Molecular-Dynamics-Simulation-Directed Rational Design of Nanoreceptors with Targeted Affinity

Water-stable boroxine structure with dynamic covalent bonds

Boroxines are significant structures in the production of covalent organic frameworks, anion receptors, self-healing materials, and others. However, their utilization in aqueous media is a formidable task due to hydrolytic instability. Here we report a water-stable boroxine structure discovered from 2-hydroxyphenylboronic acid. We find that, under ambient environments, 2-hydroxyphenylboronic acid undergoes spontaneous dehydration to form a dimer with dynamic covalent bonds and aggregation-induced enhanced emission activity. Intriguingly, upon exposure to water, the dimer rapidly transforms into a boroxine structure with excellent pH stability and water-compatible dynamic covalent bonds. Building upon these discoveries, we report the strong binding capacity of boroxines toward fluoride ions in aqueous media, and develop a boroxine-based hydrogel with high acid-base stability and reversible gel-sol transition. This discovery of the water-stable boroxine structure breaks the constraint of boroxines not being applicable in aqueous environments, opening a new era of researches in boroxine chemistry.

Achieving Molecular Recognition of Structural Analogues in Surface-Enhanced Raman Spectroscopy: Inducing Charge and Geometry Complementarity to Mimic Molecular Docking

Molecular recognition of complex isomeric biomolecules remains challenging in surface-enhanced Raman scattering (SERS) spectroscopy due to their small Raman cross-sections and/or poor surface affinities. To date, the use of molecular probes has achieved excellent molecular sensitivities but still suffers from poor spectral specificity. Here, we induce "charge and geometry complementarity" between probe and analyte as a key strategy to achieve high spectral specificity for effective SERS molecular recognition of structural analogues. We employ 4-mercaptopyridine (MPY) as the probe, and chondroitin sulfate (CS) disaccharides with isomeric sulfation patterns as our proof-of-concept study. Our experimental and in silico studies reveal that "charge and geometry complementarity" between MPY's binding pocket and the CS sulfation patterns drives the formation of site-specific, multidentate interactions at the respective CS isomerism sites, which "locks" each CS in its analogue-specific complex geometry, akin to molecular docking events. Leveraging the resultant spectral fingerprints, we achieve > 97 % classification accuracy for 4 CSs and 5 potential structural interferences, as well as attain multiplex CS quantification with < 3 % prediction error. These insights could enable practical SERS differentiation of biologically important isomers to meet the burgeoning demand for fast-responding applications across various fields such as biodiagnostics, food and environmental surveillance.

Molecular Mechanisms Underlying Detection Sensitivity in Nanoparticle-Assisted NMR Chemosensing

Nanoparticle-assisted nuclear magnetic resonance (NMR) chemosensing exploits monolayer-protected nanoparticles as supramolecular hosts to detect small molecules in complex mixtures via nuclear Overhauser effect experiments with detection limits down to the micromolar range. Still, the structure-sensitivity relationships at the basis of such detection limits are little understood. In this work, we integrate NMR spectroscopy and atomistic molecular dynamics simulations to examine the covariates that affect the sensitivity of different NMR chemosensing experiments [saturation transfer difference (STD), water STD, and high-power water-mediated STD]. Our results show that the intensity of the observed signals correlates with the number and duration of the spin-spin interactions between the analytes and the nanoparticles and/or between the analytes and the nanoparticles' solvation molecules. In turn, these parameters depend on the location and dynamics of each analyte inside the monolayer. This insight will eventually facilitate the tailoring of experimental and computational setups to the analyte's chemistry, making NMR chemosensing an even more effective technique in practical use.

On-nanoparticle monolayers as a solute-specific, solvent-like phase

In addition to modifying surface properties, self-assembled monolayers, SAMs, on nanoparticles can selectively incorporate small molecules from the surrounding solution. This selectivity has been used in the design of substrate-specific catalytic systems but its degree has not been quantified. This work uses catalytic centers embedded in on-nanoparticle hydrophobic SAMs to monitor and quantify the partitioning of molecules between the bulk solvent and these monolayers. A combination of experiments and theory allows us to relate the logarithm of the incorporation-into-SAM constant to the "bulk" log P values, characterizing the incoming substrates. These results are in line with classic, semi-empirical linear free energy relationships between partitioning solvent systems; in this way, they substantiate the view of nanoscopic on-particle SAMs acting akin to a bulk solvent phase.

NanoModeler CG: A Tool for Modeling and Engineering Functional Nanoparticles at a Coarse-Grained Resolution

Functionalized metal nanoparticles (NPs) are macromolecular assemblies with a tunable physicochemical profile that makes them interesting for biotechnology, materials science, and energy conversion. In this regard, molecular simulations offer a way to scrutinize the structural and dynamical features of monolayer-protected NPs and their interactions with relevant matrices. Previously, we developed NanoModeler, a webserver that automates the preparation of functionalized gold NPs for atomistic molecular dynamics (MD) simulations. Here, we present NanoModeler CG (www.nanomodeler.it), a new release of NanoModeler that now also allows the building and parametrizing of monolayer-protected metal NPs at a coarse-grained (CG) resolution. This new version extends our original methodology to NPs of eight different core shapes, conformed by up to 800,000 beads and coated by eight different monolayer morphologies. The resulting topologies are compatible with the Martini force field but are easily extendable to any other set of parameters parsed by the user. Finally, we demonstrate NanoModeler CG's capabilities by reproducing experimental structural features of alkylthiolated NPs and rationalizing the brush-to-mushroom phase transition of PEGylated anionic NPs. By automating the construction and parametrization of functionalized NPs, the NanoModeler series offers a standardized way to computationally model monolayer-protected nanosized systems.

Current Trends and Changes in Use of Membrane Molecular Dynamics Simulations within Academia and the Pharmaceutical Industry

There has been an almost exponential increase in the use of molecular dynamics simulations in basic research and industry over the last 5 years, with almost a doubling in the number of publications each year. Many of these are focused on neurological membranes, and biological membranes in general, applied to the medical industry. A smaller portion have utilized membrane simulations to answer more basic questions related to the function of specific proteins, chemicals or biological processes. This review covers some newer studies, alongside studies from the last two decades, to determine changes in the field. Some of these are basic, while others are more profound, such as multi-component embedded membrane machinery. It is clear that many facets of the discipline remain the same, while the focus on and uses of the technology are broadening in scope and utilization as a general research tool. Analysis of recent literature provides an overview of the current methodologies, covers some of the recent trends or advances and tries to make predictions of the overall path membrane molecular dynamics will follow in the coming years. In general, the overview presented is geared towards the general scientific community, who may wish to introduce the use of these methodologies in light of these changes, making molecular dynamic simulations more feasible for general scientific or medical research.

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.

Fluorescent Sensing of Glutathione and Related Bio-Applications

Glutathione (GSH), as the most abundant low-molecular-weight biological thiol, plays significant roles in vivo. Abnormal GSH levels have been demonstrated to be related to the dysfunction of specific physiological activities and certain kinds of diseases. Therefore, the sensing of GSH is emerging as a critical issue. Cancer, with typical high morbidity and mortality, remains one of the most serious diseases to threaten public health. As it is clear that much more concentrated GSH is present at tumor sites than at normal sites, the in vivo sensing of GSH offers an option for the early diagnosis of cancer. Moreover, by monitoring the amounts of GSH in specific microenvironments, effective diagnosis of ROS levels, neurological diseases, or even stroke has been developed as well. In this review, we focus on the fluorescent methodologies for GSH detection, since they can be conveniently applied in living systems. First, the fluorescent sensing methods are introduced. Then, the principles for fluorescent sensing of GSH are discussed. In addition, the GSH-sensing-related biological applications are reviewed. Finally, the future opportunities in in the areas of fluorescent GSH sensing-in particular, fluorescent GSH-sensing-prompted disease diagnosis-are addressed.

A Bionic-Homodimerization Strategy for Optimizing Modulators of Protein-Protein Interactions: From Statistical Mechanics Theory to Potential Clinical Translation

Emerging protein-protein interaction (PPI) modulators have brought out exciting ability as therapeutics in human diseases, but its clinical translation has been greatly hampered by the limited affinity. Inspired by the homodimerize structure of antibody, the homodimerization contributes hugely to generating the optimized affinity is conjectured. Herein, a statistical-mechanics-theory-guided method is established to quantize the affinity of ligands with different topologies through analyzing the change of enthalpy and the loss of translational and rotational entropies. A peptide modulator for p53-MDM2 termed CPAP is used to homodimerize connecting, and this simple homodimerization can significantly increase the affinity. To realize the cellular internalization and tumor accumulation, Dimer CPAP and Mono CPAP are nanoengineered into gold(I)-CPAP supermolecule by the aurophilic interaction-driven self-assembly. Nano-Dimer CPAP potently suppressed tumor growth in lung cancer allograft model and a patient-derived xenograft model in more action than Nano-Mono CPAP, while keeping a favorable drug safety profile. This work not only presents a physico-mechanical method for calculating the affinity of PPI modulators, but also provides a simple yet robust homodimerization strategy to optimize the affinity of PPI modulators.

Engineering the Aggregation of Dyes on Ligand-Shell Protected Gold Nanoparticles to Promote Plexcitons Formation

The ability to control the light–matter interaction in nanosystems is a major challenge in the field of innovative photonics applications. In this framework, plexcitons are promising hybrid light–matter states arising from the strong coupling between plasmonic and excitonic materials. However, strategies to precisely control the formation of plexcitons and to modulate the coupling between the plasmonic and molecular moieties are still poorly explored. In this work, the attention is focused on suspensions of hybrid nanosystems prepared by coupling cationic gold nanoparticles to tetraphenyl porphyrins in different aggregation states. The role of crucial parameters such as the dimension of nanoparticles, the pH of the solution, and the ratio between the nanoparticles and dye concentration was systematically investigated. A variety of structures and coupling regimes were obtained. The rationalization of the results allowed for the suggestion of important guidelines towards the control of plexcitonic systems.

Role of Ionic Strength in the Formation of Stable Supramolecular Nanoparticle-Protein Conjugates for Biosensing

Monolayer-protected gold nanoparticles (AuNPs) exhibit distinct physical and chemical properties depending on the nature of the ligand chemistry. A commonly employed NP monolayer comprises hydrophobic molecules linked to a shell of PEG and terminated with functional end group, which can be charged or neutral. Different layers of the ligand shell can also interact in different manners with proteins, expanding the range of possible applications of these inorganic nanoparticles. AuNP-fluorescent Green Fluorescent Protein (GFP) conjugates are gaining increasing attention in sensing applications. Experimentally, their stability is observed to be maintained at low ionic strength conditions, but not at physiologically relevant conditions of higher ionic strength, limiting their applications in the field of biosensors. While a significant amount of fundamental work has been done to quantify electrostatic interactions of colloidal nanoparticle at the nanoscale, a theoretical description of the ion distribution around AuNPs still remains relatively unexplored. We perform extensive atomistic simulations of two oppositely charged monolayer-protected AuNPs interacting with fluorescent supercharged GFPs co-engineered to have complementary charges. These simulations were run at different ionic strengths to disclose the role of the ionic environment on AuNP–GFP binding. The results highlight the capability of both AuNPs to intercalate ions and water molecules within the gold–sulfur inner shell and the different tendency of ligands to bend inward allowing the protein to bind not only with the terminal ligands but also the hydrophobic alkyl chains. Different binding stability is observed in the two investigated cases as a function of the ligand chemistry.

Selective NMR detection of N-methylated amines using cavitand-decorated silica nanoparticles as receptors

We report a strategy for the realization of NMR chemosensors based on the spontaneous self-assembly of lower rim pyridinium-functionalized tetraphopshonate cavitands on commercial silica nanoparticles. These nanohybrids enable the selective...

Thiolate end-group regulates ligand arrangement, hydration and affinity for small compounds in monolayer-protected gold nanoparticles

The ability to control the properties of monolayer protected gold nanoparticles (MPNPs) discloses unrevealed features stemming from collective properties of the ligands forming the monolayer and presents opportunities to design new materials. To date, the influence of ligand end-group size and capacity to form hydrogen bonds on structure and hydration of small MPNPs (<5 nm) has been poorly studied. Here, we show that both features determine ligands order, solvent accessibility, capacity to host hydrophobic compounds and interfacial properties of MPNPs. The polarity perceived by a radical probe and its binding constant with the monolayer investigated by electron spin resonance is rationalized by molecular dynamics simulations, which suggest that larger space-filling groups - trimethylammonium, zwitterionic and short polyethylene glycol - favor a radial organization of the thiolates, whereas smaller groups - as sulfonate - promote the formation of bundles. Zwitterionic ligands create a surface network of hydrogen bonds, which affects nanoparticle hydrophobicity and maximize the partition equilibrium constant of the probe. This study discloses the role of the chemistry of the end-group on monolayer features with effects that span from molecular- to nano-scale and opens the door to a shift in the conception of new MPNPs exploiting the end-group as a novel design motif.

On the Metal-Aided Catalytic Mechanism for Phosphodiester Bond Cleavage Performed by Nanozymes

Recent studies have shown that gold nanoparticles (AuNPs) functionalized with Zn(II) complexes can cleave phosphate esters and nucleic acids. Remarkably, such synthetic nanonucleases appear to catalyze metal (Zn)-aided hydrolytic reactions of nucleic acids similar to metallonuclease enzymes. To clarify the reaction mechanism of these nanocatalysts, here we have comparatively analyzed two nanonucleases with a >10-fold difference in the catalytic efficiency for the hydrolysis of the 2-hydroxypropyl-4-nitrophenylphosphate (HPNP, a typical RNA model substrate). We have used microsecond-long atomistic simulations, integrated with NMR experiments, to investigate the structure and dynamics of the outer coating monolayer of these nanoparticles, either alone or in complex with HPNP, in solution. We show that the most efficient one is characterized by coating ligands that promote a well-organized monolayer structure, with the formation of solvated bimetallic catalytic sites. Importantly, we have found that these nanoparticles can mimic two-metal-ion enzymes for nucleic acid processing, with Zn ions that promote HPNP binding at the reaction center. Thus, the two-metal-ion-aided hydrolytic strategy of such nanonucleases helps in explaining their catalytic efficiency for substrate hydrolysis, in accordance with the experimental evidence. These mechanistic insights reinforce the parallelism between such functionalized AuNPs and proteins toward the rational design of more efficient catalysts.

Association Mechanism of Peptide-Coated Metal Nanoparticles with Model Membranes: A Coarse-Grained Study

Functionalized metal nanoparticles (NPs) hold great promise as innovative tools in nanomedicine. However, one of the main challenges is how to optimize their association with the cell membrane, which is critical for their effective delivery. Recent findings show high cellular uptake rates for NPs coated with the polycationic cell-penetrating peptide gH625-644 (gH), although the underlying internalization mechanism is poorly understood. Here, we use extended coarse-grained simulations and free energy calculations to study systems that simultaneously include metal NPs, peptides, lipids, and sterols. In particular, we investigate the first encounter between multicomponent model membranes and 2.5 nm metal NPs coated with gH (gHNPs), based on the evidence from scanning transmission electron microscopy. By comparing multiple membrane and (membranotropic) NP models, we found that gHNP internalization occurs by forming an intermediate state characterized by specific stabilizing interactions formed by peptide-coated nanoparticles with multicomponent model membranes. This association mechanism is mainly characterized by interactions of gH with the extracellular solvent and the polar membrane surface. At the same time, the NP core interacts with the transmembrane (cholesterol-rich) fatty phase.

Specific and nondisruptive interaction of guanidium-functionalized gold nanoparticles with neutral phospholipid bilayers

Understanding and controlling the interaction between nanoparticles and biological entities is fundamental to the development of nanomedicine applications. In particular, the possibility to realize nanoparticles capable of directly targeting neutral lipid membranes would be advantageous to numerous applications aiming at delivering nanoparticles and their cargos into cells and biological vesicles. Here, we use experimental and computational methodologies to analyze the interaction between liposomes and gold nanoparticles (AuNPs) featuring cationic headgroups in their protecting monolayer. We find that in contrast to nanoparticles decorated with other positively charged headgroups, guanidinium-coated AuNPs can bind to neutral phosphatidylcholine liposomes, inducing nondisruptive membrane permeabilization. Atomistic molecular simulations reveal that this ability is due to the multivalent H-bonding interaction between the phosphate residues of the liposome's phospholipids and the guanidinium groups. Our results demonstrate that the peculiar properties of arginine magic, an effect responsible for the membranotropic properties of some naturally occurring peptides, are also displayed by guanidinium-bearing functionalized AuNPs.

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.

Hybrid nanoreceptors for high sensitivity detection of small molecules by NMR chemosensing

"Nanoparticle-assisted NMR chemosensing" combines magnetization transfer NMR techniques with the recognition abilities of gold nanoparticles (AuNPs) to isolate the NMR spectrum of relevant organic species in mixtures. The efficiency of the magnetization transfer is crucial to set the detection limit of the technique. To this aim, a second generation of nanoreceptors obtained by the self-organization of 2 nm AuNPs onto the surface of bigger silica nanoparticles shows better magnetization transfer performances, allowing the detection of analytes in water down to 10 μM concentration using standard instrumentation.

Not only in silico drug discovery: Molecular modeling towards in silico drug delivery formulations

The use of methods at molecular scale for the discovery of new potential active ligands, as well as previously unknown binding sites for target proteins, is now an established reality. Literature offers many successful stories of active compounds developed starting from insights obtained in silico and approved by Food and Drug Administration (FDA). One of the most famous examples is raltegravir, a HIV integrase inhibitor, which was developed after the discovery of a previously unknown transient binding area thanks to molecular dynamics simulations. Molecular simulations have the potential to also improve the design and engineering of drug delivery devices, which are still largely based on fundamental conservation equations. Although they can highlight the dominant release mechanism and quantitatively link the release rate to design parameters (size, drug loading, et cetera), their spatial resolution does not allow to fully capture how phenomena at molecular scale influence system behavior. In this scenario, the "computational microscope" offered by simulations at atomic scale can shed light on the impact of molecular interactions on crucial parameters such as release rate and the response of the drug delivery device to external stimuli, providing insights that are difficult or impossible to obtain experimentally. Moreover, the new paradigm brought by nanomedicine further underlined the importance of such computational microscope to study the interactions between nanoparticles and biological components with an unprecedented level of detail. Such knowledge is a fundamental pillar to perform device engineering and to achieve efficient and safe formulations. After a brief theoretical background, this review aims at discussing the potential of molecular simulations for the rational design of drug delivery systems.

Mechanistic Understanding From Molecular Dynamics Simulation in Pharmaceutical Research 1: Drug Delivery

In this review, we outline the growing role that molecular dynamics simulation is able to play as a design tool in drug delivery. We cover both the pharmaceutical and computational backgrounds, in a pedagogical fashion, as this review is designed to be equally accessible to pharmaceutical researchers interested in what this new computational tool is capable of and experts in molecular modeling who wish to pursue pharmaceutical applications as a context for their research. The field has become too broad for us to concisely describe all work that has been carried out; many comprehensive reviews on subtopics of this area are cited. We discuss the insight molecular dynamics modeling has provided in dissolution and solubility, however, the majority of the discussion is focused on nanomedicine: the development of nanoscale drug delivery vehicles. Here we focus on three areas where molecular dynamics modeling has had a particularly strong impact: (1) behavior in the bloodstream and protective polymer corona, (2) Drug loading and controlled release, and (3) Nanoparticle interaction with both model and biological membranes. We conclude with some thoughts on the role that molecular dynamics simulation can grow to play in the development of new drug delivery systems.

Dispersion state phase diagram of citrate-coated metallic nanoparticles in saline solutions

The fundamental interactions underlying citrate-mediated chemical stability of metal nanoparticles, and their surface characteristics dictating particle dispersion/aggregation in aqueous solutions, are largely unclear. Here, we developed a theoretical model to estimate the stoichiometry of small, charged ligands (like citrate) chemisorbed onto spherical metallic nanoparticles and coupled it with atomistic molecular dynamics simulations to define the uncovered solvent-accessible surface area of the nanoparticle. Then, we integrated coarse-grained molecular dynamics simulations and two-body free energy calculations to define dispersion state phase diagrams for charged metal nanoparticles in a range of medium’s ionic strength, a known trigger for aggregation. Ultraviolet-visible spectroscopy experiments of citrate-capped nanocolloids validated our predictions and extended our results to nanoparticles up to 35 nm. Altogether, our results disclose a complex interplay between the particle size, its surface charge density, and the ionic strength of the medium, which ultimately clarifies how these variables impact colloidal stability.

Citrate-stabilized metallic colloids are key materials towards chemosensing and catalysis applications. Here the authors introduce a new theoretical model to estimate how the stoichiometry of citrate molecules absorbed onto spherical metallic nanoparticles influences their aggregation phenomena.

Dispersion state phase diagram of citrate-coated metallic nanoparticles in saline solutions

The fundamental interactions underlying citrate-mediated chemical stability of metal nanoparticles, and their surface characteristics dictating particle dispersion/aggregation in aqueous solutions, are largely unclear. Here, we developed a theoretical model to estimate the stoichiometry of small, charged ligands (like citrate) chemisorbed onto spherical metallic nanoparticles and coupled it with atomistic molecular dynamics simulations to define the uncovered solvent-accessible surface area of the nanoparticle. Then, we integrated coarse-grained molecular dynamics simulations and two-body free energy calculations to define dispersion state phase diagrams for charged metal nanoparticles in a range of medium’s ionic strength, a known trigger for aggregation. Ultraviolet-visible spectroscopy experiments of citrate-capped nanocolloids validated our predictions and extended our results to nanoparticles up to 35 nm. Altogether, our results disclose a complex interplay between the particle size, its surface charge density, and the ionic strength of the medium, which ultimately clarifies how these variables impact colloidal stability.

Molecular Features for Probing Small Amphiphilic Molecules with Self-Assembled Monolayer-Protected Nanoparticles

The sensing of small molecules poses the challenge of developing devices able to discriminate between compounds that may be structurally very similar. Here, attention has been paid to the use of self-assembled monolayer (SAM)-protected gold nanoparticles since they enable a modular approach to tune single-molecule affinity and selectivity simply by changing functional moieties (i.e., covering ligands), along with multivalent molecular recognition. To date, the discovery of monolayers suitable for a specific molecular target has relied on trial-and-error approaches, with ligand chemistry being the main criterion used to modulate selectivity and sensitivity. By using molecular dynamics, we showcase that either individual molecular characteristics and/or collective features such as ligand flexibility, monolayer organization, ligand local ordering, and interfacial solvent properties can also be exploited conveniently. The knowledge of the molecular mechanisms that drive the recognition of small molecules on SAM-covered nanoparticles will critically expand our ability to manipulate and control such supramolecular systems.

Nanoparticle-assisted NMR spectroscopy: A chemosensing perspective

Sensing methodologies for the detection of target compounds in mixtures are important in many different contexts, ranging from medical diagnosis to environmental analysis and quality assessment. Ideally, such detection methods should allow for both identification and quantification of the targets, minimizing the possibility of false positives. With very few exceptions, most of the available sensing techniques rely on the selective interaction of the analyte with some detector, which in turn produces a signal as a result of the interaction. This approach hence provides indirect information on the targets, whose identity is generally ensured by comparison with known standards, if available, or by the selectivity of the sensor system itself. Pursuing a different approach, NMR chemosensing aims at generating signals directly from the analytes, in the form of a (complete) NMR spectrum. In this way, not only are the targets unequivocally identified, but it also becomes possible to identify and assign the structures of unknown species. In this review we show how relaxation- and diffusion-based NMR techniques, assisted by appropriate nanoparticles, can be used to edit the ¹H NMR spectrum of a mixture and extract the signals of specific target compounds. Monolayer-protected nanoparticles, in particular those made from gold, are well suited to this task because they provide a versatile, protein-size support to build or incorporate supramolecular receptors. Remarkably, the self-organized and multifunctional nature of the nanoparticle coating allows exploitation of different kinds of non-covalent interactions, to provide tailored binding sites for virtually any class of molecules. From the NMR standpoint, the reduced translational and rotational diffusion rates of bulky nanoparticles offer a way to manipulate the states of the monolayer spins and build a reservoir of magnetization that can be selectively transferred to the interacting analytes. In addition, the low correlation time and the enhanced rigidity of the coating molecules (due to their grafting and crowding on the particle surface) promote efficient spin diffusion, useful in saturation transfer experiments. The optimized combination of NMR experiments and nanoreceptors can ultimately allow the detection of relevant analytes in the micromolar concentration range, paving the way to applications in the diagnostic field and beyond.