MagiC: Software Package for Multiscale Modeling

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.

CGCompiler: Automated Coarse-Grained Molecule Parametrization via Noise-Resistant Mixed-Variable Optimization

Coarse-grained force fields (CG FFs) such as the Martini model entail a predefined, fixed set of Lennard-Jones parameters (building blocks) to model virtually all possible nonbonded interactions between chemically relevant molecules. Owing to its universality and transferability, the building-block coarse-grained approach has gained tremendous popularity over the past decade. The parametrization of molecules can be highly complex and often involves the selection and fine-tuning of a large number of parameters (e.g., bead types and bond lengths) to optimally match multiple relevant targets simultaneously. The parametrization of a molecule within the building-block CG approach is a mixed-variable optimization problem: the nonbonded interactions are discrete variables, whereas the bonded interactions are continuous variables. Here, we pioneer the utility of mixed-variable particle swarm optimization in automatically parametrizing molecules within the Martini 3 coarse-grained force field by matching both structural (e.g., RDFs) as well as thermodynamic data (phase-transition temperatures). For the sake of demonstration, we parametrize the linker of the lipid sphingomyelin. The important advantage of our approach is that both bonded and nonbonded interactions are simultaneously optimized while conserving the search efficiency of vector guided particle swarm optimization (PSO) methods over other metaheuristic search methods such as genetic algorithms. In addition, we explore noise-mitigation strategies in matching the phase-transition temperatures of lipid membranes, where nucleation and concomitant hysteresis introduce a dominant noise term within the objective function. We propose that noise-resistant mixed-variable PSO methods can both improve and automate parametrization of molecules within building-block CG FFs, such as Martini.

OpenMSCG: A Software Tool for Bottom-Up Coarse-Graining

The "bottom-up" approach to coarse-graining, for building accurate and efficient computational models to simulate large-scale and complex phenomena and processes, is an important approach in computational chemistry, biophysics, and materials science. As one example, the Multiscale Coarse-Graining (MS-CG) approach to developing CG models can be rigorously derived using statistical mechanics applied to fine-grained, i.e., all-atom simulation data for a given system. Under a number of circumstances, a systematic procedure, such as MS-CG modeling, is particularly valuable. Here, we present the development of the OpenMSCG software, a modularized open-source software that provides a collection of successful and widely applied bottom-up CG methods, including Boltzmann Inversion (BI), Force-Matching (FM), Ultra-Coarse-Graining (UCG), Relative Entropy Minimization (REM), Essential Dynamics Coarse-Graining (EDCG), and Heterogeneous Elastic Network Modeling (HeteroENM). OpenMSCG is a high-performance and comprehensive toolset that can be used to derive CG models from large-scale fine-grained simulation data in file formats from common molecular dynamics (MD) software packages, such as GROMACS, LAMMPS, and NAMD. OpenMSCG is modularized in the Python programming framework, which allows users to create and customize modeling "recipes" for reproducible results, thus greatly improving the reliability, reproducibility, and sharing of bottom-up CG models and their applications.

Contrastive Learning of Coarse-Grained Force Fields

Coarse-grained models have proven helpful for simulating complex systems over long time scales to provide molecular insights into various processes. Methodologies for systematic parametrization of the underlying energy function or force field that describes the interactions among different components of the system are of great interest for ensuring simulation accuracy. We present a new method, potential contrasting, to enable efficient learning of force fields that can accurately reproduce the conformational distribution produced with all-atom simulations. Potential contrasting generalizes the noise contrastive estimation method with umbrella sampling to better learn the complex energy landscape of molecular systems. When applied to the Trp-cage protein, we found that the technique produces force fields that thoroughly capture the thermodynamics of the folding process despite the use of only α-carbons in the coarse-grained model. We further showed that potential contrasting could be applied over large data sets that combine the conformational ensembles of many proteins to improve force field transferability. We anticipate potential contrasting as a powerful tool for building general-purpose coarse-grained force fields.

Bottom-up Coarse-Graining: Principles and Perspectives

Large-scale computational molecular models provide scientists a means to investigate the effect of microscopic details on emergent mesoscopic behavior. Elucidating the relationship between variations on the molecular scale and macroscopic observable properties facilitates an understanding of the molecular interactions driving the properties of real world materials and complex systems (e.g., those found in biology, chemistry, and materials science). As a result, discovering an explicit, systematic connection between microscopic nature and emergent mesoscopic behavior is a fundamental goal for this type of investigation. The molecular forces critical to driving the behavior of complex heterogeneous systems are often unclear. More problematically, simulations of representative model systems are often prohibitively expensive from both spatial and temporal perspectives, impeding straightforward investigations over possible hypotheses characterizing molecular behavior. While the reduction in resolution of a study, such as moving from an atomistic simulation to that of the resolution of large coarse-grained (CG) groups of atoms, can partially ameliorate the cost of individual simulations, the relationship between the proposed microscopic details and this intermediate resolution is nontrivial and presents new obstacles to study. Small portions of these complex systems can be realistically simulated. Alone, these smaller simulations likely do not provide insight into collectively emergent behavior. However, by proposing that the driving forces in both smaller and larger systems (containing many related copies of the smaller system) have an explicit connection, systematic bottom-up CG techniques can be used to transfer CG hypotheses discovered using a smaller scale system to a larger system of primary interest. The proposed connection between different CG systems is prescribed by (i) the CG representation (mapping) and (ii) the functional form and parameters used to represent the CG energetics, which approximate potentials of mean force (PMFs). As a result, the design of CG methods that facilitate a variety of physically relevant representations, approximations, and force fields is critical to moving the frontier of systematic CG forward. Crucially, the proposed connection between the system used for parametrization and the system of interest is orthogonal to the optimization used to approximate the potential of mean force present in all systematic CG methods. The empirical efficacy of machine learning techniques on a variety of tasks provides strong motivation to consider these approaches for approximating the PMF and analyzing these approximations.

A Bottom-Up Coarse-Grained Model for Nucleosome-Nucleosome Interactions with Explicit Ions

The nucleosome core particle (NCP) is a large complex of 145-147 base pairs of DNA and eight histone proteins and is the basic building block of chromatin that forms the chromosomes. Here, we develop a coarse-grained (CG) model of the NCP derived through a systematic bottom-up approach based on underlying all-atom MD simulations to compute the necessary CG interactions. The model produces excellent agreement with known structural features of the NCP and gives a realistic description of the nucleosome-nucleosome attraction in the presence of multivalent cations (Mg(H₂O)₆²⁺ or Co(NH₃)₆³⁺) for systems comprising 20 NCPs. The results of the simulations reveal structural details of the NCP-NCP interactions unavailable from experimental approaches, and this model opens the prospect for the rigorous modeling of chromatin fibers.

Inverse Boltzmann Iterative Multi-Scale Molecular Dynamics Study between Carbon Nanotubes and Amino Acids

Our work uses Iterative Boltzmann Inversion (IBI) to study the coarse-grained interaction between 20 amino acids and the representative carbon nanotube CNT55L3. IBI is a multi-scale simulation method that has attracted the attention of many researchers in recent years. It can effectively modify the coarse-grained model derived from the Potential of Mean Force (PMF). IBI is based on the distribution result obtained by All-Atom molecular dynamics simulation; that is, the target distribution function and the PMF potential energy are extracted, and then, the initial potential energy extracted by the PMF is used to perform simulation iterations using IBI. Our research results have been through more than 100 iterations, and finally, the distribution obtained by coarse-grained molecular simulation (CGMD) can effectively overlap with the results of all-atom molecular dynamics simulation (AAMD). In addition, our work lays the foundation for the study of force fields for the simulation of the coarse-graining of super-large proteins and other important nanoparticles.

Iterative integral equation methods for structural coarse-graining

In this paper, new Newton and Gauss-Newton methods for iterative coarse-graining based on integral equation theory are evaluated and extended. In these methods, the potential update is calculated from the current and target radial distribution function, similar to iterative Boltzmann inversion, but gives a potential update of quality comparable with inverse Monte Carlo. This works well for the coarse-graining of molecules to single beads, which we demonstrate for water. We also extend the methods to systems that include coarse-grained bonded interactions and examine their convergence behavior. Finally, using the Gauss-Newton method with constraints, we derive a model for single bead methanol in implicit water, which matches the osmotic pressure of the atomistic reference. An implementation of all new methods is provided for the open-source VOTCA package.

Learning to Model G-Quadruplexes: Current Methods and Perspectives

G-quadruplexes have raised considerable interest during the past years for the development of therapies against cancer. These noncanonical structures of DNA may be found in telomeres and/or oncogene promoters, and it has been observed that the stabilization of such G-quadruplexes may disturb tumor cell growth. Nevertheless, the mechanisms leading to folding and stabilization of these G-quadruplexes are still not well established, and they are the focus of much current work in this field. In seminal works, stabilization was observed to be produced by cations. However, subsequent studies showed that different kinds of small molecules, from planar and nonplanar organic molecules to square-planar and octahedral metal complexes, may also lead to the stabilization of G-quadruplexes. Thus, the comprehension and rationalization of the interaction of these small molecules with G-quadruplexes are also important topics of current interest in medical applications. To shed light on the questions arising from the literature on the formation of G-quadruplexes, their stabilization, and their interaction with small molecules, synergies between experimental studies and computational works are needed. In this review, we mainly focus on in silico approaches and provide a broad compilation of different leading studies carried out to date by different computational methods. We divide these methods into twomain categories: (a) classical methods, which allow for long-timescale molecular dynamics simulations and the corresponding analysis of dynamical information, and (b) quantum methods (semiempirical, quantum mechanics/molecular mechanics, and density functional theory methods), which allow for the explicit simulation of the electronic structure of the system but, in general, are not capable of being used in long-timescale molecular dynamics simulations and, therefore, give a more static picture of the relevant processes.

The coarse-grained models of poly(ethylene oxide) and poly(propylene oxide) homopolymers and poloxamers in big multipole water (BMW) and MARTINI frameworks

Polyethylene oxide (PEO) and poly(propylene oxide) (PPO), especially their tri-block copolymers PEO-PPO-PEO (poloxamers), have a broad range of applications in biotechnology and medical science. Understanding their specific interactions with biomembranes is the key to unveil the unique features of poloxamers either as membrane-healing or membrane pore-forming agents. Based on the coarse-graining convention of the MARTINI force field and the big multipole water (BMW) model, which has a three charged site topology and can reproduce the correct dipole moment of four-water clusters, we generated coarse-grained (CG) models with analytical and numerical potentials for PEO and PPO homopolymers and poloxamers in dilute solution. The effective bonded interaction potentials between CG beads were determined from the probability distributions of bond lengths, angles and dihedrals that are determined from atomistic simulations. The nonbonded interaction parameters were fine-tuned to reproduce the conformational properties of atomistic PEO and PPO homopolymers and poloxamers via extensive CG simulations of PEO and PPO homopolymers and poloxamers in a BMW water environment. The reported CG models provide a promising framework for a comprehensive understanding of the microstructural, conformational, and dynamic properties of poloxamers and their delicate interactions with other species in an explicit water environment.

A multiscale analysis of DNA phase separation: from atomistic to mesoscale level

DNA condensation and phase separation is of utmost importance for DNA packing in vivo with important applications in medicine, biotechnology and polymer physics. The presence of hexagonally ordered DNA is observed in virus capsids, sperm heads and in dinoflagellates. Rigorous modelling of this process in all-atom MD simulations is presently difficult to achieve due to size and time scale limitations. We used a hierarchical approach for systematic multiscale coarse-grained (CG) simulations of DNA phase separation induced by the three-valent cobalt(III)-hexammine (CoHex³⁺). Solvent-mediated effective potentials for a CG model of DNA were extracted from all-atom MD simulations. Simulations of several hundred 100-bp-long CG DNA oligonucleotides in the presence of explicit CoHex³⁺ ions demonstrated aggregation to a liquid crystalline hexagonally ordered phase. Following further coarse-graining and extraction of effective potentials, we conducted modelling at mesoscale level. In agreement with electron microscopy observations, simulations of an 10.2-kb-long DNA molecule showed phase separation to either a toroid or a fibre with distinct hexagonal DNA packing. The mechanism of toroid formation is analysed in detail. The approach used here is based only on the underlying all-atom force field and uses no adjustable parameters and may be generalised to modelling chromatin up to chromosome size.

Implicit solvent systematic coarse-graining of dioleoylphosphatidylethanolamine lipids: From the inverted hexagonal to the bilayer structure

Lamellar and hexagonal lipid structures are of particular importance in the biological processes such as membrane fusion and budding. Atomistic simulations of formation of these phases and transitions between them are computationally prohibitive, hence development of coarse-grained models is an important part of the methodological development in this area. Here we apply systematic bottom-up coarse-graining to model different phase structures formed by 1,2-dioleoylphosphatidylethanolamine (DOPE) lipid molecules. We started from atomistic simulations of DOPE lipids in water carried out at two different water/lipid molar ratio corresponding to the lamellar L_α and inverted hexagonal H_II structures at low and high lipid concentrations respectively. The atomistic trajectories were mapped to coarse-grained trajectories, in which each lipid was represented by 14 coarse-grained sites. Then the inverse Monte Carlo method was used to compute the effective coarse-grained potentials which for the coarse-grain model reproduce the same structural properties as the atomistic simulations. The potentials derived from the low concentration atomistic simulation were only able to form a bilayer structure, while both L_α and H_II lipid phases were formed in simulations with potentials obtained at high concentration. The typical atomistic configurations of lipids at high concentration combine fragments of both lamellar and non-lamellar structures, that is reflected in the extracted coarse-grained potentials which become transferable and can form a wide range of structures including the inverted hexagonal, bilayer, tubule, vesicle and micellar structures.

Graph-Based Approach to Systematic Molecular Coarse-Graining

A novel methodology is introduced here to generate coarse-grained (CG) representations of molecular models for simulations. The proposed strategy relies on basic graph-theoretic principles and is referred to as graph-based coarse-graining (GBCG). It treats a given system as a molecular graph and derives a corresponding CG representation by using edge contractions to combine nodes in the graph, which correspond to atoms in the molecule, into CG sites. A key element of this methodology is that the nodes are combined according to well-defined protocols that rank-order nodes based on the underlying chemical connectivity. By iteratively performing these operations, successively coarser representations of the original atomic system can be produced to yield a systematic set of CG mappings with hierarchical resolution in an automated fashion. These capabilities are demonstrated in the context of several test systems, including toluene, pentadecane, a polysaccharide dimer, and a rhodopsin protein. In these examples, GBCG yields multiple, intuitive structures that naturally preserve the chemical topology of the system. Importantly, these representations are rendered from algorithmic implementation rather than an arbitrary ansatz, which, until now, has been the conventional approach for defining CG mapping schemes. Overall, the results presented here indicate that GBCG is efficient, robust, and unambiguous in its application, making it a valuable tool for future CG modeling.

Molecular Dynamics Simulations of Membrane Proteins: An Overview

Simulations of membrane proteins have been rising in popularity in the past decade. Advancements in technology and force fields made it possible to simulate behavior of membrane proteins. Membrane protein simulations can now be used as supporting evidence for experimental findings, for elucidating protein mechanisms, and validating protein crystal structures. Unrelated to experimental data, these simulations can also serve to investigate larger scale processes like protein sorting, protein-membrane interactions, and more. In this review, the history as well as the state-of-the-art methodologies in membrane protein simulations will be summarized. An emphasis will be put on how to set up the system and on the current models for the different components of the simulation system. An overview of the available tools for membrane protein simulation will be given, and current limitations and prospects will also be discussed.

Coarse-Grained Simulation of Rodlike Higher-Order Quadruplex Structures at Different Salt Concentrations

We present a coarse-grained (CG) model of a rodlike higher-order quadruplex with explicit monovalent salts, which was developed from radial distribution functions of an underlying reference atomistic molecular dynamics simulation using inverse Monte Carlo technique. This work improves our previous CG model and extends its applicability beyond the minimal salt conditions, allowing its use at variable ionic strengths. The strategies necessary for the model development are clearly explained and discussed. The effects of the number of stacked quadruplexes and varied salt concentration on the elasticity of the rodlike higher-order quadruplex structures are analyzed. The CG model reproduces the deformations of the terminal parts in agreement with experimental observations without introducing any special parameters for terminal beads and reveals slight differences in the rise and twist of the G-quartet arrangement along the studied biopolymer. The conclusions of our study can be generalized for other G-quartet-based structures.

The Theory of Ultra-Coarse-Graining. 3. Coarse-Grained Sites with Rapid Local Equilibrium of Internal States

When viewed through a coarse-grained lens, important molecular and biophysical systems can appear to undergo discrete, switch-like state changes in addition to more continuous configurational motions. One of our recent papers described a theory for bottom-up coarse-graining of the equilibrium statistics of models with such behavior, called ultra-coarse-grained (UCG) models, and a follow up paper described an implementation when the states of the coarse-grained sites or "beads" change rarely. However, not all systems with this discrete behavior fall under that special limit. This article develops the general UCG theory for the opposite limit, that is, where the internal states of the CG particles or beads adjust rapidly so as to always remain effectively at quasi-equilibrium no matter what the positions of the coarse-grained particles. This rapid local equilibrium allows ultra-coarse-graining to mix standard coarse-grained force fields by using local order parameters to control the degree of mixing, which adds an environmental dependence and many-body effects to the coarse-grained model while requiring minimal new coding. This article first presents the definition of such UCG force fields as well as their fitting procedures from atomistic-scale data, and then it presents three examples of UCG simulations with an approach that we call UCG with rapid local equilibrium (UCG-RLE). We then present an application of UCG-RLE using the full bottom-up methodology to coarse-grain and simulate cooperative hydrophobic association of neopentane in methanol solvent. UCG-RLE force matching does a superior job of matching solute-solute correlation functions and solute cluster size distributions compared to the more standard force-matched models not having coarse-grained sites with discrete internal states.

Thermodynamics of Hydrophobic Amino Acids in Solution: A Combined Experimental-Computational Study

We present a joint experimental-computational study to quantitatively describe the thermodynamics of hydrophobic leucine amino acids in aqueous solution. X-ray scattering data were acquired at a series of solute and salt concentrations to effectively measure interleucine interactions, indicating that a major scattering peak is observed consistently at q = 0.83 Å^-1. Atomistic molecular dynamics simulations were then performed and compared with the scattering data, achieving high consistency at both small and wider scattering angles (q = 0-1.5 Å^-1). This experimental-computational consistence enables a first glimpse of the leucine-leucine interacting landscape, where two leucine molecules are aligned mostly in a parallel fashion, as opposed to antiparallel, but also allows us to derive effective leucine-leucine interactions in solution. Collectively, this combined approach of employing experimental scattering and molecular simulation enables quantitative characterization of effective intermolecular interactions of hydrophobic amino acids, critical for protein function and dynamics such as protein folding.

Multiscale Modelling of Bionano Interface

We present a framework for coarse-grained modelling of the interface between foreign nanoparticles (NP) and biological fluids and membranes. Our model includes united-atom presentations of membrane lipids and globular proteins in implicit solvent, which are based on all-atom structures of the corresponding molecules and parameterised using experimental data or atomistic simulation results. The NPs are modelled by homogeneous spheres that interact with the beads of biomolecules via a central force that depends on the NP size. The proposed methodology is used to predict the adsorption energies for human blood plasma proteins on NPs of different sizes as well as the preferred orientation of the molecules upon adsorption. Our approach allows one to rank the proteins by their binding affinity to the NP, which can be used for predicting the composition of the NP-protein corona for the corresponding material. We also show how the model can be used for studying NP interaction with a lipid bilayer membrane and thus can provide a mechanistic insight for modelling NP toxicity.

Systematic hierarchical coarse-graining with the inverse Monte Carlo method

We outline our coarse-graining strategy for linking micro- and mesoscales of soft matter and biological systems. The method is based on effective pairwise interaction potentials obtained in detailed ab initio or classical atomistic Molecular Dynamics (MD) simulations, which can be used in simulations at less accurate level after scaling up the size. The effective potentials are obtained by applying the inverse Monte Carlo (IMC) method [A. P. Lyubartsev and A. Laaksonen, Phys. Rev. E 52(4), 3730-3737 (1995)] on a chosen subset of degrees of freedom described in terms of radial distribution functions. An in-house software package MagiC is developed to obtain the effective potentials for arbitrary molecular systems. In this work we compute effective potentials to model DNA-protein interactions (bacterial LiaR regulator bound to a 26 base pairs DNA fragment) at physiological salt concentration at a coarse-grained (CG) level. Normally the IMC CG pair-potentials are used directly as look-up tables but here we have fitted them to five Gaussians and a repulsive wall. Results show stable association between DNA and the model protein as well as similar position fluctuation profile.

Molecular Dynamics Simulation Study of Parallel Telomeric DNA Quadruplexes at Different Ionic Strengths: Evaluation of Water and Ion Models

Most molecular dynamics (MD) simulations of DNA quadruplexes have been performed under minimal salt conditions using the Åqvist potential parameters for the cation with the TIP3P water model. Recently, this combination of parameters has been reported to be problematic for the stability of quadruplex DNA, especially caused by the ion interactions inside or near the quadruplex channel. Here, we verify how the choice of ion parameters and water model can affect the quadruplex structural stability and the interactions with the ions outside the channel. We have performed a series of MD simulations of the human full-parallel telomeric quadruplex by neutralizing its negative charge with K(+) ions. Three combinations of different cation potential parameters and water models have been used: (a) Åqvist ion parameters, TIP3P water model; (b) Joung and Cheatham ion parameters, TIP3P water model; and (c) Joung and Cheatham ion parameters, TIP4Pew water model. For the combinations (b) and (c), the effect of the ionic strength has been evaluated by adding increasing amounts of KCl salt (50, 100, and 200 mM). Two independent simulations using the Åqvist parameters with the TIP3P model show that this combination is clearly less suited for the studied quadruplex with K(+) as counterions. In both simulations, one ion escapes from the channel, followed by significant deformation of the structure, leading to deviating conformation compared to that in the reference crystallographic data. For the other combinations of ion and water potentials, no tendency is observed for the channel ions to escape from the quadruplex channel. In addition, the internal mobility of the three loops, torsion angles, and counterion affinity have been investigated at varied salt concentrations. In summary, the selection of ion and water models is crucial as it can affect both the structure and dynamics as well as the interactions of the quadruplex with its counterions. The results obtained with the TIP4Pew model are found to be closest to the experimental data at all of the studied ion concentrations.

Multiscale coarse-grained modelling of chromatin components: DNA and the nucleosome

To model large biomolecular systems, such as cell and organelles an atomistic description is not currently achievable and is not generally practical. Therefore, simplified coarse-grained (CG) modelling becomes a necessity. One of the most important cellular components is chromatin, a large DNA-protein complex where DNA is highly compacted. Recent progress in coarse graining modelling of the major chromatin components, double helical DNA and the nucleosome core particle (NCP) is presented. First, general principles and approaches allowing rigorous bottom-to-top generation of interaction potentials in the CG models are presented. Then, recent CG models of DNA are reviewed and their adequacy is benchmarked against experimental data on the salt dependence of DNA flexibility (persistence length). Furthermore, a few recent CG models of the NCP are described and their application for studying salt-dependent NCP-NCP interaction is discussed. An example of a multiscale approach to CG modelling of chromatin is presented where interactions and self-assembly of thousands of NCPs in solution are observed.

A Solvent-Mediated Coarse-Grained Model of DNA Derived with the Systematic Newton Inversion Method

We present a new class of coarse-grained (CG) force fields (FFs) for B-DNA with explicit ions suited for large-scale mesoscale simulations at microsecond-micrometer scale using a wide spectrum of particle simulation methods from molecular dynamics to dissipative particle dynamics. The effective solvent-mediated pairwise interactions making up the FFs are obtained by inverting radial distribution functions and other particle-particle distributions obtained from all-atom simulations of numbers of octadecamer DNA fragments from the Ascona B-DNA library. The inverse Monte Carlo (IMC) method, later known as Newton inversion (NI) (Lyubartsev, A. P.; Laaksonen, A. Phys. Rev. E, 1995, 52, 3730-3737), was used together with the iterative Boltzmann inversion (IBI) scheme to compute the effective CG potentials. We show that this systematic structure-based approach is capable of providing converged potentials that accurately reproduce the structural features of the underlying atomistic system within a few percents of relative difference. We also show that a simple one-site-per-nucleotide model with 10 intramolecular pair interaction potentials is able to reproduce key features of DNA, for example, the persistence length and its dependence on the ionic concentration, experimentally determined around 50 nm at physiological salt concentration.

A simple and transferable all-atom/coarse-grained hybrid model to study membrane processes

We present an efficient all-atom/coarse-grained hybrid model and apply it to membrane processes. This model is an extension of the all-atom/ELBA model applied previously to processes in water. Here, we improve the efficiency of the model by implementing a multiple-time step integrator that allows the atoms and the coarse-grained beads to be propagated at different timesteps. Furthermore, we fine-tune the interaction between the atoms and the coarse-grained beads by computing the potential of mean force of amino acid side chain analogs along the membrane normal and comparing to atomistic simulations. The model was independently validated on the calculation of small-molecule partition coefficients. Finally, we apply the model to membrane peptides. We studied the tilt angle of the Walp23 and Kalp23 helices in two different model membranes and the stability of the glycophorin A dimer. The model is efficient, accurate, and straightforward to use, as it does not require any extra interaction particles, layers of atomistic solvent molecules or tabulated potentials, thus offering a novel, simple approach to study membrane processes.

Relative Entropy and Optimization-Driven Coarse-Graining Methods in VOTCA

We discuss recent advances of the VOTCA package for systematic coarse-graining. Two methods have been implemented, namely the downhill simplex optimization and the relative entropy minimization. We illustrate the new methods by coarse-graining SPC/E bulk water and more complex water-methanol mixture systems. The CG potentials obtained from both methods are then evaluated by comparing the pair distributions from the coarse-grained to the reference atomistic simulations. In addition to the newly implemented methods, we have also added a parallel analysis framework to improve the computational efficiency of the coarse-graining process.

Multiscale modelling of nucleosome core particle aggregation

The nucleosome core particle (NCP) is the basic building block of chromatin. Under the influence of multivalent cations, isolated mononucleosomes exhibit a rich phase behaviour forming various columnar phases with characteristic NCP-NCP stacking. NCP stacking is also a regular element of chromatin structure in vivo. Understanding the mechanism of nucleosome stacking and the conditions leading to self-assembly of NCPs is still incomplete. Due to the complexity of the system and the need to describe electrostatics properly by including the explicit mobile ions, novel modelling approaches based on coarse-grained (CG) methods at the multiscale level becomes a necessity. In this work we present a multiscale CG computer simulation approach to modelling interactions and self-assembly of solutions of NCPs induced by the presence of multivalent cations. Starting from continuum simulations including explicit three-valent cobalt(III)hexammine (CoHex(3+)) counterions and 20 NCPs, based on a previously developed advanced CG NCP model with one bead per amino acid and five beads per two DNA base pair unit (Fan et al 2013 PLoS One 8 e54228), we use the inverse Monte Carlo method to calculate effective interaction potentials for a 'super-CG' NCP model consisting of seven beads for each NCP. These interaction potentials are used in large-scale simulations of up to 5000 NCPs, modelling self-assembly induced by CoHex(3+). The systems of 'super-CG' NCPs form a single large cluster of stacked NCPs without long-range order in agreement with experimental data for NCPs precipitated by the three-valent polyamine, spermidine(3+).

Multiscale simulations of human telomeric G-quadruplex DNA

We present a coarse-grain (CG) model of human telomeric G-quadruplex, obtained using the inverse Monte Carlo (IMC) and iterative Boltzmann inversion (IBI) techniques implemented within the software package called MagiC. As a starting point, the 2HY9 human telomeric [3 + 1] hybrid, a 26-nucleobase sequence, was modeled performing a 1 μs long atomistic molecular dynamics (MD) simulation. The chosen quadruplex includes two kinds of loops and all possible combinations of relative orientations of guanine strands that can be found in quadruplexes. The effective CG potential for a one bead per nucleotide model has been developed from the radial distribution functions of this reference system. The obtained potentials take into account explicitly the interaction with counterions, while the effect of the solvent is included implicitly. The structural properties of the obtained CG model of the quadruplex provided a perfect match to those resulting from the reference atomistic MD simulation. The same set of interaction potentials was then used to simulate at the CG level another quadruplex topology (PDB id 1KF1 ) that can be formed by the human telomeric DNA sequence. This quadruplex differs from 2HY9 in the loop topology and G-strand relative orientation. The results of the CG MD simulations of 1KF1 are very encouraging and suggest that the CG model based on 2HY9 can be used to simulate quadruplexes with different topologies. The CG model was further applied to a higher order human telomeric quadruplex formed by the repetition, 20 times, of the 1KF1 quadruplex structure. In all cases, the developed model, which to the best of our knowledge is the first model of quadruplexes at the CG level presented in the literature, reproduces the main structural features remarkably well.

COFFDROP: A Coarse-Grained Nonbonded Force Field for Proteins Derived from All-Atom Explicit-Solvent Molecular Dynamics Simulations of Amino Acids

We describe the derivation of a set of bonded and nonbonded coarse-grained (CG) potential functions for use in implicit-solvent Brownian dynamics (BD) simulations of proteins derived from all-atom explicit-solvent molecular dynamics (MD) simulations of amino acids. Bonded potential functions were derived from 1 μs MD simulations of each of the 20 canonical amino acids, with histidine modeled in both its protonated and neutral forms; nonbonded potential functions were derived from 1 μs MD simulations of every possible pairing of the amino acids (231 different systems). The angle and dihedral probability distributions and radial distribution functions sampled during MD were used to optimize a set of CG potential functions through use of the iterative Boltzmann inversion (IBI) method. The optimized set of potential functions—which we term COFFDROP (COarse-grained Force Field for Dynamic Representation Of Proteins)—quantitatively reproduced all of the “target” MD distributions. In a first test of the force field, it was used to predict the clustering behavior of concentrated amino acid solutions; the predictions were directly compared with the results of corresponding all-atom explicit-solvent MD simulations and found to be in excellent agreement. In a second test, BD simulations of the small protein villin headpiece were carried out at concentrations that have recently been studied in all-atom explicit-solvent MD simulations by Petrov and Zagrovic (PLoS Comput. Biol.2014, 5, e1003638). The anomalously strong intermolecular interactions seen in the MD study were reproduced in the COFFDROP simulations; a simple scaling of COFFDROP’s nonbonded parameters, however, produced results in better accordance with experiment. Overall, our results suggest that potential functions derived from simulations of pairwise amino acid interactions might be of quite broad applicability, with COFFDROP likely to be especially useful for modeling unfolded or intrinsically disordered proteins.

The power of coarse graining in biomolecular simulations

Computational modeling of biological systems is challenging because of the multitude of spatial and temporal scales involved. Replacing atomistic detail with lower resolution, coarse grained (CG), beads has opened the way to simulate large-scale biomolecular processes on time scales inaccessible to all-atom models. We provide an overview of some of the more popular CG models used in biomolecular applications to date, focusing on models that retain chemical specificity. A few state-of-the-art examples of protein folding, membrane protein gating and self-assembly, DNA hybridization, and modeling of carbohydrate fibers are used to illustrate the power and diversity of current CG modeling.

Perspective: Coarse-grained models for biomolecular systems

By focusing on essential features, while averaging over less important details, coarse-grained (CG) models provide significant computational and conceptual advantages with respect to more detailed models. Consequently, despite dramatic advances in computational methodologies and resources, CG models enjoy surging popularity and are becoming increasingly equal partners to atomically detailed models. This perspective surveys the rapidly developing landscape of CG models for biomolecular systems. In particular, this review seeks to provide a balanced, coherent, and unified presentation of several distinct approaches for developing CG models, including top-down, network-based, native-centric, knowledge-based, and bottom-up modeling strategies. The review summarizes their basic philosophies, theoretical foundations, typical applications, and recent developments. Additionally, the review identifies fundamental inter-relationships among the diverse approaches and discusses outstanding challenges in the field. When carefully applied and assessed, current CG models provide highly efficient means for investigating the biological consequences of basic physicochemical principles. Moreover, rigorous bottom-up approaches hold great promise for further improving the accuracy and scope of CG models for biomolecular systems.

Evaluation of reduced point charge models of proteins through Molecular Dynamics simulations: application to the Vps27 UIM-1-Ubiquitin complex

Reduced point charge models of amino acids are designed, (i) from local extrema positions in charge density distribution functions built from the Poisson equation applied to smoothed molecular electrostatic potential (MEP) functions, and (ii) from local maxima positions in promolecular electron density distribution functions. Corresponding charge values are fitted versus all-atom Amber99 MEPs. To easily generate reduced point charge models for protein structures, libraries of amino acid templates are built. The program GROMACS is used to generate stable Molecular Dynamics trajectories of an Ubiquitin-ligand complex (PDB: 1Q0W), under various implementation schemes, solvation, and temperature conditions. Point charges that are not located on atoms are considered as virtual sites with a nul mass and radius. The results illustrate how the intra- and inter-molecular H-bond interactions are affected by the degree of reduction of the point charge models and give directions for their implementation; a special attention to the atoms selected to locate the virtual sites and to the Coulomb-14 interactions is needed. Results obtained at various temperatures suggest that the use of reduced point charge models allows to probe local potential hyper-surface minima that are similar to the all-atom ones, but are characterized by lower energy barriers. It enables to generate various conformations of the protein complex more rapidly than the all-atom point charge representation.