Coarse-grained Model Research Articles

Accessing the electronic structure of liquid crystalline semiconductors with bottom-up electronic coarse-graining

A bottom-up electronic coarse-graining model predicts morphology-dependent electronic structure and charge transport properties for liquid-crystal semiconductors, without back-mapping or ad nauseam quantum chemistry calculations.

DNA Barcodes Using a Dual Nanopore Device.

We report a novel method based on the current blockade (CB) characteristics obtained from a dual nanopore device that can determine DNA barcodes with near-perfect accuracy using a Brownian dynamics simulation strategy. The method supersedes our previously reported velocity correction algorithm (S. Seth and A. Bhattacharya, RSC Advances, 11:20781-20787, 2021), taking advantage of the better measurement of the time-of-flight (TOF) protocol offered by the dual nanopore setup. We demonstrate the efficacy of the method by comparing our simulation data from a coarse-grained model of a polymer chain consisting of 2048 excluded volume beads of diameter 𝜎 = 24bp using with those obtained from experimental CB data from a 48,500bp λ-phage DNA, providing a base pair resolution in simulation. The simulation time scale is compared to the experimental time scale by matching the simulated time-of-flight (TOF) velocity distributions with those obtained experimentally (Rand et al., ACS Nano, 16:5258-5273, 2022). We then use the evolving coordinates of the dsDNA and the molecular features to reconstruct the current blockade characteristics on the fly using a volumetric model based on the effective van der Waal radii of the species inside and in the immediate vicinity of the pore. Our BD simulation mimics the control-zoom-in-logic to understand the origin of the TOF distributions due to the relaxation of the out-of-equilibrium conformations followed by a reversal of the electric fields. The simulation algorithm is quite general and can be applied to differentiate DNA barcodes from different species.

Effect of coat-protein concentration on the self-assembly of bacteriophage MS2 capsids around RNA.

Self-assembly is a vital part of the life cycle of certain icosahedral RNA viruses. Furthermore, the assembly process can be harnessed to make icosahedral virus-like particles (VLPs) from coat protein and RNA in vitro. Although much previous work has explored the effects of RNA-protein interactions on the assembly products, relatively little research has explored the effects of coat-protein concentration. We mix coat protein and RNA from bacteriophage MS2, and we use a combination of gel electrophoresis, dynamic light scattering, and transmission electron microscopy to investigate the assembly products. We show that with increasing coat-protein concentration, the products transition from well-formed MS2 VLPs to "monster" particles consisting of multiple partial capsids to RNA-protein condensates consisting of large networks of RNA and partially assembled capsids. We argue that the transition from well-formed to monster particles arises because the assembly follows a nucleation-and-growth pathway in which the nucleation rate depends sensitively on the coat-protein concentration, such that at high protein concentrations, multiple nuclei can form on each RNA strand. To understand the formation of the condensates, which occurs at even higher coat-protein concentrations, we use Monte Carlo simulations with coarse-grained models of capsomers and RNA. These simulations suggest that the formation of condensates occurs by the adsorption of protein to the RNA followed by the assembly of capsids. Multiple RNA molecules can become trapped when a capsid grows from capsomers attached to two different RNA molecules or when excess protein bridges together growing capsids on different RNA molecules. Our results provide insight into an important biophysical process and could inform design rules for making VLPs for various applications.

PSPICA Force Field Extended for Proteins and Peptides.

Many coarse-grained (CG) molecular dynamics (MD) studies have been performed to investigate biological processes involving proteins and lipids. CG force fields (FFs) in these MD studies often use implicit or nonpolar water models to reduce computational costs. CG-MD using water models cannot properly describe electrostatic screening effects owing to the hydration of ionic segments and thus cannot appropriately describe molecular events involving water channels and pores through lipid membranes. To overcome this issue, we developed a protein model in the pSPICA FF, in which a polar CG water model showing the proper dielectric response was adopted. The developed CG model greatly improved the transfer free energy profiles of charged side chain analogues across the lipid membrane. Application studies on melittin-induced membrane pores and mechanosensitive channels in lipid membranes demonstrated that CG-MDs using the pSPICA FF correctly reproduced the structure and stability of the pores and channels. Furthermore, the adsorption behavior of the highly charged nona-arginine peptides on lipid membranes changed with salt concentration, indicating the pSPICA FF is also useful for simulating protein adsorption on membrane surfaces.

Temperature-Transferable Coarse-Grained Models for Volumetric Properties of Poly(lactic Acid).

A new coarse-grained (CG) model, for which each monomer is mapped as one bead at its center of mass, was developed for simulating the volumetric properties of the polylactide (PLA) bulk. The three bonded CG potentials are first parametrized against the strain energies of the dimer, trimer, and tetramer, and the nonbonded CG potentials are then optimized to match the melt densities of the decamer. With the derived CG potentials, molecular dynamics (MD) simulations are found to reproduce thermal expansion and glass transition. By rescaling the dihedral and nonbonded potentials with temperature-independent factors, the glass transition temperature (Tg) is also satisfactorily restored with little modifications on the volumetric expansive coefficients at both rubbery and glassy states. Therefore, the finally optimized CG potentials exhibit excellent temperature transferability, as rationalized by the Simha-Boyer relation. Furthermore, it is confirmed that the dihedral torsions and nonbonded interactions play key roles in glass transition. Also, the simulated bulk moduli and conformational properties in a wide temperature range compare well with the referenced data. The proposed multiscale scheme has great potential in simulating thermo-mechanical properties of PLA and other polymers.

Enhanced Coarse-Grained Molecular Dynamics Simulation with a Smoothed Hybrid Potential Using a Neural Network Model.

In all-atom (AA) molecular dynamics (MD) simulations, the rugged energy profile of the force field makes it challenging to reproduce spontaneous structural changes in biomolecules within a reasonable calculation time. Existing coarse-grained (CG) models, in which the energy profile is set to a global minimum around the initial structure, are unsuitable to explore the structural dynamics between metastable states far away from the initial structure without any bias. In this study, we developed a new hybrid potential composed of an artificial intelligence (AI) potential and minimal CG potential related to the statistical bond length and excluded volume interactions to accelerate the transition dynamics while maintaining the protein character. The AI potential is trained by energy matching using a diverse structural ensemble sampled via multicanonical (Mc) MD simulation and the corresponding AA force field energy, profile of which is smoothed by energy minimization. By applying the new methodology to chignolin and TrpCage, we showed that the AI potential can predict the AA energy with significantly high accuracy, as indicated by a correlation coefficient (R-value) between the true and predicted energies exceeding 0.89. In addition, we successfully demonstrated that CGMD simulation based on the smoothed hybrid potential can significantly enhance the transition dynamics between various metastable states while preserving protein properties compared to those obtained with conventional CGMD and AAMD.

Studying on the upper limit of the coarse-grained DEM model for simulating mixing processes in a rolling drum with complex wall boundary

Compared with the original Discrete Element Method, the coarse-grained DEM significantly improves simulation efficiency. In CG DEM, the coarse-grained ratio is defined as the diameter of the coarse-grained particle divided by the diameter of the original particle. When the upper limit of coarse-grained ratio is used, the simulation is the most efficient. The upper value for simulating mixing process of spherical fuel particles was determined using a multi-level methodology. The adequacy of the upper value of the coarse-grained ratio was assessed by comparing the mixing characteristics and kinetic properties of the coarse-grained particle systems with those of the original particle systems in a rotating drum with blades. The impact of rotation speed and particle number on the upper value was examined and discussed. It was observed that the kinetic energy increases with an increase in the coarse-grained ratio, limiting the efficiency improvement. To ensure kinetic energy conservation, a scaling law for the restitution coefficient was proposed. Combined with the Reduced Particle Stiffness method, the simulation time of the coarse-grained case is only 1/3030 that of the original case (with a particle number of 2.3 million) when the upper limit of the coarse-grained ratio was used (12.0).

Energy spectrum analysis on a red blood cell model.

It is important to understand the dynamics of red blood cells (RBCs) in blood flow. This requires the formulation of coarse-grained RBC models that reproduce the hydrodynamic properties of blood accurately. One of the models that successfully reproduces the rheology and morphology of blood has been proposed by Fedosov et al. [Comput. Methods Appl. Mech. Eng. 199, 1937-1948 (2010)]. The proposed RBC model contains several parameters whose values are determined by either various experiments or physical requirements. In this study, we developed a new method of determining parameter values precisely from the fluctuations of the RBC membrane. Specifically, we studied the relationship between the spectra of the fluctuations and model parameters. Characteristic peaks were observed in the spectra, whose peak frequencies were dependent on the parameter values. In addition, we investigated the spectra of the radius of gyration. We identified the peaks originating from the spring potential and the volume-conserving potential appearing in the spectra. These results lead to the precise experimental determination of the parameters used in the RBC model.

Optimizing Excipient Properties to Prevent Aggregation in Biopharmaceutical Formulations.

Excipients are included within protein biotherapeutic solution formulations to improve colloidal and conformational stability but are generally not designed for the specific purpose of preventing aggregation and improving cryoprotection in solution. In this work, we have explored the relationship between the structure and antiaggregation activity of excipients by utilizing coarse-grained molecular dynamics modeling of protein-excipient interaction. We have studied human serum albumin as a model protein, and we report the interaction of 41 excipients (polysorbates, fatty alcohol ethoxylates, fatty acid ethoxylates, phospholipids, glucosides, amino acids, and others) in terms of the reduction of solvent accessible surface area of aggregation-prone regions, proposed as a mechanism of aggregation prevention. Polyoxyethylene sorbitan had the greatest degree of interaction with aggregation-prone regions, decreasing the solvent accessible surface area of APRs by 20.7 nm2 (40.1%). Physicochemical descriptors generated by Mordred are employed to probe the structure-property relationship using partial least-squares regression. A leave-one-out cross-validated model had a root-mean-square error of prediction of 4.1 nm2 and a mean relative error of prediction of 0.077. Generally, longer molecules with a large number of alcohol-terminated PEG units tended to interact more, with qualitatively different protein interactions, wrapping around the protein. Shorter or less ethoxylated compounds tend to form hemimicellar clusters at the protein surface. We propose that an improved design would feature many short chains of 5 to 10 PEG units in many distinct branches and at least some hydrophobic content in the form of medium-length or greater aliphatic chains (i.e., six or more carbon atoms). The combination of molecular dynamics simulation and quantitative modeling is an important first step in an all-purpose protein-independent model for the computer-aided design of stabilizing excipients.

Disentanglement of Surface and Confinement Effects for Diene Metathesis in Mesoporous Confinement.

We study the effects of a planar interface and confinement on a generic catalytically activated ring-closing polymerization reaction near an unstructured catalyst. For this, we employ a coarse-grained polymer model using grand-canonical molecular dynamics simulations with a Monte Carlo reaction scheme. Inspired by recent experiments in the group of M. Buchmeiser that demonstrated an increase in ring-closing selectivity under confinement, we show that both the interface effects, i.e., placing the catalyst near a planar wall, and the confinement effects, i.e., locating the catalyst within a pore, lead to an increase of selectivity. We furthermore demonstrate that curvature effects for cylindrical mesopores (2 nm < d < 12.3 nm) influence the distribution of the chain ends, leading to a further increase in selectivity. This leads us to speculate that specially corrugated surfaces might also help to enhance catalytically activated polymerization processes.

Molecular dynamics study on micelle-small molecule interactions: developing a strategy for an extensive comparison

Theoretical predictions of the solubilizing capacity of micelles and vesicles present in intestinal fluid are important for the development of new delivery techniques and bioavailability improvement. A balance between accuracy and computational cost is a key factor for an extensive study of numerous compounds in diverse environments. In this study, we aimed to determine an optimal molecular dynamics (MD) protocol to evaluate small-molecule interactions with micelles composed of bile salts and phospholipids. MD simulations were used to produce free energy profiles for three drug molecules (danazol, probucol, and prednisolone) and one surfactant molecule (sodium caprate) as a function of the distance from the colloid center of mass. To address the challenges associated with such tasks, we compared different simulation setups, including freely assembled colloids versus pre-organized spherical micelles, full free energy profiles versus only a few points of interest, and a coarse-grained model versus an all-atom model. Our findings demonstrate that combining these techniques is advantageous for achieving optimal performance and accuracy when evaluating the solubilization capacity of micelles.Graphical abstractAll-atom (AA) and coarse-grained (CG) umbrella sampling (US) simulations and point-wise free energy (FE) calculations were compared to their efficiency to computationally analyze the solubilization of active pharmaceutical ingredients in intestinal fluid colloids.

Many-body potential for simulating the self-assembly of polymer-grafted nanoparticles in a polymer matrix

Many-body interactions between polymer-grafted nanoparticles (NPs) play a key role in promoting their assembly into low-dimensional structures within polymer melts, even when the particles are spherical and isotropically grafted. However, capturing such interactions in simulations of NP assembly is very challenging because explicit modeling of the polymer grafts and melt chains is highly computationally expensive, even using coarse-grained models. Here, we develop a many-body potential for describing the effective interactions between spherical polymer-grafted NPs in a polymer matrix through a machine-learning approach. The approach involves using permutationally invariant polynomials to fit two- and three-body interactions derived from the potential of mean force calculations. The potential developed here reduces the computational cost by several orders of magnitude, thereby, allowing us to explore assembly behavior over large length and time scales. We show that the potential not only reproduces previously known assembled phases such as 1D strings and 2D hexagonal sheets, which generally cannot be achieved using isotropic two-body potentials, but can also help discover interesting phases such as networks, clusters, and gels. We demonstrate how each of these assembly morphologies intrinsically arises from a competition between two- and three-body interactions. Our approach for deriving many-body effective potentials can be readily extended to other colloidal systems, enabling researchers to make accurate predictions of their behavior and dissect the role of individual interaction energy terms of the overall potential in the observed behavior.

Simulation of aqueous solutes using the adaptive solvent-scaling (AdSoS) scheme

The Adaptive Solvent-Scaling (AdSoS) scheme [J. Chem. Phys. 155 (2021) 094107] is an adaptive-resolution approach for performing simulations of a solute embedded in a fine-grained (FG) solvent region surrounded by a coarse-grained (CG) solvent region, with a continuous FG ↔ CG switching of the solvent resolution across a buffer layer. Instead of relying on a distinct CG solvent model, AdSoS is based on CG models defined by a dimensional scaling of the FG solvent by a factor s, accompanied by the s-dependent modulation of its mass and interaction parameters. The latter changes are designed to achieve an isomorphism between the dynamics of the FG and CG models, and to preserve the dispersive and dielectric solvation properties of the solvent with respect to a solute at FG resolution. As a result, the AdSoS scheme minimizes the thermodynamic mismatch between different regions of the adaptive-resolution system. The present article generalizes the scheme initially introduced for a pure atomic liquid in slab geometry to more practically relevant situations involving (i) a molecular dipolar solvent (e.g., water); (ii) a radial geometry (i.e., spherical rather than planar layers); and (iii) the inclusion of a solute (e.g., water molecule, dipeptide, ion, or ion pair).

Can a coarse-grained water model capture the key physical features of the hydrophobic effect?

Coarse-grained (CG) molecular dynamics can be a powerful method for probing complex processes. However, most CG force fields use pairwise nonbonded interaction potentials sets, which can limit their ability to capture complex multi-body phenomena such as the hydrophobic effect. As the hydrophobic effect primarily manifests itself due to the nonpolar solute affecting the nearby hydrogen bonding network in water, capturing such effects using a simple one CG site or “bead” water model is a challenge. In this work, we systematically test the ability of CG one site water models for capturing critical features of the solvent environment around a hydrophobe as well as the potential of mean force (PMF) of neopentane association. We study two bottom-up models: a simple pairwise (SP) force-matched water model constructed using the multiscale coarse-graining method and the Bottom-Up Many-Body Projected Water (BUMPer) model, which has implicit three-body correlations. We also test the top-down monatomic (mW) and the Machine Learned mW (ML-mW) water models. The mW models perform well in capturing structural correlations but not the energetics of the PMF. BUMPer outperforms SP in capturing structural correlations and also gives an accurate PMF in contrast to the two mW models. Our study highlights the importance of including three-body interactions in CG water models, either explicitly or implicitly, while in general highlighting the applicability of bottom-up CG water models for studying hydrophobic effects in a quantitative fashion. This assertion comes with a caveat, however, regarding the accuracy of the enthalpy–entropy decomposition of the PMF of hydrophobe association.

Coarse-Graining and Forecasting Atomic Material Simulations with Descriptors.

Atomic simulations of materials require significant resources to generate, store, and analyze. Here, descriptor functions are proposed as a general, metric latent space for atomic structures, ideal for use in large-scale simulations. Descriptors can regress a broad range of properties, including character-dependent dislocation densities, stress states, or radial distribution functions. A vector autoregressive model can generate trajectories over yield points, resample from new initial conditions and forecast trajectory futures. A forecast confidence, essential for practical application, is derived by propagating forecasts through the Mahalanobis outlier distance, providing a powerful tool to assess coarse-grained models. Application to nanoparticles and yielding of nanoscale dislocation networks confirms low uncertainty forecasts are accurate and resampling allows for the propagation of smooth property distributions. Yielding is associated with a collapse in the intrinsic dimension of the descriptor manifold, which is discussed in relation to the yield surface.

Coarse-grained molecular dynamics study on submicron structuring of calcium silicate hydrate with enhanced tensile modulus and strength

Potential of cement-based materials for energy harvesting is encoded in their elusive mesoporous structure, and understanding the sub-micron structure of calcium silicate hydrate (C–S–H), a major binding phase of cement, is an important yet challenging task. Nanoscale C–S–H is ductile, while macroscopic performance is brittle, suggesting a gap in the transition of material properties from the nano-to the macro-scale, and a chance in finetuning the submicron (∼100 nm) structure to effectively deliver the atomistic properties to the bulk performance. The refined sub-micron structure of C–S–H can enhance the strength and modulus of concrete, thus providing a base material with better mechanical properties and durability. Here, two critical structural features of C–S–H, including agglomeration of globules and stacking of layers, are modeled by spherical and oblate ellipsoidal units, respectively. Coarse-grained modeling and peridynamics simulations find that the packing of ellipsoidal units is better than that of spherical units, from the perspectives of size distribution, morphology, fractality of pores and tensile properties. The analyses on the mesoporous structure shows that the ellipsoid-packing is more homogeneous than the sphere-packing model, and it yields a refined structure with enhancement in tensile modulus and strength. The submicron finetuning enhancement of C–S–H can be realized through different physical and chemical approaches, like inducing lateral growth of silicate chains by chemical additives, scaffolding materials, and conditioned hydration. This coarse-grained simulation work gives novel insights and fundamental strategies for toughening the cement matrix through delivering ductile nanoscale performance of C–S–H to macroscale cementitious materials, and provides superior performance for concrete in existing and new structures.

Effects of Ionic Strength on the Morphology, Scattering, and Mechanical Response of Neurofilament-Derived Protein Brushes.

Protein brushes not only play a key role in the functionality of neurofilaments but also have wide applications in biomedical materials. Here, we investigate the effect of ionic strength on the morphology of protein brushes using continuous-space self-consistent field theory. A coarse-grained multiblock charged macromolecular model is developed to capture the chemical identity of amino acid sequences. For neurofilament heavy (NFH) brushes at pH 2.4, we predict three morphological regimes: swollen brushes, condensed brushes, and coexisting brushes, which consist of both a dense inner layer and a diffuse outer layer. The brush height predicted by our theory is in good agreement with the experimental data for a wide range of ionic strengths. The dramatic height decrease is a result of the electrostatic screening-induced transition from the overlapping state to the isolated state of the coexisting brushes. We also studied the evolution of the scattering and mechanical responses accompanying the morphological change. The oscillation in the reflectivity spectra characterizes the existence and microstructure of the inner condensed layer, whereas the shoulder in the force spectra signifies a swollen morphology.

First coarse grain then scale: How to estimate diffusion coefficients of confined molecules.

An approach for approximating position and orientation dependent translational and rotational diffusion coefficients of rigid molecules of any shape suspended in a viscous fluid under geometric confinement is proposed. It is an extension of the previously developed scheme for evaluating near-wall diffusion of macromolecules, now applied to any geometry of boundaries. The method relies on shape based coarse-graining combined with scaling of mobility matrix components by factors derived based on energy dissipation arguments for Stokes flows. Tests performed for a capsule shaped molecule and its coarse-grained model, a dumbbell, for three different types of boundaries (a sphere, an open cylinder, and two parallel planes) are described. An almost perfect agreement between mobility functions of the detailed and coarse-grained models, even close to boundary surfaces, is obtained. The proposed method can be used to simplify hydrodynamic calculations and reduce errors introduced due to coarse-graining of molecular shapes.

3D feasibility of 2D RNA-RNA interaction paths by stepwise folding simulations.

The structure of an RNA, and even more so its interactions with other RNAs, provide valuable information about its function. Secondary structure-based tools for RNA-RNA interaction predictions provide a quick way to identify possible interaction targets and structures. However, these tools ignore the effect of steric hindrance on the tertiary (3D) structure level, and do not consider whether a suitable folding pathway exists to form the interaction. As a consequence, these tools often predict interactions that are unrealistically long and could be formed (in three dimensions) only by going through highly entangled intermediates. Here, we present a computational pipeline to assess whether a proposed secondary (2D) structure interaction is sterically feasible and reachable along a plausible folding pathway. To this end, we simulate the folding of a series of 3D structures along a given 2D folding path. To avoid the complexity of large-scale atomic resolution simulations, our pipeline uses coarse-grained 3D modeling and breaks up the folding path into small steps, each corresponding to the extension of the interaction by 1 or 2 bp. We apply our pipeline to analyze RNA-RNA interaction formation for three selected RNA-RNA complexes. We find that kissing hairpins, in contrast to interactions in the exterior loop, are difficult to extend and tend to get stuck at an interaction length of 6 bp. Our tool, including source code, documentation, and sample data, is available at www.github.com/irenekb/RRI-3D.

Coarse-Graining with Equivariant Neural Networks: A Path Toward Accurate and Data-Efficient Models.

Machine learning has recently entered into the mainstream of coarse-grained (CG) molecular modeling and simulation. While a variety of methods for incorporating deep learning into these models exist, many of them involve training neural networks to act directly as the CG force field. This has several benefits of which the most significant is accuracy. Neural networks can inherently incorporate multibody effects during the calculation of CG forces, and a well-trained neural network force field outperforms pairwise basis sets generated from essentially any methodology. However, this comes at a significant cost. First, these models are typically slower than pairwise force fields, even when accounting for specialized hardware, which accelerates the training and integration of such networks. The second and the focus of this paper is the need for a considerable amount of data to train such force fields. It is common to use 10s of microseconds of molecular dynamics data to train a single CG model, which approaches the point of eliminating the CG model's usefulness in the first place. As we investigate in this work, this "data-hunger" trap from neural networks for predicting molecular energies and forces can be remediated in part by incorporating equivariant convolutional operations. We demonstrate that, for CG water, networks that incorporate equivariant convolutional operations can produce functional models using data sets as small as a single frame of reference data, while networks without these operations cannot.