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We study active topological glass under spherical confinement, allowing us to exceed the chain lengths simulated previously and determine the critical exponents of the arrested conformations. We find a previously unresolved "tank-treading" dynamic mode of active segments along the ring contour. This mode can enhance active-passive phase separation in the state of active topological glass when both diffusional and conformational relaxation of the rings are significantly suppressed. Within the observational time, we see no systematic trends in the positioning of the separated active domains within the confining sphere. The arrested state exhibits coherent stochastic rotations. We discuss possible connections of the conformational and dynamic features of the system to chromosomes enclosed in the nucleus of a living cell.

Peptide–polymer conjugates have been regarded as primary stronghold in biohybrid nanomedicine, which has seen extensive development due to its intrinsic property to provide complementary functions of both the peptide material and the synthetic polymer platform. Here we present an advanced macromolecular therapeutic that targets two exclusive classes of important diseases (namely, the HIV and cancer) that are implicated by extremely different causative agents. Using a facile thiol-reactive monomer, the eventual polymer facilitates multivalent conjugation of an endogenous peptide WSC02 that targets the CXCR4 chemokine receptor. The biohybrid material demonstrated both potent antiviral effects against HIV-1 as well as inhibiting cancer stem cell migration thus establishing the foundation for multimodal nanotherapeutics that simultaneously target more than one class of disease implications.

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Board-shaped polymers form sanidic mesophases: assemblies of parallel lamellae of stacked polymer backbones separated by disordered side chains. Sanidics vary significantly with respect to polymer order inside their lamellae, making them "stepping stones" toward the crystalline state. Therefore, they are potentially interesting for studying crystallization and technological applications. Building on earlier mesoscopic models of the most disordered sanidics Σ

The prosperity and lifestyle of our society are very much governed by achievements in condensed matter physics, chemistry and materials science, because new products for sectors such as energy, the environment, health, mobility and information technology (IT) rely largely on improved or even new materials. Examples include solid-state lighting, touchscreens, batteries, implants, drug delivery and many more. The enormous amount of research data produced every day in these fields represents a gold mine of the twenty-first century. This gold mine is, however, of little value if these data are not comprehensively characterized and made available. How can we refine this feedstock; that is, turn data into knowledge and value? For this, a FAIR (findable, accessible, interoperable and reusable) data infrastructure is a must. Only then can data be readily shared and explored using data analytics and artificial intelligence (AI) methods. Making data 'findable and AI ready' (a forward-looking interpretation of the acronym) will change the way in which science is carried out today. In this Perspective, we discuss how we can prepare to make this happen for the field of materials science.

A fully atomistic modelling of biological macromolecules at relevant length- and time-scales is often cumbersome or not even desirable, both in terms of computational effort required and it a posteriori analysis. This difficulty can be overcome with the use of multi-resolution models, in which different regions of the same system are concurrently described at different levels of detail. In enzymes, computationally expensive atomistic detail is crucial in the modelling of the active site in order to capture e.g. the chemically subtle process of ligand binding. In contrast, important yet more collective properties of the remainder of the protein can be reproduced with a coarser description. In the present work, we demonstrate the effectiveness of this approach through the calculation of the binding free energy of hen egg white lysozyme (HEWL) with the inhibitor di-N-acetylchitotriose. Particular attention is posed to the impact of the mapping, i.e. the selection of atomistic and coarse-grained residues, on the binding free energy. It is shown that, in spite of small variations of the binding free energy with respect to the active site resolution, the separate contributions coming from different energetic terms (such as electrostatic and van der Waals interactions) manifest a stronger dependence on the mapping, thus pointing to the existence of an optimal level of intermediate resolution.

Intrinsically disordered proteins (IDPs) play an important role in an array of biological processes but present a number of fundamental challenges for computational modeling. Recently, simple polymer models have regained popularity for interpreting the experimental characterization of IDPs. Homopolymer theory provides a strong foundation for understanding generic features of phenomena ranging from single-chain conformational dynamics to the properties of entangled polymer melts, but is difficult to extend to the copolymer context. This challenge is magnified for proteins due to the variety of competing interactions and large deviations in side-chain properties. In this work, we apply a simple physics-based coarse-grained model for describing largely disordered conformational ensembles of peptides, based on the premise that sampling sterically forbidden conformations can compromise the faithful description of both static and dynamical properties. The Hamiltonian of the employed model can be easily adjusted to investigate the impact of distinct interactions and sequence specificity on the randomness of the resulting conformational ensemble. In particular, starting with a bead-spring-like model and then adding more detailed interactions one by one, we construct a hierarchical set of models and perform a detailed comparison of their properties. Our analysis clarifies the role of generic attractions, electrostatics, and side-chain sterics, while providing a foundation for developing efficient models for IDPs that retain an accurate description of the hierarchy of conformational dynamics, which is nontrivially influenced by interactions with surrounding proteins and solvent molecules.

The solvent quality determines the collapsed or the expanded state of a polymer. For example, a polymer dissolved in a poor solvent collapses, whereas in a good solvent it opens up. While this standard understanding is generally valid, there are examples when a polymer collapses even in a mixture of two good solvents. This phenomenon, commonly known as co-non-solvency, is usually associated with smart polymers. Moreover, recent experiments have shown that the elastin-like polypeptides (ELPs) show co-non-solvency behavior in aqueous-ethanol mixtures. In this study, we investigate the phase behavior of ELPs in aqueous binary mixtures using molecular dynamics simulations of all-atom and complementary explicit solvent generic models. The model is parameterized by mapping the solvation free energy obtained from the all-atom simulations onto the generic interaction parameters. For this purpose, we derive segment based generic parameters for four different peptides, namely proline (P), valine (V), glycine (G) and alanine (A). Here we compare the conformational behavior of two ELP sequences, namely VPGGG and VPGVG, in aqueous-ethanol and -urea mixtures. Consistent with recent experiments, we find that ELPs show co-non-solvency in aqueous-ethanol mixtures. Ethanol molecules have preferential binding with all ELP residues and thus driving the coil-to-globule transition. On the contrary, ELP conformations show weak variation in aqueous-urea mixtures. Our simulations suggest that the glycine residues dictate the overall behavior of ELPs in aqueous-urea, where urea molecules have a rather weak preferential binding with glycine, i.e., less than kT. While the validation of the latter findings will require more detailed experimental investigation, the results presented here may provide a new twist to the present understanding of cosolvent interactions with peptides and proteins., Comment: Accepted for publication in Macromolecules

Responsiveness of polypeptides and polymers in aqueous solution plays an important role in biomedical applications and in designing advanced functional materials. Elastin-like polypeptides (ELPs) are a well-known class of synthetic intrinsically disordered proteins (IDPs), which exhibit a lower critical solution temperature (LCST) in pure water. The LCST transition can be further tuned by proline isomerization. Here, we study and compare the influence of cis/trans proline isomerization on the collapse of single ELPs in aqueous solution. Our results reveal that cis isomers play an important role in tuning the phase behavior of ELPs by hindering peptide-water hydrogen bonding while promoting intramolecular interactions., Comment: Under review of ACS Macro Letters

The creation of synthetic polymer nanoobjects with well-defined hierarchical structures is important for a wide range of applications such as nanomaterial synthesis, catalysis, and therapeutics. Inspired by the programmability and precise three-dimensional architectures of biomolecules, here we demonstrate the strategy of fabricating controlled hierarchical structures through self-assembly of folded synthetic polymers. Linear poly(2-hydroxyethyl methacrylate) of different lengths are folded into cyclic polymers and their self-assembly into hierarchical structures is elucidated by various experimental techniques and molecular dynamics simulations. Based on their structural similarity, macrocyclic brush polymers with amphiphilic block side chains are synthesized, which can self-assemble into wormlike and higher-ordered structures. Our work points out the vital role of polymer folding in macromolecular self-assembly and establishes a versatile approach for constructing biomimetic hierarchical assemblies., Synthetic polymer nano-objects with well-defined hierarchical structures are important for a wide range of applications such as nanomaterial synthesis, catalysis, and therapeutics. Here the authors demonstrate the strategy of fabricating controlled hierarchical structures through self-assembly of folded synthetic polymers.

Recent theoretical studies have demonstrated that the behaviour of molecular knots is a sensitive indicator of polymer structure. Here, we use knots to verify the ability of two state-of-the-art algorithms—configuration assembly and hierarchical backmapping—to equilibrate high-molecular-weight (MW) polymer melts. Specifically, we consider melts with MWs equivalent to several tens of entanglement lengths and various chain flexibilities, generated with both strategies. We compare their unknotting probability, unknotting length, knot spectra, and knot length distributions. The excellent agreement between the two independent methods with respect to knotting properties provides an additional strong validation of their ability to equilibrate dense high-MW polymeric liquids. By demonstrating this consistency of knotting behaviour, our study opens the way for studying topological properties of polymer melts beyond time and length scales accessible to brute-force molecular dynamics simulations.

International audience; A true challenge in designing multiresponsive complex macro-molecular architectures is to tune their conformation at will by changing the gradients of external stimuli. However, the lack of a clear molecular-level understanding, establishing a delicate interplay between segment-based interaction details and large-scale macromolecular properties, has hindered the implementation of design principles for a long time. Combining molecular simulations, together with complementary polymer synthesis and characterization, we propose a molecular-level design principle of multiresponsive copolymer architectures. For this purpose, we use the co-nonsolvency concept that is associated with polymer collapse in miscible good solvents. We show how the responsiveness of different polymer blocks can provide fully flexible conformational tuning and, therefore, may serve as a guiding principle for smart material design.

Room-temperature ionic liquids (RTILs) stand out among molecular liquids for their rich physicochemical characteristics, including structural and dynamic heterogeneity. The significance of electrostatic interactions in RTILs results in long characteristic length- and timescales, and has motivated the development of a number of coarse-grained (CG) simulation models. In this study, we aim to better understand the connection between certain CG parameterization strategies and the dynamical properties and transferability of the resulting models. We systematically compare five CG models: a model largely parameterized from experimental thermodynamic observables; a refinement of this model to increase its structural accuracy; and three models that reproduce a given set of structural distribution functions by construction, with varying intramolecular parameterizations and reference temperatures. All five CG models display limited structural transferability over temperature, and also result in various effective dynamical speedup factors, relative to a reference atomistic model. On the other hand, the structure-based CG models tend to result in more consistent cation–anion relative diffusion than the thermodynamic-based models, for a single thermodynamic state point. By linking short- and long-timescale dynamical behaviors, we demonstrate that the varying dynamical properties of the different CG models can be largely collapsed onto a single curve, which provides evidence for a route to constructing dynamically-consistent CG models of RTILs.

In the Hamiltonian adaptive resolution simulation method (H–AdResS) it is possible to simulate coexisting atomistic (AT) and ideal gas representations of a physical system that belong to different subdomains within the simulation box. The Hamiltonian includes a field that bridges both models by smoothly switching on (off) the intermolecular potential as particles enter (leave) the AT region. In practice, external one-body forces are calculated and applied to enforce a reference density throughout the simulation box, and the resulting external potential adds up to the Hamiltonian. This procedure suggests an apparent dependence of the final Hamiltonian on the system’s thermodynamic state that challenges the method’s statistical mechanics consistency. In this paper, we explicitly include an external potential that depends on the switching function. Hence, we build a grand canonical potential for this inhomogeneous system to find the equivalence between H–AdResS and density functional theory (DFT). We thus verify that the external potential inducing a constant density profile is equal to the system’s excess chemical potential. Given DFT’s one-to-one correspondence between external potential and equilibrium density, we find that a Hamiltonian description of the system is compatible with the numerical implementation based on enforcing the reference density across the simulation box. In the second part of the manuscript, we focus on assessing our approach’s convergence and computing efficiency concerning various model parameters, including sample size and solute concentrations. To this aim, we compute the excess chemical potential of water, aqueous urea solutions and Lennard–Jones (LJ) mixtures. The results’ convergence and accuracy are convincing in all cases, thus emphasising the method’s robustness and capabilities.

Soft matter covers a wide range of materials based on linear or branched polymers, gels and rubbers, amphiphilic (macro)molecules, colloids, and self-assembled structures. These materials have applications in various industries, all highly important for our daily life, and they control all biological functions; therefore, controlling and tailoring their properties is crucial. One way to approach this target is defect engineering, which aims to control defects in the material's structure, and/or to purposely add defects into it to trigger specific functions. While this approach has been a striking success story in crystalline inorganic hard matter, both for mechanical and electronic properties, and has also been applied to organic hard materials, defect engineering is rarely used in soft matter design. In this review, we present a survey on investigations on defects and/or defect engineering in nine classes of soft matter composed of liquid crystals, colloids, linear polymers with moderate degree of branching, hyperbranched polymers and dendrimers, conjugated polymers, polymeric networks, self-assembled amphiphiles and proteins, block copolymers and supramolecular polymers. This overview proposes a promising role of this approach for tuning the properties of soft matter.

This work shows how glassy behavior emerges from the active, directed polymer dynamics that is triggered by the non-equilibrium phase separation.

The creation of synthetic polymer nanoobjects with well-defined hierarchical structures is important for a wide range of applications such as nanomaterial synthesis, catalysis, and therapeutics. Inspired by the programmability and precise three-dimensional architectures of biomolecules, here wedemonstrate the strategy of fabricating controlled hierarchical structures through self-assembly of folded synthetic polymers. Linear poly(2-hydroxyethyl methacrylate) of different lengths are folded into cyclic polymers and their self-assembly into hierarchical structures is elucidated by various experimental techniques and molecular dynamics simulations. Based on their structural similarity, macrocyclic brush polymers with amphiphilic block side chains are synthesized, which can self-assemble into wormlike structures and higher-ordered networks. Our work points out the vital role of polymer folding in macromolecular self-assembly and establishes a versatile approach for constructing biomimetic hierarchical assemblies.

Elastin-like polypeptides (ELPs) are well-known proline-rich stimulus-responsive polymers. They have broad applications ranging from drug delivery to green chemistry. Recently, the authors have shown that the cis/trans proline isomerization can be used to regulate their conformational behavior while keeping the lower critical solution temperature (LCST) unchanged in pure water. In aqueous ethanol mixtures, ELPs typically exhibit an expanded-collapsed-expanded transition known as the co-non-solvency phenomenon. Since it is unclear how proline isomerization affects the solvation behavior of ELPs in aqueous ethanol mixtures, an all-atom insight on single ELPs has been provided to address this question. It is found that if all proline residues are in the cis state, the peptides only experience a collapsed-expanded transition as ethanol concentration increases, i.e., the initial collapse vanishes because cis isomers prefer the compact structures in pure water. The data from the authors also suggest that proline isomerization does not change the shift in solvation free energy of an ELP with given sequence, but it varies the affinity of the peptide to both the solvent and cosolvent molecules.

The utilization of solution���processable organic semiconducting (OSC) polymers and the development of industrial���relevant printing techniques enable cost���efficient fabrication of optoelectronic devices for the mass market. Yet, the adaptation of viscoelastic properties of a functional ink to the respective printing technology is challenging. One crucial parameter is the formulation of the ink, which can be adjusted by selecting the combination of solvents that are mixed with the OSC. The current study considers model functional inks composed of a poly���phenylene���vinylene���based OSC and two solvents, empirically known to be good. Their quality is quantified using the Hansen solubility parameters. The influence of the composition of the solvent mixture on structural, dynamical, and rheological behavior of the ink is investigated with light scattering, viscometry, and rheometry. Although both solvents are considered good, polymer aggregation is found at all compositions. Aggregation depends on composition in a nontrivial way. For dilute and semi���dilute inks, the effects of aggregates on the ink viscosity are hidden by the difference in viscosities of the neat solvents. For elevated concentrations, the aggregates produce a hysteresis in the shear���dependent viscosity, which should be considered when developing a functional ink for a particular printing technique.

We study a coarse-grained model for a binary blend system composed of cis-polyisoprene and vinyl-polybutadiene. Since the slow relaxation dynamics of polymers may require very long simulation times, coarse-grained descriptions are regularly used in order to reduce computational cost while keeping the essential physics. Relaxation dynamics of a coarse-grained model is sometimes accelerated by the smooth coarse-grained potentials. However, the magnitude of the acceleration may be different in different components in a multi-component system. In order to simulate a time-scale consistent dynamics, the acceleration effects should be the same across the different components. Here, we investigate a time-scale consistent coarse-grained model for a binary polymer blend. For the coarse-grained equation of motion, we adopt the Langevin equation and adjust the friction coefficients by focusing on the relaxation times of the first normal mode of the polymers. A united-atom model is used as a reference system of the coarse-graining. Since it is found that the solubility parameter of the atomistic model is much larger than the experimental result, our simulation model is not applicable for the quantitative predictions, but we utilize it as a example system to study a time scale mismatch of a coarse-grained model. We find that the coarse-grained potentials and the friction coefficients derived for one blend composition captures different compositions of the blend. Furthermore, it is found that the magnitude of the acceleration effects of the blend rarely depends on the composition ratio. This implies that our coarse-grained model can be used for inhomogeneous systems.

Intrinsically disordered proteins (IDPs) play an important role in an array of biological processes but present a number of fundamental challenges for computational modeling. Recently, simple polymer models have re-gained popularity for interpreting the experimental characterization of IDPs. Homopolymer theory provides a strong foundation for understanding generic features of phenomena ranging from single-chain conformational dynamics to the properties of entangled polymer melts, but is difficult to extend to the copolymer context. This challenge is magnified for proteins due to the variety of competing interactions and large deviations in side-chain properties. In this work, we apply a simple physics-based coarse-grained model for describing largely disordered conformational ensembles of peptides, based on the premise that sampling sterically-forbidden conformations can compromise the faithful description of both static and dynamical properties. The Hamiltonian of the employed model can be easily adjusted to investigate the impact of distinct interactions and sequence specificity on the randomness of the resulting conformational ensemble. In particular, starting with a bead-spring-like model and then adding more detailed interactions one by one, we construct a hierarchical set of models and perform a detailed comparison of their properties. Our analysis clarifies the role of generic attractions, electrostatics and side-chain sterics, while providing a foundation for developing efficient models for IDPs that retain an accurate description of the hierarchy of conformational dynamics, which is nontrivially influenced by interactions with surrounding proteins and solvent molecules.

The programming of nanomaterials at molecular length-scales to control architecture and function represents a pinnacle in soft materials synthesis. Although elusive in synthetic materials, Nature has evolutionarily refined macromolecular synthesis with perfect atomic resolution across three-dimensional space that serves specific functions. We show that biomolecules, specifically proteins, provide an intrinsic macromolecular backbone for the construction of anisotropic brush polymers with monodisperse lengths via grafting-from strategy. Using human serum albumin as a model, its sequence was exploited to chemically transform a single cysteine, such that the expression of said functionality is asymmetrically placed along the backbone of the eventual brush polymer. This positional monofunctionalization strategy was connected with biotin-streptavidin interactions to demonstrate the capabilities for site-specific self-assembly to create higher ordered architectures. Supported by systematic experimental and computational studies, we envisioned that this macromolecular platform provides unique avenues and perspectives in macromolecular design for both nanoscience and biomedicine.

Motivated by the chromosomes enclosed in a cell nucleus, we study a spherically confined system of a small number of long unknotted and nonconcatenated polymer rings in a melt and systematically compare it with the bulk results. We find that universal scaling exponents of the bulk system also apply in the confined case; however, certain important differences arise. First, due to confinement effects, the static and threading properties of the rings depend on their radial position within the confining sphere. Second, the rings’ dynamics is overall subdiffusive, but anisotropic along the directions parallel and perpendicular to the sphere’s radius. The radial center of mass displacements of the rings are in general much smaller than the angular ones, which is caused by the confinement-induced inhomogeneous radial distribution of the whole rings within the sphere. Finally, we find enhanced contact times between rings as compared to the bulk, which indicates slow and predominantly coordinated pathways of the relaxation of the system.

We propose an open-boundary molecular dynamics method in which an atomistic system is in contact with an infinite particle reservoir at constant temperature, volume and chemical potential. In practice, following the Hamiltonian adaptive resolution strategy, the system is partitioned into a domain of interest and a reservoir of non-interacting, ideal gas, particles. An external potential, applied only in the interfacial region, balances the excess chemical potential of the system. To ensure that the size of the reservoir is infinite, we introduce a particle insertion/deletion algorithm to control the density in the ideal gas region. We show that it is possible to study non-equilibrium phenomena with this open-boundary molecular dynamics method. To this aim, we consider a prototypical confined liquid under the influence of an external constant density gradient. The resulting pressure-driven flow across the atomistic system exhibits a velocity profile consistent with the corresponding solution of the Navier-Stokes equation. In contrast to available computational methods in which external forces drive the system far from equilibrium, this approach conserves momentum and closely resembles experimental conditions. The presented method can be used to study various direct and indirect out-of-equilibrium conditions in complex molecular systems.