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Stabilizing liquid-liquid interfaces, whether between miscible or immiscible liquids, is crucial for a wide range of applications, including energy storage, microreactors, and biomimetic structures. In this study, a versatile approach for stabilizing the water-oil interface is presented using the morphological transitions that occur during the self-assembly of anionic, cationic, and nonionic surfactants mixed with fatty acid oils. The morphological transitions underlying this approach are characterized and extensively studied through small-angle X-ray scattering (SAXS), rheometry, and microscopy techniques. Dissipative particle dynamics (DPD) as a simulation tool is adopted to investigate these morphological transitions both in the equilibrium ternary system as well as in the dynamic condition of the water-oil interface. Such a versatile strategy holds promise for enhancing applications such as liquid-in-liquid 3D printing. Moreover, it has the potential to revolutionize a wide range of fields where stabilizing liquid-liquid interfaces not only offers unprecedented opportunities for fine-tuning nanostructural morphologies but also imparts interesting practical features to the resulting liquid shapes. These features include perfusion capabilities, self-healing, and porosity, which could have significant implications for various industries., (© 2024 Wiley‐VCH GmbH.)

The crowded interior of a living cell makes performing experiments on simpler in vitro systems attractive. Although these reveal interesting phenomena, their biological relevance can be questionable. A topical example is the phase separation of intrinsically disordered proteins into biomolecular condensates, which is proposed to underlie the membrane-less compartmentalization of many cellular functions. How a cell reliably controls biochemical reactions in compartments open to the compositionally-varying cytoplasm is an important question for understanding cellular homeostasis. Computer simulations are often used to study the phase behavior of model biomolecular condensates, but the number of relevant parameters increases as the number of protein components increases. It is unfeasible to exhaustively simulate such models for all parameter combinations, although interesting phenomena are almost certainly hidden in their high-dimensional parameter space. Here, we have studied the phase behavior of a model biomolecular condensate in the presence of a polymeric crowding agent. We used a novel compute framework to execute dozens of simultaneous simulations spanning the protein/crowder concentration space. We then combined the results into a graphical representation for human interpretation, which provided an efficient way to search the model's high-dimensional parameter space. We found that steric repulsion from the crowder drives a near-critical system across the phase boundary, but the molecular arrangement within the resulting biomolecular condensate is rather insensitive to the crowder concentration and molecular weight. We propose that a cell may use the local cytoplasmic concentration to assist the formation of biomolecular condensates, while relying on the dense phase to reliably provide a stable, structured, fluid milieu for cellular biochemistry despite being open to its changing environment.

Biomolecular condensates play numerous roles in cells by selectively concentrating client proteins while excluding others. These functions are likely to be sensitive to the spatial organization of the scaffold proteins forming the condensate. We use coarse-grained molecular simulations to show that model intrinsically-disordered proteins phase separate into a heterogeneous, structured fluid characterized by a well-defined length scale. The proteins are modelled as semi-flexible polymers with punctate, multifunctional binding sites in good solvent conditions. Their dense phase is highly solvated with a spatial structure that is more sensitive to the separation of the binding sites than their affinity. We introduce graph theoretic measures to quantify their heterogeneity, and find that it increases with increasing binding site number, and exhibits multi-timescale dynamics. The model proteins also swell on passing from the dilute solution to the dense phase. The simulations predict that the structure of the dense phase is modulated by the location and affinity of binding sites distant from the termini of the proteins, while sites near the termini more strongly affect its phase behaviour. The relations uncovered between the arrangement of weak interaction sites on disordered proteins and the material properties of their dense phase can be experimentally tested to give insight into the biophysical properties, pathological effects, and rational design of biomolecular condensates.

Associative surfactants systems involving polar oils have recently been shown to stabilize immiscible liquids by forming nanostructures at the liquid interface and have been used to print soft materials. Although these associating surfactant systems show great promise for creating nanostructured soft materials, a fundamental understanding of the self-assembly process is still unknown. In this study, a ternary phase diagram for a system of cationic surfactant cetylpyridinium chloride monohydrate (CPCl), a polar oil (oleic acid), and water is established using experiment and simulation, to study the equilibrium phase behavior. A combination of visual inspection, small-angle X-ray scattering (SAXS), and rheological measurements was employed to establish the phase behavior and properties of the self-assembled materials. Dissipative particle dynamics (DPD) is used to simulate the formation of the morphologies in this system and support the experimental results. The ternary phase diagram obtained from the simulations agrees with the experimental results, indicating the robustness of the computational simulation as a supplement to the mesoscale experimental systems. We observe that morphological transitions ( e.g. , micelle-to-bilayer and vesicle-to-lamellar) are in agreement between experiments and simulations across the ternary diagram. DPD simulations correctly predict that associative surfactant systems form new nanoscale phases due to the co-assembly of the components. The established ternary phase diagram and the DPD model pave the way towards predicting and controlling the formation of different mesostructures like lamellar or vesicles, opening new avenues to tailor and synthesize desired morphologies for applications related to liquid-in-liquid 3D printing.

Phospholipid membranes surround the cell and its internal organelles, and their multicomponent nature allows the formation of domains that are important in cellular signalling, the immune system, and bacterial infection. Cytoplasmic compartments are also created by the phase separation of intrinsically disordered proteins into biomolecular condensates. The ubiquity of lipid membranes and protein condensates raises the question of how three-dimensional droplets might interact with two-dimensional domains, and whether this coupling has physiological or pathological importance. Here, we explore the equilibrium morphologies of a dilute phase of a model disordered protein interacting with an ideal-mixing, two-component lipid membrane using coarse-grained molecular simulations. We find that the proteins can wet the membrane with and without domain formation, and form phase separated droplets bound to membrane domains. Results from much larger simulations performed on a novel non-von-Neumann compute architecture called POETS, which greatly accelerates their execution compared to conventional hardware, confirm the observations. Reducing the wall clock time for such simulations requires new architectures and computational techniques. We demonstrate here an inter-disciplinary approach that uses real-world biophysical questions to drive the development of new computing hardware and simulation algorithms.

The pathological growth of amyloid fibrils in neurons underlies the progression of neurodegenerative diseases including Alzheimer's and Parkinson's disease. Fibrils form when soluble monomers oligomerise in the cytoplasm. Their subsequent growth occurs via nucleated polymerization mechanisms involving the free ends of the fibrils augmented by secondary nucleation of new oligomers at their surface. Amyloid fibrils possess a complex interactome with diffusing cytoplasmic proteins that regulates many aspects of their growth, seeding capacity, biochemical activity and transition to pathological inclusions in diseased brains. Changes to their surface are also expected to modify their interactome, pathogenicity and spreading in the brain. Many assays visualise fibril formation, growth and inclusion formation by decorating monomeric proteins with fluorescent tags such as GFP. Recent studies from our group suggest that tags with sizes comparable to the fibril radius may modify the fibril surface accessibility and thus their PTM pattern, interactome and ability to form inclusions. Using coarse-grained molecular simulations of a single alpha synuclein fibril tagged with GFP we find that thermal fluctuations of the tags create a non-monotonic, size-dependent sieve around the fibril that perturbs its interactome with diffusing species. Our results indicate that experiments using tagged and untagged monomers to study the growth and interactome of fibrils should be compared with caution, and the confounding effects of the tags are more complex than a reduction in surface accessibility. The prevalence of fluorescent tags in amyloid fibril growth experiments suggests this has implications beyond the specific alpha synuclein fibrils we model here., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Hilal Lashuel has received funding from industry to support research on neurodegenerative diseases, including from Merck Serono, UCB, Idorsia and Abbvie. These companies had no specific role in the in the conceptualization and preparation of and decision to publish this work. H.A.L is also the co-founder and Chief Scientific Officer of ND BioSciences SA, a company that develops diagnostics and treatments for neurodegenerative diseases based on platforms that reproduce the complexity and diversity of proteins implicated in neurodegenerative diseases and their pathologies., (© 2021 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.)

Many bacteria secrete toxic protein complexes that modify and disrupt essential processes in the infected cell that can lead to cell death. To conduct their action, these toxins often need to cross the cell membrane and reach a specific substrate inside the cell. The investigation of these protein complexes is essential not only for understanding their biological functions but also for the rational design of targeted drug delivery vehicles that must navigate across the cell membrane to deliver their therapeutic payload. Despite the immense advances in experimental techniques, the investigations of the toxin entry mechanism have remained challenging. Computer simulations are robust complementary tools that allow for the exploration of biological processes in exceptional detail. In this review, we first highlight the strength of computational methods, with a special focus on all-atom molecular dynamics, coarse-grained, and mesoscopic models, for exploring different stages of the toxin protein entry mechanism. We then summarize recent developments that are significantly advancing our understanding, notably of the glycolipid-lectin (GL-Lect) endocytosis of bacterial Shiga and cholera toxins. The methods discussed here are also applicable to the design of membrane-penetrating nanoparticles and the study of the phenomenon of protein phase separation at the surface of the membrane. Finally, we discuss other likely routes for future development.

Phase separation of immiscible fluids is a common phenomenon in polymer chemistry, and is recognized as an important mechanism by which cells compartmentalize their biochemical reactions. Biomolecular condensates are condensed fluid droplets in cells that form by liquid-liquid phase separation of intrinsically-disordered proteins. They have a wide range of functions and are associated with chronic neurodegenerative diseases in which they become pathologically rigid. However, it remains unclear how their material properties depend on the molecular structure of the proteins. Here we explore the phase behaviour and structure of a model biomolecular condensate composed of semi-flexible polymers with attractive end-caps using coarse-grained simulations. The model contains the minimal molecular features that are sufficient to observe liquid-liquid phase separation of soluble polymers into a porous, three-dimensional network in which their end-caps reversibly bind at junctions. The distance between connected junctions scales with the polymer length as a self-avoiding random walk over a wide range of concentration with a weak affinity-dependent prefactor. By contrast, the average number of polymers that meet at the junctions depends on the end-cap affinity but only weakly on the polymer length. The structured porosity of the condensed phase suggests a mechanism for cells to regulate biomolecular condensates. Protein interaction sites may be turned on or off to modulate the condensate's porosity and therefore the diffusion and interaction of additional proteins.

Clustering of extracellular ligands and proteins on the plasma membrane is required to perform specific cellular functions, such as signaling and endocytosis. Attractive forces that originate in perturbations of the membrane's physical properties contribute to this clustering, in addition to direct protein-protein interactions. However, these membrane-mediated forces have not all been equally considered, despite their importance. In this review, we describe how line tension, lipid depletion, and membrane curvature contribute to membrane-mediated clustering. Additional attractive forces that arise from protein-induced perturbation of a membrane's fluctuations are also described. This review aims to provide a survey of the current understanding of membrane-mediated clustering and how this supports precise biological functions., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

The bacterial Shiga toxin interacts with its cellular receptor, the glycosphingolipid globotriaosylceramide (Gb3 or CD77), as a first step to entering target cells. Previous studies have shown that toxin molecules cluster on the plasma membrane, despite the apparent lack of direct interactions between them. The precise mechanism by which this clustering occurs remains poorly defined. Here, we used vesicle and cell systems and computer simulations to show that line tension due to curvature, height, or compositional mismatch, and lipid or solvent depletion cannot drive the clustering of Shiga toxin molecules. By contrast, in coarse-grained computer simulations, a correlation was found between clustering and toxin nanoparticle-driven suppression of membrane fluctuations, and experimentally we observed that clustering required the toxin molecules to be tightly bound to the membrane surface. The most likely interpretation of these findings is that a membrane fluctuation-induced force generates an effective attraction between toxin molecules. Such force would be of similar strength to the electrostatic force at separations around 1 nm, remain strong at distances up to the size of toxin molecules (several nanometers), and persist even beyond. This force is predicted to operate between manufactured nanoparticles providing they are sufficiently rigid and tightly bound to the plasma membrane, thereby suggesting a route for the targeting of nanoparticles to cells for biomedical applications.

Large-scale brain initiatives such as the US BRAIN initiative and the European Human Brain Project aim to marshall a vast amount of data and tools for the purpose of furthering our understanding of brains. Fundamental to this goal is that neuronal morphologies must be seamlessly reconstructed and aggregated on scales up to the whole rodent brain. The experimental labor needed to manually produce this number of digital morphologies is prohibitively large. The BigNeuron initiative is assembling community-generated, open-source, automated reconstruction algorithms into an open platform, and is beginning to generate an increasing flow of high-quality reconstructed neurons. We propose a novel extension of this workflow to use this data stream to generate an unlimited number of statistically equivalent, yet distinct, digital morphologies. This will bring automated processing of reconstructed cells into digital neurons to the wider neuroscience community, and enable a range of morphologically accurate computational models.

The bacterial Shiga toxin is composed of an enzymatically active A-subunit, and a receptor-binding homopentameric B-subunit (STxB) that mediates intracellular toxin trafficking. Upon STxB-mediated binding to the glycolipid globotriaosylceramide (Gb3) at the plasma membrane of target cells, Shiga toxin is internalized by clathrin-dependent and independent endocytosis. The formation of tubular membrane invaginations is an essential step in the clathrin-independent STxB uptake process. However, the mechanism by which STxB induces these invaginations has remained unclear. Using a combination of all-atom molecular dynamics and Monte Carlo simulations we show that the molecular architecture of STxB enables the following sequence of events: the Gb3 binding sites on STxB are arranged such that tight avidity-based binding results in a small increment of local curvature. Membrane-mediated clustering of several toxin molecules then creates a tubular membrane invagination that drives toxin entry into the cell. This mechanism requires: (1) a precise molecular architecture of the STxB binding sites; (2) a fluid bilayer in order for the tubular invagination to form. Although, STxB binding to the membrane requires specific interactions with Gb3 lipids, our study points to a generic molecular design principle for clathrin-independent endocytosis of nanoparticles.

We present a first-draft digital reconstruction of the microcircuitry of somatosensory cortex of juvenile rat. The reconstruction uses cellular and synaptic organizing principles to algorithmically reconstruct detailed anatomy and physiology from sparse experimental data. An objective anatomical method defines a neocortical volume of 0.29 ± 0.01 mm(3) containing ~31,000 neurons, and patch-clamp studies identify 55 layer-specific morphological and 207 morpho-electrical neuron subtypes. When digitally reconstructed neurons are positioned in the volume and synapse formation is restricted to biological bouton densities and numbers of synapses per connection, their overlapping arbors form ~8 million connections with ~37 million synapses. Simulations reproduce an array of in vitro and in vivo experiments without parameter tuning. Additionally, we find a spectrum of network states with a sharp transition from synchronous to asynchronous activity, modulated by physiological mechanisms. The spectrum of network states, dynamically reconfigured around this transition, supports diverse information processing strategies., Paperclip: VIDEO ABSTRACT., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Biological cells are highly dynamic, and continually move material around their own volume and between their interior and exterior. Much of this transport encapsulates the material inside phospholipid vesicles that shuttle to and from, fusing with, and budding from, other membranes. A feature of vesicles that is crucial for this transport is their ability to fuse to target membranes and release their contents to the distal side. In industry, some personal care products contain vesicles to help transport reagents across the skin, and research on drug formulation shows that packaging active compounds inside vesicles delays their clearance from the blood stream. In this chapter, we survey the biological role and physicochemical properties of phospholipids, and describe progress in coarse-grained simulations of vesicles and vesicle fusion. Because coarse-grained simulations retain only those molecular details that are thought to influence the large-scale processes of interest, they act as a model embodying our current understanding. Comparing the predictions of these models with experiments reveals the importance of the retained microscopic details and also the deficiencies that can suggest missing details, thereby furthering our understanding of the complex dynamic world of vesicles.

Amphiphilic vesicles are ubiquitous in living cells and industrially interesting as drug delivery vehicles. Vesicle self-assembly proceeds rapidly from nanometer to micrometer length scales and is too fast to image experimentally but too slow for molecular dynamics simulations. Here, we use parallel dissipative particle dynamics (DPD) to follow spontaneous vesicle self-assembly for up to 445 μs with near-molecular resolution. The mean mass and radius of gyration of growing amphiphilic clusters obey power laws with exponents of 0.85 ± 0.03 and 0.41 ± 0.02, respectively. We show that DPD provides a computational window onto fluid dynamics on scales unreachable by other explicit-solvent simulations.

The expulsion of material from a cell by fusion of vesicles at the plasma membrane, and the entry of a virus by membrane invagination are complex membrane-associated processes whose control is crucial to cell survival. Our ability to visualize the dynamics of such processes experimentally is limited by spatial resolution and the speed of molecular rearrangements. The increase in computing power of the last few decades enables the construction of computational tools for observing cellular processes in silico. As experiments yield increasing amounts of data on the protein and lipid constituents of the cell, computer simulations parametrized using this data are beginning to allow models of cellular processes to be interrogated in ways unavailable in the laboratory. Mesoscopic simulations retain only those molecular features that are believed to be relevant to the processes of interest. This allows the dynamics of spatially heterogeneous membranes and the crowded cytoplasmic environment to be followed at a modest computational cost. The price for such power is that the atomic detail of the constituents is much lower than in atomistic Molecular Dynamics simulations. We argue that this price is worth paying because mesoscopic simulations can generate new insight into the complex, dynamic life of a cell.

The material parameters (area stretch modulus and bending rigidity) of two-component amphiphilic membranes are determined from dissipative particle dynamics simulations. The preferred area per molecule for each species is varied so as to produce homogeneous mixtures or nonhomogeneous mixtures that form domains. If the latter mixtures are composed of amphiphiles with the same tail length, but different preferred areas per molecule, their material parameters increase monotonically as a function of composition. By contrast, mixtures of amphiphiles that differ in both tail length and preferred area per molecule form both homogeneous and nonhomogeneous mixtures that both exhibit smaller values of their material properties compared to the corresponding pure systems. When the same nonhomogeneous mixtures of amphiphiles are assembled into planar membrane patches and vesicles, the resulting domain shapes are different when the bending rigidities of the domains are sufficiently different. Additionally, both bilayer and monolayer domains are observed in vesicles. We conclude that the evolution of the domain shapes is influenced by the high curvature of the vesicles in the simulation, a result that may be relevant for biological vesicle membranes.

Biological membranes are examples of 'smart' materials whose properties and behaviour emerge from the propagation across many scales of the molecular characteristics of their constituents. Artificial smart materials, such as drug delivery vehicles and biosensors, often rely on modifying naturally occurring soft matter, such as polymers and lipid vesicles, so that they possess useful behaviour. However, the complexity of natural membranes, both in their static properties, exemplified in their phase behaviour, and in their dynamic properties, as in the kinetics of their formation and interactions, hinders their rational modification. Mesoscopic simulations, such as dissipative particle dynamics (DPD), allow in silico experiments to be easily and cheaply performed on complex, soft materials requiring as input only the molecular structure of the constituents at a coarse-grained level. They can therefore act as a guide to experimenters prior to performing costly assays. Additionally, mesoscopic simulations provide the only currently feasible window on the length- and timescales relevant to important biophysical processes such as vesicle fusion. We review here the development of computational models of bilayer membranes, and in particular the use of mesoscopic simulations to follow the molecular rearrangements that occur during membrane fusion.

Dissipative particle dynamics is used to extract the material parameters (bending and area stretch moduli) of a bilayer membrane patch. Some experiments indicate that the area stretch modulus of lipid vesicles varies little as the chain length of the lipids composing the bilayer increases. Here we show that making the interactions between the hydrophilic head groups of the model amphiphiles proportional to the hydrophobic tail length reproduces the above result for the area stretch modulus. We also show that the area stretch modulus of bilayers composed of amphiphiles with the same number of tail beads but with asymmetric chains is less than that of bilayers with symmetric chains. The effects on the bilayer density and lateral stress profiles of changes to the amphiphile architecture are also presented.

Maintaining the integrity of their protective plasma membrane is a primary requirement of cells. Accordingly, cellular events that breach the membrane are tightly regulated. Artificial vesicles used in drug delivery must also stay intact until they have reached the desired target. In both cases, the intrinsic resistance of the membrane to rupture must be overcome to allow the efflux of the vesicle's contents. Here, we use mesoscopic simulations to study the fusion of 28-nm-diameter vesicles to 50 x 50 nm(2) planar membrane patches over 2 mus. We monitor the time evolution of 93 different fusion attempts. This allows us to construct a global morphology diagram, using the initial tensions of the vesicle and the planar membrane patch as control parameters, and to determine the corresponding fusion statistics. All successful fusion events are observed to occur within 350 ns, which reflects the presence of alternative pathways for the tension relaxation.

We study the lateral and transverse diffusion of amphiphiles in two-component bilayer membranes, using a coarse-grained model for amphiphilic molecules and combined Monte Carlo-Molecular Dynamics simulations. Membrane structural properties, such as the mean thickness, are also measured. The dependence of such properties on membrane composition, inter-molecular interactions, and amphiphile stiffness is determined. In particular, we show that addition of shorter amphiphiles drives the model membrane towards a more fluid state, with increased amphiphile lateral diffusion rates. These results can be understood in the framework of a simple free-volume model. Furthermore, we observe an increase in the trans-membrane diffusion when the interaction energy of amphiphiles with their neighboring molecules is decreased.

The growth of thermally induced pores in a two-dimensional model fluid membrane is investigated by Monte Carlo simulation. Holes appear in the membrane via an activated process, and their subsequent growth is controlled by an edge energy per unit length or line tension. The barrier height and line tension, together with a lateral tension, are the independent parameters of the model. In the resulting phase diagram, a rupture transition separates an intact membrane from a disintegrated state. The approach to the ruptured state shows distinct regimes. Reducing the barrier height at large line tension produces multiple, quasi-independent, small holes whose behavior is dominated by their edge energy, whereas at lower line tensions shape fluctuations of the holes facilitate their coalescence into a single large hole. At a small value of line tension and large barrier height, a single hole spontaneously permeabilizes the membrane in an entropically driven phase transition. Entropy dominates pore growth for line tensions not far below those measured for artificial vesicles. Permeabilization of lipid bilayers by certain peptides involves perturbing lipid-lipid cohesive energies, and our simulations show that at small line tensions the entropy of hole shape fluctuations destroys the model membrane's stability.

A computer simulation is used to investigate hole formation in a model membrane. The model parameters are the stress applied to the membrane, and the edge energy per unit length along the hole boundary (edge tension). Even at zero stress, the membrane has an entropically driven instability against hole formation. Within the model, the minimum edge tension required for the stability of a typical biological membrane is in the region of 1 x 10(-11) J/m, which is similar to the edge tension obtained in many measurements of biomembranes. At the zero-stress instability threshold, the hole shape is the same as a self-avoiding ring, but under compression, the hole shape assumes a branched polymer form. In the presence of large holes at zero stress, the membrane itself behaves like a branched polymer. The boundaries of the phase diagram for membrane stability are obtained, and general features of the rate of membrane rupture under stress are investigated. A model in which the entropy of hole formation is proportional to the hole perimeter is used to interpret the simulation results at small stress near the instability threshold.