The Interplay of Ligand Properties and Core Size Dictates the Hydrophobicity of Monolayer-Protected Gold Nanoparticles

Influence of branched ligand architectures on nanoparticle interactions with lipid bilayers

Gold nanoparticles functionalized with organic cationic ligands have shown promise as biomedical agents, but their interactions with cellular membranes are not yet well-understood and design rules for ligands that promote desired cellular interactions are lacking. Past experimental studies have demonstrated that ligand lipophilicity, quantified by the ligand end group partition coefficient, can be used as a descriptor for predicting nano-bio interactions, but such a descriptor is incapable accounting for ligand architecture, such as chain branching. To probe the effects of ligand end group architecture on ligand-lipid interactions, we perform molecular dynamics simulations to investigate how ligand alkyl chain branching modulates the thermodynamics and mechanisms of nanoparticle adsorption to lipid membranes. We designed four pairs of 2 nm diameter gold nanoparticles where each pair had ligand end groups with similar lipophilicity but varying alkyl chain architecture (e.g., one long alkyl chain vs. two short chains) to isolate branching effects from lipophilicity. Free energy calculations and mechanistic insight revealed that alkyl end group branching can decrease free energy barriers for adsorption by disrupting ligand monolayer packing, increasing end group protrusions that lead to favorable ligand intercalation with minimal membrane disruption. Furthermore, increased end group branching promotes adsorption by increasing the exposure of nonpolar surface area to water, which results in a greater reduction of free energy upon exposure to the nonpolar core of the lipid bilayer. These results show that ligand chain architecture can modulate nano-bio interactions, limiting the exclusive use of lipophilicity as a descriptor to predict cellular uptake of surface-functionalized nanoparticles.

Probing the Influence of Hydrophobicity of Modified Gold Nanoparticles in Modulating the Lipid Surface Behavior Using Vibrational Sum Frequency Generation Spectroscopy

A deep understanding of how the surface modifications of nanoparticles impact their interactions with cell membranes is vital for advancing safe and effective biomedical applications. Among the pivotal factors governing these interactions, the hydrophobicity of nanoparticles plays a crucial role, predominantly driven by the hydrophobic interactions with the cell membrane. Herein, we study the influence of the hydrophobic alkyl chain length of thiol-capped gold nanoparticles (GNPs) on lipid surfaces with the help of vibrational sum frequency generation spectroscopy. We have utilized the zwitterionic 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) lipid monolayer as a representative model of cell membranes on the water surface. Our findings revealed that GNPs capped with the thiol ligand having a shorter alkyl chain such as heptanethiol (HT, C7) show minimal changes in the C-H stretching vibrations while interacting with the lipid monolayer. These observations could be attributed to the perturbation of the lipid chain due to hydrophobic-hydrophobic interactions between the alkyl chain of thiol-capped GNPs and the hydrophobic group of the lipid membrane or simply by the adsorption of GNPs at the interface without disrupting the monolayer structure. However, with increasing the chain length of thiol-capped GNPs from decanethiol (DDT, C10) to octadecanethiol (ODT, C18), the extent of spectral change in the C-H stretching vibration is increased. The controlled experiment performed with the deuterated lipids conforms that the changes observed in the C-H stretching vibration after adding HT (C7) GNPs are only because of their presence in the surface without altering the monolayer structure. However, in the case of DT (C10) and DDT (C12) GNPs, the strong hydrophobic interactions between the monolayer and the alkyl chain of the thiol-capped GNPs result in the increased orientational order of the monolayer. Moreover, in the case of ODT (C18) GNPs, the very long alkyl chain induces pronounced perturbations in the monolayer structure with net disordering of the monolayer. These observations are further supported by the spectral changes observed in the O-H vibration of the interfacial water molecules. Our findings reveal the crucial role of the hydrophobic nature of GNPs in influencing the interface. Understanding these effects is crucial for drug delivery applications and improving the stability and effectiveness of lipid-based systems.

Nanocrystal Assemblies: Current Advances and Open Problems

We explore the potential of nanocrystals (a term used equivalently to nanoparticles) as building blocks for nanomaterials, and the current advances and open challenges for fundamental science developments and applications. Nanocrystal assemblies are inherently multiscale, and the generation of revolutionary material properties requires a precise understanding of the relationship between structure and function, the former being determined by classical effects and the latter often by quantum effects. With an emphasis on theory and computation, we discuss challenges that hamper current assembly strategies and to what extent nanocrystal assemblies represent thermodynamic equilibrium or kinetically trapped metastable states. We also examine dynamic effects and optimization of assembly protocols. Finally, we discuss promising material functions and examples of their realization with nanocrystal assemblies.

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.

Pickering Emulsions of Thermotropic Liquid Crystals Stabilized by Amphiphilic Gold Nanoparticles

We report emulsions of thermotropic liquid crystals (LCs) in water that are stabilized using amphiphilic gold nanoparticles (AuNPs) and retain their ability to respond to aqueous analytes for extended periods (e.g., up to 1 year after preparation). These LC emulsions exhibit exceptional colloidal stability that results from the adsorption of AuNPs that are functionalized with thiol-terminated poly(ethylene glycol) (PEG-thiol) and hexadecanethiol (C16-thiol) to LC droplet interfaces. These stabilized LC emulsions respond to the presence of model anionic (SDS), cationic (C12TAB), and nonionic (C12E4) surfactants in the surrounding aqueous media, as evidenced by ordering transitions in the LC droplets that can be readily observed using polarized light microscopy. Our results reveal significant differences in the sensitivity of the stabilized LC droplets toward each of these analytes. In particular, these stabilized droplets can detect the cationic C12TAB at concentrations that are lower than those required for bare LC droplets under similar experimental conditions (0.5 and 2 mM, respectively). These results demonstrate an enhanced sensitivity of the LC toward C12TAB when the PEG/C16-thiol-coated AuNPs are adsorbed at LC droplet interfaces. In contrast, the concentrations of SDS required to observe optical transformations in the stabilized LC droplets are higher than those required for the bare LC droplets, suggesting that the presence of the PEG/C16-thiol AuNPs reduces the sensitivity of the LC toward this analyte. When combined, our results show that this Pickering stabilization approach using amphiphilic AuNPs as stabilizing agents for LC-in-water emulsions provides a promising platform for developing LC droplet-based optical sensors with long-term colloidal stability as well as opportunities to tune the sensitivity and selectivity of the response to target aqueous analytes.

DNA-Based Gold Nanoparticle Assemblies: From Structure Constructions to Sensing Applications

Gold nanoparticles (Au NPs) have become one of the building blocks for superior assembly and device fabrication due to the intrinsic, tunable physical properties of nanoparticles. With the development of DNA nanotechnology, gold nanoparticles are organized in a highly precise and controllable way under the mediation of DNA, achieving programmability and specificity unmatched by other ligands. The successful construction of abundant gold nanoparticle assembly structures has also given rise to the fabrication of a wide range of sensors, which has greatly contributed to the development of the sensing field. In this review, we focus on the progress in the DNA-mediated assembly of Au NPs and their application in sensing in the past five years. Firstly, we highlight the strategies used for the orderly organization of Au NPs with DNA. Then, we describe the DNA-based assembly of Au NPs for sensing applications and representative research therein. Finally, we summarize the advantages of DNA nanotechnology in assembling complex Au NPs and outline the challenges and limitations in constructing complex gold nanoparticle assembly structures with tailored functionalities.

Solvent Effects in Ligand Stripping Behavior of Colloidal Nanoparticles

Inorganic colloidal nanoparticle (NP) properties can be tuned by stripping stabilizing ligands using a poor solvent. However, the mechanism behind ligand stripping is poorly understood, in part because in situ measurements of ligand stripping are challenging at the nanoscale. Here, we investigate ethanol solvent-mediated oleylamine ligand stripping from magnetite (Fe₃O₄) NPs in different compositions of ethanol/hexane mixtures using atomistic molecular dynamics (MD) simulations and thermogravimetric analysis (TGA). Our study elucidates a complex interplay of ethanol interactions with system components and indicates the existence of a threshold concentration of ∼34 vol % ethanol, above which ligand stripping saturates. Moreover, hydrogen bonding between ethanol and stripped ligands inhibits subsequent readsorption of the ligands on the NP surface. A proposed modification of the Langmuir isotherm explains the role of the enthalpy of mixing of the ligands and solvents on the ligand stripping mechanism. A good agreement between the MD predictions and TGA measurements of ligand stripping from Fe₃O₄ NPs validates the simulation observations. Our findings demonstrate that the ligand coverage of NPs can be controlled by using a poor solvent below the threshold concentration and highlight the importance of ligand-solvent interactions that modulate the properties of colloidal NPs. The study also provides an approach for a detailed in silico study of ligand stripping and exchange from colloidal NPs that are crucial for applications of NPs spanning self-assembly, optoelectronics, nanomedicine, and catalysis.

Janus Ligand-Tethered Nanoparticles at Liquid-Liquid Interfaces

We investigate the structural properties of Janus ligand-tethered nanoparticles at liquid-liquid interfaces using coarse-grained molecular dynamics simulations. The effect of interactions between different chains and liquids is discussed. We consider the Janus particles with symmetrical interactions with the liquids which correspond to supplementary wettability and particles with uncorrelated interactions. Simulation results indicate that the Janus hairy particles trapped in the interface region have different configurations characterized by the vertical displacement distance, the orientation of the Janus line relative to the interface, and the particle shape. The Janus hairy particles present abundant morphologies, including dumbbell-like and typical core-shell, at the interface. The shape of adsorbed particles is analyzed in detail. The simulation data are compared with those predicted by a simple phenomenological approach. This work can promote the applications of Janus hairy particles in nanotechnology.

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.

Hybrid Nanoparticles at Fluid-Fluid Interfaces: Insight from Theory and Simulation

Hybrid nanoparticles that combine special properties of their different parts have numerous applications in electronics, optics, catalysis, medicine, and many others. Of the currently produced particles, Janus particles and ligand-tethered (hairy) particles are of particular interest both from a practical and purely cognitive point of view. Understanding their behavior at fluid interfaces is important to many fields because particle-laden interfaces are ubiquitous in nature and industry. We provide a review of the literature, focusing on theoretical studies of hybrid particles at fluid-fluid interfaces. Our goal is to give a link between simple phenomenological models and advanced molecular simulations. We analyze the adsorption of individual Janus particles and hairy particles at the interfaces. Then, their interfacial assembly is also discussed. The simple equations for the attachment energy of various Janus particles are presented. We discuss how such parameters as the particle size, the particle shape, the relative sizes of different patches, and the amphiphilicity affect particle adsorption. This is essential for taking advantage of the particle capacity to stabilize interfaces. Representative examples of molecular simulations were presented. We show that the simple models surprisingly well reproduce experimental and simulation data. In the case of hairy particles, we concentrate on the effects of reconfiguration of the polymer brushes at the interface. This review is expected to provide a general perspective on the subject and may be helpful to many researchers and technologists working with particle-laden layers.

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.

Spotting Local Environments in Self-Assembled Monolayer-Protected Gold Nanoparticles

Organic-inorganic (O-I) nanomaterials are versatile platforms for an incredible high number of applications, ranging from heterogeneous catalysis to molecular sensing, cell targeting, imaging, and cancer diagnosis and therapy, just to name a few. Much of their potential stems from the unique control of organic environments around inorganic sites within a single O-I nanomaterial, which allows for new properties that were inaccessible using purely organic or inorganic materials. Structural and mechanistic characterization plays a key role in understanding and rationally designing such hybrid nanoconstructs. Here, we introduce a general methodology to identify and classify local (supra)molecular environments in an archetypal class of O-I nanomaterials, i.e., self-assembled monolayer-protected gold nanoparticles (SAM-AuNPs). By using an atomistic machine-learning guided workflow based on the Smooth Overlap of Atomic Positions (SOAP) descriptor, we analyze a collection of chemically different SAM-AuNPs and detect and compare local environments in a way that is agnostic and automated, i.e., with no need of a priori information and minimal user intervention. In addition, the computational results coupled with experimental electron spin resonance measurements prove that is possible to have more than one local environment inside SAMs, being the thickness of the organic shell and solvation primary factors in the determining number and nature of multiple coexisting environments. These indications are extended to complex mixed hydrophilic-hydrophobic SAMs. This work demonstrates that it is possible to spot and compare local molecular environments in SAM-AuNPs exploiting atomistic machine-learning approaches, establishes ground rules to control them, and holds the potential for the rational design of O-I nanomaterials instructed from data.

Shape Transformations and Self-Assembly of Hairy Particles under Confinement

Molecular dynamics simulations are used to investigate the behavior of polymer-tethered nanoparticles between two inert or attractive walls. The confinement in pores creates new possibilities for controlling the shape transformation of individual hairy particles and their self-organization. We introduce a minimalistic model of the system; only chain-wall interactions are assumed to be attractive, while the others are softly repulsive. We show how the shape of isolated particles can be controlled by changing the wall separation and the strength of the interaction with the surfaces. For attractive walls, we found two types of structures, “bridges” and “mounds”. The first structures are similar to flanged spools in which the chains are connected with both walls and form bridges between them. We observed various bridges, symmetrical and asymmetrical spools, hourglasses, and pillars. The bridge-like structures can be “nano-oscillators” in which the cores jump from one wall to the other. We also study the self-assembly of a dense fluid of hairy particles in slit-like pores and analyze how the system morphology depends on interactions with the surfaces and the wall separation. The hairy particles form layers parallel to the walls. Different ordered structures, resembling two-dimensional crystalline lattices, are reported. We demonstrate that hairy particles are a versatile soft component forming a variety of structures in the slits.

Binding of polar and hydrophobic molecules at the LiCoO2 (001)-water interface: force field development and molecular dynamics simulations

A classical model in the framework of the INTERFACE force field has been developed for treating the LiCoO₂ (LCO) (001)/water interface. In comparison to ab initio molecular dynamics (MD) simulations based on density functional theory, MD simulations using the classical model lead to generally reliable descriptions of interfacial properties, such as the density distribution of water molecules. Water molecules in close contact with the LCO surface form a strongly adsorbed layer, which leads to a free energy barrier for the adsorption of polar or charged molecules to the LCO surface. Moreover, due to the strong hydrogen bonding interactions with the LCO surface, the first water layer forms an interface that exhibits hydrophobic characters, leading to favorable adsorption of non-polar molecules to the interface. Therefore, despite its highly polar nature, the LCO (001) surface binds not only polar/charged but also non-polar solutes. As an application, the model is used to analyze the adsorption of reduced nicotinamide adenine dinucleotide (NADH) and its molecular components to the LCO (001) surface in water. The results suggest that recently observed redox activity of NADH at the LCO/water interface was due to the co-operativity between the ribose component, which drives binding to the LCO surface, and the nicotinamide moiety, which undergoes oxidation.

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.

Adsorption of Polymer-Tethered Particles on Solid Surfaces

We explore the behavior of polymer-tethered particles on solid surfaces using coarse-grained molecular dynamics simulations. Segment-segment, segment-core, and core-core interactions are assumed to be purely repulsive, while the segment-substrate interactions are attractive. We analyze changes in the internal structure of single hairy particles on the surfaces with the increasing strength of the segment-substrate interactions. For this purpose, we calculate the density profiles along the x, y, z axes and the mass dipole moments. The adsorbed hairy particles are found to be symmetrical in a plane parallel to the substrate but strongly asymmetric in the vertical direction. On stronger adsorbents, the particle canopies become flattened and the cores lie closer to the wall. We consider the adsorption of hairy nanoparticles dispersed in systems of different initial particle densities. We show how the strength of segment-substrate interactions affects the structure of the adsorbed phase, the particle-wall potential of the average force, the excess adsorption isotherms, and the real adsorption isotherms.

Therapeutic nanoparticles in the brain: A review of types, physicochemical properties and challenges

One of the main obstacles in the treatment of neurological diseases, perhaps the biggest one, is the delivery of therapeutic compounds to the central nervous system, and nanoparticles are promising tools to overcome this challenge. Different types of nanoparticles may be used as delivery systems, including liposomes, carbon nanotubes, and dendrimers. Nevertheless, these nanoparticles must display characteristics to be useful in brain drug delivery, such as stability, permeability to blood vessels, biocompatibility, and specificity. All of these aspects are intrinsically related to the physicochemical properties of nanoformulations: size, composition, electric charge, hydrophobicity, mucoadherence, permeability to the blood-brain barrier, and many others. Furthermore, there are challenging hindrances involved in the development and application of nanoparticles - hence the importance of studying and understanding these pharmaceutical tools.

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.

Adsorption on Ligand-Tethered Nanoparticles

We use coarse-grained molecular dynamics simulations to study adsorption on ligand-tethered particles. Nanoparticles with attached flexible and stiff ligands are considered. We discuss how the excess adsorption isotherm, the thickness of the polymer corona, and its morphology depend on the number of ligands, their length, the size of the core, and the interaction parameters. We investigate the adsorption-induced structural transitions of polymer coatings. The behavior of systems involving curved and flat "brushes" is compared.

Computational Insight on the Interaction of Common Blood Proteins with Gold Nanoparticles

Protein interactions with engineered gold nanoparticles (AuNPs) and the consequent formation of the protein corona are very relevant and poorly understood biological phenomena. The nanoparticle coverage affects protein binding modalities, and the adsorbed protein sites influence interactions with other macromolecules and cells. Here, we studied four common blood proteins, i.e., hemoglobin, serum albumin, α1-antiproteinase, and complement C3, interacting with AuNPs covered by hydrophobic 11-mercapto-1-undecanesulfonate (MUS). We use Molecular Dynamics and the Martini coarse−grained model to gain quantitative insight into the kinetics of the interaction, the physico-chemical characteristics of the binding site, and the nanoparticle adsorption capacity. Results show that proteins bind to MUS−capped AuNPs through strong hydrophobic interactions and that they adapt to the AuNP surfaces to maximize the contact surface, but no dramatic change in the secondary structure of the proteins is observed. We suggest a new method to calculate the maximum adsorption capacity of capped AuNPs based on the effective surface covered by each protein, which better represents the realistic behavior of these systems.

Molecular Recognition by Gold Nanoparticle-Based Receptors as Defined through Surface Morphology and Pockets Fingerprint

Ligand shell-protected gold nanoparticles can form nanoreceptors that recognize and bind to specific molecules in solution, with numerous potential innovative applications in science and industry. At this stage, the challenge is to rationally design such nanoreceptors to optimize their performance and boost their further development. Toward this aim, we have developed a new computational tool, Nanotron. This allows the analysis of molecular dynamics simulations of ligand shell-protected nanoparticles to define their exact surface morphology and pocket fingerprints of binding cavities in the coating monolayer. Importantly, from dissecting the well-characterized pairing formed by the guest salicylate molecule and specific host nanoreceptors, our work reveals that guest binding at such nanoreceptors occurs via preformed deep pockets in the host. Upon the interaction with the guest, such pockets undergo an induced-fit-like structural optimization for best host-guest fitting. Our findings and methodological advancement will accelerate the rational design of new-generation nanoreceptors.

Molecular Dynamics Simulations of a Catalytic Multivalent Peptide-Nanoparticle Complex

Molecular modeling of a supramolecular catalytic system is conducted resulting from the assembling between a small peptide and the surface of cationic self-assembled monolayers on gold nanoparticles, through a multiscale iterative approach including atomistic force field development, flexible docking with Brownian Dynamics and µs-long Molecular Dynamics simulations. Self-assembly is a prerequisite for the catalysis, since the catalytic peptides do not display any activity in the absence of the gold nanocluster. Atomistic simulations reveal details of the association dynamics as regulated by defined conformational changes of the peptide due to peptide length and sequence. Our results show the importance of a rational design of the peptide to enhance the catalytic activity of peptide-nanoparticle conjugates and present a viable computational approach toward the design of enzyme mimics having a complex structure-function relationship, for technological and nanomedical applications.