Coarse-grained Force Research Articles

Unveiling Nucleosome Dynamics: A Comparative Study Using All-Atom and Coarse-Grained Simulations Enhanced by Principal Component Analysis.

This study investigates nucleosome dynamics using both all-atom and coarse-grained (CG) molecular dynamics simulations, focusing on the SIRAH force field. Simulations are performed for two nucleosomal DNA sequences, ASP and Widom-601, over six microseconds at physiological salt concentrations. Comparative analysis of structural parameters, such as groove widths and base pair geometries, reveals good agreement between atomistic and CG models, though CG simulations exhibit broader conformational sampling and greater breathing motion of DNA ends. Principal component analysis (PCA) is applied to DNA structural parameters, revealing multiple free energy minima, especially in CG simulations. These findings highlight the potential of the SIRAH CG force field for studying large-scale nucleosome dynamics, offering insights into DNA repositioning and sequence-dependent behavior.

Graph neural network coarse-grain force field for the molecular crystal RDX

Condense phase molecular systems organize in wide range of distinct molecular configurations, including amorphous melt and glass as well as crystals often exhibiting polymorphism, that originate from their intricate intra- and intermolecular forces. While accurate coarse-grain (CG) models for these materials are critical to understand phenomena beyond the reach of all-atom simulations, current models cannot capture the diversity of molecular structures. We introduce a generally applicable approach to develop CG force fields for molecular crystals combining graph neural networks (GNN) and data from an all-atom simulations and apply it to the high-energy density material RDX. We address the challenge of expanding the training data with relevant configurations via an iterative procedure that performs CG molecular dynamics of processes of interest and reconstructs the atomistic configurations using a pre-trained neural network decoder. The multi-site CG model uses a GNN architecture constructed to satisfy translational invariance and rotational covariance for forces. The resulting model captures both crystalline and amorphous states for a wide range of temperatures and densities.

Exploring the Properties of Curved Lipid Membranes: Comparative Analysis of Atomistic and Coarse-Grained Force Fields.

Curvature emerges as a fundamental membrane characteristic crucial for diverse biological processes, including vesicle formation, cell signaling, and membrane trafficking. Increasingly valuable insights into atomistic details governing curvature-dependent membrane properties are provided by computer simulations. Nevertheless, the underlying force field models are conventionally calibrated and tested in relation to experimentally derived parameters of planar bilayers, thereby leaving uncertainties concerning their consistency in reproducing curved lipid systems. In this study we compare the depiction of buckled phosphatidylcholine (POPC) and POPC-cholesterol membranes by four popular force field models. Aside from agreement with respect to general trends in curvature dependence of a number of parameters, we observe a few qualitative differences. Among the most prominent ones is the difference between atomistic and coarse grained force fields in their representation of relative compressibility of the polar headgroup region and hydrophobic lipid core. Through a number of downstream effects, this discrepancy can influence the way in which curvature modulates the behavior of membrane bound proteins depending on the adopted simulation model.

The acquisition and calibration of coarse-grained force field parameters for graphene/nitrile rubber nanocomposites

Graphene (GNS) is an ideal reinforcing material for developing high-performance nitrile rubber (NBR) nanocomposites to withstand complex and harsh working conditions. The mesoscale molecular simulation is significant in exploring the microscopic mechanism of nanocomposites. Initially, a coarse-grained (CG) scheme for NBR was established, and the potential parameter files were obtained through the iterative Boltzmann inversion method. Then, the bond and angle parameters were fitted by harmonic potential, the non-bonded interactions were fitted by LJ 12–6 potential. The CG force field parameters were coefficients of the potential function. The parameters such as the stiffness values, potential well parameter, etc., were calibrated by stretching the NBR molecular chains, and comparing the density and stretching performances of the NBR all-atomic and CG models. A rapid method for obtaining the non-bonded parameters between NBR and GNS was proposed based on the radial distribution function curves. The non-bonded parameters were calibrated by comparing GNS pull-out simulations, and the density and tensile properties of the GNS/NBR models. Finally, the CG force field parameters applicable to the GNS/NBR nanocomposites were obtained. The study is expected to be significant for subsequent mesoscale studies on GNS/NBR nanocomposites.

Coarse-Grained Modeling of Liquid-Liquid Phase Separation in Cells: Challenges and Opportunities.

Liquid-liquid phase separation (LLPS) within cells gives rise to membraneless organelles, which play pivotal roles in numerous cellular functions. A comprehensive understanding of the functional aspects of intrinsically disordered protein (IDP) condensates necessitates elucidating their inherent structures and establishing correlations with biological functions. Coarse-grained (CG) molecular dynamics (MD) simulations present a promising avenue for gaining insights into LLPS mechanisms of biomacromolecules. Essential to this endeavor is the development of tailored CG force fields for MD simulations, incorporating the full spectrum of biomolecules involved in the formation of condensates and accounting for real-time biochemical reactions coupled to the LLPS. Moreover, developing accurate theoretical frameworks and establishing links between condensate structure and its function are imperative for a thorough comprehension of LLPS of biological systems.

Bartender: Martini 3 Bonded Terms via Quantum Mechanics-Based Molecular Dynamics.

Coarse-grained (CG) molecular dynamics (MD) simulations have grown in applicability over the years. The recently released version of the Martini CG force field (Martini 3) has been successfully applied to simulate many processes, including protein-ligand binding. However, the current ligand parametrization scheme is manual and requires an a priori reference all-atom (AA) simulation for benchmarking. For systems with suboptimal AA parameters, which are often unknown, this translates into a CG model that does not reproduce the true dynamical behavior of the underlying molecule. Here, we present Bartender, a quantum mechanics (QM)/MD-based parametrization tool written in Go. Bartender harnesses the power of QM simulations and produces reasonable bonded terms for Martini 3 CG models of small molecules in an efficient and user-friendly manner. For small, ring-like molecules, Bartender generates models whose properties are indistinguishable from the human-made models. For more complex, drug-like ligands, it is able to fit functional forms beyond simple harmonic dihedrals and thus better captures their dynamical behavior. Bartender has the power to both increase the efficiency and the accuracy of Martini 3-based high-throughput applications by producing numerically stable and physically realistic CG models.

Coarse-grained force field for ZIF-8: A study on adsorption, diffusion, and structural properties.

Metal-organic frameworks (MOFs) are revolutionizing a spectrum of industries, from groundbreaking gas storage solutions to transformative biological system applications. The intricate architecture of these materials necessitates the use of advanced computational techniques for a comprehensive understanding of their molecular structure and prediction of their physical properties. Coarse-grained (CG) simulations shine a spotlight on the often-neglected influences of defects, pressure effects, and spatial disorders on the performance of MOFs. These simulations are not just beneficial but indispensable for high-demand applications, such as mixed matrix membranes and intricate biological system interfaces. In this work, we propose an optimized CG force field tailored for ZIF-8. Our work provides a deep dive into sorption isotherms and diffusion coefficients of small molecules. We demonstrate the structural dynamics of ZIF-8, particularly how it responds to pressurization, which affects its crystal structure and leads to local changes in aperture size and area. Emphasizing the game-changing potential of CG simulations, we explore the characteristics of amorphization in ZIF-8. Through computational exploration, we aim to bridge the knowledge gap, enhancing the potential applications of nanoporous materials for various applications.

Force matching and iterative Boltzmann inversion coarse grained force fields for ZIF-8.

Despite the intense activity at electronic and atomistic resolutions, coarse grained (CG) modeling of metal-organic frameworks remains largely unexplored. One of the main reasons for this is the lack of adequate CG force fields. In this work, we present iterative Boltzmann inversion and force matching (FM) force fields for modeling ZIF-8 at three different coarse grained resolutions. Their ability to reproduce structure, elastic tensor, and thermal expansion is evaluated and compared with that of MARTINI force fields considered in previous work [Alvares et al., J. Chem. Phys. 158, 194107 (2023)]. Moreover, MARTINI and FM are evaluated for their ability to depict the swing effect, a subtle phase transition ZIF-8 undergoes when loaded with guest molecules. Overall, we found that all our force fields reproduce structure reasonably well. Elastic constants and volume expansion results are analyzed, and the technical and conceptual challenges of reproducing them are explained. Force matching exhibits promising results for capturing the swing effect. This is the first time these CG methods, widely applied in polymer and biomolecule communities, are deployed to model porous solids. We highlight the challenges of fitting CG force fields for these materials.

Exploring Free Energies of Specific Protein Conformations Using the Martini Force Field.

Coarse-grained (CG) level molecular dynamics simulations are routinely used to study various biomolecular processes. The Martini force field is currently the most widely adopted parameter set for such simulations. The functional form of this and several other CG force fields enforces secondary protein structure support by employing a variety of harmonic potentials or restraints that favor the protein's native conformation. We propose a straightforward method to calculate the energetic consequences of transitions between predefined conformational states in systems in which multiple factors can affect protein conformational equilibria. This method is designed for use within the Martini force field and involves imposing conformational transitions by linking a Martini-inherent elastic network to the coupling parameter λ. We demonstrate the applicability of our method using the example of five biomolecular systems that undergo experimentally characterized conformational transitions between well-defined structures (Staphylococcal nuclease, C-terminal segment of surfactant protein B, LAH4 peptide, and β2-adrenergic receptor) as well as between folded and unfolded states (GCN4 leucine zipper protein). The results show that the relative free energy changes associated with protein conformational transitions, which are affected by various factors, such as pH, mutations, solvent, and lipid membrane composition, are correctly reproduced. The proposed method may be a valuable tool for understanding how different conditions and modifications affect conformational equilibria in proteins.

SIRAH Late Harvest: Coarse-Grained Models for Protein Glycosylation.

Glycans constitute one of the most complex families of biological molecules. Despite their crucial role in a plethora of biological processes, they remain largely uncharacterized because of their high complexity. Their intrinsic flexibility and the vast variability associated with the many combination possibilities have hampered their experimental determination. Although theoretical methods have proven to be a valid alternative to the study of glycans, the large size associated with polysaccharides, proteoglycans, and glycolipids poses significant challenges to a fully atomistic description of biologically relevant glycoconjugates. On the other hand, the exquisite dependence on hydrogen bonds to determine glycans' structure makes the development of simplified or coarse-grained (CG) representations extremely challenging. This is particularly the case when glycan representations are expected to be compatible with CG force fields that include several molecular types. We introduce a CG representation able to simulate a wide variety of polysaccharides and common glycosylation motifs in proteins, which is fully compatible with the CG SIRAH force field. Examples of application to N-glycosylated proteins, including antibody recognition and calcium-mediated glycan-protein interactions, highlight the versatility of the enlarged set of CG molecules provided by SIRAH.

Exploring ssDNA translocation through α-hemolysin using coarse-grained steered molecular dynamics.

Protein nanopores have proven to be effective for single-molecule studies, particularly for single-stranded DNA (ssDNA) translocation. Previous experiments demonstrated their ability to distinguish differences in purine and pyrimidine bases and in the orientation of the ssDNA molecule inside nanopores. Unfortunately, the microscopic details of ssDNA translocation over experimental time scales, which are not accessible through all-atom molecular dynamics (MD), have yet to be examined. However, coarse-grained (CG) MD simulations enable systems to be simulated over longer characteristic times closer to experiments than all-atom MD. This paper studies ssDNA translocation through α-hemolysin nanopores exploiting steered MD using the MARTINI CG force field. The impacts of the sequence length, orientation inside the nanopore and DNA charges on translocation dynamics as well as the conformational dynamics of ssDNA during the translocation are explored. Our results highlight the efficacy of CG molecular dynamics in capturing the experimental properties of ssDNA translocation, including a wide distribution in translocation times per base. In particular, the phosphate charges of the DNA molecule are crucial in the translocation dynamics and impact the translocation rate. Additionally, the influence of the ssDNA molecule orientation on the translocation rate is explained by the conformational differences of ssDNA inside the nanopore during its translocation. Our study emphasizes the significance of obtaining sufficient statistics via CG MD, which can elucidate the great variety of translocation processes.

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.

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 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.

Nonlinear Mori–Zwanzig theory and quadratic coarse-grained coordinates for complex molecular systems

We first introduce the Zwanzig–Kawasaki version of the generalized Langevin equation and show as a preamble and under some hypothesis about the relaxation of the fluctuations in the orthogonal subspace, that the commonly used term for the Markovian approximation of the dissipation is rigorously vanishing, necessitating the use of the next-order term, in an integral series we introduce. Independently, we provide thereafter a comprehensive description of complex coarse-grained molecules which, in addition to the classical positions and momenta of their centers of mass, encompasses their shapes, angular momenta and internal energies. The dynamics of these quantities is then derived as the coarse-grained forces, torques, microscopic stresses, energy transfers, from the coarse-grained potential built with their Berne-like anisotropic interactions. By incorporating exhaustively the quadratic combinations of the atomic degrees of freedom, this novel approach enriches considerably the dynamics at the coarse-grained level and could serve as a foundation for developing numerical models more holistic and accurate than dissipative particle dynamics for the simulation of complex molecular systems. This advancement opens up new possibilities for understanding and predicting the behavior of such systems in various scientific and engineering applications.

Transferable Implicit Solvation via Contrastive Learning of Graph Neural Networks.

Implicit solvent models are essential for molecular dynamics simulations of biomolecules, striking a balance between computational efficiency and biological realism. Efforts are underway to develop accurate and transferable implicit solvent models and coarse-grained (CG) force fields in general, guided by a bottom-up approach that matches the CG energy function with the potential of mean force (PMF) defined by the finer system. However, practical challenges arise due to the lack of analytical expressions for the PMF and algorithmic limitations in parameterizing CG force fields. To address these challenges, a machine learning-based approach is proposed, utilizing graph neural networks (GNNs) to represent the solvation free energy and potential contrasting for parameter optimization. We demonstrate the effectiveness of the approach by deriving a transferable GNN implicit solvent model using 600,000 atomistic configurations of six proteins obtained from explicit solvent simulations. The GNN model provides solvation free energy estimations much more accurately than state-of-the-art implicit solvent models, reproducing configurational distributions of explicit solvent simulations. We also demonstrate the reasonable transferability of the GNN model outside of the training data. Our study offers valuable insights for deriving systematically improvable implicit solvent models and CG force fields from a bottom-up perspective.

Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics.

Coarse-grained (CG) molecular dynamics enables the study of biological processes at temporal and spatial scales that would be intractable at an atomistic resolution. However, accurately learning a CG force field remains a challenge. In this work, we leverage connections between score-based generative models, force fields, and molecular dynamics to learn a CG force field without requiring any force inputs during training. Specifically, we train a diffusion generative model on protein structures from molecular dynamics simulations, and we show that its score function approximates a force field that can directly be used to simulate CG molecular dynamics. While having a vastly simplified training setup compared to previous work, we demonstrate that our approach leads to improved performance across several protein simulations for systems up to 56 amino acids, reproducing the CG equilibrium distribution and preserving the dynamics of all-atom simulations such as protein folding events.

Neural potentials of proteins extrapolate beyond training data.

We evaluate neural network (NN) coarse-grained (CG) force fields compared to traditional CG molecular mechanics force fields. We conclude that NN force fields are able to extrapolate and sample from unseen regions of the free energy surface when trained with limited data. Our results come from 88 NN force fields trained on different combinations of clustered free energy surfaces from four protein mapped trajectories. We used a statistical measure named total variation similarity to assess the agreement between reference free energy surfaces from mapped atomistic simulations and CG simulations from trained NN force fields. Our conclusions support the hypothesis that NN CG force fields trained with samples from one region of the proteins' free energy surface can, indeed, extrapolate to unseen regions. Additionally, the force matching error was found to only be weakly correlated with a force field's ability to reconstruct the correct free energy surface.

A coarse‐grained molecular dynamics study on the mechanical behavior of carbon nanotubes reinforced vulcanized natural rubber composites

AbstractIn the present study, coarse‐grained (CG) molecular models of carbon nanotubes (CNTs) strengthened vulcanized natural rubber (VNR) composites are constructed to systematically investigate the effects of length, inter‐tube cross‐linking, and polymer‐grafting of CNTs on the stress–strain behavior of VNR composites under uniaxial tension. The interfacial CG force field between CNTs and VNR is derived via the energy matching approach. The results demonstrate that increasing the length of CNTs is able to effectively enhance the mechanical performance of VNR composites, and the reinforcing efficiency increases gradually and tends to stabilize with increasing CNT length. Moreover, the cross‐linking and polymer‐grafting of CNTs are also effective approaches to significantly enhance the mechanical performance of VNR composites. The enhancement mechanism of CNTs is interpreted from the perspective of CNT networks, VNR networks, CNT‐VNR interface networks, and the interfacial load‐transfer behavior. Specifically, the dispersion of CNTs, the orientation of CNTs and VNR molecular chains, and the wrapping and interlocking behaviors between CNTs and the VNR matrix reveal a detailed structure‐mechanics relationship of CNTs‐reinforced VNR composites at the molecular level. These findings present theoretical instruction for the structural design of high‐performance rubber composites.Highlights A coarse‐grained model of carbon nanotube‐natural rubber composites is built. The composite network structures are quantitatively characterized. The length, cross‐linking, and grafting effects of carbon nanotubes are studied. The reinforcement mechanisms of carbon nanotubes are revealed.

Design single-stranded DNA aptamer of cluster of differentiation 47 protein by stochastic tunnelling-basin hopping-discrete molecular dynamics method

The formation of the Cluster of Differentiation 47 (CD47, PDB code: 2JJT)/signal regulatory protein α (SIRPα) complex is very important as it protects healthy cells from immune clearance while promoting macrophage phagocytosis for tumour elimination. Although several antibodies have been developed for cancer therapy, new function-blocking aptamers are still under development. This study aims to design the aptamer AptCD47, which can block the formation of the CD47/SIRPα complex. This study employs the MARTINI coarse-grained (CG) force field and the stochastic tunnelling-basin hopping-discrete molecular dynamics (STUN-BH-DMD) method to identify the most stable AptCD47/CD47 complexes. Coarse-grained molecular dynamics (CGMD) simulations were used to obtain root-mean-square deviation (RMSD) and root-mean-square fluctuation (RMSF) analyses. The results demonstrate that the formation of AptCD47/CD47 complexes renders the CD47 structure more stable than the single CD47 molecule in a water environment. The minimum energy pathway (MEP) obtained by the nudged elastic band (NEB) method indicates that the binding processes of 5′-ATTCAATTCC-3′ and 5′-AGTGCAATCT-3′ to CD47 are barrierless, which is much lower than the binding barrier of SIRPα to CD47 of about 14.23 kcal/mol. Therefore, these two AptCD47/CD47 complexes can create a high spatial binding barrier for SIRPα, preventing the formation of a stable CD47/SIRPα complex. The proposed numerical process with the MARTINI CG force field can be used to design CD47 aptamers that efficiently block SIRPα from binding to CD47. Communicated by Ramaswamy H. Sarma