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Bio-based semicrystalline polylactide (PLA) has a growing value as a substitute for fossil-based polyesters in technical applications and as a biocompatible material in medicine. The complexity of the behavior of PLA, determined by the presence of different stereoisomers, the role of electrostatic interactions, and its slow crystallization rate, makes computer simulations an important tool to discover new approaches to control the properties of PLA-based materials. The goal of this review is to summarize the efforts to simulate PLA materials with different levels of detail, including the quantum mechanical approach, all-atom modeling, and coarse-grained particle models. We focus on the validation of the models and the ways to cross-check the results with other simulation and experimental data. Special attention is devoted to the simulations of PLA in the presence of water, which provide insights into molecular mechanisms of hydrolytic degradation of PLA, especially at the initial stage, when the structural changes can not yet be detected by experimental methods. Ultimately, the selection of the appropriate simulation methods can facilitate material design, by combining the throughput and level of detail necessary for the job. [ABSTRACT FROM AUTHOR]

Most coarse-grained models of individual capsomers associated with viruses employ rigid building blocks that do not exhibit shape adaptation during self-assembly. We develop a coarse-grained general model of viral capsomers that incorporates their stretching and bending energies while retaining many features of the rigid-body models, including an overall trapezoidal shape with attractive interaction sites embedded in the lateral walls to favor icosahedral capsid assembly. Molecular dynamics simulations of deformable capsomers reproduce the rich self-assembly behavior associated with a general T = 1 icosahedral virus system in the absence of a genome. Transitions from non-assembled configurations to icosahedral capsids to kinetically-trapped malformed structures are observed as the steric attraction between capsomers is increased. An assembly diagram in the space of capsomer–capsomer steric attraction and capsomer deformability reveals that assembling capsomers of higher deformability into capsids requires increasingly large steric attraction between capsomers. Increasing capsomer deformability can reverse incorrect capsomer–capsomer binding, facilitating transitions from malformed structures to symmetric capsids; however, making capsomers too soft inhibits assembly and yields fluid-like structures. [ABSTRACT FROM AUTHOR]

DNA carries the genetic information required for the synthesis of RNA and proteins and plays an important role in many processes of biological development. Understanding the three-dimensional (3D) structures and dynamics of DNA is crucial for understanding their biological functions and guiding the development of novel materials. In this review, we discuss the recent advancements in computer methods for studying DNA 3D structures. This includes molecular dynamics simulations to analyze DNA dynamics, flexibility, and ion binding. We also explore various coarse-grained models used for DNA structure prediction or folding, along with fragment assembly methods for constructing DNA 3D structures. Furthermore, we also discuss the advantages and disadvantages of these methods and highlight their differences. [ABSTRACT FROM AUTHOR]

The systematic development of coarse-grained (CG) models via the Mori–Zwanzig projector operator formalism requires the explicit description of a deterministic drift term, a dissipative memory term and a random fluctuation term. The memory and fluctuating terms are related by the fluctuation–dissipation relation and are more challenging to sample and describe than the drift term due to complex dependence on space and time. This work proposes a rational basis for a Markovian data-driven approach to approximating the memory and fluctuating terms. We assumed a functional form for the memory kernel and under broad regularity hypothesis, we derived bounds for the error committed in replacing the original term with an approximation obtained by its asymptotic expansions. These error bounds depend on the characteristic time scale of the atomistic model, representing the decay of the autocorrelation function of the fluctuating force; and the characteristic time scale of the CG model, representing the decay of the autocorrelation function of the momenta of the beads. Using appropriate parameters to describe these time scales, we provide a quantitative meaning to the observation that the Markovian approximation improves as they separate. We then proceed to show how the leading-order term of such expansion can be identified with the Markovian approximation usually considered in the CG theory. We also show that, while the error of the approximation involving time can be controlled, the Markovian term usually considered in CG simulations may exhibit significant spatial variation. It follows that assuming a spatially constant memory term is an uncontrolled approximation which should be carefully checked. We complement our analysis with an application to the estimation of the memory in the CG model of a one-dimensional Lennard–Jones chain with different masses and interactions, showing that even for such a simple case, a non-negligible spatial dependence for the memory term exists. [ABSTRACT FROM AUTHOR]

Plastic pollution has become a global environmental threat, which leads to an increasing concern over the consequences of plastic exposition on global health. Plastic nanoparticles have been shown to influence the folding of proteins and influence the formation of aberrant amyloid proteins, therefore potentially triggering the development of systemic and local amyloidosis. This work aims to study the interaction between nanoplastics and β-amyloid fibrils to better understand the potential role of nanoplastics in the outbreak of neurodegenerative disorders. Using microsecond-long coarse-grained molecular dynamics simulations, we investigated the interactions between neutral and charged nanoparticles made of the most common plastic materials (i.e., polyethylene, polypropylene, and polystyrene) and β-amyloid fibrils. We observe that the occurrence of contacts, region of amyloid fibril involved, and specific amino acids mediating the interaction depend on the type and charge of the nanoparticles. [ABSTRACT FROM AUTHOR]

The ESCASA algorithm for analytical estimation of proton positions from coarse‐grained geometry developed in our recent work has been implemented in modeling protein structures with the highly coarse‐grained UNRES model of polypeptide chains (two sites per residue) and nuclear magnetic resonance (NMR) data. A penalty function with the shape of intersecting gorges was applied to treat ambiguous distance restraints, which automatically selects consistent restraints. Hamiltonian replica exchange molecular dynamics was used to carry out the conformational search. The method was tested with both unambiguous and ambiguous restraints producing good‐quality models with GDT_TS from 7.4 units higher to 14.4 units lower than those obtained with the CYANA or MELD software for protein‐structure determination from NMR data at the all‐atom resolution. The method can thus be applied in modeling the structures of flexible proteins, for which extensive conformational search enabled by coarse‐graining is more important than high modeling accuracy. [ABSTRACT FROM AUTHOR]

Coarse-grained (CG) protein models have become indispensable tools for studying many biological protein details, from conformational dynamics to the organization of protein macro-complexes, and even the interaction of proteins with other molecules. The Martini force field is one of the most widely used CG models for bio-molecular simulations, partly because of the enormous success of its protein model. With the recent release of a new and improved version of the Martini force field – Martini 3 – a new iteration of its protein model was also made available. The Martini 3 protein force field is an evolution of its Martini 2 counterpart, aimed at improving many of the shortcomings that had been previously identified. In this mini-review, we first provide a general overview of the model and then focus on the successful advances made in the short time since its release, many of which would not have been possible before. Furthermore, we discuss reported limitations, potential directions for model improvement and comment on what the likely future development and application avenues are. • Computational methods like Martini play key roles in deciphering protein function. • The Martini 3 protein model improves on past iterations, addressing many shortcomings. • Despite success, soon after release, reports note hindrances to its applicability. • We explore improvement directions and discuss future development and applications. [ABSTRACT FROM AUTHOR]

Aliphatic isocyanates and polyisocyanates are central molecules in the fabrication of polyurethanes, coatings, and adhesives and, due to their excellent mechanical and stability properties, are continuously investigated in advanced applications; however, despite the growing interest in isocyanate-based systems, atomistic simulations on them have been limited by the lack of accurate parametrizations for these molecular species. In this review, we will first provide an overview of current research on isocyanate systems to highlight their most promising applications, especially in fields far from their typical usage, and to justify the need for further modeling works. Next, we will discuss the state of their modeling, from first-principle studies to atomistic molecular dynamics simulations and coarse-grained approaches, highlighting the recent advances in atomistic modeling. Finally, the most promising lines of research in the modeling of isocyanates are discussed in light of the possibilities opened by novel approaches, such as machine learning. [ABSTRACT FROM AUTHOR]

Coarse-grained (CG) modelling with the Martini force field has come of age. By combining a variety of bead types and sizes with a new mapping approach, the newest version of the model is able to accurately simulate large biomolecular complexes at millisecond timescales. In this perspective, we discuss possible applications of the Martini 3 model in drug discovery and development pipelines and highlight areas for future development. Owing to its high simulation efficiency and extended chemical space, Martini 3 has great potential in the area of drug design and delivery. However, several aspects of the model should be improved before Martini 3 CG simulations can be routinely employed in academic and industrial settings. These include the development of automatic parameterisation protocols for a variety of molecule types, the improvement of backmapping procedures, the description of protein flexibility and the development of methodologies enabling efficient sampling. We illustrate our view with examples on key areas where Martini could give important contributions such as drugs targeting membrane proteins, cryptic pockets and protein-protein interactions and the development of soft drug delivery systems. [ABSTRACT FROM AUTHOR]

[Display omitted] Carbon nanotubes (CNTs) represent a novel platform for cellular delivery of therapeutic peptides. Chemically-functionalized CNTs may enhance peptide uptake by improving their membrane targeting properties. Using coarse-grained (CG) molecular dynamics (MD) simulations, we investigate membrane interactions of a peptide conjugated to pristine and chemically-modified CNTs. As proof of principle, we focus on their interactions with PM2, an amphipathic stapled peptide that inhibits the E3 ubiquitin ligase HDM2 from negatively regulating the p53 tumor suppressor. CNT interaction with both simple planar lipid bilayers as well as spherical lipid vesicles was studied, the latter as a surrogate for curved cellular membranes. Membrane permeation was rapid and spontaneous for both pristine and oxidized CNTs when unconjugated. This was slowed upon addition of a noncovalently attached peptide surface "sheath", which may be an effective way to slow CNT entry and avert membrane rupture. The CNT conjugates were observed to "desheath" their peptide layer at the bilayer interface upon insertion, leaving their cargo behind in the outer leaflet. This suggests that a synergy may exist to optimize CNT safety whilst enhancing the delivery efficiency of "hitchhiking" therapeutic molecules. [ABSTRACT FROM AUTHOR]

Coarse-grained (CG) modelling with the Martini force field has come of age. By combining a variety of bead types and sizes with a new mapping approach, the newest version of the model is able to accurately simulate large biomolecular complexes at millisecond timescales. In this perspective, we discuss possible applications of the Martini 3 model in drug discovery and development pipelines and highlight areas for future development. Owing to its high simulation efficiency and extended chemical space, Martini 3 has great potential in the area of drug design and delivery. However, several aspects of the model should be improved before Martini 3 CG simulations can be routinely employed in academic and industrial settings. These include the development of automatic parameterisation protocols for a variety of molecule types, the improvement of backmapping procedures, the description of protein flexibility and the development of methodologies enabling efficient sampling. We illustrate our view with examples on key areas where Martini could give important contributions such as drugs targeting membrane proteins, cryptic pockets and protein–protein interactions and the development of soft drug delivery systems.

We study the nascent behavior of three model coarse-grained proteins in six rigid all-atom structures representing ribosomes that come from three domains of life. The synthesis of the proteins is implemented as a growth process. The geometry of the exit tunnel is quantified and shown to differ between the domains of life: both in volume and the size of constriction sites. This results in different characteristic times of capture within the tunnel and various probabilities of the escape. One of the proteins studied is the bacterial YibK which is knotted in its native state. A fraction of the trajectories results in knotting and the probability of doing so is largest for the bacterial ribosomes. Relaxing the condition of the rigidness of the ribosomes should result in a better avoidance of trapping and better proper folding.

Towards cellular-scale structural modeling, multiscale biomolecular simulation is gaining much attention. Here, I review methodological aspects of coarse-grained (CG) biomolecular simulations. I begin with conceptual argument of coarse graining for proteins where the idea behind Gō models is discussed. Then, statistical physics and theories of coarse graining are described. I then exemplify a class of CG models for proteins, nucleic acids, and lipids, where about 10 non-hydrogen atoms are grouped into one CG particles. Finally, I discuss three sources of speeding up by coarse graining. [ABSTRACT FROM AUTHOR]

Aliphatic isocyanates and polyisocyanates are central molecules in the fabrication of polyurethanes, coatings, and adhesives and, due to their excellent mechanical and stability properties, are continuously investigated in advanced applications; however, despite the growing interest in isocyanate-based systems, atomistic simulations on them have been limited by the lack of accurate parametrizations for these molecular species. In this review, we will first provide an overview of current research on isocyanate systems to highlight their most promising applications, especially in fields far from their typical usage, and to justify the need for further modeling works. Next, we will discuss the state of their modeling, from first-principle studies to atomistic molecular dynamics simulations and coarse-grained approaches, highlighting the recent advances in atomistic modeling. Finally, the most promising lines of research in the modeling of isocyanates are discussed in light of the possibilities opened by novel approaches, such as machine learning.

Molecular dynamics simulations often involve excessive degrees of freedom, and therefore the computational cost is prohibitively high. Coarse-graining high-dimensional molecular systems is a well-established procedure to increase the space- and time- scales accessible. The coarse-level potential energy must be estimated to derive correctly structural and dynamical properties of the reduced system. Here we examine the artificial neural networks as a potential tool to represent high-dimensional potential-energy surfaces. The simulated coarse configurations are fed to the neural network while taking care to represent each pseudo-atom's chemical environment properly. Then, the neural network is trained to reproduce the molecular energy. In this work, we present two approaches designed to preserve the physical symmetries of the coarse system. The first approach introduces symmetry functions and the second local coordinates. We implement these techniques, and we apply and compare them for two molecular systems, a bulk methane system, and a liquid water system. [ABSTRACT FROM AUTHOR]

Multicomponent biomolecular aggregates, i.e., systems consisting of more than one type of biomolecular component co-assembling into one aggregate, provide an interesting design space for engineering unique biomaterials. In this study, we examine the co-assembly of two lipomimetic oligopeptide block copolymers selected for their lipid-like amphiphilicity and highly similar architectures into nanofibers via coarse-grained MD simulations. We focus on the behavior of these peptides due to incremental differences in size by selecting two peptides that differ in length by exactly one amino acid residue. We find that the longer peptide sequence displays greater self-association properties. [Display omitted] • Lipomimetic peptides (V 6 K 2 and V 6 K 3) aggregate to form fibrillar structures. • Hydrophobic tail groups form the cores of the fibers while hydrophilic heads form surface groups. • Steric consequences of different sizes of the headgroups cause varying degrees of head group interactions. • Head group interactions form an extended percolating network across the surface of the fiber. • Interactions between charged head groups facilitated by salt bridging. [ABSTRACT FROM AUTHOR]

Coarse-graining is a powerful tool for extending the reach of dynamic models of proteins and other biological macromolecules. Topological coarse-graining, in which biomolecules or sets thereof are represented via graph structures, is a particularly useful way of obtaining highly compressed representations of molecular structures, and simulations operating via such representations can achieve substantial computational savings. A drawback of coarse-graining, however, is the loss of atomistic detail—an effect that is especially acute for topological representations such as protein structure networks (PSNs). Here, we introduce an approach based on a combination of machine learning and physically-guided refinement for inferring atomic coordinates from PSNs. This “neural upscaling” procedure exploits the constraints implied by PSNs on possible configurations, as well as differences in the likelihood of observing different configurations with the same PSN. Using a 1 μs atomistic molecular dynamics trajectory of Aβ1–40, we show that neural upscaling is able to effectively recapitulate detailed structural information for intrinsically disordered proteins, being particularly successful in recovering features such as transient secondary structure. These results suggest that scalable network-based models for protein structure and dynamics may be used in settings where atomistic detail is desired, with upscaling employed to impute atomic coordinates from PSNs.

Background: Despite the advances in computational techniques and computing resources, the atomistic simulations are still limited in terms of attainable simulation time (up to 1-100 μs) and system size (up to millions of atoms). Various coarse-grained models provide a valuable alternative to the fully atomic simulations but they largely rely upon the rigorous parametrization procedure. The latter is required to obtain a set of coarse-grained force field parameters adequately reproducing the behavior of the original full-atom system. Here, we report an automated pipeline designed for the parametrization of coarse-grained models of small organic compounds and polymers. While it is tailored to the MARTINI coarse-graining framework, it can also be applied to other force fields following a similar coarse-graining strategy. Methods: Given the all-atom trajectory and the mapping (i.e., a table describing the correspondence between atoms and coarse-grained particles), the parametrization procedure iteratively optimizes bonded parameters of the coarse-grained model such that the bond, angle, (if required) dihedral histograms obtained from a series of coarse-grained simulations gradually converge to the target distributions derived from the full-atom trajectory mapped to the CG resolution. Results: The developed pipeline accepts as an input a full-atom trajectory of a molecule to be parametrized as well as the mapping between the full-atom and coarse-grained models. Based on this input, the initial coarse-grained model is built using Boltzmann inversion, which is a subject for the following iterative optimization. At each iteration, the convergence of bonded terms is checked based on the overlap of respective histograms derived from full-atom and coarse-grained simulations and, in case of satisfactory convergence, the optimization is stopped. In addition to the parametrization of individual compounds, the pipeline also allows deriving models for polymers consisting of repetitive units. The proposed here approach has been successfully used to create coarse-grained topologies for various small molecular weight compounds, such as drugs and antiseptics, as well as for detergents (DDM) and amphiphilic polymers (SMA, DIBMA) stabilizing lipodiscs – nanosized lipid particles widely exploited in structural studies of membrane proteins. The later models proved themselves particularly helpful for the interpretation of experimental data from small-angle scattering, EM, and EPR experiments Conclusion: We expect that the developed pipeline would support the fast and straightforward development of coarse-grained models for simulations of biomolecular systems and new materials. Such simulations can be especially useful for the interpretation of experimental data obtained by, e.g., small-angle scattering and Cryo-EM experiments. The pipeline is available online at https://github.com/porekhov/cgmktop.

A large number of low-resolution models have been proposed in the last decades to reduce the computational cost of molecular dynamics simulations for bio-nano systems, such as those involving the interactions of proteins with functionalized nanoparticles (NPs). For the proteins, “minimalist” models at the one-bead-per residue (Cα-based) level and with implicit solvent are well established. For the gold NPs, widely explored for biotechnological applications, mesoscale (MS) models treating the NP core with a single spheroidal object are commonly proposed. In this representation, the surface details (coating, roughness, etc.) are lost. These, however, and the specificity of the functionalization, have been shown to have fundamental roles for the interaction with proteins. We presented a mixed-resolution coarse-grained (CG) model for gold NPs in which the surface chemistry is reintroduced as superficial smaller beads. We compared molecular dynamics simulations of the amyloid β2-microglobulin represented at the minimalist level interacting with NPs represented with this model or at the MS level. Our finding highlights the importance of describing the surface of the NP at a finer level as the chemical-physical properties of the surface of the NP are crucial to correctly understand the protein-nanoparticle association.

Coarse-grained simulation schemes are increasingly gaining popularity in the scientific community because of the significant speed up granted, allowing a considerable expansion of the accessible time and size scales accessible to molecular simulations. However, the number of compatible force fields capable of representing ensembles containing different molecular species (i.e., Protein, DNA, etc) is still limited. Here, we present a set of parameters and simplified representation for lipids compatible with the SIRAH force field for coarse-grained simulations (). We show that the present model not only achieves a correct reproduction of structural parameters as area per lipid and thickness, but also dynamic descriptors such as diffusion coefficient, order parameters, and proper temperature driven variations. Adding phospholipid membranes to the existing aqueous solution, protein and DNA representations of the SIRAH force field permit considering the most common problems tackled by the biomolecular simulation community. [ABSTRACT FROM AUTHOR]

The PACE force field presents an attractive model for conducting molecular dynamics simulations of membrane-protein systems. PACE is a hybrid model, in which lipids and solvents are coarse-grained consistent with the MARTINI mapping, while proteins are described by a united atom model. However, given PACE is linked to MARTINI, which is widely used to study membranes, the behavior of proteins interacting with membranes has only been limitedly examined in PACE. In this study, PACE is used to examine the behavior of several peptides in membrane environments, namely WALP peptides, melittin and influenza hemagglutinin fusion peptide (HAfp). Overall, we find PACE provides an improvement over MARTINI for modeling helical peptides, based on the membrane insertion energetics for WALP16 and more realistic melittin pore dynamics. Our studies on HAfp, which forms a helical hairpin structure, do not show the hairpin structure to be stable, which may point toward a deficiency in the model. © 2017 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Coarse-grained (CG) models allow enlarging the size and time scales that are reachable by atomistic molecular dynamics simulations. A CG force field (FF) for lipids and amino acids that possesses a polarizable water model has been developed following the MARTINI parametrization strategy, the BMW-MARTINI [1]. We tested the BMW-MARTINI FF capability to describe some structural and thermodynamical properties of lipid monolayers and bilayers. We found that, since the surface tension values of oil/water interfaces calculated with the model are not correct, compression isotherms of lipid monolayers present artifacts. Also, this FF predicts DPPC and DAPC bilayers to remain in the L α phase at temperatures as low as 283 K, contrary to the expected from their experimental Tm values. Finally, simulations at constant temperature of bilayers of saturated lipids belonging to PC homologous, showed an increase in the mean molecular area (Mma) upon increasing the chain length, inversely to the experimental observation. We refined BMW-MARTINI FF by modifying as few parameters as possible in order to bring simulated and experimental measurements closer. We have also modified structural parameters of the lipid geometry that do not have direct influence in global properties of the bilayer membranes or monolayers, but serve to approach the obtained CG geometry to atomistic reference values. The refined FF is able to better reproduce phase transition temperatures and Mma for saturated PC bilayers than BMW-MARTINI and MARTINI FF. Finally, the simulated surface pressure-Mma isotherms of PC monolayers resemble the experimental ones and eliminate serious artifacts of previous models. [ABSTRACT FROM AUTHOR]

Skin's effectiveness as a barrier to permeation of water and other chemicals rests almost entirely in the outermost layer of the epidermis, the stratum corneum (SC), which consists of layers of corneocytes surrounded by highly organized lipid lamellae. As the only continuous path through the SC, transdermal permeation necessarily involves diffusion through these lipid layers. The role of the SC as a protective barrier is supported by its exceptional lipid composition consisting of ceramides (CERs), cholesterol (CHOL), and free fatty acids (FFAs) and the complete absence of phospholipids, which are present in most biological membranes. Molecular simulation, which provides molecular level detail of lipid configurations that can be connected with barrier function, has become a popular tool for studying SC lipid systems. We review this ever-increasing body of literature with the goals of (1) enabling the experimental skin community to understand, interpret and use the information generated from the simulations, (2) providing simulation experts with a solid background in the chemistry of SC lipids including the composition, structure and organization, and barrier function, and (3) presenting a state of the art picture of the field of SC lipid simulations, highlighting the difficulties and best practices for studying these systems, to encourage the generation of robust reproducible studies in the future. This review describes molecular simulation methodology and then critically examines results derived from simulations using atomistic and then coarse-grained models. • Molecular simulation can promote understanding of the stratum corneum barrier. • Meaningful results require force field validation and simulation documentation. • Coarse-grained models allow for multilayer, multicomponent lipid self-assembly. • Multilayer simulations most closely approximate experiments. • Permeability calculated from simulations are not directly comparable to experiment. [ABSTRACT FROM AUTHOR]

In nanopore force spectroscopy (NFS) a charged polymer is threaded through a channel of molecular dimensions. When an electric field is applied across the insulating membrane, the ionic current through the nanopore reports on polymer translocation, unzipping, dissociation, and so forth. We present a new model that can be applied in molecular dynamics simulations of NFS. Although simplified, it does reproduce experimental trends and all-atom simulations. The scaled conductivities in bulk solution are consistent with experimental results for NaCl for a wide range of electrolyte concentrations and temperatures. The dependence of the ionic current through a nanopore on the applied voltage is symmetric and, in the voltage range used in experiments (up to 2 V), linear and in good agreement with experimental data. The thermal stability and geometry of DNA is well represented. The model was applied to simulations of DNA hairpin unzipping in nanopores. The results are in good agreement with all-atom simulations: the scaled translocation times and unzipping sequence are similar. © 2015 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

This review is a tutorial for scientists interested in the problem of protein structure prediction, particularly those interested in using coarse-grained molecular dynamics models which are optimized using lessons learned from the energy landscape theory of protein folding. We also present a review of the results of the AMH/AMC/AMW/AWSEM family of coarse-grained molecular dynamics protein folding models to illustrate the points covered in the first part of the article. Accurate coarse-grained structure prediction models can be used to investigate a wide range of conceptual and mechanistic issues outside of protein structure prediction; specifically, the paper concludes by reviewing how AWSEM has, in recent years, been able to elucidate questions related to the unusual kinetic behavior of artificially designed proteins, multidomain protein misfolding, and the initial stages of protein aggregation. [ABSTRACT FROM AUTHOR]

Viruses are particularly challenging systems to study via molecular simulation methods. Virus capsids typically consist of over 100 subunit proteins and reach dimensions of over 100 nm; solvated viruses capsid systems can be over 1 million atoms in size. In this review, I will present recent developments which have attempted to overcome the significant computational expense to perform simulations which can inform experimental studies, make useful predictions about biological phenomena and calculate material properties relevant to nanotechnology design efforts. [ABSTRACT FROM AUTHOR]

The timescales of biological processes, primarily those inherent to the molecular mechanisms of disease, are long (>μs) and involve complex interactions of systems consisting of many atoms (>106). Simulating these systems requires an advanced computational approach, and as such, coarse-grained (CG) models have been developed and highly optimised for accelerator hardware, primarily graphics processing units (GPUs). In this review, I discuss the implementation of CG models for biologically relevant systems, and show how such models can be optimised and perform well on GPU-accelerated hardware. Several examples of GPU implementations of CG models for both molecular dynamics and Monte Carlo simulations on purely GPU and hybrid CPU/GPU architectures are presented. Both the hardware and algorithmic limitations of various models, which depend greatly on the application of interest, are discussed. [ABSTRACT FROM AUTHOR]

Abstract: To simulate molecular processes on biologically relevant length- and timescales, coarse-grained (CG) models of biomolecular systems with tens to even hundreds of residues per CG site are required. One possible way to build such models is explored in this article: an elastic network model (ENM) is employed to define the CG variables. Free energy surfaces are approximated by Taylor series, with the coefficients found by force-matching. CG potentials are shown to undergo renormalization due to roughness of the energy landscape and smoothing of it under coarse-graining. In the case study of hen egg-white lysozyme, the entropy factor is shown to be of critical importance for maintaining the native structure, and a relationship between the proposed ENM-mode-based CG models and traditional CG-bead-based models is discussed. The proposed approach uncovers the renormalizable character of CG models and offers new opportunities for automated and computationally efficient studies of complex free energy surfaces. [Copyright &y& Elsevier]

The interaction between proteins and inorganic surfaces or nanoparticles is important for several aspects of bionanoscience, ranging from the interaction of nanoparticles with living biosystems to the use of surface-immobilized proteins as elements of sensing devices. Atomistic simulations of the interaction between proteins and inorganic surfaces can provide a microscopic picture behind such interactions. At present, classical molecular dynamics simulations, explicitly including all atoms of the systems and the water solvent, represent the best compromise between computational cost and accuracy in the description of the systems. Nevertheless, the time scale that can be routinely investigated with these methods is still limited to tens of nanoseconds. Coarse-grained models, where many protein atoms are condensed in a single effective particle, can extend this time scale by orders of magnitude. Here, we demonstrate how a recently proposed coarse-graining scheme can be used to simulate ubiquitin (a small model protein ubiquitous in eukaryotic cells) on gold surfaces in water. In particular, we verify that the coarse-grained model gives results coherent with those obtained with the fully atomistic simulations, at a much smaller computational cost. [ABSTRACT FROM AUTHOR]

Dimension reduction is often necessary when attempting to reach longer length and time scales in molecular simulations. It is realized by constraining degrees of freedom or by coarse-graining the system. When evaluating the accuracy of a dimensional reduction, there is a practical challenge: the models yield vectors with different lengths, making a comparison by calculating their dot product impossible. This article investigates mapping procedures for normal mode analysis. We first review a horizontal mapping procedure for the reduced Hessian techniques, which projects out degrees of freedom. We then design a vertical mapping procedure for the 'implosion' of the all-atom (AA) Hessian to a coarse-grained scale that is based upon vibrational subsystem analysis. This latter method derives both effective force constants and an effective kinetic tensor. Next, a series of metrics is presented for comparison across different scales, where special attention is given to proper mass-weighting. The dimension-dependent metrics, which require prior mapping for proper evaluation, are frequencies, overlap of normal mode vectors, probability similarity, Hessian similarity, collectivity of modes, and thermal fluctuations. The dimension-independent metrics are shape derivatives, elastic modulus, vibrational free energy differences, heat capacity, and projection on a predefined basis set. The power of these metrics to distinguish between reasonable and unreasonable models is tested on a toy alpha helix system and a globular protein; both are represented at several scales: the AA scale, a Gō-like model, a canonical elastic network model, and a network model with intentionally unphysical force constants. Published 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Molecular dynamics simulations of a coarse-grained, embedded-charge model of lysozyme aqueous solutions are compared with small-angle neutron scattering experiments. Measures concern different solutions with a 10% by weight protein concentration and an increasing pH in the range 2-6. The model is based on a soft-core modification of the original Carlsson-Malmsten-Linse model, where in particular all residues carrying an appreciable amount of residual charge, as a function of the pH, are explicitly taken into account in the overall macromolecular interaction. Simulations reproduce qualitatively the experimental trend of the structure factor such as, in particular, the observed change from a low-pH regime, dominated by repulsive interactions, to behaviour mainly determined by attractive forces, at higher pH. Possible improvements of the model, towards a better reproduction of the structural properties of the real solution, are proposed. [ABSTRACT FROM AUTHOR]

Abstract: The interaction of the Alzheimer''s amyloid beta peptide, Aβ40, with sodium dodecyl sulfate (SDS) micelles, together with the self-assembly of SDS molecules around the peptide from an initial random distribution were studied using atomistic and coarse-grained (CG) molecular dynamics simulations. In atomistic simulations, the peptide structure in the micelle was characterized by two helical regions connected through a short hinge. The initial structure of the system was shown to affect the simulation results. The atomistic self-assembly of SDS molecules resulted in a 38-molecule micelle around the peptide, along with some globules and individual molecules. Coarse-grained simulation results, however, did not show such a difference, and at the end of all CG simulations, a complete 60-molecule micelle was obtained, with the peptide located at the interface of the micelle with water. The obtained CG radial density profiles and SDS micelle size and shape properties were identical for all CG simulations. [Copyright &y& Elsevier]

Molecular dynamics simulations have become a standard tool for the investigation of biological and soft matter systems. Water models serve as the basis of force fields used in molecular dynamics simulations of these systems. This article reports on an examination of the utility of a set of coarse-grained (CG) water models, with different resolutions, interaction potentials (Lennard-Jones, Morse), and cut-off distances. The relationships between the parameters under specific choices of the above options and the thermodynamic properties, such as density, surface tension, and compressibility, were found to fit simple mathematical equations. The limits of applicability of these CG water models were explored by checking the melting temperature. If a CG site is mapped to one or two real water molecules, a simple model with appropriate combinations of cut-off distances, functional forms, and parameters can be found to simultaneously match the experimental values of density, surface tension, and compressibility under ambient conditions. If more water molecules are included in a CG site, either the melting temperature approaches or surpasses room temperature, or the surface tension and compressibility cannot both be matched simultaneously. In striving for computational efficiency, it is still possible to find a simple CG water model with three water molecules contained in a CG bead that generates a liquid state of water with realistic values of density, surface tension and compressibility at ambient condition, but coarser models are not recommended. [ABSTRACT FROM AUTHOR]

The article presents a new method for the computation of free energies and ensemble canonical averages of one-dimensional coarse-grained models in materials science. The three dominant computational approaches used for the calculation are Markov chains methods, Monte Carlo methods and molecular dynamics methods. The new method is based upon a thermodynamic limit process, and makes use of ergodic theorems and large deviations theory.

We developed a software package (RedMD) to perform molecular dynamics simulations and normal mode analysis of reduced models of proteins, nucleic acids, and their complexes. With RedMD one can perform molecular dynamics simulations in a microcanonical ensemble, with Berendsen and Langevin thermostats, and with Brownian dynamics. We provide force field and topology generators which are based on the one-bead per residue/nucleotide elastic network model and its extensions. The user can change the force field parameters with the command line options that are passed to generators. Also, the generators can be modified, for example, to add new potential energy functions. Normal mode analysis tool is available for elastic or anisotropic network models. The program is written in C and C++ languages and the structure/topology of a molecule is based on an XML format. OpenMP technology for shared-memory architectures was used for code parallelization. The code is distributed under GNU public licence and available at http://bionano.icm.edu.pl/software/. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2009 [ABSTRACT FROM AUTHOR]

Abstract: Proteins are dynamical objects; their structure fluctuations are often the key to their function and keen attention need therefore be paid to it. Molecular dynamics (MD) simulations are a widely used technique to study dynamics of both proteins and nucleic acids. However, simulating molecular machines containing several thousands of amino acids, on long enough time scales to observe relevant structural deformation, remains challenging. Normal mode analysis (NMA) is better suited to study the slow dynamics of proteins. We briefly describe the theory underlying NMA and the simplifications used to render it tractable for (large) proteins. We also describe different kinds of analyses that can be performed on the eigenvectors to characterize the dynamical properties of the system. A number of validation studies are then summarized. Finally we describe NMA servers available on the Internet. [Copyright &y& Elsevier]

Coarse-graining is a powerful tool for extending the reach of dynamic models of proteins and other biological macromolecules. Topological coarse-graining, in which biomolecules or sets thereof are represented via graph structures, is a particularly useful way of obtaining highly compressed representations of molecular structures, and simulations operating via such representations can achieve substantial computational savings. A drawback of coarse-graining, however, is the loss of atomistic detail—an effect that is especially acute for topological representations such as protein structure networks (PSNs). Here, we introduce an approach based on a combination of machine learning and physically-guided refinement for inferring atomic coordinates from PSNs. This "neural upscaling" procedure exploits the constraints implied by PSNs on possible configurations, as well as differences in the likelihood of observing different configurations with the same PSN. Using a 1 μs atomistic molecular dynamics trajectory of A β 1 – 40 , we show that neural upscaling is able to effectively recapitulate detailed structural information for intrinsically disordered proteins, being particularly successful in recovering features such as transient secondary structure. These results suggest that scalable network-based models for protein structure and dynamics may be used in settings where atomistic detail is desired, with upscaling employed to impute atomic coordinates from PSNs. [ABSTRACT FROM AUTHOR]

New force fields that are both simple and accurate are needed for computationally efficient molecular simulation studies to give insight into the actual features of the protein folding process. In this work, we assess a force field based on a new combination of two coarse-grained potentials taken from the bibliography. These potentials have already been proved efficient in representing different types of interactions, namely the side-chain interactions and the backbone hydrogen bonds. Now we combine them weighing their contribution to the global energy with a very simplified parameterization. To assess this combination of potentials, we use our evolutionary method to carry out energy minimization experiments for a set of all-α, all-β, and (α + β) protein structures. Our results, based on the assembly of short rigid native fragments, suggest that this combination of potentials can be successfully employed in coarse-grained folding simulations. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2008 [ABSTRACT FROM AUTHOR]

Abstract: The packaging of double-stranded DNA into bacteriophages leads to the arrangement of the genetic material into highly-packed and ordered structures. Although modern experimental techniques reveal the most probable location of DNA inside viral capsids, the individual conformations of DNA are yet to be determined. In the current study we present the results of molecular dynamics simulations of the DNA packaging into several bacteriophages performed within the framework of a coarse-grained model. The final DNA conformations depend on the size and shape of the capsid, as well as the size of the protein portal, if any. In particular, isometric capsids with small or absent portals tend to form concentric spools, whereas the presence of a large portal favors coaxial spooling; slightly and highly elongated capsids result in folded and twisted toroidal conformations, respectively. The results of the simulations also suggest that the predominant factor in defining the global DNA arrangement inside bacteriophages is the minimization of the bending stress upon packaging. [Copyright &y& Elsevier]

Abstract: We present a one-bead coarse-grained model that enables dynamical simulations of proteins on the time scale of tens of microseconds. The parameterization of the force field includes accurate conformational terms that allow for fast and reliable exploration of the configurational space. The model is applied to the dynamics of flap opening in HIV-1 protease. The experimental structure of the recently crystallized semi-open conformation of HIV-1 protease is well reproduced in the simulation, which supports the accuracy of our model. Thanks to very long simulations and extensive sampling of opening and closing events, we also investigate the thermodynamics and kinetics of the opening process. We have shown that the effect of the solvent slows down the dynamics to the experimentally observed time scales. The model is found to be reliable for application to substrate docking simulations, which are currently in progress. [Copyright &y& Elsevier]

Abstract: A variety of small cageless chaperones have been discovered that can assist protein folding without the consumption of ATP. These include mini-chaperones (catalytically active fragments of larger chaperones), as well as small proteins such as α-casein and detergents acting as “artificial chaperones.” These chaperones all possess exposed hydrophobic patches on their surface that act as recognition sites for misfolded proteins. They lack the complexity of chaperonins (that encapsulate proteins in their inner rings) and their study can offer insight into the minimal requirements for chaperone function. We use molecular dynamics simulations to investigate how a cageless chaperone, modeled as a sphere of tunable hydrophobicity, can assist folding of a substrate protein. We find that under steady-state (non-stress) conditions, cageless chaperones that bind to a single substrate protein increase folding yields by reducing the time the substrate spends in an aggregation-prone state in a dual manner: (a) by competing for aggregation-prone hydrophobic sites on the surface of a protein, hence reducing the time the protein spends unprotected in the bulk and (b) by accelerating folding rates of the protein. In both cases, the chaperone must bind to and hold the protein loosely enough to allow the protein to change its conformation and fold while bound. Loose binding may enable small cageless chaperones to help proteins fold and avoid aggregation under steady-state conditions, even at low concentrations, without the consumption of ATP. [Copyright &y& Elsevier]

Abstract: A coarse-grained model for a set of three polyimide isomers is developed. Each polyimide is comprised of 3,3′,4,4′-biphenyltetracarboxylic dianhydride (BPDA) and one of three APB isomers: 1,3-bis(4-aminophenoxy)benzene, 1,4-bis(4-aminophenoxy)benzene or 1,3-bis(3-aminophenoxy)benzene. The coarse-grained model is constructed as a series of linked vectors following the contour of the polymer backbone. Beads located at the midpoint of each vector define centers for long range interaction energies between monomer subunits. A bulk simulation of each coarse-grained polyimide model is performed with a dynamic Monte Carlo procedure. These coarse-grained models are then reverse-mapped to fully atomistic models. The coarse-grained models show the expected trends in decreasing chain dimensions with increasing meta linkage in the APB section of the repeat unit, although these differences were minor due to the relatively short chains simulated here. Considerable differences are seen among the dynamic Monte Carlo properties of the three polyimide isomers. Decreasing relaxation times are seen with increasing meta linkage in the APB section of the repeat unit. The packing behavior of the three isomers is compared with the atomistic and coarse-grained models. [Copyright &y& Elsevier]