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It is known that metal nanoparticles (NPs) may be dynamic and atoms may move within them even at fairly low temperatures. Characterizing such complex dynamics is key for understanding NPs’ properties in realistic regimes, but detailed information on, e.g., the stability, survival, and interconversion rates of the atomic environments (AEs) populating them are non-trivial to attain. Here we show a machine learning approach that allows us to reconstruct the complex atomic dynamics of metal NPs from high-dimensional data extracted from molecular dynamics simulations. Using different-shape gold NPs as a representative example, an AEs’ dictionary allows us to label step-by-step the individual atoms in the NPs, identifying the native and non-native AEs and populating them along the MD simulations at various temperatures. Tracking the emergence, annihilation, lifetime, and dynamic interconversion of the AEs, our approach permits estimating a “statistical equivalent identity” for metal NPs based on the intrinsic atomic dynamics present within them.

The most advanced structure prediction methods are powerless in exploring the conformational ensemble of disordered peptides and proteins and for this reason the "protein folding problem" remains unsolved. We present a novel methodology that enables the accurate prediction of spectroscopic fingerprints (Circular Dichroism, Infrared, Raman, and Raman Optical Activity), and by this allows for "tidying up" the conformational ensembles of disordered peptides and disordered regions in proteins. This concept is elaborated for and applied to a dodecapeptide, whose spectroscopic fingerprint is measured and theoretically predicted by means of enhanced-sampling Molecular Dynamics coupled with Quantum Mechanical calculations. Following this approach, we demonstrate that peptides lacking a clear propensity for ordered secondary-structure motifs are not randomly, but only conditionally disordered. This means that their conformational landscape, or phase-space, can be well represented by a basis-set of conformers including about 10 to 100 structures. The implications of this finding have profound consequences both for the interpretation of experimental electronic and vibrational spectral features of peptides in solution and for the theoretical prediction of these features using accurate and computationally expensive techniques. The here-derived methods and conclusions are expected to fundamentally impact the rationalization of so-far elusive structure-spectra relationships for disordered peptides and proteins, towards improved and versatile structure prediction methods.

This work reports for the first time a quantum mechanical study of the interactions of a model benzodiazepine drug, i.e., nitrazepam, with various models of amorphous silica surfaces, differing in structural and interface properties. The interest in these systems is related to the use of mesoporous silica as carrier in drug delivery. The adopted computational procedure has been chosen to investigate whether silica–drug interactions favor the drug degradation mechanism or not, hindering the beneficial pharmaceutical effect. Computed structural, energetics, and vibrational properties represent a relevant comparison for future experiments. Our simulations demonstrate that adsorption of nitrazepam on amorphous silica is a strongly exothermic process in which a partial proton transfer from the surface to the drug is observed, highlighting a possible catalytic role of silica in the degradation reaction of benzodiazepines.

Spatial confinement is widely employed by Nature to attain unique efficiency in controlling chemical reactions. Notable examples are enzymes, which selectively bind reactants and exquisitely regulate their conversion into products. In the attempt to mimic natural catalytic systems, supramolecular metal-organic cages capable of encapsulating guests in their cavity and of controlling/accelerating chemical reactions under confinement are attracting increasing interest. However, the complex nature of these systems, where reactants/products continuously exchange in-and-out the host, makes it often difficult to elucidate the factors controlling the reactivity in dynamic regimes. As a case study, here we focus on a coordination cage that can encapsulate amide guests and enhance their hydrolysis by favoring their mechanical twisting towards reactive molecular configurations under confinement. We designed an advanced multiscale simulation approach that allows us to reconstruct the reactivity in such host-guest systems in dynamic regimes. In this way, we can characterize the amide encapsulation/expulsion in/out the cage cavity (thermodynamics and kinetics), coupling such host-guest dynamic equilibrium with the characteristic hydrolysis reaction constants. All computed kinetic/thermodynamic data are then combined, obtaining a statistical estimation of reaction acceleration in the host-guest system that is found in optimal agreement with the available experimental trends. This shows how, to understand the key factors controlling accelerations/variations in the reaction under confinement, it is necessary to take into account all dynamic processes that occur as intimately entangled in such host-guest systems. This also provides us with a flexible computational framework, useful to build structure-dynamics-property relationships for a variety of reactive host-guest systems.

Metals are traditionally considered hard matter. However, it is well known that their atomic lattices may become dynamic and undergo reconfigurations even well below the melting temperature. The innate atomic dynamics of metals is directly related to their bulk and surface properties. Understanding their complex structural dynamics is, thus, important for many applications but is not easy. Here, we report deep-potential molecular dynamics simulations allowing to resolve at an atomic resolution the complex dynamics of various types of copper (Cu) surfaces, used as an example, near the Hüttig ([Formula: see text] of melting) temperature. The development of deep neural network potential trained on density functional theory calculations provides a dynamically accurate force field that we use to simulate large atomistic models of different Cu surface types. A combination of high-dimensional structural descriptors and unsupervized machine learning allows identifying and tracking all the atomic environments (AEs) emerging in the surfaces at finite temperatures. We can directly observe how AEs that are non-native in a specific (ideal) surface, but that are, instead, typical of other surface types, continuously emerge/disappear in that surface in relevant regimes in dynamic equilibrium with the native ones. Our analyses allow estimating the lifetime of all the AEs populating these Cu surfaces and to reconstruct their dynamic interconversions networks. This reveals the elusive identity of these metal surfaces, which preserve their identity only in part and in part transform into something else under relevant conditions. This also proposes a concept of “statistical identity” for metal surfaces, which is key to understanding their behaviors and properties.

We undertake steps to overcome four challenges that have hindered the understanding of ZnO/biomolecule interfaces at the atomic scale: parametrization of a classical force field, ZnO surface termination and amino acid protonation state in methanol, and convergence of enhanced sampling molecular dynamics simulations. We predict adsorption free energies for histidine, serine, cysteine, and tryptophan in remarkable agreement with experimental measurements obtained via a novel indicator-displacement assay. Adsorption is driven by direct surface/amino-acid interactions mediated by terminal hydroxyl groups and stabilized by strongly structured methanol solvation shells.

Background: Silica continues to represent an intriguing topic of fundamental and applied research across various scientific fields, from geology to physics, chemistry, cell biology, and particle toxicology. The pathogenic activity of silica is variable, depending on the physico-chemical features of the particles. In the last 50 years, crystallinity and capacity to generate free radicals have been recognized as relevant features for silica toxicity. The ‘surface’ also plays an important role in silica toxicity, but this term has often been used in a very general way, without defining which properties of the surface are actually driving toxicity. How the chemical features (e.g., silanols and siloxanes) and configuration of the silica surface can trigger toxic responses remains incompletely understood. Main body: Recent developments in surface chemistry, cell biology and toxicology provide new avenues to improve our understanding of the molecular mechanisms of the adverse responses to silica particles. New physicochemical methods can finely characterize and quantify silanols at the surface of silica particles. Advanced computational modelling and atomic force microscopy offer unique opportunities to explore the intimate interactions between silica surface and membrane models or cells. In recent years, interdisciplinary research, using these tools, has built increasing evidence that surface silanols are critical determinants of the interaction between silica particles and biomolecules, membranes, cell systems, or animal models. It also has become clear that silanol configuration, and eventually biological responses, can be affected by impurities within the crystal structure, or coatings covering the particle surface. The discovery of new molecular targets of crystalline as well as amorphous silica particles in the immune system and in epithelial lung cells represents new possible toxicity pathways. Cellular recognition systems that detect specific features of the surface of silica particles have been identified. Conclusions: Interdisciplinary research bridging surface chemistry to toxicology is progressively solving the puzzling issue of the variable toxicity of silica. Further interdisciplinary research is ongoing to elucidate the intimate mechanisms of silica pathogenicity, to possibly mitigate or reduce surface reactivity. Keywords: Silica, Silicosis, Lung cancer, Auto-immune diseases, Surface reactivity, Silanol, Coating, Modelling, Spectroscopy, Atomic force microscopy

Melatonin (MT) is a molecule of paramount importance in all living organisms, due to its presence in many biological activities, such as circadian (sleep–wake cycle) and seasonal rhythms (reproduction, fattening, molting, etc.). Unfortunately, it suffers from poor solubility and, to be used as a drug, an appropriate transport vehicle has to be developed, in order to optimize its release in the human tissues. As a possible drug-delivery system, β-cyclodextrin (βCD) represents a promising scaffold which can encapsulate the melatonin, releasing when needed. In this work, we present a computational study supported by experimental IR spectra on inclusion MT/βCD complexes. The aim is to provide a robust, accurate and, at the same time, low-cost methodology to investigate these inclusion complexes both with static and dynamic simulations, in order to study the main actors that drive the interactions of melatonin with β-cyclodextrin and, therefore, to understand its release mechanism.

The interaction of silica (SiO2) with biological systems is complex and contradictory. On the one hand, silica is at the basis of several biomineralization processes (e.g., in sponges). On the other hand, silica nanoparticles and dust may lead to silicosis and, at the cellular level, hemolysis. These toxic responses are strongly dependent on the silica polymorph and their root causes are still under debate. Both silica biomineralization and silica-induced nanotoxicity could be related to similar mechanisms of molecular recognition between the cellular membranes and the surface of the SiO2 particles. On the basis of this hypothesis, we employed classical molecular dynamics simulations, coupled to advanced sampling techniques, to achieve an atomistic picture of the interactions between different types of silica nanoparticles and the membrane of erythrocytes. Our predicted free-energy profiles associated with membrane crossing give no evidence for segregation of nanoparticles at the membrane/water interface,...

Li

Li7La3Zr2O12 (LLZO) and related compounds are considered as promising candidates for future all-solid-state Li-ion battery applications. Still, the processing of those materials into thin membranes with the right stoichiometry and crystal structure is difficult and laborious. The sensitivity of the Li-ion conductive garnets against moisture and the associated Li+/H+ cation exchange makes their processing even more difficult. Formulation of suitable polymer/ceramic hybrid solid state electrolytes could be a prosperous way to reach the future large scale production of solid state Li-ion batteries. In fact, solvent mediated and/or slurry based wet-processing of the LLZO, e.g., tape-casting, could result in irreversible Li-ion loss of the pristine material due to Li+/H+ cation exchange. The concomitant structural changes and loss in functionality in terms of Li-ion conductivity are the results of the above process. Therefore, in the present work a systematic study on the chemical stability and structural retention of Al-substituted LLZO in different solvents is reported. It was found that Li+/H+ exchange in LLZO occurs upon solvent immersion, and its magnitude is dependent on the availability of −OH functional groups of the solvent molecules. As a result, a larger degree of Li+/H+ exchange causes higher increase of the lattice parameter of the LLZO, determined by synchrotron diffraction analyses. The expansion of the cubic unit cell was ascertained, when Li+ was replaced by H+ in the host lattice, by ab initio computational studies. The application of the most common solvent as dispersion medium, i.e., high purity water, causes the most significant Li+/H+ exchange and, therefore, structural change, while acetonitrile was proven to be the best suitable solvent for wet postprocessing of LLZO. Finally, computational calculations suggested that the Li+/H+ exchange could result in diminished ionic, i.e., mixed Li+-H+, conductivity due to the insertion of protons with lower mobility than that of Li-ions.

Adsorption of enzymes on solid surfaces may lead to conformational changes that reduce their catalytic conversion activity and are thus detrimental to the efficiency of biotechnology or biosensing applications. This work is a joint theoretical and experimental endeavor in which we identify and quantify the conformational changes that chymotrypsin undergoes when in contact with the surface of amorphous silica nanoparticles. For this purpose, we use circular dichroism spectroscopy, standard molecular dynamics, and advanced-sampling methods. Only the combination of these techniques allowed us to pinpoint a destabilization effect of silica on specific structural motifs of chymotrypsin. They are linked by the possibility of theoretically predicting CD spectra, allowing us to elucidate the source of the experimentally observed spectral changes. We find that chymotrypsin loses part of its helical content upon adsorption, with minor perturbation of its overall tertiary structure, associated with changes in the aromatic interactions. We demonstrate that the C-terminal helical fragment is unfolded as an isolated oligopeptide in pure water, folded as an α-helix as terminus of chymotrypsin in solution, and again partly disordered when the protein is adsorbed on silica. We believe that the joint methodology introduced in this manuscript has a direct general applicability to investigate any biomolecule-inorganic surface system. Methods to theoretically predict circular dichroism spectra from atomistic simulations were compared and improved. The drawbacks of the approaches are discussed; in particular, the limited capability of advanced-sampling MD schemes to explore the conformational phase space of large proteins and the dependency of the predicted ellipticity bands on the choice of calculation parameters.

Among materials usually employed for drug delivery, silica plays a key role, particularly in its mesoporous form. Although much research has been performed on the topic of silica drug delivery, the understanding of the interactions occurring between the material surface and drug molecules is still scarce, despite this knowledge is essential for determining the final performance of a drug-delivery system (DDS). Molecular modeling can give a precious insight on this issue, acting as a virtual microscope to study the processes occurring inside the drug carrier. Ab initio simulations, in particular, can accurately predict geometries and enthalpies of adsorption, infrared and nuclear magnetic resonance spectra, and other experimental observables that can help pharmaceutical researchers to better predict the features of novel DDSs.

The asymmetric hydrogenation of β-keto esters over platinum nanoparticles (NPs) functionalized with different α-amino acids was explored. By systematic variation of the structural complexity of the functionalizing ligands, we determined structure–selectivity relationships and further improved our understanding of ligand–reactant interactions. We identified attractive interactions between the lipophilic substituents of ligands and reactants as one of the major contributing factors governing the mutual orientation of ligands and reactants and, thus, the stereoselectivity of the catalytic reaction. For the first time, an enantiomeric excess (ee) above 80% is reported for supported ligand-functionalized NPs, which demonstrates the potential of these materials as a novel type of asymmetric heterogeneous catalyst. Our results reveal that the molecular principles employed in homogeneous catalysis can be utilized to achieve stereoselective catalytic reactions even on rather non-uniform surfaces like small NPs. This opens up yet unexplored possibilities for manipulating reactions on catalytic surfaces to control selectivity.

A density functional theory (PBE functional) investigation is carried out, in which a model of an amorphous silica surface is functionalized by ortho-benzoquinone. Surface functionalization with catechol and quinone-based compounds is relevant in biomedical fields, from prosthetic implants to dentistry, to develop multifunctional coatings with antimicrobial properties. The present study provides atomistic information on the specific interactions between the functionalizing agent and the silanol groups at the silica surface. The distinct configurations of the functional groups, the hydrogen bond pattern, the role of dispersion forces and the simulated IR spectra provide detailed insight into the features of this model surface coating. Ab initio molecular dynamics gives further insights into the mobility of the functionalizing groups. As a final step, we studied the condensation reaction with allylamine, via Schiff base formation, to ground subsequent simulations on condensation with model peptides of antimicrobial activity.

Hydroxyapatite [HA, Ca10(PO4)6(OH)2], the main constituent of bones and teeth enamels, is a widely studied and employed biomaterial. Its applications span from dental to orthopedic implants, including bone tissue engineering scaffolds, coating, filler and many others. Previous theoretical and experimental studies have already characterized the physical–chemical foundations of water adsorption on a number of HA surfaces, an essential step in the mechanism of biomaterial integration. Here, we extend such knowledge by simulating, at a hybrid DFT level of theory, different HA surface terminations, both stoichiometric and non-stoichiometric, as free and in interaction with water. Such a goal is achieved at an unprecedented accuracy, with a large all-electron basis set and including dispersion forces contributions. The calculated results are then compared with experimental micro-calorimetric data, showing a good agreement in the loading trend of the (010) surfaces. More generally, this theoretical approach is confirmed to be an efficient tool to analyze these biomaterials, giving the possibility to investigate the HA behavior toward more complex molecules, from amino acids to collagen, at the here-presented level of theory, to shed some light on the complex biomineralization process of human bones and teeth.

Confinement in mesoporous silica can greatly increase the solubility of pharmaceutical compounds. Propionic acid derivatives (a very popular class of drugs that include ibuprofen and ketoprofen) would greatly benefit from such technology, given their common apolar character. However, it is still debated whether, after confinement, these drugs are adsorbed on the pore walls as individual molecules or they keep the H-bonded dimeric structure that exists in their crystalline form. Their physical state inside the mesopores could have important consequences on the final performances of the drug delivery system. We employed accurate periodic density functional theory simulations, both static and dynamic, to investigate the issue. We simulated ibuprofen, as a model for all propionic acid derivatives, adsorbed both as a monomer and as a dimer inside a realistic model for the MCM-41 mesoporous silica. We found that adsorption is energetically favored in both cases, driven by both vdW and H-bond interactions. However, through ab initio molecular dynamics, we observed a continuous forming, breaking and reforming of these interactions. In the end, by comparing computed energetics, vibrational spectra and mobility, we were able to provide some important clues on the physical state of this class of drugs inside mesoporous silica, helping to define which drug family (monomer or dimer) is more probable after confinement.

Fully ab initio treatment of complex solid systems needs computational software which is able to efficiently take advantage of the growing power of high performance computing (HPC) architectures. Recent improvements in CRYSTAL, a periodic ab initio code that uses a Gaussian basis set, allows treatment of very large unit cells for crystalline systems on HPC architectures with high parallel efficiency in terms of running time and memory requirements. The latter is a crucial point, due to the trend toward architectures relying on a very high number of cores with associated relatively low memory availability. An exhaustive performance analysis shows that density functional calculations, based on a hybrid functional, of low-symmetry systems containing up to 100,000 atomic orbitals and 8000 atoms are feasible on the most advanced HPC architectures available to European researchers today, using thousands of processors. © 2012 Wiley Periodicals, Inc.

This study demonstrates the feasibility of periodic all-electron hybrid density functional theory calculations in the description of protein crystals, using crambin as a test case., Molecular simulations of proteins have been usually accomplished through empirical or semi-empirical potentials, due to the large size and inherent complexity of these biological systems. On the other hand, a theoretical description of proteins based on quantum-mechanical methods would however provide an unbiased characterization of their electronic properties, possibly offering a link between these and the ultimate biological activity. Yet, such approaches have been historically hindered by the large amount of requested computational power. Here we demonstrate the feasibility of periodic all-electron density functional theory calculations in the description of the crystal of the protein crambin (46 aminoacids), which is determined with exceptional structural accuracy. We have employed the hybrid B3LYP functional, coupled to an empirical description of London interactions (D*) to simulate the crambin crystal with an increasing amount of lattice water molecules in the cell (up to 172H2O per cell). The agreement with the experiment is good for both protein geometry and protein–water interactions. The energetics was computed to predict crystal formation energies, protein–water and protein–protein interaction energies. We studied the role of dispersion interactions which are crucial for holding the crambin crystal in place. B3LYP-D* electrostatic potential and dipole moment of crambin as well as the electronic charge flow from crambin to the solvating water molecules (0.0015e per H2O) have also been predicted. These results proved that quantum-mechanical simulations of small proteins, both free and in their crystalline state, are now feasible in a reasonable amount of time, by programs capable of exploiting high performance computing architectures, allowing the study of protein properties not easily amenable through classical force fields.

Ordered mesoporous silicas have been widely investigated as drug carriers in several fields, from tissue engineering to cancer therapy. The knowledge of the specific interactions between the surface of mesoporous silicas and drugs is necessary to guide development of new and improved drug delivery systems. However, such knowledge is still scarce, due to the arduous interpretation of experimental results. In this work, we characterize the incorporation of clotrimazole, a common antifungal drug, inside ordered mesoporous silica by means of a joint computational and experimental approach. Experimentally the drug was loaded through supercritical CO2 and its adsorption investigated through infrared spectroscopy, N2 adsorption isotherms, and thermogravimetric analysis. Modeling involved static and dynamic Density Functional Theory simulations of clotrimazole adsorbed on realistic models of amorphous silica surfaces. A good agreement between the computational and the experimental results was obtained, concerning...

In silico modeling of acidic (CH2COOH) or basic (CH2NH2) functionalized silica surfaces has been carried out by means of a density functional approach based on a gradient-corrected functional to provide insight into the characterization of experimentally functionalized surfaces via a plasma method. Hydroxylated surfaces of crystalline cristobalite (sporting 4.8 OH/nm2) mimic an amorphous silica interface as unsubstituted material. To functionalize the silica surface we transformed the surface Si-OH groups into Si-CH2COOH and Si-CH2NH2 moieties to represent acidic/basic chemical character for the substitution. Structures, energetics, electronic, and vibrational properties were computed and compared as a function of the increasing loading of the functional groups (from 1 to 4 per surface unit cell). Classical molecular dynamics simulations of selected cases have been performed through a Reax-FF reactive force field to assess the mobility of the surface added chains. Both DFT and force field calculations identify the CH2NH2 moderate surface loading (1 group per unit cell) as the most stable functionalization, at variance with the case of the CH2COOH group, where higher loadings are preferred (2 groups per unit cell). The vibrational fingerprints of the surface functionalities, which are the ?(C=O) stretching and ?(NH2) bending modes for acidic/basic cases, have been characterized as a function of substitution percentage in order to guide the assignment of the experimental data. The final results highlighted the different behavior of the two types of functionalization. On the one hand, the frequency associated with the ?(C=O) mode shifts to lower wavenumbers as a function of the H-bond strength between the surface functionalities (both COOH and SiOH groups), and on the other hand, the ?(NH2) frequency shift seems to be caused by a subtle balance between the H-bond donor and acceptor abilities of the NH2 moiety. Both sets of data are in general agreement with experimental measurements on the corresponding silica-functionalized materials and provide finer details for a deeper interpretation of experimental spectra.

Carbonate apatite is a material of the utmost importance as it represents the inorganic fraction of biological hard tissues in bones and teeth. Here we study the static and dynamic features of CO 3 2 − ion in the apatitic channel of carbonate apatite (A- type substitution), by applying both static and dynamic quantum mechanical calculations based on density functional methods with B3LYP-D * and PBE functionals. The static calculations reveal a number of almost energetically equivalent carbonate con fi gurations in the channel, leading to cell parameters compatible with the P 3 ̅space group assigned by the experimental X-ray structure determination. Ab initio isothermal − isobaric molecular dynamics simulations provide insights on the CO 3 2 − mobility, showing that at the temperature of the experimental structural determination the CO 3 2 − moiety undergoes a dynamic disorder, as the carbonate group is almost free to move within the apatitic channel enhancing its exchangeability with other anions. Carbonate apatite is a material of the utmost importance as it represents the inorganic fraction of biological hard tissues in bones and teeth. Here we study the static and dynamic features of CO32- ion in the apatitic channel of carbonate apatite (A-type substitution), by applying both static and dynamic quantum mechanical calculations based on density functional methods with B3LYP-D* and PBE functionals. The static calculations reveal a number of almost energetically equivalent carbonate configurations in the channel, leading to cell parameters compatible with the P3Ì... space group assigned by the experimental X-ray structure determination. Ab initio isothermal-isobaric molecular dynamics simulations provide insights on the CO32- mobility, showing that at the temperature of the experimental structural determination the CO32- moiety undergoes a dynamic disorder, as the carbonate group is almost free to move within the apatitic channel enhancing its exchangeability with other anions. © 2013 American Chemical Society.

Amorphous silica is widely employed in pharmaceutical formulations both as a tableting, anticaking agent and as a drug delivery system, whereas MCM-41 mesoporous silica has been recently proposed as an efficient support for the controlled release of drugs. Notwithstanding the relevance of this topic, the atomistic details about the specific interactions between the surfaces of the above materials and drugs and the energetic of adsorption are almost unknown. In this work, we resort to a computational ab initio approach, based on periodic Density Functional Theory (DFT), to study the adsorption behavior of two popular drugs (aspirin and ibuprofen) on two models of an amorphous silica surface characterized by different hydrophilic/hydrophobic properties due to different SiOH surface groups' density. Particular effort was devoted to understand the role of dispersive (vdW) interactions in the adsorption mechanism and their interplay with H-bond interactions. On the hydrophilic silica surface, the H-bond pattern of the Si-OH groups rearranges to comply with the formation of new H-bond interactions triggered by the adsorbed drug. The interaction energy of ibuprofen with the hydrophilic model of the silica surface is computed to be very close to the sublimation energy of the ibuprofen molecular crystal, accounting for the experimental evidence of ibuprofen crystal amorphization induced by the contact with the mesoporous silica material. For both surface models, dispersion interactions play a crucial role in dictating the features of the drug/silica system, and they become dominant for the hydrophobic surface. It was proved that a competition may exist between directional H-bonds and nonspecific dispersion driven interactions, with important structural and energetic consequences for the adsorption. The results of this work emphasize the inadequacy of plain DFT methods to model adsorption processes involving inorganic surfaces and drugs of moderate size, due to the missing term accounting for London dispersion interactions.