databank. The proposed framework will clarify the assumptions and physical significance of knowledge-based potentials, allow for their systematic improvement, and provide new insight into many-body correlations and cooperativity in folded proteins. protein structure prediction | coarse-grained models | inverse problems | Yvon--Born--Green theory doi/ 10.1073/pnas.1006428107

The interactions between multiscale coarse graining (MS-CG) and coarse grained (CG) sites are described with the help of statistical methods and by application of liquid-state theory. The findings suggest that central pair potentials give rise to isotropic homogenous systems in the MS-Cg methodology with critical three-body correlation effects.

Spectrally resolved infrared stimulated vibrational echo data were obtained for sperm whale carbonmonoxymyoglobin (MbCO) at 300 K. The dephasing dynamics of the CO ligand were measured and compared with the dephasing dynamics calculated with molecular dynamics simulations for MbCO with the residue histidine-64 (His64). Results show that the dynamics of the His64 can be considered as a major source of dephasing for the CO ligand.

The proposition that the classical mechanical nonlinear response function for a thermal ensemble of anharmonic oscillators diverges in time is presented along with arguments favoring it. It has been found that, within the domain of classical mechanics, infrared pulse sequences can provide a sensitive probe of the stabilities of classical trajectories, even for a system in thermodynamic equilibrium.