Computational model of integrin adhesion elongation under an actin fiber.

Cells create physical connections with the extracellular environment through adhesions. Nascent adhesions form at the leading edge of migrating cells and either undergo cycles of disassembly and reassembly, or elongate and stabilize at the end of actin fibers. How adhesions assemble has been addressed in several studies, but the exact role of actin fibers in the elongation and stabilization of nascent adhesions remains largely elusive. To address this question, here we extended our computational model of adhesion assembly by incorporating an actin fiber that locally promotes integrin activation. The model revealed that an actin fiber promotes adhesion stabilization and elongation. Actomyosin contractility from the fiber also promotes adhesion stabilization and elongation, by strengthening integrin-ligand interactions, but only up to a force threshold. Above this force threshold, most integrin-ligand bonds fail, and the adhesion disassembles. In the absence of contraction, actin fibers still support adhesions stabilization. Collectively, our results provide a picture in which myosin activity is dispensable for adhesion stabilization and elongation under an actin fiber, offering a framework for interpreting several previous experimental observations.

On the significance of membrane unfolding in mechanosensitive cell spreading: Its individual and synergistic effects.

Mechanosensitivity of cell spread area to substrate stiffness has been established both through experiments and different types of mathematical models of varying complexity including both the mechanics and biochemical reactions in the cell. What has not been addressed in previous mathematical models is the role of cell membrane dynamics on cell spreading, and an investigation of this issue is the goal of this work. We start with a simple mechanical model of cell spreading on a deformable substrate and progressively layer mechanisms to account for the traction dependent growth of focal adhesions, focal adhesion induced actin polymerization, membrane unfolding/exocytosis and contractility. This layering approach is intended to progressively help in understanding the role each mechanism plays in reproducing experimentally observed cell spread areas. To model membrane unfolding we introduce a novel approach based on defining an active rate of membrane deformation that is dependent on membrane tension. Our modeling approach allows us to show that tension-dependent membrane unfolding plays a critical role in achieving the large cell spread areas experimentally observed on stiff substrates. We also demonstrate that coupling between membrane unfolding and focal adhesion induced polymerization works synergistically to further enhance cell spread area sensitivity to substrate stiffness. This enhancement has to do with the fact that the peripheral velocity of spreading cells is associated with contributions from the different mechanisms by either enhancing the polymerization velocity at the leading edge or slowing down of the retrograde flow of actin within the cell. The temporal evolution of this balance in the model corresponds to the three-phase behavior observed experimentally during spreading. In the initial phase membrane unfolding is found to be particularly important.

Prediction and Interpretation of Polymer Properties Using the Graph Convolutional Network.

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.

Comparison of Machine Learning Methods towards Developing Interpretable Polyamide Property Prediction.

Polyamides are often used for their superior thermal, mechanical, and chemical properties. They form a diverse set of materials that have a large variation in properties between linear to aromatic compounds, which renders the traditional quantitative structure-property relationship (QSPR) challenging. We use extended connectivity fingerprints (ECFP) and traditional QSPR fingerprints to develop machine learning models to perform high fidelity prediction of glass transition temperature (Tg), melting temperature (Tm), density (ρ), and tensile modulus (E). The non-linear model using random forest is in general found to be more accurate than linear regression; however, using feature selection or regularization, the accuracy of linear models is shown to be improved significantly to become comparable to the more complex nonlinear algorithm. We find that none of the models or fingerprints were able to accurately predict the tensile modulus E, which we hypothesize is due to heterogeneity in data and data sources, as well as inherent challenges in measuring it. Finally, QSPR models revealed that the fraction of rotatable bonds, and the rotational degree of freedom affects polyamide properties most profoundly and can be used for back of the envelope calculations for a quick estimate of the polymer attributes (glass transition temperature, melting temperature, and density). These QSPR models, although having slightly lower prediction accuracy, show the most promise for the polymer chemist seeking to develop an intuition of ways to modify the chemistry to enhance specific attributes.

Constructing surface models of silicate glasses using molecular dynamics to understand the effect of pH on the hydration properties.

In this work, we use realistic silicate glass surface models, with molecular dynamics simulations, and present an algorithm for proper atomic partial charge assignment, consistent with measurable internal dipoles. The immersion energy is calculated for different silicate glass compositions in solutions of varying pH. We use molecular dynamics to elucidate the differences in the structure of water between mono- and divalent cations. The immersion energy of the glass surface is found to increase with an increase in ionic surface density and pH. This can be attributed to the stronger interaction between water and cations, as opposed to the interactions between water and silanol groups. The developed models and methods provide new insights into the structure of glass-solution interfaces and the effect of cation surface density in common nanoscale environments.

Multivalent Binding of a Ligand-Coated Particle: Role of Shape, Size, and Ligand Heterogeneity.

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 α5ß1-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.

Computational approaches for investigating interfacial adhesion phenomena of polyimide on silica glass.

This manuscript provides a comprehensive study of adhesion behavior and its governing mechanisms when polyimide undergoes various modes of detachment from silica glass. Within the framework of steered molecular dynamics, we develop three different adhesion measurement techniques: pulling, peeling, and sliding. Such computational methodologies can be applied to investigate heterogeneous materials with differing interfacial adhesion modes. Here, a novel hybrid potential involving a combination of the INTERFACE force field in conjunction with ReaxFF and including Coulombic and Lennard-Jones interactions is employed to study such interfaces. The studies indicate that the pulling test requires the largest force and the shortest distance to detachment as the interfacial area is separated instantaneously, while the peeling test is observed to exhibit the largest distance for detachment because it separates via line-by-line adhesion. Two kinds of polyimides, aromatic and aliphatic type, are considered to demonstrate the rigidity dependent adhesion properties. The aromatic polyimide, which is more rigid due to the stronger charge transfer complex between chains, requires a greater force but a smaller distance at detachment than the aliphatic polyimide for all of the three methodologies.

Center or periphery? Modeling the effects of focal adhesion placement during cell spreading.

Focal adhesions are often observed at the cell's periphery. We provide an explanation for this observation using a system-level mathematical model of a cell interacting with a two-dimensional substrate. The model describes the biological cell as a hypoelastic continuum material whose behavior is coupled to a deformable, linear elastic substrate via focal adhesions that are represented by collections of linear elastic attachments between the cell and the substrate. The evolution of the focal adhesions is coupled to local intracellular stresses which arise from mechanical cell-substrate interactions. Using this model we show that the cell has at least three mechanisms through which it can control its intracellular stresses: focal adhesion position, size, and attachment strength. We also propose that one reason why focal adhesions are typically located on the cell periphery instead of its center is because peripheral focal adhesions allow the cell to be more sensitive to changes in the microenvironment. This increased sensitivity is caused by the fact that peripherally located focal adhesions allow the cells to modulate its intracellular properties over a much larger portion of the cell area.

An actin-filament-binding interface on the Arp2/3 complex is critical for nucleation and branch stability.

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.

Interaction of lipid membrane with nanostructured 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.

Interaction of nanoparticles with lipid membrane.

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.