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

Biomimetic Plasmonic Nanophages by Head/Tail Self-Assembling: Gold Nanoparticle/Virus Interactions

Gold nanoparticles (AuNPs), because of their dual plasmonic and catalytic functionalities, are among the most promising nanomaterials for the development of therapeutic and diagnostic tools for severe diseases such as cancer and neurodegeneration. Bacteriophages, massively present in human biofluids, are emerging as revolutionary biotechnological tools as they can be engineered to display multiple specific binding moieties, providing effective targeting ability, high stability, low cost, and sustainable production. Coupling AuNPs with phages can lead to an advanced generation of nanotools with great potential for biomedical applications. In the present study, we analyzed the interactions between differently sized AuNPs and filamentous M13 phages, establishing an advanced characterization platform that combines analytical techniques and computational models for an in-depth understanding of these hybrid self-assembling systems. A precise and structurally specific interaction of the AuNP-M13 hybrid complexes was observed, leading to a peculiar head/tail "tadpole-like" configuration. In silico simulations allowed explaining the mechanisms underlying the preferential assembly route and providing information about AuNPs' size-dependent interplay with specific M13 capsid proteins. The AuNP-M13 structures were proven to be biomimetic, eluding the formation of biomolecular corona. By keeping the biological identity of the virion, hybrid nanostructures maintained their natural recognition/targeting ability even in the presence of biomolecular crowding. In addition, we were able to tune the hybrid nanostructures' tropism toward E. coli based on the AuNP size. Overall, our results set the fundamental basis and a standard workflow for the development of phage-based targeting nanotools, valuable for a wide spectrum of nanotechnology applications.

Mechanism of RGD-conjugated nanodevice binding to its target protein integrin αVβ3 by atomistic molecular dynamics and machine learning

Active targeting strategies have been proposed to enhance the selective uptake of nanoparticles (NPs) by diseased cells, and recent experimental findings have proven the effectiveness of this approach. However, no mechanistic studies have yet revealed the atomistic details of the interactions between ligand-activated NPs and integrins. As a case study, here we investigate, by means of advanced molecular dynamics simulations (MD) and machine learning methods (namely equilibrium MD, binding free energy calculations and training of self-organized maps), the interaction of a cyclic-RGD-conjugated PEGylated TiO2 NP (the nanodevice) with the extracellular segment of integrin αVβ3 (the target), the latter experimentally well-known to be over-expressed in several solid tumors. Firstly, we proved that the cyclic-RGD ligand binding to the integrin pocket is established and kept stable even in the presence of the cumbersome realistic model of the nanodevice. In this respect, the unsupervised machine learning analysis allowed a detailed comparison of the ligand/integrin binding in the presence and in the absence of the nanodevice, which unveiled differences in the chemical features. Then, we discovered that unbound cyclic RGDs conjugated to the NP largely contribute to the interactions between the nanodevice and the integrin. Finally, by increasing the density of cyclic RGDs on the PEGylated TiO2 NP, we observed a proportional enhancement of the nanodevice/target binding. All these findings can be exploited to achieve an improved targeting selectivity and cellular uptake, and thus a more successful clinical outcome.

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.

Separation of protein corona from nanoparticles under intracellular acidic conditions: effect of protonation on nanoparticle-protein and protein-protein interactions

Protein coronas separate from nanoparticles under intracellular acidic conditions however, competitive adsorption of multiple proteins and their protein network formation under different pH conditions have not yet been systematically studied at the atomic scale. Herein, we report all-atom molecular dynamics simulations of plasma proteins (human serum albumin and immunoglobulin gamma-1 chain C) adsorbed to 10 nm-sized carboxyl-terminated polystyrene (PS) nanoparticles at different protonation states that mimic extracellular and intracellular pH conditions of 7, 6-5, and 4.5. Binding free energies are calculated from umbrella sampling simulations, showing the significantly weakened binding between PS particles and proteins at the protonation state at pH 4.5, in agreement with experiments showing the separation of protein corona from nanoparticles at pH 4.5. Mixtures of multiple proteins and PS particles are also simulated, showing much less protein adsorption and protein cluster formation at the protonation state at pH 4.5 than that at higher pH values, which are further confirmed by calculating the diffusivities and hydrodynamic radii of individual proteins. In particular, electrostatic particle-protein and protein-protein interactions are significantly weakened by a combination of particle and protein protonation rather than by particle protonation alone, to an extent dependent on different proteins. These findings help explain the experimental observations regarding separation of protein corona from nanoparticles under intracellular acidic conditions at pH 4.5 but not at higher pH, supporting that acidification cannot be the only reason for this separation during the process of endosome maturation.

Deconstructing Electrostatics of Functionalized Metal Nanoparticles from Molecular Dynamics Simulations

Gold nanoparticles (NPs) with different surface functionalizations can selectively interact with specific proteins, allowing a wide range of possible applications in biotechnology and biomedicine. To prevent their tendency to aggregate and to modulate their interaction with charged biomolecules or substrates (e.g., for biosensing applications), they can be functionalized with charged groups, introducing a mutual interaction which can be modulated by changing the ionic strength of the solvent. In silico modeling of these systems is often addressed with low-resolution models, which must account for these effects in the, often implicit, solvent representation. Here, we present a systematic conformational dynamic characterization of ligand-coated gold nanoparticles with different sizes, charges, and functionalizations by means of atomistic molecular dynamics simulations. Based on these, we deconstruct their electrostatic properties and propose a general representation of their average-long-range interactions extendable to different sizes, charges, and ionic strengths. This study clarifies in detail the role of the different features of the NP (charge, size, structure) and of the ionic strength in determining the details of the interparticle interaction and represents the first step toward a general strategy for the parametrization of NP coarse-grained models able to account for varying ionic strengths.

Nanoparticle-mediated cancer cell therapy: basic science to clinical applications

Every sixth person in the world dies due to cancer, making it the second leading severe cause of death after cardiovascular diseases. According to WHO, cancer claimed nearly 10 million deaths in 2020. The most common types of cancers reported have been breast (lung, colon and rectum, prostate cases), skin (non-melanoma) and stomach. In addition to surgery, the most widely used traditional types of anti-cancer treatment are radio- and chemotherapy. However, these do not distinguish between normal and malignant cells. Additional treatment methods have evolved over time for early detection and targeted therapy of cancer. However, each method has its limitations and the associated treatment costs are quite high with adverse effects on the quality of life of patients. Use of individual atoms or a cluster of atoms (nanoparticles) can cause a paradigm shift by virtue of providing point of sight sensing and diagnosis of cancer. Nanoparticles (1-100 nm in size) are 1000 times smaller in size than the human cell and endowed with safer relocation capability to attack mechanically and chemically at a precise location which is one avenue that can be used to destroy cancer cells precisely. This review summarises the extant understanding and the work done in this area to pave the way for physicians to accelerate the use of hybrid mode of treatments by leveraging the use of various nanoparticles.

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.

Differences in protein distribution, conformation, and dynamics in hard and soft coronas: dependence on protein and particle electrostatics

We perform all-atom molecular dynamics simulations of a 9 nm-thick protein layer, which consists of serum albumin (SA) or a mixture of SA and immunoglobulin gamma-1, formed on 10 nm-sized cationic, anionic, and neutral polystyrene particles. More than half of the proteins are densely concentrated within a distance of ∼3 nm from the particle surface, while fewer proteins are broadly distributed in the range of 3-9 nm from the particle. This compares favorably with the experimental observations of a hard corona as the first layer adjacent to the particle and a soft corona as a loose protein-network. The conformation and diffusivity of the proteins vary in different positions of the layer, and are to an extent dependent on the protein and particle electrostatics. These, combined with free energy calculations, show that the protein and particle charges do not significantly modify the strength of protein-particle binding but do influence the distribution of proteins in the layer. In particular, a free protein more strongly binds to the complex of a protein and particle than to either one, showing the synergistic effect of already adsorbed proteins and a particle. This helps explain the experimental observation regarding the formation of a denser protein layer and the stronger protein-protein interaction in the hard corona than the soft corona.

Biomechanical activation of blood platelets via adhesion to von Willebrand factor studied with mesoscopic simulations

Platelet adhesion and activation are essential initial processes of arterial and microvascular hemostasis, where high hydrodynamic forces from the bloodflow impede coagulation. The process relies on von Willebrand factor (VWF)-a linear multimeric protein of blood plasma plays a pivotal role in mechanochemical regulation of shear-induced platelet aggregation (SIPA). Adhesive interactions between VWF and glycoprotein receptors GPIb are crucial for platelet recruitment under high shear stress in fluid. Recent advances in experimental studies revealed that mechanical tension on the extracellular part of GPIb may trigger a cascade of biochemical reactions in platelets leading to activation of integrins [Formula: see text] (also known as GPIIb/IIIa) and strengthening of the adhesion. The present paper is aimed at investigation of this process by three-dimensional computer simulations of platelet adhesion to surface-grafted VWF multimers in pressure-driven flow of platelet-rich plasma. The simulations demonstrate that GPIb-mediated mechanotransduction is a feasible way of platelet activation and stabilization of platelet aggregates under high shear stress. Quantitative understanding of mechanochemical processes involved in SIPA would potentially promote the discovery of new anti-platelet medication and the development of multiscale numerical models of platelet thrombosis and hemostasis.

Aggregation behavior of nanoparticles: Revisiting the phase diagram of colloids

Surface functionalization of metal nanoparticles (NPs), e.g., using peptides and proteins, has recently attracted a considerable attention in the field of design of therapeutics and diagnostics. The possibility of diverse functionalization allows them to selectively interact with proteins, while the metal core ensures solubility, making them tunable therapeutic agents against diseases due to mis-folding or aggregation. On the other hand, their action is limited by possible self-aggregation, which could be, however, prevented based on the full understanding of their phase diagram as a function of the environmental variables (temperature, ionic strength of the solution, concentration) and intrinsic characteristics (size, charge, amount, and type of functional groups). A common modeling strategy to study the phase behavior is to represent the NPs as spheres interacting via effective potentials implicitly accounting for the solvation effects. Their size put the NPs into the class of colloids, albeit with particularly complex interactions including both attractive and repulsive features, and a consequently complex phase diagram. In this work, we review the studies exploring the phases of these systems starting from those with only attractive or repulsive interactions, displaying a simpler disperse-clustered-aggregated transitions. The phase diagram is here interpreted focusing on the universal aspects, i.e., those dependent on the general feature of the potentials, and available data are organized in a parametric phase diagram. We then consider the potentials with competing attractive short range well and average-long-range repulsive tail, better representing the NPs. Through the proper combination of the attractive only and repulsive only potentials, we are able to interpret the appearance of novel phases, characterized by aggregates with different structural characteristics. We identify the essential parameters that stabilize the disperse phase potentially useful to optimize NP therapeutic activity and indicate how to tune the phase behavior by changing environmental conditions or the NP chemical–physical properties.

Mechanistic insights into the size-dependent effects of nanoparticles on inhibiting and accelerating amyloid fibril formation

The aggregation of peptides into amyloid fibrils has been linked to ageing-related diseases, such as Alzheimer's and type 2 diabetes. Interfaces, particularly those with large nanostructured surfaces, can affect the kinetics of peptide aggregation, which ranges from complete inhibition to strong acceleration. While a number of physiochemical parameters determine interfacial effects, we focus here on the role of nanoparticle (NP) size and curvature. We used thioflavin T (ThT) fluorescence assays to demonstrate the size-dependent effects of NPs on amyloid fibril formation for the peptides Aβ₄₀, NNFGAIL, GNNQQNY and VQIYVK. While 5 nm gold NPs (AuNP-5) retarded or inhibited the aggregation of all peptides except NNFGAIL, larger 20 nm gold NPs (AuNP-20) tended to accelerate or not influence peptide aggregation. Differences in the NP effects for the peptides resulted from the different peptide properties (size, tendency to aggregate) and associated surface binding affinities. Additional dynamic light scattering (DLS), electron microscopy, and atomic force microscopy (AFM) experiments with the Aβ₄₀ peptide confirmed size-dependent NP effects on peptide aggregation, and also suggested a structural influence on the formed fibrils. NPs can serve as a surface for the adsorption of peptide monomers and enable nucleation to oligomers and fibril formation. However, molecular dynamics (MD) simulations showed that peptide oligomers were less stable at smaller NPs. High surface curvatures destabilized prefibrillar structures, which provides a possible explanation for inhibitory effects on fibril growth, provided that peptide-NP surface binding was relevant for fibril formation. These mechanistic insights can support the design of future nanostructured materials.

The Role of in silico Research in Developing Nanoparticle-Based Therapeutics

Nanoparticles (NPs) hold great potential as therapeutics, particularly in the realm of drug delivery. They are effective at functional cargo delivery and offer a great degree of amenability that can be used to offset toxic side effects or to target drugs to specific regions in the body. However, there are many challenges associated with the development of NP-based drug formulations that hamper their successful clinical translation. Arguably, the most significant barrier in the way of efficacious NP-based drug delivery systems is the tedious and time-consuming nature of NP formulation—a process that needs to account for downstream effects, such as the onset of potential toxicity or immunogenicity, in vivo biodistribution and overall pharmacokinetic profiles, all while maintaining desirable therapeutic outcomes. Computational and AI-based approaches have shown promise in alleviating some of these restrictions. Via predictive modeling and deep learning, in silico approaches have shown the ability to accurately model NP-membrane interactions and cellular uptake based on minimal data, such as the physicochemical characteristics of a given NP. More importantly, machine learning allows computational models to predict how specific changes could be made to the physicochemical characteristics of a NP to improve functional aspects, such as drug retention or endocytosis. On a larger scale, they are also able to predict the in vivo pharmacokinetics of NP-encapsulated drugs, predicting aspects such as circulatory half-life, toxicity, and biodistribution. However, the convergence of nanomedicine and computational approaches is still in its infancy and limited in its applicability. The interactions between NPs, the encapsulated drug and the body form an intricate network of interactions that cannot be modeled with absolute certainty. Despite this, rapid advancements in the area promise to deliver increasingly powerful tools capable of accelerating the development of advanced nanoscale therapeutics. Here, we describe computational approaches that have been utilized in the field of nanomedicine, focusing on approaches for NP design and engineering.

Effect of dopamine-functionalization, charge and pH on protein corona formation around TiO2 nanoparticles

Inorganic nanoparticles (NPs) are gaining increasing attention in nanomedicine because of their stimuli responsiveness, which allows combining therapy with diagnosis. However, little information is known about their interaction with intracellular or plasma proteins when they are introduced in a biological environment. Here we present atomistic molecular dynamics (MD) simulations investigating the case study of dopamine-functionalized TiO₂ nanoparticles and two proteins that are overexpressed in cancer cells, i.e. PARP1 and HSP90, since experiments proved them to be the main components of the corona in cell cultures. The mechanism and the nature of the interaction (electrostatic, van der Waals, H-bonds, etc.) is unravelled by defining the protein residues that are more frequently in contact with the NPs, the extent of contact surface area and the variations in the protein secondary structures, at different pH and ionic strength conditions of the solution where they are immersed to simulate a realistic biological environment. The effects of the NP surface functionalization and charge are also considered. Our MD results suggest that less acidic intracellular pH conditions in the presence of cytosolic ionic strength enhance PARP1 interaction with the nanoparticle, whereas the HSP90 contribution is partly weakened, providing a rational explanation to existing experimental observations.

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 P^NM, 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.

Between Two Walls: Modeling the Adsorption Behavior of β-Glucosidase A on Bare and SAM-Functionalized Gold Surfaces

The efficient immobilization of enzymes on surfaces remains a complex but central issue in the biomaterials field, which requires us to understand this process at the atomic level. Using a multiscale approach combining all-atom molecular dynamics and coarse-grain Brownian dynamics simulations, we investigated the adsorption behavior of β-glucosidase A (βGA) on bare and self-assembled monolayer (SAM)-functionalized gold surfaces. We monitored the enzyme position and orientation during the molecular dynamics (MD) trajectories and measured the contacts it forms with both surfaces. While the adsorption process has little impact on the protein conformation, it can nonetheless perturb its mechanical properties and catalytic activity. Our results show that compared to the SAM-functionalized surface, the adsorption of βGA on bare gold is more stable, but less specific, and more likely to disrupt the enzyme's function. This observation emphasizes the fact that the structural organization of proteins at the solid interface is a key point when designing devices based on enzyme immobilization, as one must find an acceptable stability-activity trade-off.

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.

Molecular Modeling of Protein Corona Formation and Its Interactions with Nanoparticles and Cell Membranes for Nanomedicine Applications

The conformations and surface properties of nanoparticles have been modified to improve the efficiency of drug delivery. However, when nanoparticles flow through the bloodstream, they interact with various plasma proteins, leading to the formation of protein layers on the nanoparticle surface, called protein corona. Experiments have shown that protein corona modulates nanoparticle size, shape, and surface properties and, thus, influence the aggregation of nanoparticles and their interactions with cell membranes, which can increases or decreases the delivery efficiency. To complement these experimental findings and understand atomic-level phenomena that cannot be captured by experiments, molecular dynamics (MD) simulations have been performed for the past decade. Here, we aim to review the critical role of MD simulations to understand (1) the conformation, binding site, and strength of plasma proteins that are adsorbed onto nanoparticle surfaces, (2) the competitive adsorption and desorption of plasma proteins on nanoparticle surfaces, and (3) the interactions between protein-coated nanoparticles and cell membranes. MD simulations have successfully predicted the competitive binding and conformation of protein corona and its effect on the nanoparticle-nanoparticle and nanoparticle-membrane interactions. In particular, simulations have uncovered the mechanism regarding the competitive adsorption and desorption of plasma proteins, which helps to explain the Vroman effect. Overall, these findings indicate that simulations can now provide predications in excellent agreement with experimental observations as well as atomic-scale insights into protein corona formation and interactions.

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.

Dynamics of Human Serum Albumin Corona Formation on Gold Nanorods with Different Surface Ligands In Silico

The interaction between human serum albumin (HSA) and nanoparticles (NPs) to form HSA corona has widely been studied since endogenous functions of albumin are highly attractive for drug delivery. However, a full understanding of the molecular dynamics and factors behind the formation of HSA corona, including interactions between HSA and different surface ligands and between neighboring HSA molecules, resulting in conformational change of HSA is presently lacking. Here, we assembled 14 HSA molecules around gold nanorods (AuNRs) with different surface chemistries (bare gold surface, cetyltrimethylammonium bromide (CTAB), polystyrene sulfonate (PSS), and polydiallyldimethylammonium chloride (PDADMAC)) in silico and examined the dynamics of HSA corona formation using coarse-grained molecular dynamics for 300 ns of simulation. We observed that PDADMAC, being more flexible than PSS, resulted in all HSA molecules moving toward AuNR-PDADMAC, while the instability of CTAB on AuNR resulted in fewer HSA molecules moving toward AuNR-CTAB compared to AuNR-PSS. HSA molecules around AuNR-PDADMAC also exhibited the largest conformational change in terms of their radius of gyration (R_g) and root mean square deviation (RMSD). In the absence of surface ligands, HSA molecules around the bare AuNR were susceptible to steric hindrance with conformational change observed in terms of their RMSD but not their R_g unlike that of HSA molecules around AuNR-PDADMAC. The insights gained from the inclusion of neighboring HSA molecules in the simulation of corona formation could be more representative than examining a single adsorbed HSA molecule on AuNRs with different surface passivations.

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

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

The Current Landscape of Novel Formulations and the Role of Mathematical Modeling in Their Development

Drug delivery is an integral part of the drug development process, influencing safety and efficacy of active pharmaceutical ingredients. The application of nanotechnology has enabled the discovery of novel formulations for numerous therapeutic purposes across multiple disease areas. However, evaluation of novel formulations in clinical scenarios is slow and hampered due to various ethical and logistical barriers. Computational models have the ability to integrate existing domain knowledge and mathematical correlations, to rationalize the feasibility of using novel formulations for safely enhancing drug delivery, identifying suitable candidates, and reducing the burden on preclinical and clinical studies. In this review, types of novel formulations and their application through several routes of administration and the use of modeling approaches that can find application in different stages of the novel formulation development process are discussed.

Protein Corona on Gold Nanoparticles Studied with Coarse-Grained Simulations

Understanding protein corona formation in an aqueous environment at the molecular and atomistic levels is critical to applications such as biomolecule-detection and drug delivery. In this work, we employed mesoscopic coarse-grained simulations to study ovispirin-1 and lysozyme protein coronas on bare gold nanoparticles. Our study showed that protein corona formation is governed by protein-surface and protein-protein interactions, as well as the surface hydrophobic effect. The corona structure was found to be dependent on protein types and the size of nanoparticles. Ovispirin proteins form homogeneous single-layered adsorption in comparison with the lysozyme's inhomogeneous multilayered aggregates on gold NP surfaces. The decrease in nanoparticle size leads to more angular degrees of freedom for protein adsorption orientation. Subsequent atomistic molecular dynamics simulations further demonstrate the loss of secondary structure of ovispirin upon adsorption and the heterogeneity of its local structure.

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.

Automation and low-cost proteomics for characterization of the protein corona: experimental methods for big data

Nanoparticles used in biological settings are exposed to proteins that adsorb on the surface forming a protein corona. These adsorbed proteins dictate the subsequent cellular response. A major challenge has been predicting what proteins will adsorb on a given nanoparticle surface. Instead, each new nanoparticle and nanoparticle modification must be tested experimentally to determine what proteins adsorb on the surface. We propose that any future predictive ability will depend on large datasets of protein-nanoparticle interactions. As a first step towards this goal, we have developed an automated workflow using a liquid handling robot to form and isolate protein coronas. As this workflow depends on magnetic separation steps, we test the ability to embed magnetic nanoparticles within a protein nanoparticle. These experiments demonstrate that magnetic separation could be used for any type of nanoparticle in which a magnetic core can be embedded. Higher-throughput corona characterization will also require lower-cost approaches to proteomics. We report a comparison of fast, low-cost, and standard, slower, higher-cost liquid chromatography coupled with mass spectrometry to identify the protein corona. These methods will provide a step forward in the acquisition of the large datasets necessary to predict nanoparticle-protein interactions.

NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment

Nanotechnology has enabled the discovery of a multitude of novel materials exhibiting unique physicochemical (PChem) properties compared to their bulk analogues. These properties have led to a rapidly increasing range of commercial applications; this, however, may come at a cost, if an association to long-term health and environmental risks is discovered or even just perceived. Many nanomaterials (NMs) have not yet had their potential adverse biological effects fully assessed, due to costs and time constraints associated with the experimental assessment, frequently involving animals. Here, the available NM libraries are analyzed for their suitability for integration with novel nanoinformatics approaches and for the development of NM specific Integrated Approaches to Testing and Assessment (IATA) for human and environmental risk assessment, all within the NanoSolveIT cloud-platform. These established and well-characterized NM libraries (e.g. NanoMILE, NanoSolutions, NANoREG, NanoFASE, caLIBRAte, NanoTEST and the Nanomaterial Registry (>2000 NMs)) contain physicochemical characterization data as well as data for several relevant biological endpoints, assessed in part using harmonized Organisation for Economic Co-operation and Development (OECD) methods and test guidelines. Integration of such extensive NM information sources with the latest nanoinformatics methods will allow NanoSolveIT to model the relationships between NM structure (morphology), properties and their adverse effects and to predict the effects of other NMs for which less data is available. The project specifically addresses the needs of regulatory agencies and industry to effectively and rapidly evaluate the exposure, NM hazard and risk from nanomaterials and nano-enabled products, enabling implementation of computational 'safe-by-design' approaches to facilitate NM commercialization.

Adsorption of Fibrinogen on Silica Surfaces-The Effect of Attached Nanoparticles

When a biomaterial is inserted into the body, proteins rapidly adsorb onto its surface, creating a conditioning protein film that functions as a link between the implant and adhering cells. Depending on the nano-roughness of the surface, proteins will adsorb in different amounts, with different conformations and orientations, possibly affecting the subsequent attachment of cells to the surface. Thus, modifications of the surface nanotopography of an implant may prevent biomaterial-associated infections. Fibrinogen is of particular importance since it contains adhesion epitopes that are recognized by both eukaryotic and prokaryotic cells, and can therefore influence the adhesion of bacteria. The aim of this study was to model adsorption of fibrinogen to smooth or nanostructured silica surfaces in an attempt to further understand how surface nanotopography may affect the orientation of the adsorbed fibrinogen molecule. We used a coarse-grained model, where the main body of fibrinogen (visible in the crystal structure) was modeled as rigid and the flexible α C-chains (not visible in the crystal structure) were modeled as completely disordered. We found that the elongated fibrinogen molecule preferably adsorbs in such a way that it protrudes further into solution on a nanostructured surface compared to a flat one. This implicates that the orientation on the flat surface increases its bio-availability.

Effects of Nanoparticle Electrostatics and Protein-Protein Interactions on Corona Formation: Conformation and Hydrodynamics

All-atom molecular dynamics simulations of plasma proteins (human serum albumin, fibrinogen, immunoglobulin gamma-1 chain-C, complement C3, and apolipoprotein A-I) adsorbed onto 10 nm sized cationic, anionic, and neutral polystyrene (PS) particles in water are performed. In simulations of a single protein with a PS particle, proteins eventually bind to all PS particles, regardless of particle charge, in agreement with experiments showing the binding between anionic proteins and particles, which is further confirmed by calculating the binding free energies from umbrella sampling simulations. Simulations of mixtures of multiple proteins and a PS particle show the formation of the protein layer on the surface via the adsorption competition between proteins, which influences the binding affinity and structure of adsorbed proteins. In particular, diffusivities are much higher for proteins bound to the particle surface or to the boundary of the protein layer than for those bound to both the particle surface and other proteins, indicating the dependence of protein mobility on their positions in the layer. These findings help to explain in detail experimental observations regarding the replacement of plasma proteins at the early stage of corona formation and the difference in the binding strength of proteins in inner and outer protein-layers.

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.

Loop formation and translational diffusion of intrinsically disordered proteins

The conformational flexibility and dynamics of unfolded peptide chains is of major interest in the context of protein folding and protein functioning. The rate with which amino acids at different positions along the peptide chain meet sets an upper speed limit for protein folding. By using single-molecule photo-induced energy transfer spectroscopy, we have systematically measured end-to-end and end-to-internal site contact formation rates for several intrinsically disordered protein fragments (IDPs) (11 to 41 amino acids) and have also determined their hydrodynamic radius using dual-focus fluorescence correlation spectroscopy. For interpreting the measured values, we have developed a Brownian dynamics model (based on bead-rod chain dynamics in a thermal bath including hydrodynamic interactions) which quantitatively reproduces all measured data surprisingly well while requiring only two fit parameters. The model provides a complete picture of the peptides' dynamics and allows us to translate the experimental rates and radii into molecular properties of the peptides: We find a persistence length of l_{P}=5.2±1.9Å, a hydrodynamic radius of a=3.5±0.7Å per amino acid, and that excluded volume effects play an important role in the dynamics of IDPs.

Molecular Modeling for Nanomaterial-Biology Interactions: Opportunities, Challenges, and Perspectives

Injection of nanoparticles (NP) into the bloodstream leads to the formation of a so-called "nano-bio" interface where dynamic interactions between nanoparticle surfaces and blood components take place. A common consequence is the formation of the protein corona, that is, a network of adsorbed proteins that can strongly alter the surface properties of the nanoparticle. The protein corona and the resulting structural changes experienced by adsorbed proteins can lead to substantial deviations from the expected cellular uptake as well as biological responses such as NP aggregation and NP-induced protein fibrillation, NP interference with enzymatic activity, or the exposure of new antigenic epitopes. Achieving a detailed understanding of the nano-bio interface is still challenging due to the synergistic effects of several influencing factors like pH, ionic strength, and hydrophobic effects, to name just a few. Because of the multiscale complexity of the system, modeling approaches at a molecular level represent the ideal choice for a detailed understanding of the driving forces and, in particular, the early events at the nano-bio interface. This review aims at exploring and discussing the opportunities and perspectives offered by molecular modeling in this field through selected examples from literature.

Low-Resolution Models for the Interaction Dynamics of Coated Gold Nanoparticles with β2-microglobulin

A large number of low-resolution models have been proposed in the last decades to reduce the computational cost of molecular dynamics simulations for bio-nano systems, such as those involving the interactions of proteins with functionalized nanoparticles (NPs). For the proteins, “minimalist” models at the one-bead-per residue (Cα-based) level and with implicit solvent are well established. For the gold NPs, widely explored for biotechnological applications, mesoscale (MS) models treating the NP core with a single spheroidal object are commonly proposed. In this representation, the surface details (coating, roughness, etc.) are lost. These, however, and the specificity of the functionalization, have been shown to have fundamental roles for the interaction with proteins. We presented a mixed-resolution coarse-grained (CG) model for gold NPs in which the surface chemistry is reintroduced as superficial smaller beads. We compared molecular dynamics simulations of the amyloid β2-microglobulin represented at the minimalist level interacting with NPs represented with this model or at the MS level. Our finding highlights the importance of describing the surface of the NP at a finer level as the chemical-physical properties of the surface of the NP are crucial to correctly understand the protein-nanoparticle association.

Mathematical modeling in cancer nanomedicine: a review

Cancer continues to be among the leading healthcare problems worldwide, and efforts continue not just to find better drugs, but also better drug delivery methods. The need for delivering cytotoxic agents selectively to cancerous cells, for improved safety and efficacy, has triggered the application of nanotechnology in medicine. This effort has provided drug delivery systems that can potentially revolutionize cancer treatment. Nanocarriers, due to their capacity for targeted drug delivery, can shift the balance of cytotoxicity from healthy to cancerous cells. The field of cancer nanomedicine has made significant progress, but challenges remain that impede its clinical translation. Several biophysical barriers to the transport of nanocarriers to the tumor exist, and a much deeper understanding of nano-bio interactions is necessary to change the status quo. Mathematical modeling has been instrumental in improving our understanding of the physicochemical and physiological underpinnings of nanomaterial behavior in biological systems. Here, we present a comprehensive review of literature on mathematical modeling works that have been and are being employed towards a better understanding of nano-bio interactions for improved tumor delivery efficacy.

Microscopy and Cell Biology: New Methods and New Questions

Understanding the cellular basis of human health and disease requires the spatial resolution of microscopy and the molecular-level details provided by spectroscopy. This review highlights imaging methods at the intersection of microscopy and spectroscopy with applications in cell biology. Imaging methods are divided into three broad categories: fluorescence microscopy, label-free approaches, and imaging tools that can be applied to multiple imaging modalities. Just as these imaging methods allow researchers to address new biological questions, progress in biological sciences will drive the development of new imaging methods. We highlight four topics in cell biology that illustrate the need for new imaging tools: nanoparticle-cell interactions, intracellular redox chemistry, neuroscience, and the increasing use of spheroids and organoids. Overall, our goal is to provide a brief overview of individual imaging methods and highlight recent advances in the use of microscopy for cell biology.

Role of Ligand Conformation on Nanoparticle-Protein Interactions

Engineered biomedical nanoparticles (NPs) administered via intravenous routes are prone to associate to serum proteins. The protein corona can mask the NP surface functionalization and hamper the delivery of the NP to its biological target. The design of corona-free NPs relies on our understanding of the chemical-physical features of the NP surface driving the interaction with serum proteins. Here, we address, by computational means, the interaction between human serum albumin (HSA) and a prototypical monolayer-protected Au nanoparticle. We show that both the chemical composition (charge, hydrophobicity) and the conformational preferences of the ligands decorating the NP surface affect the NP propensity to bind HSA.

Impact of nanoparticles on amyloid peptide and protein aggregation: a review with a focus on gold nanoparticles

Society is increasingly exposed to nanoparticles as they are ubiquitous in nature and introduced as man-made air pollutants and as functional ingredients in cosmetic products as well as in nanomedicine. Nanoparticles differ in size, shape and material properties. In addition to their intended function, the side effects on biochemical processes in organisms remain unclear. Nanoparticles can significantly influence the nucleation and aggregation process of peptides. The development of several neurodegenerative diseases, such as Alzheimer's disease, is related to the aggregation of peptides into amyloid fibrils. However, there is no comprehensive or universal mechanism to predict or explain apparent acceleration or inhibition of these aggregation processes. In this work, selected studies and possible mechanisms for amyloid peptide nucleation and aggregation, in the presence of nanoparticles, are highlighted. These studies are discussed in the context of recent data from our group on the role of gold nanoparticles in amyloid peptide aggregation using experimental methods and large-scale molecular dynamics simulations. A complex interplay of the surface properties of the nanoparticles, the properties of the peptides, as well as the resulting forces between both the nanoparticles and the peptides, appear to determine whether amyloid peptide aggregation is influenced, catalysed or inhibited by the presence of nanoparticles.

Label-free in-flow detection of receptor recognition motifs on the biomolecular corona of nanoparticles

Nanomedicine, nanotargeting and nanotherapeutics have in the last few years faced several difficulties in translating the promising results obtained in vitro to an in vivo scenario. The origin of this discrepancy might be found in the lack of a detailed and realistic characterization of the biological surface of nanoparticles. Despite the capability to engineer nanomaterials with a great variety and a precise control of the surface functionalization, the targeting capability is lost when the nanoparticles are embedded in complex biological media, due to the formation of a biological layer (biomolecular corona). This biological layer represents the ultimate nanoparticle surface, likely to interact with the cell machinery. Therefore, in addition to traditional nanoparticle characterization techniques, a more insightful investigation of the biomolecular corona is needed, including the capability to assess the orientation and functionality of specific key molecular features. Here we present a method for the rapid screening of exposed protein recognition motifs on the surface of nanoparticles exploiting quartz crystal microbalance (QCM). We quantify accessible functional epitopes of transferrin-coated nanoparticles and correlate them to differences in nanoparticle size and functionalization. The target recognition occurs label free in flow, thereby pushing our investigation into a more in vivo-like scenario. Our method is applicable to a wide array of nanoparticles and therefore holds the potential to become an advanced technique for the classification of all kinds of nanobioconstructs based on their biological external functionality.

Nanoparticle-Cell Interactions: Relevance for Public Health

Nanoparticles, especially metal oxide nanoparticles, are used in a wide range of commercial and industrial applications that result in direct human contact, such as titanium dioxide nanoparticles in paints, food colorings, and cosmetics, or indirectly through release of nanoparticle-containing materials into the environment. Workers who process nanoparticles for downstream applications are exposed to especially high concentrations of nanoparticles. For physical chemists, nanoparticles present an interesting area of study as the small size of nanoparticles changes the properties from that of the bulk material, leading to novel properties and reactivity. For the public health community, this reduction in particle size means that exposure limits and outcomes that were determined from bulk material properties are not necessarily valid. Informed determination of exposure limits requires a fundamental understanding of how nanoparticles interact with cells. This Feature Article highlights the areas of intersection between physical chemistry and public health in understanding nanoparticle-cell interactions, with a focus on titanium dioxide nanoparticles. It provides an overview of recent research examining the interaction of titanium dioxide nanoparticles with cells in the absence of UV light and provides recommendations for additional nanoparticle-cell research in which physical chemistry expertise could help to inform the public health community.

Nano- and microparticles at fluid and biological interfaces

Systems with interfaces are abundant in both technological applications and biology. While a fluid interface separates two fluids, membranes separate the inside of vesicles from the outside, the interior of biological cells from the environment, and compartmentalize cells into organelles. The physical properties of interfaces are characterized by interface tension, those of membranes are characterized by bending and stretching elasticity. Amphiphilic molecules like surfactants that are added to a system with two immiscible fluids decrease the interface tension and induce a bending rigidity. Lipid bilayer membranes of vesicles can be stretched or compressed by osmotic pressure; in biological cells, also the presence of a cytoskeleton can induce membrane tension. If the thickness of the interface or the membrane is small compared with its lateral extension, both can be described using two-dimensional mathematical surfaces embedded in three-dimensional space. We review recent work on the interaction of particles with interfaces and membranes. This can be micrometer-sized particles at interfaces that stabilise emulsions or form colloidosomes, as well as typically nanometer-sized particles at membranes, such as viruses, parasites, and engineered drug delivery systems. In both cases, we first discuss the interaction of single particles with interfaces and membranes, e.g. particles in external fields, non-spherical particles, and particles at curved interfaces, followed by interface-mediated interaction between two particles, many-particle interactions, interface and membrane curvature-induced phenomena, and applications.

Exploring Protein-Nanoparticle Interactions with Coarse-Grained Protein Folding Models

Understanding the fundamental biophysics behind protein-nanoparticle (NP) interactions is essential for the design and engineering bio-NP systems. The authors describe the development of a coarse-grained protein-NP model that utilizes a structure centric protein model. A key feature of the protein-NP model is the quantitative inclusion of the hydrophobic character of residues in the protein and their interactions with the NP surface. In addition, the curvature of the NP is taken into account, capturing the protein behavior on NPs of different size. The authors evaluate this model by comparison with experimental results for structure and adsorption of a model protein interacting with an NP. It is demonstrated that the simulation results recapitulate the structure of the small α/β protein GB1 on the NP for data from circular dichroism and fluorescence spectroscopy. In addition, the calculated protein adsorption free energy agrees well with the experimental value. The authors predict the dependence of protein folding on the NP size, surface chemistry, and temperature. The model has the potential to guide NP design efforts by predicting protein behavior on NP surfaces with various chemical properties and curvatures.

Regimes of Biomolecular Ultrasmall Nanoparticle Interactions

Ultrasmall nanoparticles (USNPs), usually defined as NPs with core in the size range 1-3 nm, are a class of nanomaterials which show unique physicochemical properties, often different from larger NPs of the same material. Moreover, there are also indications that USNPs might have distinct properties in their biological interactions. For example, recent in vivo experiments suggest that some USNPs escape the liver, spleen, and kidney, in contrast to larger NPs that are strongly accumulated in the liver. Here, we present a simple approach to study the biomolecular interactions at the USNPs bio-nanointerface, opening up the possibility to systematically link these observations to microscopic molecular principles.

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.