Dopamine-Decorated TiO2 Nanoparticles in Water: A QM/MM vs an MM Description

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.

Molecular Dynamics for the Optimal Design of Functionalized Nanodevices to Target Folate Receptors on Tumor Cells

Atomistic details on the mechanism of targeting activity by biomedical nanodevices of specific receptors are still scarce in the literature, where mostly ligand/receptor pairs are modeled. Here, we use atomistic molecular dynamics (MD) simulations, free energy calculations, and machine learning approaches on the case study of spherical TiO2 nanoparticles (NPs) functionalized with folic acid (FA) as the targeting ligand of the folate receptor (FR). We consider different FA densities on the surface and different anchoring approaches, i.e., direct covalent bonding of FA γ-carboxylate or through polyethylene glycol spacers. By molecular docking, we first identify the lowest energy conformation of one FA inside the FR binding pocket from the X-ray crystal structure, which becomes the starting point of classical MD simulations in a realistic physiological environment. We estimate the binding free energy to be compared with the existing experimental data. Then, we increase complexity and go from the isolated FA to a nanosystem decorated with several FAs. Within the simulation time framework, we confirm the stability of the ligand-receptor interaction, even in the presence of the NP (with or without a spacer), and no significant modification of the protein secondary structure is observed. Our study highlights the crucial role played by the spacer, FA protonation state, and density, which are parameters that can be controlled during the nanodevice preparation step.

The application of QM/MM simulations in heterogeneous catalysis

The QM/MM simulation method is provenly efficient for the simulation of biological systems, where an interplay of extensive environment and delicate local interactions drives a process of interest through a funnel on a complex energy landscape. Recent advances in quantum chemistry and force-field methods present opportunities for the adoption of QM/MM to simulate heterogeneous catalytic processes, and their related systems, where similar intricacies exist on the energy landscape. Herein, the fundamental theoretical considerations for performing QM/MM simulations, and the practical considerations for setting up QM/MM simulations of catalytic systems, are introduced; then, areas of heterogeneous catalysis are explored where QM/MM methods have been most fruitfully applied. The discussion includes simulations performed for adsorption processes in solvent at metallic interfaces, reaction mechanisms within zeolitic systems, nanoparticles, and defect chemistry within ionic solids. We conclude with a perspective on the current state of the field and areas where future opportunities for development and application exist.

Molecular dynamics simulations of cRGD-conjugated PEGylated TiO2 nanoparticles for targeted photodynamic therapy

The conjugation of high-affinity cRGD-containing peptides is a promising approach in nanomedicine to efficiently reduce off-targeting effects and enhance the cellular uptake by integrin-overexpressing tumor cells. Herein we utilize atomistic molecular dynamics simulations to evaluate key structural-functional parameters of these targeting ligands for an effective binding activity towards α_Vβ₃ integrins. An increasing number of cRGD ligands is conjugated to PEG chains grafted to highly curved TiO₂ nanoparticles to unveil the impact of cRGD density on the ligand's presentation, stability, and conformation in an explicit aqueous environment. We find that a low density leads to an optimal spatial presentation of cRGD ligands out of the "stealth" PEGylated layer around the nanosystem, favoring a straight upward orientation and spaced distribution of the targeting ligands in the bulk-water phase. On the contrary, high densities favor over-clustering of cRGD ligands, driven by a concerted mechanism of enhanced ligand-ligand interactions and reduced water accessibility over the ligand's molecular surface. These findings strongly suggest that the ligand density modulation is a key factor in the design of cRGD-targeting nanodevices to maximize their binding efficiency into over-expressed α_Vβ₃ integrin receptors.

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.

Exploring the drug loading mechanism of photoactive inorganic nanocarriers through molecular dynamics simulations

Inorganic nanoparticles are gaining increasing attention as drug carriers because they respond to external physical stimuli, allowing therapy to be combined with diagnosis. Their drawback is low drug loading capacity, which can be improved by proper and efficacious functionalization. In this computational study, we take TiO2 spherical nanoparticles as prototype photoresponsive inorganic nanoparticles and we fully decorate them with two different types of bifunctional ligands: TETTs and DOPACs, which present different surface anchoring groups (silanol or catechol) but the same drug tethering COOH group, although in different concentrations (3 vs. 1), thus causing different steric hindrances. Then, we put these two types of nanocarriers in bulk water and in the presence of several DOX molecules and let the systems evolve through molecular dynamics (MD) simulations, clearly observing drug loading on the nanocarriers. This comparative MD study allows the investigation of the loading mechanism, performance of a conformational analysis and establishment of the guiding interactions through an energy decomposition analysis. We learn that DOX mostly interacts with the functionalized NPs through electrostatics, as a consequence of the protonated amino group, although several H-bonds are also established both with the ligands and with the oxide surface. Different ligands induce a different electrostatic potential around the NP; therefore, those which lead to the formation of more negative hotspots (here TETTs) are found to favour DOX binding. The leading role of electrostatics can provide a rational explanation for a pH-dependent drug release mechanism that is often invoked for DOX when reaching diseased cells because under anomalous acidic conditions both the NP surface and the carboxylate groups of the ligands are expected to get protonated, which of course would weaken, if not totally quench, the interaction of the nanocarrier with protonated DOX.