Machine-learning based prediction of small molecule-surface interaction potentials.

Predicting the adsorption affinity of a small molecule to a target surface is of importance to a range of fields, from catalysis to drug delivery and human safety, but a complex task to perform computationally when taking into account the effects of the surrounding medium. We present a flexible machine-learning approach to predict potentials of mean force (PMFs) and adsorption energies for chemical-surface pairs from the separate interaction potentials of each partner with a set of probe atoms. We use a pre-existing library of PMFs obtained via atomistic molecular dynamics simulations for a variety of inorganic materials and molecules to train the model. We find good agreement between original and predicted PMFs in both training and validation groups, confirming the predictive power of this approach, and demonstrate the flexibility of the model by producing PMFs for molecules and surfaces outside the training set.

A hard-sphere model of protein corona formation on spherical and cylindrical nanoparticles.

A nanoparticle (NP) immersed in biological media rapidly forms a corona of adsorbed proteins, which later controls the eventual fate of the particle and the route through which adverse outcomes may occur. The composition and timescale for the formation of this corona are both highly dependent on both the NP and its environment. The deposition of proteins on the surface of the NP can be imitated by a process of random sequential adsorption, and, based on this model, we develop a rate-equation treatment for the formation of a corona represented by hard spheres on spherical and cylindrical NPs. We find that the geometry of the NP significantly alters the composition of the corona through a process independent of the rate constants assumed for adsorption and desorption of proteins, with the radius and shape of the NP both influencing the corona. We further investigate the roles of protein mobility on the surface of the NP and changes in the concentration of proteins.

First principles characterisation of bio-nano interface.

Nanomaterials possess a wide range of potential applications due to their novel properties and exceptionally high activity as a result of their large surface to volume ratios compared to bulk matter. The active surface may present both advantage and risk when the nanomaterials interact with living organisms. As the overall biological impact of nanomaterials is triggered and mediated by interactions at the bio-nano interface, an ability to predict those from the atomistic descriptors, especially before the material is produced, can present enormous advantage for the development of nanotechnology. Fast screening of nanomaterials and their variations for specific biological effects can be enabled using computational materials modelling. The challenge lies in the range of scales that needs to be crossed from the material-specific atomistic representation to the relevant length scales covering typical biomolecules (proteins and lipids). In this work, we present a systematic multiscale approach that allows one to evaluate crucial interactions at the bionano interface from the first principles without any prior information about the material and thus establish links between the details of the nanomaterials structure to protein-nanoparticle interactions. As an example, an advanced computational characterization of titanium dioxide nanoparticles (6 different surfaces of rutile and anatase polymorphs) has been performed. We computed characteristics of the titanium dioxide interface with water using density functional theory for electronic density, used these parameters to derive an atomistic force field, and calculated adsorption energies for essential biomolecules on the surface of titania nanoparticles via direct atomistic simulations and coarse-grained molecular dynamics. Hydration energies, as well as adsorption energies for a set of 40 blood proteins are reported.

Preliminary evaluation of a novel, fully automated, Telenostic device for rapid field-diagnosis of cattle parasites.

New ideas for diagnostics in clinical parasitology are needed to overcome some of the difficulties experienced in the widespread adoption of detection methods for gastrointestinal parasites in livestock. Here we provide an initial evaluation of the performance of a newly developed automated device (Telenostic) to identify and quantify parasitic elements in fecal samples. This study compared the Telenostic device with the McMaster and Mini-FLOTAC for counting of strongyle eggs in a fecal sample. Three bovine fecal samples were examined, in triplicate, on each of the three fecal egg-counting devices. In addition, both manual (laboratory technician) and automated analysis (image analysis algorithm) were performed on the Telenostic device to calculate fecal egg counts (FEC). Overall, there were consistent egg counts reported across the three devices and calculation methods. The Telenostic device compared very favourably to the Mini-FLOTAC and McMaster. Only in sample C, a significant difference (P < 0.05) was observed between the egg counts obtained by Mini-FLOTAC and by the other methods. From this limited dataset it can be concluded that the Telenostic-automated test is comparable to currently used benchmark FEC methods, while improving the workflow, test turn-around time and not requiring trained laboratory personnel to operate or interpret the results.

Role of contact inhibition of locomotion and junctional mechanics in epithelial collective responses to injury.

Epithelial tissues form physically integrated barriers against the external environment protecting organs from infection and invasion. Within each tissue, epithelial cells respond to different challenges that can potentially compromise tissue integrity. In particular, cells collectively respond to injuries by reorganizing their cell-cell junctions and migrating directionally towards the sites of damage. Notwithstanding, the mechanisms that drive collective responses in epithelial aggregates remain poorly understood. In this work, we develop a minimal mechanistic model that is able to capture the essential features of epithelial collective responses to injuries. We show that a model that integrates the mechanics of cells at the cell-cell and cell-substrate interfaces as well as contact inhibition of locomotion (CIL) correctly predicts two key properties of epithelial response to injury as: (1) local relaxation of the tissue and (2) collective reorganization involving the extension of cryptic lamellipodia that extend, on average, up to 3 cell diameters from the site of injury and morphometric changes in the basal regions. Our model also suggests that active responses (like the actomyosin purse string and softening of cell-cell junctions) are needed to drive morphometric changes in the apical region. Therefore, our results highlight the importance of the crosstalk between junctional biomechanics, cell substrate adhesion, and CIL, as well as active responses, in guiding the collective rearrangements that are required to preserve the epithelial barrier in response to injury.

Hydrodynamic lift forces on solutes in a tilted nanopillar array: A computer simulation study.

We study solute transport in a microfluidic channel, where the walls hold an array of tilted rigid nanopillars. By solving numerically the flow equations in the channel, we show that a combination of hydrodynamic effects with excluded volume interactions between the solute particles and the pillars leads to a hydrodynamic lift effect, which varies with the particle size, and depends in a strongly nonlinear fashion on the flow rate. We show that the lift force can be sufficiently strong to drive the solute accumulation or removal from the pillar region and can be switched to the opposite direction by variation of the shear rate or driving pressure. We also demonstrate that the nanopillar array can be used to selectively attract particles of certain size and enhance solute trapping at the surface.

Multiscale Modelling of Bionano Interface.

We present a framework for coarse-grained modelling of the interface between foreign nanoparticles (NP) and biological fluids and membranes. Our model includes united-atom presentations of membrane lipids and globular proteins in implicit solvent, which are based on all-atom structures of the corresponding molecules and parameterised using experimental data or atomistic simulation results. The NPs are modelled by homogeneous spheres that interact with the beads of biomolecules via a central force that depends on the NP size. The proposed methodology is used to predict the adsorption energies for human blood plasma proteins on NPs of different sizes as well as the preferred orientation of the molecules upon adsorption. Our approach allows one to rank the proteins by their binding affinity to the NP, which can be used for predicting the composition of the NP-protein corona for the corresponding material. We also show how the model can be used for studying NP interaction with a lipid bilayer membrane and thus can provide a mechanistic insight for modelling NP toxicity.

Electrophoresis of Janus particles: A molecular dynamics simulation study.

In this work, we use molecular dynamics and lattice-Boltzmann simulations to study the properties of charged Janus particles in an electric field. We show that for a relatively small net charge and a thick electrostatic diffuse layer, mobilities of Janus particles and uniformly charged colloids of the same net charge are identical. However, for higher charges and thinner diffuse layers, the Janus particles always show lower electrophoretic mobility. We also demonstrate that the Janus particles align with the electric field and the angular deviation from the field's direction is related to their dipole moment. We show that the latter is affected by the thickness of electrostatic diffuse layer and strongly correlates with the electrophoretic mobility.

Coarse-grained model of adsorption of blood plasma proteins onto nanoparticles.

We present a coarse-grained model for evaluation of interactions of globular proteins with nanoparticles (NPs). The protein molecules are represented by one bead per aminoacid and the nanoparticle by a homogeneous sphere that interacts with the aminoacids via a central force that depends on the nanoparticle size. The proposed methodology is used to predict the adsorption energies for six common human blood plasma proteins on hydrophobic charged or neutral nanoparticles of different sizes as well as the preferred orientation of the molecules upon adsorption. Our approach allows one to rank the proteins by their binding affinity to the nanoparticle, which can be used for predicting the composition of the NP-protein corona. The predicted ranking is in good agreement with known experimental data for protein adsorption on surfaces.

Multiscale modelling of biomolecular corona formation on metallic surfaces.

In the realm of food industry, the choice of non-consumable materials used plays a crucial role in ensuring consumer safety and product quality. Aluminum is widely used in food packaging and food processing applications, including dairy products. However, the interaction between aluminum and milk content requires further investigation to understand its implications. In this work, we present the results of multiscale modelling of the interaction between various surfaces, that is (100), (110), and (111), of fcc aluminum with the most abundant milk proteins and lactose. Our approach combines atomistic molecular dynamics, a coarse-grained model of protein adsorption, and kinetic Monte Carlo simulations to predict the protein corona composition in the deposited milk layer on aluminum surfaces. We consider a simplified model of milk, which is composed of the six most abundant milk proteins found in natural cow milk and lactose, which is the most abundant sugar found in dairy. Through our study, we ranked selected proteins and lactose adsorption affinities based on their corresponding interaction strength with aluminum surfaces and predicted the content of the naturally forming biomolecular corona. Our comprehensive investigation sheds light on the implications of aluminum in food processing and packaging, particularly concerning its interaction with the most abundant milk proteins and lactose. By employing a multiscale modelling approach, we simulated the interaction between metallic aluminum surfaces and the proteins and lactose, considering different crystallographic orientations. The results of our study provide valuable insights into the mechanisms of lactose and protein deposition on aluminum surfaces, which can aid in the general understanding of protein corona formation.

In silico prediction of protein binding affinities onto core-shell PEGylated noble metal nanoparticles for rational design of drug nanocarriers.

Polymer-coated nanoparticles (NP) are commonly used as drug carriers or theranostic agents. Their uptake rates are modulated by the interactions with essential serum proteins such as transferrin and albumin. Understanding the control parameters of these interactions is crucial for improving the efficiency of these nanoscale devices. In this work, we perform a multiscale computational study of protein adsorption onto polyethylene glycol (PEG) coated gold and silver NPs, producing protein-NP adsorption rankings as a function of PEG grafting density, which are validated against previously reported experimental protein-NP binding constants. Furthermore, the applied nano-docking method provides information on the preferred orientation of proteins immobilised on the surface of NPs. We propose a method of construction of model core-shell NPs in silico. The presented protocol can provide molecular level insights for the experimental development of biosensors, nanocarriers, or other nanoplatforms where information on the preferred orientation of protein at the bio-nano interface is crucial, and enables fast in silico prescreening of assays of various nanocarriers, i.e., combinations of proteins, NPs, and coatings.

Milk Protein Adsorption on Metallic Iron Surfaces.

Food processing and consumption involves multiple contacts between biological fluids and solid materials of processing devices, of which steel is one of the most common. Due to the complexity of these interactions, it is difficult to identify the main control factors in the formation of undesirable deposits on the device surfaces that may affect safety and efficiency of the processes. Mechanistic understanding of biomolecule-metal interactions involving food proteins could improve management of these pertinent industrial processes and consumer safety in the food industry and beyond. In this work, we perform a multiscale study of the formation of protein corona on iron surfaces and nanoparticles in contact with cow milk proteins. By calculating the binding energies of proteins with the substrate, we quantify the adsorption strength and rank proteins by the adsorption affinity. We use a multiscale method involving all-atom and coarse-grained simulations based on generated ab initio three-dimensional structures of milk proteins for this purpose. Finally, using the adsorption energy results, we predict the composition of protein corona on iron curved and flat surfaces via a competitive adsorption model.

Electrostatic interaction of neutral semi-permeable membranes.

We consider an osmotic equilibrium between bulk solutions of polyelectrolyte bounded by semi-permeable membranes and separated by a thin film of salt-free liquid. Although the membranes are neutral, the counter-ions of the polyelectrolyte molecules permeate into the gap and lead to a steric charge separation. This gives rise to a distance-dependent membrane potential, which translates into a repulsive electrostatic disjoining pressure. From the solution of the nonlinear Poisson-Boltzmann equation, we obtain the distribution of the potential and of ions. We then derive an explicit formula for the pressure exerted on the membranes and show that it deviates from the classical van't Hoff expression for the osmotic pressure. This difference is interpreted in terms of a repulsive electrostatic disjoining pressure originating from the overlap of counterion clouds inside the gap. We also develop a simplified theory based on a linearized Poisson-Boltzmann approach. A comparison with simulation of a primitive model for the electrolyte is provided and does confirm the validity of the theoretical predictions. Beyond the fundamental result that the neutral surfaces can repel, this mechanism not only helps to control the adhesion and long-range interactions of living cells, bacteria, and vesicles, but also allows us to argue that electrostatic interactions should play enormous role in determining behavior and functions of systems bounded by semi-permeable membranes.

Endothelial glycocalyx permeability for nanoscale solutes.

Glycocalyx has a great impact on the accessibility of the endothelial cell membranes. Although the specific interactions play a crucial role in cross-membrane solute transport, nonspecific interactions cannot be neglected. In this work, we used computational modeling to quantify the nonspecific interactions that control the distribution of nanosized solutes across the endothelial glycocalyx. We evaluated the probabilities of various nanoparticles' passage through the luminal layer to the membrane. The calculations demonstrate that excluded volume and electrostatic interactions are decisive for the solute transport as compared with van der Waals and hydrodynamic interactions. Damaged glycocalyx models showed a relatively weak efficiency in sieving plasma solutes. We estimated the energy barriers and corresponding mean first passage times for nanoscale solute transport through the model glycocalyx.

Endothelial glycocalyx plays multiple roles in the vascular system: it regulates vascular permeability, modulates the interactions between blood and endothelial cells and controls the shear stress produced by the blood flow. The defense it provides against nanoscale blood solutes such as viruses and nanoparticles is based on nonspecific interactions. Being a natural sieve, it also influences the delivery of medicines transported by the blood flow. In this work, using a computational study of the permeability of endothelial glycocalyx, we demonstrate that the mesh formed by glycan fibers forms an effective mechanical barrier against penetration by nanoscale solutes. We evaluate the characteristic energies and times for the penetration of solutes of various sizes through the glycocalyx and assess the role of different contributions to nanoparticle­glycocalyx interactions. The obtained results may help clarify glycocalyx dysfunction and design drug nanocarriers and other nanoparticle-based medical applications.

Multiscale Modeling of Bio-Nano Interactions of Zero-Valent Silver Nanoparticles.

Understanding the specifics of interaction between the protein and nanomaterial is crucial for designing efficient, safe, and selective nanoplatforms, such as biosensor or nanocarrier systems. Routing experimental screening for the most suitable complementary pair of biomolecule and nanomaterial used in such nanoplatforms might be a resource-intensive task. While a range of computational tools are available for prescreening libraries of proteins for their interactions with small molecular ligands, choices for high-throughput screening of protein libraries for binding affinities to new and existing nanomaterials are very limited. In the current work, we present the results of the systematic computational study of interaction of various biomolecules with pristine zero-valent noble metal nanoparticles, namely, AgNPs, by using the UnitedAtom multiscale approach. A set of blood plasma and dietary proteins for which the interaction with AgNPs was described experimentally were examined computationally to evaluate the performance of the UnitedAtom method. A set of interfacial descriptors (log PNM, adsorption affinities, and adsorption affinity ranking), which can characterize the relative hydrophobicity/hydrophilicity/lipophilicity of the nanosized silver and its ability to form bio(eco)corona, was evaluated for future use in nano-QSAR/QSPR studies.

Representing and describing nanomaterials in predictive nanoinformatics.

Engineered nanomaterials (ENMs) enable new and enhanced products and devices in which matter can be controlled at a near-atomic scale (in the range of 1 to 100 nm). However, the unique nanoscale properties that make ENMs attractive may result in as yet poorly known risks to human health and the environment. Thus, new ENMs should be designed in line with the idea of safe-and-sustainable-by-design (SSbD). The biological activity of ENMs is closely related to their physicochemical characteristics, changes in these characteristics may therefore cause changes in the ENMs activity. In this sense, a set of physicochemical characteristics (for example, chemical composition, crystal structure, size, shape, surface structure) creates a unique 'representation' of a given ENM. The usability of these characteristics or nanomaterial descriptors (nanodescriptors) in nanoinformatics methods such as quantitative structure-activity/property relationship (QSAR/QSPR) models, provides exciting opportunities to optimize ENMs at the design stage by improving their functionality and minimizing unforeseen health/environmental hazards. A computational screening of possible versions of novel ENMs would return optimal nanostructures and manage ('design out') hazardous features at the earliest possible manufacturing step. Safe adoption of ENMs on a vast scale will depend on the successful integration of the entire bulk of nanodescriptors extracted experimentally with data from theoretical and computational models. This Review discusses directions for developing appropriate nanomaterial representations and related nanodescriptors to enhance the reliability of computational modelling utilized in designing safer and more sustainable ENMs.

In Silico Prediction of Protein Adsorption Energy on Titanium Dioxide and Gold Nanoparticles.

The free energy of adsorption of proteins onto nanoparticles offers an insight into the biological activity of these particles in the body, but calculating these energies is challenging at the atomistic resolution. In addition, structural information of the proteins may not be readily available. In this work, we demonstrate how information about adsorption affinity of proteins onto nanoparticles can be obtained from first principles with minimum experimental input. We use a multiscale model of protein-nanoparticle interaction to evaluate adsorption energies for a set of 59 human blood serum proteins on gold and titanium dioxide (anatase) nanoparticles of various sizes. For each protein, we compare the results for 3D structures derived from experiments to those predicted computationally from amino acid sequences using the I-TASSER methodology and software. Based on these calculations and 2D and 3D protein descriptors, we develop statistical models for predicting the binding energy of proteins, enabling the rapid characterization of the affinity of nanoparticles to a wide range of proteins.

Translating Scientific Advances in the AOP Framework to Decision Making for Nanomaterials.

Much of the current innovation in advanced materials is occurring at the nanoscale, specifically in manufactured nanomaterials (MNs). MNs display unique attributes and behaviors, and may be biologically and physically unique, making them valuable across a wide range of applications. However, as the number, diversity and complexity of MNs coming to market continue to grow, assessing their health and environmental risks with traditional animal testing approaches is too time- and cost-intensive to be practical, and is undesirable for ethical reasons. New approaches are needed that meet current requirements for regulatory risk assessment while reducing reliance on animal testing and enabling safer-by-design product development strategies to be implemented. The adverse outcome pathway (AOP) framework presents a sound model for the advancement of MN decision making. Yet, there are currently gaps in technical and policy aspects of AOPs that hinder the adoption and use for MN risk assessment and regulatory decision making. This review outlines the current status and next steps for the development and use of the AOP framework in decision making regarding the safety of MNs. Opportunities and challenges are identified concerning the advancement and adoption of AOPs as part of an integrated approach to testing and assessing (IATA) MNs, as are specific actions proposed to advance the development, use and acceptance of the AOP framework and associated testing strategies for MN risk assessment and decision making. The intention of this review is to reflect the views of a diversity of stakeholders including experts, researchers, policymakers, regulators, risk assessors and industry representatives on the current status, needs and requirements to facilitate the future use of AOPs in MN risk assessment. It incorporates the views and feedback of experts that participated in two workshops hosted as part of an Organization for Economic Cooperation and Development (OECD) Working Party on Manufactured Nanomaterials (WPMN) project titled, "Advancing AOP Development for Nanomaterial Risk Assessment and Categorization", as well as input from several EU-funded nanosafety research consortia.

Understanding and Controlling Food Protein Structure and Function in Foods: Perspectives from Experiments and Computer Simulations.

The structure and interactions of proteins play a critical role in determining the quality attributes of many foods, beverages, and pharmaceutical products. Incorporating a multiscale understanding of the structure-function relationships of proteins can provide greater insight into, and control of, the relevant processes at play. Combining data from experimental measurements, human sensory panels, and computer simulations through machine learning allows the construction of statistical models relating nanoscale properties of proteins to the physicochemical properties, physiological outcomes, and tastes of foods. This review highlights several examples of advanced computer simulations at molecular, mesoscale, and multiscale levels that shed light on the mechanisms at play in foods, thereby facilitating their control. It includes a practical simulation toolbox for those new to in silico modeling.

Prediction of Chronic Inflammation for Inhaled Particles: the Impact of Material Cycling and Quarantining in the Lung Epithelium.

On a daily basis, people are exposed to a multitude of health-hazardous airborne particulate matter with notable deposition in the fragile alveolar region of the lungs. Hence, there is a great need for identification and prediction of material-associated diseases, currently hindered due to the lack of in-depth understanding of causal relationships, in particular between acute exposures and chronic symptoms. By applying advanced microscopies and omics to in vitro and in vivo systems, together with in silico molecular modeling, it is determined herein that the long-lasting response to a single exposure can originate from the interplay between the newly discovered nanomaterial quarantining and nanomaterial cycling between different lung cell types. This new insight finally allows prediction of the spectrum of lung inflammation associated with materials of interest using only in vitro measurements and in silico modeling, potentially relating outcomes to material properties for a large number of materials, and thus boosting safe-by-design-based material development. Because of its profound implications for animal-free predictive toxicology, this work paves the way to a more efficient and hazard-free introduction of numerous new advanced materials into our lives.