Complexation Mechanisms of Aqueous Amylose: Molecular Dynamics Study Using 3-Pentadecylphenol.

Molecular dynamics (MD) simulations of linear amylose fragments containing 10 to 40 glucose units were used to study the complexation of the prototypical compound, 3-pentadecylphenol (PDP)─a natural product with surfactant-like properties─in aqueous solution. The amylose-PDP binding leverages mainly hydrophobic interactions together with excluded volume effects. It was found that while the most stable complexes contained PDP inside the helical structure of the amylose in the expected guest-host (inclusion) complexation manner, at higher temperatures, the commonly observed PDP-amylose complexes often involved more nonspecific interactions than inclusion complexation. In the case where a stoichiometric excess of PDP was added to the simulation box, self-aggregation of the small molecule precluded its ability to enter the internal helical part of the oligosaccharide, and as a result, inclusion complexation became ineffective. MD simulation trajectories were analyzed preliminarily using cluster analysis (CA), followed by more rigorous solvent accessible surface area (SASA) determination over the temperature range spanning from 277 to 433 K. It was found that using the SASA of PDP corrected for its intrinsic conformational changes, together with a generic hidden Markov model (HMM), an adequate quantification of the different types of PDP-amylose aggregates was obtained to allow further analysis. The enthalpy change associated with the guest-host binding equilibrium constant (Kgh) in aqueous solution was estimated to be -75 kJ/mol, which is about twice as high as one might expect based on experimentally measured values of similar complexes in the solid state where the (unsolvated) helical structure of amylose remains rigid. On the other hand, the nonspecific binding (Kns) enthalpy change associated with PDP-amylose interactions in the same solution environment was found to be about half of the inclusion complexation value.

Nanoscale Simulation of Plastic Contaminants Migration in Packaging Materials and Potential Leaching into Model Food Fluids.

Polymers are the most commonly used packaging materials for nutrition and consumer products. The ever-growing concern over pollution and potential environmental contamination generated from single-use packaging materials has raised safety questions. Polymers used in these materials often contain impurities, including unreacted monomers and small oligomers. The characterization of transport properties, including diffusion and leaching of these molecules, is largely hampered by the long timescales involved in shelf life experiments. In this work, we employ atomistic molecular simulation techniques to explore the main mechanisms involved in the bulk and interfacial transport of monomer molecules from three polymers commonly employed as packaging materials: polyamide-6, polycarbonate, and poly(methyl methacrylate). Our simulations showed that both hopping and continuous diffusion play important roles in inbound monomer diffusion and that solvent-polymer compatibility significantly affects monomer leaching. These results provide rationalization for monomer leaching in model food formulations as well as bulky industry-relevant molecules. Through this molecular-scale characterization, we offer insights to aid in the design of polymer/consumer product interfaces with reduced risk of contamination and longer shelf life.

Drug Aggregation of Sparingly-Soluble Ionizable Drugs: Molecular Dynamics Simulations of Papaverine and Prostaglandin F2α.

Aggregation in aqueous solution can have important implications on both the in vivo exposure of a drug and its pharmaceutical manufacturability. However, the drug aggregates formed can be very small and, thus, difficult to interrogate experimentally. On the other hand, at higher supersaturations where larger aggregates are supported, the chemical system is inherently metastable and therefore likewise challenging to study from an experimental standpoint. Understanding aggregation behavior is further complicated in the case of ionizable drugs where, unlike neutral compounds, there can be uncertainty in the kinds of drug molecules (i.e., charged, neutral, or both) that become incorporated into various clusters, particularly at pH values near the pKa. In this paper, we apply physics-based all-atom molecular dynamics (MD) simulations to study aggregation in the weakly basic drug papaverine and in the weakly acidic drug prostaglandin F2α. We employ in silico tools to construct simulation workflows and comprehensive cluster analysis protocols to elucidate the size distributions and dynamics of the drug aggregates formed at both an experimentally relevant concentration and at high supersaturation. We build on a previously published treatment [Solubility of sparingly soluble ionizable drugs. Adv. Drug Deliv. Rev. 2007, 59, 568-590, DOI: 10.1016/j.addr.2007.05.008] to translate the predicted aggregate distributions of each ionized drug into corresponding pH-solubility curves that can be compared directly to experiment. Our findings show that the assumption of a single predominant (charged) aggregate can be misleading in interpreting experimental pH-solubility curves, as it does not adequately reflect the rich diversity revealed in our simulations. Beyond not accounting for the distribution of ionized drug-containing clusters actually observed in solution, for both drugs we find evidence that neutral drug molecules can also participate in the aggregation phenomena. Notably, we observe that many drug molecules remain as free monomers in solution even under simulated conditions designed to mimic those where there is significant deviation of the experimental pH-solubility curve from the Henderson-Hasselbalch (H-H) equation, often taken to be a clear signpost of drug aggregation.

Exploring the Effects of Wetting and Free Fatty Acid Deposition on an Atomistic Hair Fiber Surface Model Incorporating Keratin-Associated Protein 5-1.

The complex development of cosmetic and medical formulations relies on an ever-growing accuracy of predictive models of hair surfaces. Hitherto, modeling efforts have focused on the description of 18-methyl eicosanoic acid (18-MEA), the primary fatty acid covalently attached to the hair surface, without explicit modeling of the protein layer. Herein, the molecular details of the outermost surface of the human hair fiber surface, also called the F-layer, were studied using molecular dynamics (MD) simulations. The F-layer is composed primarily of keratin-associated proteins KAP5 and KAP10, which are decorated with 18-MEA on the outer surface of a hair fiber. In our molecular model, we incorporated KAP5-1 and evaluated the surface properties of 18-MEA through MD simulations, resulting in 18-MEA surface density, layer thickness, and tilt angles in agreement with previous experimental and computational studies. Subsequent models with reduced 18-MEA surface density were also generated to mimic damaged hair surfaces. Response to wetting of virgin and damaged hair showed rearrangement of 18-MEA on the surface, allowing for water penetration into the protein layer. To demonstrate a potential use case for these atomistic models, we deposited naturally occurring fatty acids and measured 18-MEA's response in both dry and wet conditions. As fatty acids are often incorporated in shampoo formulations, this work demonstrates the ability to model the adsorption of ingredients on hair surfaces. This study illustrates, for the first time, the complex behavior of a realistic F-layer at the molecular level and opens up the possibility of studying the adsorption behavior of larger, more complex molecules and formulations.

Shearing friction behaviour of synthetic polymers compared to a functionalized polysaccharide on biomimetic surfaces: models for the prediction of performance of eco-designed formulations.

The substitution of natural, bio-based and/or biodegradable polymers for those of petrochemical origin in consumer formulations has become an active area of research and development as the sourcing and destiny of material components becomes a more critical factor in product design. These polymers often differ from their petroleum-based counterparts in topology, raw material composition and solution behaviour. Effective and efficient reformulation that maintains comparable cosmetic performance to existing products requires a deep understanding of the differences in frictional behaviour between polymers as a function of their molecular structure. In this work, we simulate the tribological behaviour of three topologically distinct polymers in solution with surfactants and in contact with hair-biomimetic patterned surfaces. We compare a generic functionalized polysaccharide to two performant polymers used in shampoo formulations: a strongly positively charged polyelectrolyte and a zwitterionic copolymer. Topological differences are expected to affect rheological properties, as well as their direct interaction with structured biological substrates. Using a refined Martini-style coarse-grained model we describe the polymer-dependent differences in aggregation behaviour as well as selective interactions with a biomimetic model hair surface. Additionally, we introduce a formalism to characterize the response of the solution to shear as an initial study on lubrication properties, which define the sensorial performance of these systems in cosmetics (i.e., manageability, touch, etc.). The tools and techniques presented in this work illustrate the strength of molecular simulation in eco-design of formulation as a complement to experiment. These efforts help advance our understanding of how we can relate complex atomic-scale solution behaviour to relevant macroscopic properties. We expect these techniques to play an increasingly important role in advancing strategies for green polymer formulation design by providing an understanding for how new polymers could reach and even exceed the level of performance of existing polymers.

Obtaining and Characterizing Stable Bicontinuous Cubic Morphologies and Their Nanochannels in Lyotropic Liquid Crystal Membranes.

Amphiphilic monomers in polar solvents can self-assemble into lyotropic liquid crystal (LLC) bicontinuous cubic structures under the right composition and temperature conditions. After cross-linking, the resulting polymer membranes with three-dimensional (3D) continuous uniform channels are excellent candidates for filtration applications. Designing such membranes with the desired physical and chemical properties requires molecular-level understanding of the structure, which can be obtained through molecular modeling. However, building molecular models of bicontinuous cubic structures is challenging due to their narrow regime of stability and the difficulty of self-assembly of large unit cells in molecular simulations. We developed a protocol for building stable bicontinuous cubic unit cells involving both parameterization and assembly of the components. We validate the theoretical structure against experimental results for one such LLC monomer and provide insight into the structure missing in experimental data, as well as demonstrate the qualitative nature of water and solute transport through these membranes.

Capturing Subdiffusive Solute Dynamics and Predicting Selectivity in Nanoscale Pores with Time Series Modeling.

Fitting mathematical models with a direct connection to experimental observables to the outputs of molecular simulations can be a powerful tool for extracting important physical information from them. In this study, we present two new approaches that use stochastic time series modeling to predict long-time-scale behavior and macroscopic properties from molecular simulation, which can be generalized to other molecular systems where complex diffusion occurs. In our previous work, we studied long molecular dynamics (MD) simulation trajectories of a cross-linked HII phase lyotropic liquid crystal (LLC) membrane, where we observed subdiffusive solute transport behavior characterized by intermittent hops separated by periods of entrapment. In this work, we use our models to parameterize the behavior of the same systems, so we can generate characteristic trajectory realizations that can be used to predict solute mean-squared displacements (MSDs), solute flux, and solute selectivity in macroscopic length pores. First, using anomalous diffusion theory, we show how solute dynamics can be modeled as a fractional diffusion process subordinate to a continuous time random walk. From the MD simulations, we parameterize the distribution of dwell times, hop lengths between dwells, and correlation between hops. We explore two variations of the anomalous diffusion modeling approach. The first variation applies a single set of parameters to the solute displacements and the second applies two sets of parameters based on the solute's radial distance from the closest pore center. Next, we present an approach that generalizes Markov state models, treating the configurational states of the system as a Markov process where each state has distinct transport properties. For each state and transition between states, we parameterize the distribution and temporal correlation structure of positional fluctuations as a means of characterization and to allow us to predict solute MSDs. We show that both stochastic models reasonably reproduce the MSDs calculated from MD simulations. However, qualitative differences between MD and Markov state-dependent model-generated trajectories may in some cases limit their usefulness. With these parameterized stochastic models, we demonstrate how one can estimate the flux of a solute across a macroscopic length pore and, based on these quantities, the membrane's selectivity toward each solute. This work therefore helps to connect microscopic, chemically dependent solute motions that do not follow simple diffusive behavior with long-time-scale behavior, in an approach generalizable to many types of molecular systems with complex dynamics.

Statistical Inference of Transport Mechanisms and Long Time Scale Behavior from Time Series of Solute Trajectories in Nanostructured Membranes.

Appropriate time series modeling of complex diffusion in soft matter systems on the microsecond time scale can provide a path toward inferring transport mechanisms and predicting bulk properties characteristic of much longer time scales. In this work we apply nonparametric Bayesian time series analysis, more specifically the sticky hierarchical Dirichlet process autoregressive hidden Markov model (HDP-AR-HMM) to solute center-of-mass trajectories generated from long molecular dynamics (MD) simulations in a cross-linked inverted hexagonal phase lyotropic liquid crystal (LLC) membrane in order to automatically detect a variety of solute dynamical modes. We can better understand the mechanisms controlling these dynamical modes by grouping the states identified by the HDP-AR-HMM into clusters based on multiple metrics aimed at distinguishing solute behavior based on their fluctuations, dwell times in each state, and positions within the inhomogeneous membrane structure. We analyze predominant clusters in order to relate their dynamical parameters to physical interactions between solutes and the membrane. Along with parameters of individual states, the HDP-AR-HMM simultaneously infers a transition matrix which allows us to stochastically propagate solute behavior from all of the independent trajectories onto arbitrary length time scales while still preserving the qualitative behavior characteristic of the MD trajectories. This affords a direct connection to important macroscopic observables used to characterize performance like solute flux and selectivity. This work provides a promising way to simultaneously identify transport mechanisms in nanoporous materials and project complex diffusive behavior on long time scales. Our enhanced understanding of the diverse range of solute behavior allows us to hypothesize design changes to LLC monomers aimed toward controlling the rates of solute passage, thus improving the selective performance of LLC membranes.

Chemically Selective Transport in a Cross-Linked HII Phase Lyotropic Liquid Crystal Membrane.

The uniform size and complex chemical topology of the pores formed by self-assembled amphiphilic molecules such as liquid crystals make them promising candidates for selective separations. In this work, we observe the transport of water, sodium ions, and 20 small polar solutes within the pores of a lyotropic liquid crystal (LLC) membrane using atomistic molecular simulations. We find that the transport of a species is dependent not only on molecular size but also on chemical functionality. The membrane's inhomogeneous composition gives rise to radially dependent transport mechanisms with respect to the pore centers. We observe that all solutes perform intermittent hops between lengthy periods of entrapment. Three different trapping mechanisms are responsible for this behavior. First, solutes that drift out of the pore can become entangled among the dense monomer tails. Second, solutes can donate hydrogen bonds to the monomer head groups. Third, solutes can coordinate with sodium counterions. The degree to which a solute is affected by each mechanism is dependent on the chemical functionality of the solute. Using the insights developed in this study, we can begin to think about how to redesign existing LLC membranes to perform solute-specific separations.

Understanding the Nanoscale Structure of Inverted Hexagonal Phase Lyotropic Liquid Crystal Polymer Membranes.

Periodic, nanostructured porous polymer membranes made from the cross-linked inverted hexagonal phase of self-assembled lyotropic liquid crystals (LLCs) are a promising class of materials for selective separations. In this work, we investigate an experimentally characterized LLC polymer membrane using atomistic molecular modeling. In particular, we compare simulated X-ray diffraction (XRD) patterns with experimental XRD data to quantify and understand the differences between simulation and experiment. We find that the nanopores are likely composed of five columns of stacked LLC monomers which surround each hydrophilic core. Evidence suggests that these columns likely move independently of each other over longer time scales than accessible via atomistic simulation. We also find that wide-angle X-ray scattering structural features previously attributed to monomer tail tilt are likely instead due to ordered tail packing. Although this system has been reported as dry, we show that small amounts of water are necessary to reproduce all features from the experimental XRD pattern because of asymmetries introduced by hydrogen bonds between the monomer head groups and water molecules. Finally, we explore the composition and structure of the nanopores and reveal that there exists a composition gradient rather than an abrupt partition between the hydrophilic and hydrophobic regions. A caveat is that the time scales of the dynamics are extremely long for this system, resulting in simulated structures that appear too ordered, thus requiring careful examination of the metastable states observed in order to draw any conclusions. The clear picture of the nanoscopic structure of these membranes provided in this study will enable a better understanding of the mechanisms of small-molecule transport within these nanopores.