Mechanism of Ligand-Activated Nanoparticles Binding to Target Cell-Surface Receptors by All-Atom Molecular Dynamics

Active targeting strategies, which exploit the highly specific interaction between ligands exposed by nanoparticles (NPs) and cell surface targets, have been experimentally proven to enhance the in vitro selective uptake of nanomedical tools by diseased cells compared to healthy ones. However, the atomistic details of the interaction between ligand-activated nanoparticles and cell surface receptors are still poorly understood. Computational techniques are powerful tools for unveiling the role played by the nanoparticles in the ligand/target binding events and the impact of the targeting ligand density.
As a case study, we modeled the interaction between a cyclic-RGD-conjugated PEGylated TiO₂ NP (the nanodevice) and the extracellular segment of integrin α_Vβ₃ (the target). The modeled nanodevice is a representative example of a complex nanomedical tool, constituted by a photoactive TiO₂ core, coated with a polyethylene glycol (PEG) layer, partially conjugated with active targeting ligands, that is cyclic RGDs. To gain a comprehensive understanding of the underlying mechanisms driving the nanodevice/target binding, we used all-atom molecular dynamics (MD) simulations and machine learning-aided analyses.
We find that the binding of the cyclic-RGD ligand to the integrin pocket is established and stable, even in the presence of the cumbersome realistic model of the nanodevice. A self-organizing map, i.e., an unsupervised machine learning clustering tool, is trained with MD simulation data to achieve a detailed comparison of the ligand/target binding in the presence and in the absence of the nanodevice, revealing differences in the chemical features. Moreover, we discover that the cyclic RGDs conjugated to the NP, but not bound to the integrin binding site, significantly contribute to the adhesion of the nanodevice to the target receptor surface. Finally, by increasing the density of the cyclic RGD ligands on the PEGylated TiO₂ NP, we observe a proportional enhancement of the nanodevice/target interactions.
These findings highlight the crucial role of the multiple copies of the targeting ligand exposed by the nanodevice. Based on MD simulation results, it is essential to finely tune the targeting ligand density on biomedical materials coatings, to improve their selectivity for diseased cells, and, thus, leading to more successful clinical outcomes.