Comparison of two coarse-grained models of cis -polyisoprene with and without pressure correction

Surveying the energy landscape of coarse-grained mappings

Simulations of soft materials often adopt low-resolution coarse-grained (CG) models. However, the CG representation is not unique and its impact upon simulated properties is poorly understood. In this work, we investigate the space of CG representations for ubiquitin, which is a typical globular protein with 72 amino acids. We employ Monte Carlo methods to ergodically sample this space and to characterize its landscape. By adopting the Gaussian network model as an analytically tractable atomistic model for equilibrium fluctuations, we exactly assess the intrinsic quality of each CG representation without introducing any approximations in sampling configurations or in modeling interactions. We focus on two metrics, the spectral quality and the information content, that quantify the extent to which the CG representation preserves low-frequency, large-amplitude motions and configurational information, respectively. The spectral quality and information content are weakly correlated among high-resolution representations but become strongly anticorrelated among low-resolution representations. Representations with maximal spectral quality appear consistent with physical intuition, while low-resolution representations with maximal information content do not. Interestingly, quenching studies indicate that the energy landscape of mapping space is very smooth and highly connected. Moreover, our study suggests a critical resolution below which a "phase transition" qualitatively distinguishes good and bad representations.

A One-Bead-Per-Saccharide (1BPS) Model for Glycosaminoglycans

Glycosaminoglycans (GAGs) are polysaccharide compounds that play key roles in various biological processes. GAGs are important structural components of cartilage and the extracellular matrix of the brain. Due to the large size of these polysaccharides, coarse-grained approaches are indispensable for modeling these biopolymers. We develop a one-bead-per-saccharide model of chondroitin sulfates and hyaluronic acid based on an existing three-bead-per-saccharide coarse-grained model. Our coarse graining is carried out by using iterative Boltzmann inversion (IBI), including an additional coupling potential to incorporate the correlation between dihedral angles. The predictions of the model are verified against those of the existing three-bead-per-saccharin model and the experimental radius of gyration for hyaluronic acid.

Role of Terminal Groups of cis-1,4-Polyisoprene Chains in the Formation of Physical Junction Points in Natural Rubber

The terminal structures of cis-1,4-polyisoprene (PI) chains play a vital role in the excellent comprehensive performance of Hevea natural rubber (NR) with properties such as high toughness, tear-resistance, and wet skid resistance. The cis-1,4-polyisoprene chain constituting NR exhibits a distinct composition of terminal groups comprising two distinct types, namely, the ω and α terminal groups. The structures of the ω terminal [dimethyl allyl (DMA)-(trans-1,4-isoprene)2] and six kinds of α end groups of the polymer chain of NR have been explored by utilizing a newly developed 2D NMR method. In the present work, we examine different kinds of PI melt systems, and we choose various combinations of terminal groups: Hydrogen, one DMA unit with two trans isoprene units as ω end groups and ester-terminated isopentene (α1), hydroxy-terminated isopentene (α2), ester-terminated isobutane (α3), hydroxy-terminated isobutane (α4), ester-terminated 1,4-cis-isoprene (α5), and hydroxy-terminated 1,4-cis-isoprene (α6), i.e., HPIH (PI0)-pure PI (Hydrogen terminal), ωPIα1 (PII), ωPIα2 (PIII), ωPIα3 (PIIII), ωPIα4 (PIIV), ωPIα5 (PIV), and ωPIα6 (PIVI). We evaluated dynamic and static properties of PI chains such as the end-to-end vector autocorrelation function (C(t)), its average relaxation time (τ), end-to-end distance (Ree), and radius of gyration (Rg). We also estimated the diffusion coefficients of polyisoprene chains and pair correlation functions [radial distribution functions (RDFs)], potentials of mean force (PMFs) in between end residues, and survival probability (P(τ)) of end groups around the end group by analyzing the equilibrated trajectories of full-atom MD simulations. As per the examination of C(t), rotational relaxation time τ, and RDFs, we discovered that the existence of a strong hydrogen bond in α2-α2, α4-α4, and α6-α6 residues makes the dynamics of hydroxy-terminated polyisoprene chains in ωPIα2,α4,α6 melt systems slower. From the analyses of RDFs and PMFs (W(r)), the association between [α2]-[α2], [α4]-[α4], and [α6]-[α6] terminals in ωPIα2,α4,α6 melt systems is significantly stronger than in [ISO]-[ISO] [Hydrogen terminated 1,4-cis-isoprene:(ISO)] in HPIH and ω-ω, [α1]-[α1], [α3]-[α3], and [α5]-[α5] in ωPIα1,α3,α5 systems. We quantified the fraction of cluster formation of terminal groups of a given size in the seven PI melt systems by employing the criteria of PMFs. It is revealed that no stable cluster exists in the HPIH, ωPIα1, ωPIα3, and ωPIα5 melt systems. Conversely, in the ωPIα2, ωPIα4, and ωPIα6 systems, we perceived stable clusters of [(α2)p] [(α4)p] and [(α6)p] end groups where p (2 ≤ x ≤ 6). These stable clusters validate the presence of physical junction points in between hydroxy-terminated polyisoprene chains through their α2, α4, and α6 terminals. These physical junction points might be crucial for superior properties of NR such as high toughness, crack growth resistance, and strain-induced crystallization.

From System Modeling to System Analysis: The Impact of Resolution Level and Resolution Distribution in the Computer-Aided Investigation of Biomolecules

The ever increasing computer power, together with the improved accuracy of atomistic force fields, enables researchers to investigate biological systems at the molecular level with remarkable detail. However, the relevant length and time scales of many processes of interest are still hardly within reach even for state-of-the-art hardware, thus leaving important questions often unanswered. The computer-aided investigation of many biological physics problems thus largely benefits from the usage of coarse-grained models, that is, simplified representations of a molecule at a level of resolution that is lower than atomistic. A plethora of coarse-grained models have been developed, which differ most notably in their granularity; this latter aspect determines one of the crucial open issues in the field, i.e. the identification of an optimal degree of coarsening, which enables the greatest simplification at the expenses of the smallest information loss. In this review, we present the problem of coarse-grained modeling in biophysics from the viewpoint of system representation and information content. In particular, we discuss two distinct yet complementary aspects of protein modeling: on the one hand, the relationship between the resolution of a model and its capacity of accurately reproducing the properties of interest; on the other hand, the possibility of employing a lower resolution description of a detailed model to extract simple, useful, and intelligible information from the latter.

Assessing the derivation of time parameters from branched polymer coarse-grain model

The parameterization of rheological models for polymers is often obtained from experiments via the top-down approach. This procedure allows us to determine good fitting parameters for homogeneous materials but is less effective for polymer mixtures. From a molecular simulation point of view, the timescales needed to derive those parameters are often accessed through the use of coarse-grain potentials. However, these potentials are often derived from linear model systems and the transferability to a more complex structure is not straightforward. Here, we verify the transferability of a potential computed from linear polymer simulations to more complex molecular shapes and present a type of analysis, which was recently formulated in the framework of a tube theory, to a coarse-grain molecular approach in order to derive the input parameters for a rheological model. We describe the different behaviors arising from the local topological structure of molecular sub-units. Coarse-grain models and mean-field based tube theory for polymers form a powerful combination with potentially important applications.

Effectiveness of coarse graining degree and speedup on the dynamic properties of homopolymer

Evaluation of effective coarse graining (CG) degree and reasonable speedup relative to all-atomistic (AA) model was conducted to provide a basis for building appropriate larger-scale model. The reproducibility of atomistic conformation and temperature transferability both act as the analysis criteria to resolve the maximum acceptable CG degree. Taking short- and long time spans into account simultaneously in the estimation of computational speedup, a dynamic scaling factor is accessible in fitting mean squared displacement ratio of CG to AA as an exponential function. Computing loss in parallel running is an indispensable component in acceleration, which was also added in the evaluation. Subsequently, a quantified prediction of CG speedup arises as a multiplication of dynamic scaling factor, computing loss, time step, and the square of reduction in the number of degrees of freedom. Polyethylene oxide was adopted as a reference system to execute the direct Boltzmann inversion and iterative Boltzmann inversion. Bonded and non-bonded potentials were calculated in CG models with 1~4 monomers per bead. The effective CG degree was determined as two at the most with a speedup of four orders magnitude over AA in this study. Determination of effectiveness CG degree and the corresponding speedup prediction provide available tools in larger spatiotemporal-scale calculations.

Coarse-graining of polyisoprene melts using inverse Monte Carlo and local density potentials

Bottom-up coarse-graining of polymers is commonly performed by matching structural order parameters such as distribution of bond lengths, bending and dihedral angles, and pair distribution functions. In this study, we introduce the distribution of nearest-neighbors as an additional order parameter in the concept of local density potentials. We describe how the inverse-Monte Carlo method provides a framework for forcefield development that is capable of overcoming challenges associated with the parameterization of interaction terms in polymer systems. The technique is applied on polyisoprene melts as a prototype system. We demonstrate that while different forcefields can be developed that perform equally in terms of matching target distributions, the inclusion of nearest-neighbors provides a straightforward route to match both thermodynamic and conformational properties. We find that several temperature state points can also be addressed, provided that the forcefield is refined accordingly. Finally, we examine both the single-particle and the collective dynamics of the coarse-grain models, demonstrating that all forcefields present a similar acceleration relative to the atomistic systems.

Recent Progress towards Chemically-Specific Coarse-Grained Simulation Models with Consistent Dynamical Properties

Coarse-grained (CG) models can provide computationally efficient and conceptually simple characterizations of soft matter systems. While generic models probe the underlying physics governing an entire family of free-energy landscapes, bottom-up CG models are systematically constructed from a higher-resolution model to retain a high level of chemical specificity. The removal of degrees of freedom from the system modifies the relationship between the relative time scales of distinct dynamical processes through both a loss of friction and a “smoothing” of the free-energy landscape. While these effects typically result in faster dynamics, decreasing the computational expense of the model, they also obscure the connection to the true dynamics of the system. The lack of consistent dynamics is a serious limitation for CG models, which not only prevents quantitatively accurate predictions of dynamical observables but can also lead to qualitatively incorrect descriptions of the characteristic dynamical processes. With many methods available for optimizing the structural and thermodynamic properties of chemically-specific CG models, recent years have seen a stark increase in investigations addressing the accurate description of dynamical properties generated from CG simulations. In this review, we present an overview of these efforts, ranging from bottom-up parameterizations of generalized Langevin equations to refinements of the CG force field based on a Markov state modeling framework. We aim to make connections between seemingly disparate approaches, while laying out some of the major challenges as well as potential directions for future efforts.

Response of adhesive polymer interfaces to repeated mechanical loading and the spatial variation of diffusion coefficient and stresses in a deforming polymer film

Comprehensive molecular simulations are conducted to show that polymer crosslinks preserve the strength of solid-polymer (melt) interfaces when they are subjected to repeated mechanical loading. The spatial variation of the diffusion coefficient and local stresses is also investigated along the polymer thickness, during deformation. After each loading cycle, a reduction in entanglement strength is observed at the fracture site. The work of adhesion also decreases over consecutive loading cycles, when fracture is induced at the same site. Reduction in both, the work of adhesion and the entanglement strength, decreases as the crosslink density increases. Diffusion coefficient and stresses vary significantly and in a complex manner along the film thickness during the entire deformation process. These variations were due to peculiar configurations occurring at each instance of separation, which are analyzed and explained in this work. The variation of diffusion coefficient during deformation suggests that other dynamic properties, such as viscosity, also vary spatially during polymer deformation.

Hierarchical modelling of polystyrene melts: from soft blobs to atomistic resolution

We demonstrate that hierarchical backmapping strategies incorporating generic blob-based models can equilibrate melts of high-molecular-weight polymers, described with chemically specific, atomistic models. The central idea is first to represent polymers by chains of large soft blobs (spheres) and efficiently equilibrate the melt on large scales. Then, the degrees of freedom of more detailed models are reinserted step by step. The procedure terminates when the atomistic description is reached. Reinsertions are feasible computationally because the fine-grained melt must be re-equilibrated only locally. We consider polystyrene (PS) which is sufficiently complex to serve method development because of stereo-chemistry and bulky side groups. Our backmapping strategy bridges mesoscopic and atomistic scales by incorporating a blob-based, a moderately coarse-grained (CG), and a united-atom model of PS. We demonstrate that the generic blob-based model can be parameterised to reproduce the mesoscale properties of a specific polymer - here PS. The moderately CG model captures stereo-chemistry. To perform backmapping we improve and adjust several fine-graining techniques. We prove equilibration of backmapped PS melts by comparing their structural and conformational properties with reference data from smaller systems, equilibrated with less efficient methods.