A nanoscale range of surface feature curvatures where lipid membranes lose integrity and form pores has been found experimentally. The pores were experimentally observed in the l-alpha-dimyristoyl phosphatidylcholine membrane around 1.2-22 nm polar nanoparticles deposited on mica surface. Lipid bilayer envelops or closely follows surface features with the curvatures outside of that region. This finding provides essential information for the understanding of nanoparticle-lipid membrane interaction, cytotoxicity, preparation of biomolecular templates and supported lipid membranes on rough and patterned surfaces.

Tiny details of the phospholipid (DMPC) membrane morphology in close vicinity to nanostructured silica surfaces have been discovered in the atomic force microscopy experiments. The structural features of the silica surface were varied in the experiments by the deposition of silica nanoparticles of different diameter on plane and smooth silica substrates. It was found that, due to the barrier function of the lipid membrane, only particles larger than 22 nm in diameter with a smooth surface were completely enveloped by the lipid membrane. However, nanoparticles with bumpy surfaces (curvature diameter of bumps as that of particles <22 nm) were only partially enveloped by the lipid bilayer. For the range of nanostructure dimensions between 1.2 and 22 nm, the lipid membrane underwent structural rearrangements by forming pores (holes). The nanoparticles were accommodated into the pores but not enveloped by the lipid bilayer. The study also found that the lipid membrane conformed to the substrate with surface structures of dimensions less than 1.2 nm without losing the membrane integrity. The experimental results are in accord with the analytical free energy model, which describes the membrane coverage, and numerical simulations which evaluate adhesion of the membrane and dynamics as a function of surface topology. The results obtained in this study are useful for the selection of dimensions and shapes for drug-delivery cargo and for the substrate for supported lipid bilayers. They also help in qualitative understanding the role of length scales involved in the mechanisms of endocytosis and cytotoxicity of nanoparticles. These findings provide a new approach for patterning supported lipid membranes with well-defined features in the 1.2-22 nm range.

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The Arp2/3 complex polymerizes new actin filaments from the sides of existing filaments, forming Y-branched networks that are critical for actin-mediated force generation. Binding of the Arp2/3 complex to the sides of actin filaments is therefore central to its actin-nucleating and branching activities. Although a model of the Arp2/3 complex in filament branches has been proposed based on electron microscopy, this model has not been validated using independent approaches, and the functional importance of predicted actin-binding residues has not been extensively tested. Using a combination of molecular dynamics and protein-protein docking simulations, we derived an independent structural model of the interaction between two subunits of the Arp2/3 complex that are key to actin binding, ARPC2 and ARPC4, and the side of an actin filament. This model agreed remarkably well with the previous results from electron microscopy. Complementary mutagenesis experiments revealed numerous residues in ARPC2 and ARPC4 that were required for the biochemical activity of the entire complex. Functionally critical residues clustered together and defined a surface that was predicted by protein-protein docking to be buried in the interaction with actin. Moreover, key residues at this interface were crucial for actin nucleation and Y-branching, high-affinity F-actin binding, and Y-branch stability, demonstrating that the affinity of Arp2/3 complex for F actin independently modulates branch formation and stability. Our results highlight the utility of combining computational and experimental approaches to study protein-protein interactions and provide a basis for further elucidating the role of F-actin binding in Arp2/3 complex activation and function.cytoskeleton | actin branching

We present machine learning models for the prediction of thermal and mechanical properties of polymers based on the graph convolutional network (GCN). GCN-based models provide reliable prediction performances for the glass transition temperature (T g ), melting temperature (T m ), density (ρ), and elastic modulus (E) with substantial dependence on the dataset, which is the best for T g (R 2 ∼ 0.9) and worst for E (R 2 ∼ 0.5). It is found that the GCN representations for polymers provide prediction performances of their properties comparable to the popular extended-connectivity circular fingerprint (ECFP) representation. Notably, the GCN combined with the neural network regression (GCN-NN) slightly outperforms the ECFP. It is investigated how the GCN captures important structural features of polymers to learn their properties. Using the dimensionality reduction, we demonstrate that the polymers are organized in the principal subspace of the GCN representation spaces with respect to the backbone rigidity. The organization in the representation space adaptively changes with the training and through the NN layers, which might facilitate a subsequent prediction of target properties based on the relationships between the structure and the property. The GCN models are found to provide an advantage to automatically extract a backbone rigidity, strongly correlated with T g , as well as a potential transferability to predict other properties associated with a backbone rigidity. Our results indicate both the capability and limitations of the GCN in learning to describe polymer systems depending on the property.

Understanding the interaction between polyimide and inorganic surfaces is vital in controlling interfacial adhesion behavior. Here, molecular dynamics simulations are employed to study the adhesion of polyimide on both crystalline and glassy silica surfaces, and the effects of hydroxylation, silica structure, and polyimide chemistry on adhesion are investigated. The results reveal that polyimide monomers have stronger adhesion on hydroxylated surfaces compared to nonhydroxylated surfaces. Also, adhesion of polyimide onto silica glass is stronger compared to the corresponding crystalline surfaces. Finally, we explore the molecular origins of adhesion to understand why some polyimide monomers like Kapton have a stronger adhesion per unit area (adhesion density) than others like BPDA-APB. We find this occurs due to a higher density of oxygen’s in the Kapton monomer, which we found to have the highest contribution to adhesion density.

We utilize a multiscale modeling framework to study the effect of shape, size, and ligand composition on the efficacy of binding of a ligand-coated particle to a substrate functionalized with the target receptors. First, we show how molecular dynamics along with steered molecular dynamics calculations can be used to accurately parameterize the molecular-binding free energy and the effective spring constant for a receptor-ligand pair. We demonstrate this for two ligands that bind to the αβ-domain of integrin. Next, we show how these effective potentials can be used to build computational models at the meso- and continuum-scales. These models incorporate the molecular nature of the receptor-ligand interactions and yet provide an inexpensive route to study the multivalent interaction of receptors and ligands through the construction of Bell potentials customized to the molecular identities. We quantify the binding efficacy of the ligand-coated-particle in terms of its multivalency, binding free-energy landscape, and the losses in the configurational entropies. We show that 1) the binding avidity for particle sizes less than 350 nm is set by the competition between the enthalpic and entropic contributions, whereas that for sizes above 350 nm is dominated by the enthalpy of binding; 2) anisotropic particles display higher levels of multivalent binding compared to those of spherical particles; and 3) variations in ligand composition can alter binding avidity without altering the average multivalency. The methods and results presented here have wide applications in the rational design of functionalized carriers and also in understanding cell adhesion.