NanoModeler: A Webserver for Molecular Simulations and Engineering of Nanoparticles

A molecular dynamics study on the ion-mediated self-assembly of monolayer-protected nanoclusters

We studied the effects of metal and molecular cations on the aggregation of atomically precise monolayer-protected nanoclusters (MPCs) in an explicit solvent using atomistic molecular dynamics simulations. While divalent cations such as Zn2+ and Cd2+ promote aggregation by forming ligand-cation-ligand bridges between the MPCs, molecular cations such as tetraethylammonium and cholinium inhibit their aggregation by getting adsorbed into the MPC's ligand shell and reducing the ligand's motion. Here, we studied the aggregation of Au25(SR)18 nanoclusters with two types of ligands, para-mercaptobenzoic acid and D-penicillamine, as prototypical examples.

Insights into the Interactions of Peptides with Monolayer-Protected Metal Nanoclusters

Monolayer-protected atomically precise metal nanoclusters (MPCs) have potential applications in catalysis, imaging, and drug delivery. Understanding their interactions with biomolecules such as peptides is of paramount interest for their use in cell imaging and drug delivery. Here we have carried out atomistic molecular dynamics simulations to investigate the interactions between MPCs and an anticancer peptide, melittin. Melittin gets attached to the MPCs surface by the formation of multiple hydrogen bonds between its amino acid residues with MPCs ligands. Additionally, the positively charged Lys, Arg, and peptide's N-terminal strongly anchor the peptide to the MPC metal surface, providing extra stabilization.

Electrostatic-Driven Self-Assembly of Janus-like Monolayer-Protected Metal Nanoclusters

The generation of controlled microstructures of functionalized nanoparticles has been a crucial challenge in nanoscience and nanotechnology. Efforts have been made to tune ligand charge states that can affect the aggregation propensity and modulate the self-assembled structures. In this work, we modeled zwitterionic Janus-like monolayer ligand-protected metal nanoclusters (J-MPCs) and studied their self-assembly using atomistic molecular dynamics and on-the-fly probability-based enhanced sampling simulations. The oppositely charged ligand functionalization on two hemispheres of a J-MPC elicits asymmetric solvation, primarily driven by distinctive hydrogen bonding patterns in the ligand-solvent interactions. Electrostatic interactions between the oppositely charged residues in J-MPCs guide the formation of one-dimensional and ring-like self-assembled superstructures with molecular dipoles oriented in specific patterns. The pertinent atomistic insights into the intermolecular interactions governing the self-assembled structures of zwitterionic J-MPCs obtained from this work can be used to design a general strategy to create tunable microstructures of charged MPCs.

Multilayer Model of Gold Nanoparticles (AuNPs) and Its Application in the Classical Molecular Dynamics Simulation of Citrate-Capped AuNPs

Studies on the interaction between gold nanoparticles (AuNPs) and functional proteins have been useful in developing diagnostic and therapeutic agents. Such studies require a realistic computational model of AuNPs for successful molecular design works. This study offers a new multilayer model of AuNPs to address the inconsistency between its molecular mechanics' interpretation and AuNP's plasmonic nature. We performed partial charge quantum calculation of AuNPs using Au13 and Au55 models. The result showed that it has partial negative charges on the surface and partial positive charges on the inner part, indicating that the AuNP model should be composed of multiatom types. We tested the partial charge parameters of these gold (Au) atoms in classical molecular dynamics simulation (CMD) of AuNPs. The result showed that our parameters performed better in simulating the adsorption of Na+ and dicarboxy acetone in terms of consistency with surface charge density than the zero charges Au in the interface force field (IFF). We proposed that the multiple-charged AuNP model can be developed further into a simpler four-atom type of Au in a larger AuNP size.

Understanding Molecular Aggregation of Ligand-Protected Atomically-Precise Metal Nanoclusters

Monolayer-protected atomically precise nanoclusters (MPCs) are an important class of molecules due to their unique structural features and diverse applications, including bioimaging, sensors, and drug carriers. Understanding the atomistic and dynamical details of their self-assembly process is crucial for designing system-specific applications. Here, we applied molecular dynamics and on-the-fly probability-based enhanced sampling simulations to study the aggregation of Au₂₅(pMBA)₁₈ MPCs in aqueous and methanol solutions. The MPCs interact via both hydrogen bonds and π-stacks between the aromatic ligands to form stable dimers, oligomers, and crystals. The dimerization free energy profiles reveal a pivotal role of the ligand charged state and solvent mediating the molecular aggregation. Furthermore, MPCs' ligands exhibit suppressed conformational flexibility in the solid phase due to facile intercluster hydrogen bonds and π-stacks. Our work provides unprecedented molecular-level dynamical details of the aggregation process and conformational dynamics of MPCs ligands in solution and crystalline phases.

Detection and imaging of bacterial biofilms with glutathione-stabilized gold nanoclusters

Bacterial biofilms colonize chronic wounds and surfaces of medical devices, thus making the development of reliable methods for imaging and detection of biofilms crucial. Although fluorescent identification of bacteria is sensitive and non-destructive, the lack of biofilm-specific fluorescent dyes limits the application of this technique to biofilm detection. Here, we demonstrate, for the first time, that fluorescent glutathione-stabilized gold nanoclusters (GSH-AuNCs) without targeting ligands can specifically interact with extracellular matrix components of Gram-negative and Gram-positive bacterial biofilms resulting in fluorescent staining of bacterial biofilms. By contrast, fluorescent bovine serum albumin-stabilized gold nanoclusters and 11-mercaptoundecanoic acid - stabilized gold nanoclusters do not stain the extracellular matrix of biofilms. According to molecular docking studies, GSH-AuNCs show affinity to several targets in extracellular matrix, including amyloid-anchoring proteins, matrix proteins and polysaccharides. Some experimental evidence was obtained for the interaction of GSH-AuNCs with the lipopolysaccharide (LPS) that was isolated from the matrix of Azospirillum baldaniorum biofilms. Based on GSH-AuNCs properties, we propose a new fluorescent method for the measurement of biofilm biomass with a limit of detection 1.7 × 10⁵ CFU/mL. The sensitivity of the method is 10-fold higher than the standard biofilm quantification with the crystal violet assay. There is a good linear relationship between the fluorescence intensity from the biofilms and the number of CFU from the biofilms in the range from 2.6 × 10⁵ to 6.7 × 10⁷ CFU/mL. The developed nanocluster-mediated method of biofilm staining was successfully applied for quantitative detection of biofilm formation on urinary catheter surface. The presented data suggest that fluorescent GSH-AuNCs can be used to diagnose medical device-associated infections.

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.

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.

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.

Atomistic Molecular Dynamics Simulation Study on the Interaction between Atomically Precise Thiolate-Protected Gold Nanoclusters and Phospholipid Membranes

The interaction of atomically precise monolayer thiolate (SR) protected gold nanoclusters (Au NCs) with the phospholipid membranes has been studied by the all-atom molecular dynamics (AAMD) simulations. The effect of cluster size, type, and the surface charge density of protection ligand was studied. The simulation results show gold nanoclusters with different size and surface modifications have much different transmembrane behaviors. The Au₂₅(SR)₁₈ cluster was found to possess the best affinity to the phospholipid membranes among six atomically accurate clusters Au₂₅(SR)₁₈, Au₃₆(SR)₂₄, Au₄₄(SR)₂₈, Au₆₈(SR)₃₂, Au₁₄₄(SR)₆₀, and Au₃₁₄(SR)₉₆. Using the Au₂₅ NC as a model, this work also found that the aggregation mode of the surface ligands and the surface charge density are the important factors affecting the interaction between the gold nanoclusters and the phospholipid membranes. Moreover, the balance of hydrophilic and hydrophobic ligands on the surface of Au NCs is beneficial to the high permeability.

CHARMM-GUI Nanomaterial Modeler for Modeling and Simulation of Nanomaterial Systems

Molecular modeling and simulation are invaluable tools for nanoscience that predict mechanical, physicochemical, and thermodynamic properties of nanomaterials and provide molecular-level insight into underlying mechanisms. However, building nanomaterial-containing systems remains challenging due to the lack of reliable and integrated cyberinfrastructures. Here we present Nanomaterial Modeler in CHARMM-GUI, a web-based cyberinfrastructure that provides an automated process to generate various nanomaterial models, associated topologies, and configuration files to perform state-of-the-art molecular dynamics simulations using most simulation packages. The nanomaterial models are based on the interface force field, one of the most reliable force fields (FFs). The transferability of nanomaterial models among the simulation programs was assessed by single-point energy calculations, which yielded 0.01% relative absolute energy differences for various surface models and equilibrium nanoparticle shapes. Three widely used Lennard-Jones (LJ) cutoff methods are employed to evaluate the compatibility of nanomaterial models with respect to conventional biomolecular FFs: simple truncation at r = 12 Å (12 cutoff), force-based switching over 10 to 12 Å (10-12 fsw), and LJ particle mesh Ewald with no cutoff (LJPME). The FF parameters with these LJ cutoff methods are extensively validated by reproducing structural, interfacial, and mechanical properties. We find that the computed density and surface energies are in good agreement with reported experimental results, although the simulation results increase in the following order: 10-12 fsw <12 cutoff < LJPME. Nanomaterials in which LJ interactions are a major component show relatively higher deviations (up to 4% in density and 8% in surface energy differences) compared with the experiment. Nanomaterial Modeler's capability is also demonstrated by generating complex systems of nanomaterial-biomolecule and nanomaterial-polymer interfaces with a combination of existing CHARMM-GUI modules. We hope that Nanomaterial Modeler can be used to carry out innovative nanomaterial modeling and simulations to acquire insight into the structure, dynamics, and underlying mechanisms of complex nanomaterial-containing systems.

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.

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.

Carbon nanotubes for rapid capturing of SARS-COV-2 virus: revealing a mechanistic aspect of binding based on computational studies

We investigate the binding interactions of synthesized multi-walled carbon nanotubes (MWCNTs) with SARS-CoV-2 virus. Two essential components of the SARS-CoV-2 structure i.e.6LU7 (main protease of SARS-CoV-2) and 6LZG (spike receptor-binding domain complexed with its receptor ACE2) were used for computational studies. MWCNTs of different morphologies (zigzag, armchair and chiral) were synthesized through a thermal chemical vapour deposition process as a function of pyrolysis temperature. A direct correlation between radius to volume ratio of the synthesized MWCNTs and the binding energies for all three (zigzag, armchair and chiral) conformations were observed in our computational studies. Our result suggests that MWCNTs interact with the active sites of the main protease along with the host angiotensin-converting enzyme2 (ACE2) receptors. Furthermore, it is also observed that MWCNTs have significant binding affinities towards SARS-CoV-2. However, the highest free binding energy of -87.09 kcal mol-1 with 6LZG were shown by the armchair MWCNTs with SARS-CoV-2 through the simulated molecular dynamic trajectories, which could alter the SARS-CoV-2 structure with higher accuracy. The radial distribution function also confirms the density variation as a function of distance from a reference particle of MWCNTs for the study of interparticle interactions of the MWCNT and SARS-CoV-2. Due to these interesting attributes, such MWCNTs could find potential application in personal protective equipment (PPE) and diagnostic kits.

R, Sissi C, Pecina A, Riccardi L, De Vivo M, Mancin F, Scrimin P. A Gold Nanoparticle Nanonuclease Relying on a Zn(II) Mononuclear Complex

Similarly to enzymes, functionalized gold nanoparticles efficiently catalyze chemical reactions, hence the term nanozymes. Herein, we present our results showing how surface-passivated gold nanoparticles behave as synthetic nanonucleases, able to cleave pBR322 plasmid DNA with the highest efficiency reported so far for catalysts based on a single metal ion mechanism. Experimental and computational data indicate that we have been successful in creating a catalytic site precisely mimicking that suggested for natural metallonucleases relying on a single metal ion for their activity. It comprises one Zn(II) ion to which a phosphate diester of DNA is coordinated. Importantly, as in nucleic acids-processing enzymes, a positively charged arginine plays a key role by assisting with transition state stabilization and by reducing the pKa of the nucleophilic alcohol of a serine. Our results also show how designing a catalyst for a model substrate (bis-p-nitrophenylphosphate) may provide wrong indications as for its efficiency when it is tested against the real target (plasmid DNA).

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.

Web-ARM: A Web-Based Interface for the Automatic Construction of QM/MM Models of Rhodopsins

This article introduces Web-ARM, a specialized tool, online available, designed to build quantum mechanical/molecular mechanical models of rhodopsins, a widely spread family of light-responsive proteins. Web-ARM allows the rapidly building of models of rhodopsins with a documented quality and the prediction of trends in UV-vis absorption maximum wavelengths, based on their excitation energies computed at the CASPT2//CASSCF/Amber level of theory. Web-ARM builds upon the recently reported, python-based a-ARM protocol [J. Chem. Theory Comput., 2019, 15, 3134-3152] and, as such, necessitates only a crystallographic structure or a comparative model in PDB format and a very basic knowledge of the studied rhodopsin system. The user-friendly web interface uses such input to generate congruous, gas-phase models of rhodopsins and, if requested, their mutants. We present two possible applications of Web-ARM, which showcase how the interface can be employed to assist both research and educational activities in fields at the interface between chemistry and biology. The first application shows how, through Web-ARM, research projects (e.g., rhodopsin and rhodopsin mutant screening) can be carried out in significantly less time with respect to using the required computational photochemistry tools via a command line. The second application documents the use of Web-ARM in a real-life educational/training activity, through a hands-on experience illustrating the concepts of rhodopsin color tuning.

Strong dependence of the nano-bio interactions on core morphology and layer composition of ultrasmall nanostructures

The interactions between nanoparticles (NPs) and proteins, cells, and tissues, broadly known as nano-bio interactions, depend on the NP size and shape and on the characteristics of the NP coating layer, such as density, thickness, and chemical makeup. The dependence of nano-membrane interactions on the design parameters of ultrasmall nanostructures is studied by computer simulations. Considered here are spheres, plates, rings, rods, tubes, and helices made up of either bare magnetite or passivated gold, interacting with charged or zwitterionic membranes. The analysis reveals a strong dependence on shape, size, and layer composition of various quantities that characterize the nano-bio behavior, including binding modes and affinities. This sensitivity can be exploited to design nanostructures that bind preferentially to membranes or that stabilize or disrupt membrane structural integrity. The method used here is general and not limited to the ultrasmall regime, so it can be adopted to study other nano-bio interactions systematically. The implications for the distribution of NPs in cells and tissues (biodistribution) and for passive and active transmembrane transport are discussed, both important processes in biomedicine.