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Covalent adaptable networks and vitrimers are novel polymers with dynamic reversible bond exchange reactions for crosslinks, enabling them to modulate their properties between those of thermoplastics and thermosets. They have been gathering interest as materials for their recycling and self-healing properties. In this review, we discuss different molecular simulation efforts that have been used over the last decade to investigate and understand the nanoscale and molecular behaviors of covalent adaptable networks and vitrimers. In particular, molecular dynamics, Monte Carlo, and a hybrid of molecular dynamics and Monte Carlo approaches have been used to model the dynamic bond exchange reaction, which is the main mechanism of interest since it controls both the mechanical and rheological behaviors. The molecular simulation techniques presented yield sufficient results to investigate the structure and dynamics as well as the mechanical and rheological responses of such dynamic networks. The benefits of each method have been highlighted. The use of other tools such as theoretical models and machine learning has been included. We noticed, amongst the most prominent results, that stress relaxes as the bond exchange reaction happens, and that at temperatures higher than the glass transition temperature, the self-healing properties are better since more bond BERs are observed. The lifetime of dynamic covalent crosslinks follows, at moderate to high temperatures, an Arrhenius-like temperature dependence. We note the modeling of certain properties like the melt viscosity with glass transition temperature and the topology freezing transition temperature according to a behavior ruled by either the Williams–Landel–Ferry equation or the Arrhenius equation. Discrepancies between the behavior in dissociative and associative covalent adaptable networks are discussed. We conclude by stating which material parameters and atomistic factors, at the nanoscale, have not yet been taken into account and are lacking in the current literature. [ABSTRACT FROM AUTHOR]

We investigate two different types of non-Markovian coarse-grained models extracted from a linear, non-equilibrium microscopic system, featuring a tagged particle coupled to underdamped oscillators. The first model is obtained by analytically 'integrating out' the oscillators and the second is based on a derivation using projection operator techniques. We observe that these two models behave very differently when the tagged particle is exposed to external harmonic potentials or pulling forces. Most importantly, we find that the analytic model has a well defined friction kernel and can be used to extract work, consistent with the microscopic system, while the projection model corresponds to an effective equilibrium model, which cannot be used to extract work. We apply the analysis to two popular non-equilibrium systems, time-delay feedback control and the active Ornstein–Uhlenbeck process. Finally, we highlight that our study could have important consequences for dynamic coarse-graining of non-equilibrium systems going far beyond the linear systems investigated in this manuscript. [ABSTRACT FROM AUTHOR]

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]

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.

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.

This mini-review highlights recent advances on computational approaches that have been used in the characterisation of the viscoelastic response of semiflexible filamentous biomaterials. Special attention is given to the multiscale and coarse-grained approaches that might be used to model the mechanical properties of systems which involve biopolymer assemblies, for instance, actin, collagen, vimentin, microtubules, DNA, viruses, silk, amyloid fibrils, and other protein-based filaments. Besides the basic features of the most commonly used models for semiflexible filaments, I present a brief overview of the numerical approaches that can be used to extract the viscoelasticity of dilute and concentrated solutions, as well as systems with cross-linked networks. Selected examples of simulations that attempt to retrieve the complex shear moduli at experimentally relevant time and length scales, i.e., including not only the fully formed filaments and networks but also their self-assembly kinetics, are also considered.

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]

The recent breakthrough in the field of protein structure prediction shows the relevance of using knowledge-based based scoring functions in combination with a low-resolution 3D representation of protein macromolecules. The choice of not using all atoms is barely supported by any data in the literature, and is mostly motivated by empirical and practical reasons, such as the computational cost of assessing the numerous folds of the protein conformational space. Here, we present a comprehensive study, carried on a large and balanced benchmark of predicted protein structures, to see how different types of structural representations rank in either accuracy or calculation speed, and which ones offer the best compromise between these two criteria. We tested ten representations, including low-resolution, high-resolution, and coarse-grained approaches. We also investigated the generalization of the findings to other formalisms than the widely-used “potential of mean force” (PMF) method. Thus, we observed that representing protein structures by their β carbons—combined or not with Cα—provides the best speed–accuracy trade-off, when using a “total information gain” scoring function. For statistical PMFs, using MARTINI backbone and side-chains beads is the best option. Finally, we also demonstrated the necessity of training the reference state on all atom types, and of including the Cα atoms of glycine residues, in a Cβ-based representation.

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.

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.

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]

Normal mode analysis (NMA) is a fast and inexpensive approach that is largely used to gain insight into functional protein motions, and more recently to create conformations for further computational studies. However, when the protein structure is unknown, the use of computational models is necessary. Here, we analyze the capacity of NMA in internal coordinate space to predict protein motion, its intrinsic flexibility, and atomic displacements, using protein models instead of native structures, and the possibility to use it for model refinement. Our results show that NMA is quite insensitive to modeling errors, but that calculations are strictly reliable only for very accurate models. Our study also suggests that internal NMA is a more suitable tool for the improvement of structural models, and for integrating them with experimental data or in other computational techniques, such as protein docking or more refined molecular dynamics simulations. [ABSTRACT FROM AUTHOR]

Although discrete approaches are increasingly employed to model biological phenomena, it remains unclear how complex, population-level behaviours in such frameworks arise from the rules used to represent interactions between individuals. Discrete-to-continuum approaches, which are used to derive systems of coarse-grained equations describing the mean-field dynamics of a microscopic model, can provide insight into such emergent behaviour. Coarse-grained models often contain nonlinear terms that depend on the microscopic rules of the discrete framework, however, and such nonlinearities can make a model difficult to mathematically analyse. By contrast, models developed using phenomenological approaches are typically easier to investigate but have a more obscure connection to the underlying microscopic system. To our knowledge, there has been little work done to compare solutions of phenomenological and coarse-grained models. Here we address this problem in the context of angiogenesis (the creation of new blood vessels from existing vasculature). We compare asymptotic solutions of a classical, phenomenological "snail-trail" model for angiogenesis to solutions of a nonlinear system of partial differential equations (PDEs) derived via a systematic coarse-graining procedure (Pillay et al. in Phys Rev E 95(1):012410, 2017. https://doi.org/10.1103/PhysRevE.95.012410). For distinguished parameter regimes corresponding to chemotaxis-dominated cell movement and low branching rates, both continuum models reduce at leading order to identical PDEs within the domain interior. Numerical and analytical results confirm that pointwise differences between solutions to the two continuum models are small if these conditions hold, and demonstrate how perturbation methods can be used to determine when a phenomenological model provides a good approximation to a more detailed coarse-grained system for the same biological process. [ABSTRACT FROM AUTHOR]

High viscosity presents a challenge for manufacturing and drug delivery of therapeutic antibodies. The viscosity is determined by protein–protein interactions among many antibodies. Molecular simulation is a promising method to study protein–protein interactions; however, all-atom models do not allow the simulation of multiple molecules, which is necessary to compute viscosity directly. Coarse-grained models, on the other hand can do this. In this work, a 12-bead coarse-grained model based on Swan and coworkers (J. Phys. Chem. B 2018, 122, 2867–2880) was applied to study antibody interactions. Two adjustable parameters related to the short-range interactions on the variable and constant regions were determined by fitting experimental data of 20 IgG1 monoclonal antibodies at 150 mg/mL. The root-mean-square deviation improved from 1 to 0.68, and the correlation coefficient improved from 0.63 to 0.87 compared to that of a previous model that assumed the short-range interactions were the same for all the beads. Our model is also able to calculate the viscosity over a wide range of concentrations without additional parameters. A tabulated viscosity based on our model is provided to facilitate antibody screening in early-stage design.

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.

Biomolecules perform their various functions in living cells, namely in an environment that is crowded by many macromolecules. Thus, simulating the dynamics and interactions of biomolecules should take into account not only water and ions but also other binding partners, metabolites, lipids and macromolecules found in cells. In the last decade, research on how to model macromolecular crowders around proteins in order to simulate their dynamics in models of cellular environments has gained a lot of attention. In this mini-review we focus on the models of crowding agents that have been used in computer modeling studies of proteins and peptides, especially via molecular dynamics simulations.

Protein inclusions in the membranes of living cells interact via the deformations they impose on that membrane. Such membrane-mediated interactions lead to sorting and self-assembly of the inclusions, as well as to membrane remodelling, crucial for many biological processes. For the past decades, theory, numerical calculations and experiments have been using simplified models for proteins to gain quantitative insights into their behaviour. Despite challenges arising from nonlinearities in the equations, the multiple length scales involved and the nonadditive nature of the interactions, recent progress now enables for the first time a direct comparison between theoretical and numerical predictions and experiments. We review the current knowledge on the biologically most relevant case, inclusions on lipid membranes with a closed surface and discuss challenges and opportunities for further progress. [ABSTRACT FROM AUTHOR]

We focus here on the calculation of the surface tension of water by molecular simulations of planar interfaces. We provide a chronological list of the key dates in the calculation of the surface tension of water by using atomistic empirical models, polarisable, many-body and coarse-grained (CG) models. We investigate how the SPC/E, TIP4P and TIP4P/2005 atomistic force fields perform in the reproduction of the surface tension along the coexistence curve. We also study the temperature dependence of the surface tension of water for CG models. We also discuss the ability of ab initio molecular dynamics simulations to reproduce the surface tension of water at room temperature. [ABSTRACT FROM AUTHOR]

When three cyanobacterial proteins, KaiA, KaiB, and KaiC, are incubated with ATP in vitro, the phosphorylation level of KaiC hexamers shows stable oscillation with approximately 24 h period. In order to understand this KaiABC clockwork, we need to analyze both the macroscopic synchronization of a large number of KaiC hexamers and the microscopic reactions and structural changes in individual KaiC molecules. In the present paper, we explain two coarse-grained theoretical models, the many-molecule (MM) model and the single-molecule (SM) model, to bridge the gap between macroscopic and microscopic understandings. In the simulation results with these models, ATP hydrolysis in the CI domain of KaiC hexamers drives oscillation of individual KaiC hexamers and the ATP hydrolysis is necessary for synchronizing oscillations of a large number of KaiC hexamers. Sensitive temperature dependence of the lifetime of the ADP bound state in the CI domain makes the oscillation period temperature insensitive. ATPase activity is correlated to the frequency of phosphorylation oscillation in the single molecule of KaiC hexamer, which should be the origin of the observed ensemble-level correlation between the ATPase activity and the frequency of phosphorylation oscillation. Thus, the simulation results with the MM and SM models suggest that ATP hydrolysis stochastically occurring in each CI domain of individual KaiC hexamers is a key process for oscillatory behaviors of the ensemble of many KaiC hexamers. [ABSTRACT FROM AUTHOR]

Our investigation is motivated by the need to design bilayer membranes with tunable interfacial and mechanical properties for use in a range of applications, such as targeted drug delivery, sensing and imaging. We draw inspiration from biological cell membranes and focus on their principal constituents. In this paper, we present our results on the role of molecular architecture on the interfacial, structural and dynamical properties of bio-inspired membranes. We focus on four lipid architectures with variations in the head group shape and the hydrocarbon tail length. Each lipid species is composed of a hydrophilic head group and two hydrophobic tails. In addition, we study a model of the Cholesterol molecule to understand the interfacial properties of a bilayer membrane composed of rigid, single-tail molecular species. We demonstrate the properties of the bilayer membranes to be determined by the molecular architecture and rigidity of the constituent species. Finally, we demonstrate the formation of a stable mixed bilayer membrane composed of Cholesterol and one of the phospholipid species. Our approach can be adopted to design multi-component bilayer membranes with tunable interfacial and mechanical properties. We use a Molecular Dynamics-based mesoscopic simulation technique called Dissipative Particle Dynamics that resolves the molecular details of the components through soft-sphere coarse-grained models and reproduces the hydrodynamic behavior of the system over extended time scales.

Superspreading is the rapid and complete spreading of surfactant-laden droplets on hydrophobic substrates. This phenomenon has been studied for many decades by experiment, theory, and simulation, but it has been only recently that molecular-level simulation has provided significant insights into the underlying mechanisms of superspreading thanks to the development of accurate force-fields and the increase of computational capabilities. Here, we review the main advances in this area that have surfaced from Molecular Dynamics simulation of all-atom and coarse-grained models highlighting and contrasting the main results and discussing various elements of the proposed mechanisms for superspreading. We anticipate that this review will stimulate further research on the interpretation of experimental results and the design of surfactants for applications requiring efficient spreading, such as coating technology.

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.