Emergence of Photomodulated Protometabolism by Short Peptide-Based Assemblies.

In the early Earth, rudimentary enzymes must have utilized the available light energy source to modulate protometabolic processes. Herein, we report the light-responsive C-C bond manipulation via short peptide-based assemblies bound to the photosensitive molecular cofactor (azo-based photoswitch) where the energy of the light source regulated the binding sites which subsequently modulated the retro-aldolase activity. In the presence of a continual source of high-energy photons, temporal realization of a catalytically more proficient state could be achieved under nonequilibrium conditions. Further, the hydrophobic surface of peptide assemblies facilitated the binding of an orthogonal molecular catalyst that showed augmented activity (promiscuous hydrolytic activity) upon binding. This latent activity was utilized for the in situ generation of light-sensitive cofactor that subsequently modulated the retro-aldolase activity, thus creating a reaction network.

Innate dynamics and identity crisis of a metal surface unveiled by machine learning of atomic environments.

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 (∼1/3 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.

On the Interactions of Melatonin/ß-Cyclodextrin Inclusion Complex: A Novel Approach Combining Efficient Semiempirical Extended Tight-Binding (xTB) Results with Ab Initio Methods.

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, ß-cyclodextrin (ßCD) 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/ßCD 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 ß-cyclodextrin and, therefore, to understand its release mechanism.

The puzzling issue of silica toxicity: are silanols bridging the gaps between surface states and pathogenicity?

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 physico-chemical 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.

Models for biomedical interfaces: a computational study of quinone-functionalized amorphous silica surface features.

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.

Computational Study of Acidic and Basic Functionalized Crystalline Silica Surfaces as a Model for Biomaterial Interfaces.

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/nm(2)) 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 ν(C═O) stretching and δ(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 ν(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 δ(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.

Silica-based materials as drug adsorbents: first principle investigation on the role of water microsolvation on Ibuprofen adsorption.

Silica-based materials find applications as excipients and, particularly for those of mesoporous nature, as drug delivery agents for pharmaceutical formulations. Their performance can be crucially affected by water moisture, as it can modify the behavior of these formulations, by limiting their shelf life. Here we describe the role of water microsolvation on the features of ibuprofen adsorbed on a model of amorphous silica surface by means of density functional theory (DFT) simulations. Starting from the results of the simulation of ibuprofen in interaction with a dry hydrophobic amorphous silica surface, a limited number of water molecules has been added to study the configurational landscape of the microsolvated system. Structural and energetics properties, as well as the role of dispersive forces, have been investigated. Our simulations have revealed that the silica surface exhibits a higher affinity for water than for ibuprofen, even if several structures coexist at room temperature, with an active competition of ibuprofen and water for the exposed surface silanols. Dispersive interactions play a key role in this system, as pure DFT fails to correctly describe its potential energy surface. Indeed, van der Waals forces are the leading contribution to adsorption, independently of whether the drug is hydrogen-bonded directly to the surface or via water molecules.

Sampling Real-Time Atomic Dynamics in Metal Nanoparticles by Combining Experiments, Simulations, and Machine Learning.

Even at low temperatures, metal nanoparticles (NPs) possess atomic dynamics that are key for their properties but challenging to elucidate. Recent experimental advances allow obtaining atomic-resolution snapshots of the NPs in realistic regimes, but data acquisition limitations hinder the experimental reconstruction of the atomic dynamics present within them. Molecular simulations have the advantage that these allow directly tracking the motion of atoms over time. However, these typically start from ideal/perfect NP structures and, suffering from sampling limits, provide results that are often dependent on the initial/putative structure and remain purely indicative. Here, by combining state-of-the-art experimental and computational approaches, how it is possible to tackle the limitations of both approaches and resolve the atomistic dynamics present in metal NPs in realistic conditions is demonstrated. Annular dark-field scanning transmission electron microscopy enables the acquisition of ten high-resolution images of an Au NP at intervals of 0.6 s. These are used to reconstruct atomistic 3D models of the real NP used to run ten independent molecular dynamics simulations. Machine learning analyses of the simulation trajectories allow resolving the real-time atomic dynamics present within the NP. This provides a robust combined experimental/computational approach to characterize the structural dynamics of metal NPs in realistic conditions.

Machine learning of atomic dynamics and statistical surface identities in gold nanoparticles.

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. In this study, we decode the intricate atomic dynamics of metal NPs by using a machine learning approach analyzing high-dimensional data obtained 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. By tracking the emergence, annihilation, lifetime, and dynamic interconversion of the AEs, our approach permits estimating a "statistical equivalent identity" for metal NPs, providing a comprehensive picture of the intrinsic atomic dynamics that shape their properties.

Tidying up the conformational ensemble of a disordered peptide by computational prediction of spectroscopic fingerprints.

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.

A new massively parallel version of CRYSTAL for large systems on high performance computing architectures.

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.

Can Mesoporous Silica Speed Up Degradation of Benzodiazepines? Hints from Quantum Mechanical Investigations.

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.

Reconstructing reactivity in dynamic host-guest systems at atomistic resolution: amide hydrolysis under confinement in the cavity of a coordination cage.

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 an 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 of 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 amide encapsulation/expulsion in/out of the cage cavity (thermodynamics and kinetics), coupling such host-guest dynamic equilibrium with 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.

Lessons from a Challenging System: Accurate Adsorption Free Energies at the Amino Acid/ZnO Interface.

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.

Structural and Computational Assessment of the Influence of Wet-Chemical Post-Processing of the Al-Substituted Cubic Li7La3Zr2O12.

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.

Atomistic Details of Chymotrypsin Conformational Changes upon Adsorption on Silica.

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.

Elucidating the fundamental forces in protein crystal formation: the case of crambin.

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.

Does Dispersion Dominate over H-Bonds in Drug-Surface Interactions? The Case of Silica-Based Materials As Excipients and Drug-Delivery Agents.

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.