Long-Chain Lipids Facilitate Insertion of Large Nanoparticles into Membranes of Small Unilamellar Vesicles.

Insertion of hydrophobic nanoparticles into phospholipid bilayers is limited to small particles that can incorporate into a hydrophobic membrane core between two lipid leaflets. Incorporation of nanoparticles above this size limit requires the development of challenging surface engineering methodologies. In principle, increasing the long-chain lipid component in the lipid mixture should facilitate incorporation of larger nanoparticles. Here, we explore the effect of incorporating very long phospholipids (C24:1) into small unilamellar vesicles on the membrane insertion efficiency of hydrophobic nanoparticles that are 5-11 nm in diameter. To this end, we improve an existing vesicle preparation protocol and utilized cryogenic electron microscopy imaging to examine the mode of interaction and evaluate the insertion efficiency of membrane-inserted nanoparticles. We also perform classical coarse-grained molecular dynamics simulations to identify changes in lipid membrane structural properties that may increase insertion efficiency. Our results indicate that long-chain lipids increase the insertion efficiency by preferentially accumulating near membrane-inserted nanoparticles to reduce the thermodynamically unfavorable disruption of the membrane.

Ligand Lipophilicity Determines Molecular Mechanisms of Nanoparticle Adsorption to Lipid Bilayers.

The interactions of ligand-functionalized nanoparticles with the cell membrane affect cellular uptake, cytotoxicity, and related behaviors, but relating these interactions to ligand properties remains challenging. In this work, we perform coarse-grained molecular dynamics simulations to study how the adsorption of ligand-functionalized cationic gold nanoparticles (NPs) to a single-component lipid bilayer (as a model cell membrane) is influenced by ligand end group lipophilicity. A set of 2 nm diameter NPs, each coated with a monolayer of organic ligands that differ only in their end groups, was simulated to mimic NPs recently studied experimentally. Metadynamics calculations were performed to determine key features of the free energy landscape for adsorption as a function of the distance of the NP from the bilayer and the number of NP-lipid contacts. These simulations revealed that NP adsorption is thermodynamically favorable for all NPs due to the extraction of lipids from the bilayer and into the NP monolayer. To resolve ligand-dependent differences in adsorption behavior, string method calculations were performed to compute minimum free energy pathways for adsorption. These calculations revealed a surprising nonmonotonic dependence of the free energy barrier for adsorption on ligand end group lipophilicity. Large free energy barriers are predicted for the least lipophilic end groups because favorable NP-lipid contacts are initiated only through the unfavorable protrusion of lipid tail groups out of the bilayer. The smallest free energy barriers are predicted for end groups of intermediate lipophilicity which promote NP-lipid contacts by intercalating within the bilayer. Unexpectedly, large free energy barriers are also predicted for the most lipophilic end groups which remain sequestered within the ligand monolayer rather than intercalating within the bilayer. These trends are broadly in agreement with past experimental measurements and reveal how subtle variations in ligand lipophilicity dictate adsorption mechanisms and associated kinetics by influencing the interplay of lipid-ligand interactions.

Elucidating Molecular-Scale Principles Governing the Anchoring of Liquid Crystal Mixtures on Solid Surfaces.

Computational chemistry calculations are broadly useful for guiding the atom-scale design of hard-soft material interfaces including how molecular interactions of single-component liquid crystals (LCs) at inorganic surfaces lead to preferred orientations of the LC far from the surface. The majority of LCs, however, are not single-component phases but comprise of mixtures, such as a mixture of mesogens, added to provide additional functions such as responsiveness to the presence of targeted organic compounds (for chemical sensing). In such LC mixtures, little is understood about the near-surface composition and organization of molecules and how that organization propagates into the far-field LC orientation. Here, we address this broad question by using a multiscale computational approach that combines density functional theory (DFT)-based calculations and classical molecular dynamics (MD) simulations to predict the interfacial composition and organization of a binary LC mixture of 4'-cyano-4-biphenylcarbolxylic acid (CBCA) and 4'-n-pentyl-4-biphenylcarbonitrile (5CB) supported on anatase (101) titania surfaces. DFT calculations determine the surface composition and atomic-scale organization of CBCA and 5CB at the titania surface, and classical MD simulations build upon the DFT description to describe the evolution of the near-surface order into the bulk LC. A surprising finding is that the 5CB and CBCA molecules adopt orthogonal orientations at the anatase surface and that, above a threshold concentration of CBCA, this mixture of orientations evolves away from the surface to define a uniform far-field homeotropic orientation. These results demonstrate that molecular-level knowledge achieved through a combination of computational techniques permits the design and understanding of functional LC mixtures at interfaces.

Decoding Optical Responses of Contact-Printed Arrays of Thermotropic Liquid Crystals Using Machine Learning: Detection and Reporting of Aqueous Amphiphiles with Enhanced Sensitivity and Selectivity.

Surfactants and other amphiphilic molecules are used extensively in household products, industrial processes, and biological applications and are also common environmental contaminants; as such, methods that can detect, sense, or quantify them are of great practical relevance. Aqueous emulsions of thermotropic liquid crystals (LCs) can exhibit distinctive optical responses in the presence of surfactants and have thus emerged as sensitive, rapid, and inexpensive sensors or reporters of environmental amphiphiles. However, many existing LC-in-water emulsions require the use of complicated or expensive instrumentation for quantitative characterization owing to variations in optical responses among individual LC droplets. In many cases, the responses of LC droplets are also analyzed by human inspection, which can miss subtle color or topological changes encoded in LC birefringence patterns. Here, we report an LC-based surfactant sensing platform that takes a step toward addressing several of these issues and can reliably predict concentrations and types of surfactants in aqueous solutions. Our approach uses surface-immobilized, microcontact-printed arrays of micrometer-scale droplets of thermotropic LCs and hierarchical convolutional neural networks (CNNs) to automatically extract and decode rich information about topological defects and color patterns available in optical micrographs of LC droplets to classify and quantify adsorbed surfactants. In addition, we report computational capabilities to determine relevant optical features extracted by the CNN from LC micrographs, which can provide insights into surfactant adsorption phenomena at LC-water interfaces. Overall, the combination of microcontact-printed LC arrays and machine learning provides a convenient and robust platform that could prove useful for developing high-throughput sensors for on-site testing of environmentally or biologically relevant amphiphiles.

Structural features of interfacial water predict the hydrophobicity of chemically heterogeneous surfaces.

The hydrophobicity of an interface determines the magnitude of hydrophobic interactions that drive numerous biological and industrial processes. Chemically heterogeneous interfaces are abundant in these contexts; examples include the surfaces of proteins, functionalized nanomaterials, and polymeric materials. While the hydrophobicity of nonpolar solutes can be predicted and related to the structure of interfacial water molecules, predicting the hydrophobicity of chemically heterogeneous interfaces remains a challenge because of the complex, non-additive contributions to hydrophobicity that depend on the chemical identity and nanoscale spatial arrangements of polar and nonpolar groups. In this work, we utilize atomistic molecular dynamics simulations in conjunction with enhanced sampling and data-centric analysis techniques to quantitatively relate changes in interfacial water structure to the hydration free energy (a thermodynamically well-defined descriptor of hydrophobicity) of chemically heterogeneous interfaces. We analyze a large data set of 58 self-assembled monolayers (SAMs) composed of ligands with nonpolar and polar end groups of different chemical identity (amine, amide, and hydroxyl) in five mole fractions, two spatial patterns, and with scaled partial charges. We find that only five features of interfacial water structure are required to accurately predict hydration free energies. Examination of these features reveals mechanistic insights into the interfacial hydrogen bonding behaviors that distinguish different surface compositions and patterns. This analysis also identifies the probability of highly coordinated water structures as a unique signature of hydrophobicity. These insights provide a physical basis to understand the hydrophobicity of chemically heterogeneous interfaces and connect hydrophobicity to experimentally accessible perturbations of interfacial water structure.

Topological Analysis of Molecular Dynamics Simulations using the Euler Characteristic.

Molecular dynamics (MD) simulations are used in diverse scientific and engineering fields such as drug discovery, materials design, separations, biological systems, and reaction engineering. These simulations generate highly complex data sets that capture the 3D spatial positions, dynamics, and interactions of thousands of molecules. Analyzing MD data sets is key for understanding and predicting emergent phenomena and in identifying key drivers and tuning design knobs of such phenomena. In this work, we show that the Euler characteristic (EC) provides an effective topological descriptor that facilitates MD analysis. The EC is a versatile, low-dimensional, and easy-to-interpret descriptor that can be used to reduce, analyze, and quantify complex data objects that are represented as graphs/networks, manifolds/functions, and point clouds. Specifically, we show that the EC is an informative descriptor that can be used for machine learning and data analysis tasks such as classification, visualization, and regression. We demonstrate the benefits of the proposed approach through case studies that aim to understand and predict the hydrophobicity of self-assembled monolayers and the reactivity of complex solvent environments.

Interactions of Bacterial Quorum Sensing Signals with Model Lipid Membranes: Influence of Membrane Composition on Membrane Remodeling.

We report the influence of membrane composition on the multiscale remodeling of multicomponent lipid bilayers initiated by contact with the amphiphilic bacterial quorum sensing signal N-(3-oxo)-dodecanoyl-l-homoserine lactone (3-oxo-C12-AHL) and its anionic headgroup hydrolysis product, 3-oxo-C12-HS. We used fluorescence microscopy and quartz crystal microbalance with dissipation (QCM-D) to characterize membrane reformation that occurs when these amphiphiles are placed in contact with supported lipid bilayers (SLBs) composed of (i) 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) containing varying amounts of cholesterol or (ii) mixtures of DOPC and either 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine (DOPE, a conical zwitterionic lipid) or 1,2-dioleoyl-sn-glycero-3-phospho-l-serine (DOPS, a model anionic lipid). In general, we observe these mixed-lipid membranes to undergo remodeling events, including the formation and subsequent collapse of long tubules and the formation of hemispherical caps, upon introduction to biologically relevant concentrations of 3-oxo-C12-AHL and 3-oxo-C12-HS in ways that differ substantially from those observed in single-component DOPC membranes. These differences in bilayer reformation and their associated dynamics can be understood in terms of the influence of membrane composition on the time scales of molecular flip-flop, lipid packing defects, and lipid phase segregation in these materials. The lipid components investigated here are representative of classes of lipids that comprise both naturally occurring cell membranes and many useful synthetic soft materials. These studies thus represent a first step toward understanding the ways in which membrane composition can impact interactions with this important class of bacterial signaling molecules.

Ordering Transitions of Liquid Crystals Triggered by Metal Oxide-catalyzed Reactions of Sulfur Oxide Species.

Liquid crystals (LCs), when supported on reactive surfaces, undergo changes in ordering that can propagate over distances of micrometers, thus providing a general and facile mechanism to amplify atomic-scale transformations on surfaces into the optical scale. While reactions on organic and metal substrates have been coupled to LC-ordering transitions, metal oxide substrates, which offer unique catalytic activities for reactions involving atmospherically important chemical species such as oxidized sulfur species, have not been explored. Here, we investigate this opportunity by designing LCs that contain 4'-cyanobiphenyl-4-carboxylic acid (CBCA) and respond to surface reactions triggered by parts-per-billion concentrations of SO2 gas on anatase (101) substrates. We used electronic structure calculations to predict that the carboxylic acid group of CBCA binds strongly to anatase (101) in a perpendicular orientation, a prediction that we validated in experiments in which CBCA (0.005 mol %) was doped into an LC (4'-n-pentyl-4-biphenylcarbonitrile). Both experiment and computational modeling further demonstrated that SO3-like species, produced by a surface-catalyzed reaction of SO2 with H2O on anatase (101), displace CBCA from the anatase surface, resulting in an orientational transition of the LC. Experiments also reveal the LC response to be highly selective to SO2 over other atmospheric chemical species (including H2O, NH3, H2S, and NO2), in agreement with our computational predictions for anatase (101) surfaces. Overall, we establish that the catalytic activities of metal oxide surfaces offer the basis of a new class of substrates that trigger LCs to undergo ordering transitions in response to chemical species of relevance to atmospheric chemistry.

Medium-Chain Lipid Conjugation Facilitates Cell-Permeability and Bioactivity.

The majority of bioactive molecules act on membrane proteins or intracellular targets and therefore needs to partition into or cross biological membranes. Natural products often exhibit lipid modifications to facilitate critical molecule-membrane interactions, and in many cases their bioactivity is markedly reduced upon removal of a lipid group. However, despite its importance in nature, lipid-conjugation of small molecules is not commonly used in chemical biology and medicinal chemistry, and the effect of such conjugation has not been systematically studied. To understand the composition of lipids found in natural products, we carried out a chemoinformatic characterization of the "natural product lipidome". According to this analysis, lipidated natural products predominantly contain saturated medium-chain lipids (MCLs), which are significantly shorter than the long-chain lipids (LCLs) found in membranes and lipidated proteins. To study the usefulness of such modifications in probe design, we systematically explored the effect of lipid conjugation on five different small molecule chemotypes and find that permeability, cellular retention, subcellular localization, and bioactivity can be significantly modulated depending on the type of lipid tail used. We demonstrate that MCL conjugation can render molecules cell-permeable and modulate their bioactivity. With all explored chemotypes, MCL-conjugates consistently exhibited superior uptake or bioactivity compared to LCL-conjugates and either comparable or superior uptake or bioactivity to short-chain lipid (SCL)-conjugates. Together, our findings suggest that conjugation of small molecules with MCLs could be a powerful strategy for the design of probes and drugs.

Sensing Gas Mixtures by Analyzing the Spatiotemporal Optical Responses of Liquid Crystals Using 3D Convolutional Neural Networks.

We report how analysis of the spatial and temporal optical responses of liquid crystal (LC) films to targeted gases, when performed using a machine learning methodology, can advance the sensing of gas mixtures and provide important insights into the physical processes that underlie the sensor response. We develop the methodology using O3 and Cl2 mixtures (representative of an important class of analytes) and LCs supported on metal perchlorate-decorated surfaces as a model system. Although O3 and Cl2 both diffuse through LC films and undergo redox reactions with the supporting metal perchlorate surfaces to generate similar initial and final optical states of the LCs, we show that a three-dimensional convolutional neural network can extract feature information that is encoded in the spatiotemporal color patterns of the LCs to detect the presence of both O3 and Cl2 species in mixtures and to quantify their concentrations. Our analysis reveals that O3 detection is driven by the transition time over which the brightness of the LC changes, while Cl2 detection is driven by color fluctuations that develop late in the optical response of the LC. We also show that we can detect the presence of Cl2 even when the concentration of O3 is orders of magnitude greater than the Cl2 concentration. The proposed methodology is generalizable to a wide range of analytes, reactive surfaces, and LCs and has the potential to advance the design of portable LC monitoring devices (e.g., wearable devices) for analyzing gas mixtures using spatiotemporal color fluctuations.

A simple simulation-derived descriptor for the deposition of polymer-wrapped carbon nanotubes on functionalized substrates.

Controlling the deposition of polymer-wrapped single-walled carbon nanotubes (s-CNTs) onto functionalized substrates can enable the fabrication of s-CNT arrays for semiconductor devices. In this work, we utilize classical atomistic molecular dynamics (MD) simulations to show that a simple descriptor of solvent structure near silica substrates functionalized by a wide variety of self-assembled monolayers (SAMs) can predict trends in the deposition of s-CNTs from toluene. Free energy calculations and experiments indicate that those SAMs that lead to maximum disruption of solvent structure promote deposition to the greatest extent. These findings are consistent with deposition being driven by solvent-mediated interactions that arise from SAM-solvent interactions, rather than direct s-CNT-SAM interactions, and will permit the rapid computational exploration of potential substrate designs for controlling s-CNT deposition and alignment.

Predicting the Physicochemical Properties and Biological Activities of Monolayer-Protected Gold Nanoparticles Using Simulation-Derived Descriptors.

Gold nanoparticles are versatile materials for biological applications because their properties can be modulated by assembling ligands on their surface to form monolayers. However, the physicochemical properties and behaviors of monolayer-protected nanoparticles in biological environments are difficult to anticipate because they emerge from the interplay of ligand-ligand and ligand-solvent interactions that cannot be readily inferred from ligand chemical structure alone. In this work, we demonstrate that quantitative nanostructure-activity relationship (QNAR) models can employ descriptors calculated from molecular dynamics simulations to predict nanoparticle properties and cellular uptake. We performed atomistic molecular dynamics simulations of 154 monolayer-protected gold nanoparticles and calculated a small library of simulation-derived descriptors that capture nanoparticle structural and chemical properties in aqueous solution. We then parametrized QNAR models using interpretable regression algorithms to predict experimental measurements of nanoparticle octanol-water partition coefficients, zeta potentials, and cellular uptake obtained from a curated database. These models reveal that simulation-derived descriptors can accurately predict experimental trends and provide physical insight into what descriptors are most important for obtaining desired nanoparticle properties or behaviors in biological environments. Finally, we demonstrate model generalizability by predicting cell uptake trends for 12 nanoparticles not included in the original data set. These results demonstrate that QNAR models parametrized with simulation-derived descriptors are accurate, generalizable computational tools that could be used to guide the design of monolayer-protected gold nanoparticles for biological applications without laborious trial-and-error experimentation.

Identifying nonadditive contributions to the hydrophobicity of chemically heterogeneous surfaces via dual-loop active learning.

Hydrophobic interactions drive numerous biological and synthetic processes. The materials used in these processes often possess chemically heterogeneous surfaces that are characterized by diverse chemical groups positioned in close proximity at the nanoscale; examples include functionalized nanomaterials and biomolecules, such as proteins and peptides. Nonadditive contributions to the hydrophobicity of such surfaces depend on the chemical identities and spatial patterns of polar and nonpolar groups in ways that remain poorly understood. Here, we develop a dual-loop active learning framework that combines a fast reduced-accuracy method (a convolutional neural network) with a slow higher-accuracy method (molecular dynamics simulations with enhanced sampling) to efficiently predict the hydration free energy, a thermodynamic descriptor of hydrophobicity, for nearly 200 000 chemically heterogeneous self-assembled monolayers (SAMs). Analysis of this dataset reveals that SAMs with distinct polar groups exhibit substantial variations in hydrophobicity as a function of their composition and patterning, but the clustering of nonpolar groups is a common signature of highly hydrophobic patterns. Further molecular dynamics analysis relates such clustering to the perturbation of interfacial water structure. These results provide new insight into the influence of chemical heterogeneity on hydrophobicity via quantitative analysis of a large set of surfaces, enabled by the active learning approach.

Interactions of Bacterial Quorum Sensing Signals with Model Lipid Membranes: Influence of Acyl Tail Structure on Multiscale Response.

Many common bacteria use amphiphilic N-acyl-L-homoserine lactones (AHLs) as signaling molecules to coordinate group behaviors at high cell densities. Past studies demonstrate that AHLs can adsorb to and promote the remodeling of lipid membranes in ways that could underpin cell-cell or host-cell interactions. Here, we report that changes in AHL acyl tail group length and oxidation state (e.g., the presence or absence of a 3-oxo group) can lead to differences in the interactions of eight naturally occurring AHLs in solution and in contact with model lipid membranes. Our results reveal that the presence of a 3-oxo group impacts remodeling when AHLs are placed in contact with supported lipid bilayers (SLBs) of the phospholipid 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC). Whereas AHLs that have 3-oxo groups generally promote the formation of microtubules, AHLs that lack 3-oxo groups generally form hemispherical caps on the surfaces of SLBs. These results are interpreted in terms of the time scales on which AHLs translocate across bilayers to relieve asymmetrical bilayer stress. Quartz crystal microbalance with dissipation measurements also reveal that 3-oxo AHLs associate with DOPC bilayers to a greater extent than their non-3-oxo analogues. In contrast, we observed no monotonic relationship between AHL tail length and bilayer reformation. Finally, we observed that 3-oxo AHLs facilitate greater transport or leakage of molecular cargo across the membranes of DOPC vesicles relative to AHLs without 3-oxo groups, also suggesting increased bilayer disruption and destabilization. These fundamental studies hint at interactions and associated multiscale phenomena that may inform current interpretations of the behaviors of AHLs in biological contexts. These results could also provide guidance useful for the design of new classes of synthetic materials (e.g., sensor elements or drug delivery vehicles) that interact with or respond selectively to communities of bacteria that use 3-oxo AHLs for cell-cell communication.

Predicting Critical Micelle Concentrations for Surfactants Using Graph Convolutional Neural Networks.

Surfactants are amphiphilic molecules that are widely used in consumer products, industrial processes, and biological applications. A critical property of a surfactant is the critical micelle concentration (CMC), which is the concentration at which surfactant molecules undergo cooperative self-assembly in solution. Notably, the primary method to obtain CMCs experimentally-tensiometry-is laborious and expensive. In this study, we show that graph convolutional neural networks (GCNs) can predict CMCs directly from the surfactant molecular structure. In particular, we developed a GCN architecture that encodes the surfactant structure in the form of a molecular graph and trained it using experimental CMC data. We found that the GCN can predict CMCs with higher accuracy on a more inclusive data set than previously proposed methods and that it can generalize to anionic, cationic, zwitterionic, and nonionic surfactants using a single model. Molecular saliency maps revealed how atom types and surfactant molecular substructures contribute to CMCs and found this behavior to be in agreement with physical rules that correlate constitutional and topological information to CMCs. Following such rules, we proposed a small set of new surfactants for which experimental CMCs are not available; for these molecules, CMCs predicted with our GCN exhibited similar trends to those obtained from molecular simulations. These results provide evidence that GCNs can enable high-throughput screening of surfactants with desired self-assembly characteristics.

Reducing Antisolvent Use in the STRAP Process by Enabling a Temperature-Controlled Polymer Dissolution and Precipitation for the Recycling of Multilayer Plastic Films.

The recently reported processing strategy called solvent-targeted recovery and precipitation (STRAP) enables deconstruction of multilayer plastic packaging films into their constituent resins by selective dissolution. It uses a series of solvent washes that are guided by thermodynamic calculations of polymer solubility. In this work, the use of antisolvents in the STRAP process was reduced and solvent mixtures were considered to enable the temperature-controlled dissolution and precipitation of the target polymers in multilayer films. This was considered as a means to further improve the STRAP process and its estimated costs. Two STRAP approaches were compared based on different polymer precipitation techniques: precipitation by the addition of an antisolvent (STRAP-A) and precipitation by decreasing the solvent temperature (STRAP-B). Both approaches were able to separate the constituent polymers in a post-industrial film composed primarily of polyethylene (PE), ethylene vinyl alcohol (EVOH), and polyethylene terephthalate (PET) with near 100 % material efficiency. Technoeconomic analysis indicates that the minimum selling price (MSP) of the recycled resins with STRAP-B is 21.0 % lower than that achieved with STRAP-A. This provides evidence that thermally driven polymer precipitation is an option to reduce the use of antisolvents, making the STRAP process more economically and environmentally attractive. A third process, STRAP-C, was demonstrated with another post-industrial multilayer film of a different composition. The results demonstrate that this process can also recover polymers at similar costs to those of virgin resins, indicating that the STRAP technology is flexible and can remain economically competitive as the plastic feed complexity is increased.

Computational Approach for Rapidly Predicting Temperature-Dependent Polymer Solubilities Using Molecular-Scale Models.

One promising approach to recycle multicomponent plastic waste (e. g., multilayer plastic films) is selective dissolution. Selective dissolution is a solvent-mediated process in which differences in polymer solubility in a carefully chosen solvent system are exploited to recover a target polymer. Here, a computational approach was developed that rapidly predicts temperature-dependent polymer solubilities to guide the design of solvent systems for solvent-mediated polymer recycling. Polymer conformations were obtained from molecular dynamics simulations by modeling the polymer as a short oligomer and then used as input to the conductor-like screening model for real solvents (COSMO-RS) for solubility predictions. Using polyethylene (PE) and ethylene vinyl alcohol (EVOH) as representative polymers, the effect of simulation parameters was systematically studied, and predicted solubilities were found to be in good agreement with experimental measurements. The applicability of the approach was demonstrated by identifying selective solvents for PE and EVOH dissolution from a library of 524 solvents.

Bacterial Quorum Sensing Signals Promote Large-Scale Remodeling of Lipid Membranes.

We report that N-acyl-l-homoserine lactones (AHLs), a class of nonionic amphiphiles that common bacteria use as signals to coordinate group behaviors, can promote large-scale remodeling in model lipid membranes. Characterization of supported lipid bilayers (SLBs) of the phospholipid 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) by fluorescence microscopy and quartz crystal microbalance with dissipation (QCM-D) reveals the well-studied AHL signal 3-oxo-C12-AHL and its anionic head group hydrolysis product (3-oxo-C12-HS) to promote the formation of long microtubules that can retract into hemispherical caps on the surface of the bilayer. These transformations are dynamic, reversible, and dependent upon the head group structure. Additional experiments demonstrate that 3-oxo-C12-AHL can promote remodeling to form microtubules in lipid vesicles and promote molecular transport across bilayers. Molecular dynamics (MD) simulations predict differences in thermodynamic barriers to translocation of these amphiphiles across a bilayer that are reflected in both the type and extent of reformation and associated dynamics. Our experimental observations can thus be interpreted in terms of accumulation and relief of asymmetric stresses in the inner and outer leaflets of a bilayer upon intercalation and translocation of these amphiphiles. Finally, experiments on Pseudomonas aeruginosa, a pathogen that uses 3-oxo-C12-AHL for cell-to-cell signaling, demonstrate that 3-oxo-C12-AHL and 3-oxo-C12-HS can promote membrane remodeling at biologically relevant concentrations and in the absence of other biosurfactants, such as rhamnolipids, that are produced at high population densities. Overall, these results have implications for the roles that 3-oxo-C12-AHL and its hydrolysis product may play in not only mediating intraspecies bacterial communication but also processes such as interspecies signaling and bacterial control of host-cell response. Our findings also provide guidance that could prove useful for the design of synthetic self-assembled materials that respond to bacteria in ways that are useful in the context of sensing, drug delivery, and in other fundamental and applied areas.

Analysis of Charged Peptide Loop-Flipping across a Lipid Bilayer Using the String Method with Swarms of Trajectories.

The hydrophobic core of the lipid bilayer is conventionally considered a barrier to the translocation of charged species such that the translocation of even single ions occurs on long time scales. In contrast, experiments have revealed that some materials, including peptides, proteins, and nanoparticles, can translocate multiple charged moieties across the bilayer on experimentally relevant time scales. Understanding the molecular mechanisms underlying this behavior is challenging because resolving corresponding free-energy landscapes with molecular simulation techniques is computationally expensive. To address this challenge, we use atomistic molecular dynamics simulations with the swarms-of-trajectories (SOT) string method to analyze charge translocation pathways across single-component lipid bilayers as a function of multiple collective variables. We first demonstrate that the SOT string method can reproduce the free-energy barrier for the translocation of a charged lysine amino acid analogue in good agreement with the literature. We then obtain minimum free-energy pathways for the translocation, or flipping, of charged peptide loops across the lipid bilayer by utilizing trajectories from prior temperature-accelerated molecular dynamics (TAMD) simulations as initial configurations. The corresponding potential of mean force calculations reveal that the protonation of a central membrane-exposed aspartate residue substantially reduces the free-energy barrier for flipping charged loops by modulating the water content of the bilayer. These results provide new insight into the thermodynamics underlying loop-flipping processes and highlight how the combination of TAMD and the SOT string method can be used to understand complex charge translocation mechanisms.

Lipophilicity of Cationic Ligands Promotes Irreversible Adsorption of Nanoparticles to Lipid Bilayers.

A mechanistic understanding of the influence of the surface properties of engineered nanomaterials on their interactions with cells is essential for designing materials for applications such as bioimaging and drug delivery as well as for assessing nanomaterial safety. Ligand-coated gold nanoparticles have been widely investigated because their highly tunable surface properties enable investigations into the effect of ligand functionalization on interactions with biological systems. Lipophilic ligands have been linked to adverse biological outcomes through membrane disruption, but the relationship between ligand lipophilicity and membrane interactions is not well understood. Here, we use a library of cationic ligands coated on 2 nm gold nanoparticles to probe the impact of ligand end group lipophilicity on interactions with supported phosphatidylcholine lipid bilayers as a model for cytoplasmic membranes. Nanoparticle adsorption to and desorption from the model membranes were investigated by quartz crystal microbalance with dissipation monitoring. We find that nanoparticle adsorption to model membranes increases with ligand lipophilicity. The effects of ligand structure on gold nanoparticle attachment were further analyzed using atomistic molecular dynamics simulations, which showed that the increase in ligand lipophilicity promotes ligand intercalation into the lipid bilayer. Together, the experimental and simulation results could be described by a two-state model that accounts for the initial attachment and subsequent conversion to a quasi-irreversibly bound state. We find that only nanoparticles coated with the most lipophilic ligands in our nanoparticle library undergo conversion to the quasi-irreversible state. We propose that the initial attachment is governed by interaction between the ligands and phospholipid tail groups, whereas conversion into the quasi-irreversibly bound state reflects ligand intercalation between phospholipid tail groups and eventual lipid extraction from the bilayer. The systematic variation of ligand lipophilicity enabled us to demonstrate that the lipophilicity of cationic ligands correlates with nanoparticle-bilayer adsorption and suggested that changing the nonpolar ligand R group promotes a mechanism of ligand intercalation into the bilayer associated with irreversible adsorption.