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Low resolution coarse-grained (CG)models enable highly efficientsimulations of complex systems. The interactions in CG models areoften iteratively refined over multiple simulations until they reproducethe one-dimensional (1-D) distribution functions, e.g., radial distributionfunctions (rdfs), of an all-atom (AA) model. In contrast, the multiscalecoarse-graining (MS-CG) method employs a generalized Yvon–Born–Green(g-YBG) relation to determine CG potentials directly (i.e., withoutiteration) from the correlations observed for the AA model. However,MS-CG models do not necessarily reproduce the 1-D distribution functionsof the AA model. Consequently, recent studies have incorporated theg-YBG equation into iterative methods for more accurately reproducingAA rdfs. In this work, we consider a theoretical framework for aniterative g-YBG method. We numerically demonstrate that the methodrobustly determines accurate models for both hexane and also a morecomplex molecule, 3-hexylthiophene. By examining the MS-CG and iterativeg-YBG models for several distinct CG representations of both molecules,we investigate the approximations of the MS-CG method and their sensitivityto the CG mapping. More generally, we explicitly demonstrate thatCG models often reproduce 1-D distribution functions of AA modelsat the expense of distorting the cross-correlations between the correspondingdegrees of freedom. In particular, CG models that accurately reproduceintramolecular 1-D distribution functions may still provide a poordescription of the molecular conformations sampled by the AA model.We demonstrate a simple and predictive analysis for determining CGmappings that promote an accurate description of these molecular conformations. [ABSTRACT FROM AUTHOR]

Coarse-grained (CG) models often employ pair potentialsthat areparametrized to reproduce radial distribution functions (rdf's) determinedfor an atomistic model. This implies that the CG model must reproducethe corresponding atomistic mean forces. These mean forces includenot only a direct contribution from the corresponding interactionbut also correlated contributions from the surrounding environment.The many-body correlations that influence this second contributionpresent significant challenges for accurately reproducing atomisticdistribution functions. This work presents a detailed investigationof these many-body correlations and their significance for determiningCG potentials while using liquid heptane as a model system. We employa transparent geometric framework for directly determining CG potentialsthat has been previously developed within the context of the multiscalecoarse-graining and generalized Yvon–Born–Green methods.In this framework, a metric tensor quantifies the relevant many-bodycorrelations and precisely decomposes atomistic mean forces into contributionsfrom specific interactions, which then determine the CG force field.Numerical investigations reveal that this metric tensor reflects boththe CG representation and also subtle correlations between moleculargeometry and intermolecular packing, but can be largely interpretedin terms of generic considerations. Our calculations demonstrate thatcontributions from correlated interactions can significantly impactthe pair mean force and, thus, also the CG force field. Finally, aneigenvector analysis investigates the importance of these interactionsfor reproducing atomistic distribution functions. [ABSTRACT FROM AUTHOR]

Intrinsically disordered proteins (IDPs) play an important role in an array of biological processes but present a number of fundamental challenges for computational modeling. Recently, simple polymer models have regained popularity for interpreting the experimental characterization of IDPs. Homopolymer theory provides a strong foundation for understanding generic features of phenomena ranging from single-chain conformational dynamics to the properties of entangled polymer melts, but is difficult to extend to the copolymer context. This challenge is magnified for proteins due to the variety of competing interactions and large deviations in side-chain properties. In this work, we apply a simple physics-based coarse-grained model for describing largely disordered conformational ensembles of peptides, based on the premise that sampling sterically forbidden conformations can compromise the faithful description of both static and dynamical properties. The Hamiltonian of the employed model can be easily adjusted to investigate the impact of distinct interactions and sequence specificity on the randomness of the resulting conformational ensemble. In particular, starting with a bead-spring-like model and then adding more detailed interactions one by one, we construct a hierarchical set of models and perform a detailed comparison of their properties. Our analysis clarifies the role of generic attractions, electrostatics, and side-chain sterics, while providing a foundation for developing efficient models for IDPs that retain an accurate description of the hierarchy of conformational dynamics, which is nontrivially influenced by interactions with surrounding proteins and solvent molecules.

We develop an extended ensemble method for constructing transferable, low-resolution coarse-grained (CG) models of polyethylene-oxide (PEO)-based ionomer chains with varying composition at multiple temperatures. In particular, we consider ionomer chains consisting of 4 isophthalate groups, which may be neutral or sulfonated, that are linked by 13 PEO repeat units. The CG models represent each isophthalate group with a single CG site and also explicitly represent the diffusing sodium counterions but do not explicitly represent the PEO backbone. We define the extended ensemble as a collection of equilibrium ensembles that are obtained from united atom (UA) simulations at 2 different temperatures for 7 chemically distinct ionomers with varying degrees of sulfonation. We employ a global force-matching method to determine the set of interaction potentials that, when appropriately combined, provide an optimal approximation to the many-body potential of mean force for each system in the extended ensemble. This optimized xn force field employs long-ranged Coulomb potentials with system-specific dielectric constants that systematically decrease with increasing sulfonation and temperature. An empirical exponential model reasonably describes the sensitivity of the dielectric to sulfonation, but we find it more challenging to model the temperature-dependence of the dielectrics. Nevertheless, given appropriate dielectric constants, the transferable xn force field reasonably describes the ion pairing that is observed in the UA simulations as a function of sulfonation and temperature. Remarkably, despite eliminating any explicit description of the PEO backbone, the CG model predicts string-like ion aggregates that appear qualitatively consistent with the ionomer peak observed in X-ray scattering experiments and, moreover, with the temperature dependence of this peak.

This work investigates the capability of bottom-up methods for parametrizing minimal coarse-grained (CG) models of disordered and helical peptides. We consider four high-resolution peptide ensembles that demonstrate varying degrees of complexity. For each high-resolution ensemble, we parametrize a CG model via the multiscale coarse-graining (MS-CG) method, which employs a generalized Yvon-Born-Green (g-YBG) relation to determine potentials directly (i.e., without iteration) from the high-resolution ensemble. The MS-CG method accurately describes high-resolution models that fluctuate about a single conformation. However, given the minimal resolution and simple molecular mechanics potential, the MS-CG method provides a less accurate description for a high-resolution peptide model that samples a disordered ensemble with multiple distinct conformations. We employ an iterative g-YBG method to develop a CG model that more accurately describes the relevant distribution functions and free energy surfaces for this disordered ensemble. Nevertheless, this more accurate model does not reproduce the cooperative helix-coil transition that is sampled by the high resolution model. By comparing the different models, we demonstrate that the errors in the MS-CG model primarily stem from the lack of cooperative interactions afforded by the minimal representation and molecular mechanics potential. This work demonstrates the potential of the MS-CG method for accurately modeling complex biomolecular structures, but also highlights the importance of more complex potentials for modeling cooperative transitions with a minimal CG representation.