Gold nanoparticles (GNPs) have attracted significant interest as materials for biomedical applications because they can be detected in vivo
, deliver drugs, and target specific sites in the body (e.g.
cancer cells). GNPs are often coated by self-assembled monolayers (SAMs) consisting of alkanethiol ligands with varying backbone and terminal end groups. SAM-protected GNPs are advantageous because of their ease of fabrication with tunable parameters, such as the gold core size, the ligand chain length, and ligand end group chemistry. However, a challenge that inhibits the use of GNPs in biomedical applications is the lack of understanding in how changing these parameters affects the interactions between GNPs and biomolecules, such as proteins or lipid bilayers. The experimental trial-and-error exploration of GNP-biomolecule interactions is cost-prohibitive, motivating the use of alternative techniques to narrow the GNP design space. One promising method to guide GNP design is to relate ligand-specific descriptors to experimental outcomes. For example, recent work has related GNP hydrophobicity â quantified by the octanol-water partition coefficient (logP) of ligand end groups â to phenomena including bilayer adsorption, antimicrobial behavior, and cellular uptake. This approach thus provides some ability to select GNP coatings a priori
that achieve desired behavior. However, a single ligand end group descriptor is insufficient to characterize changes to GNP surface properties that emerge from variations in GNP size or collective interactions between surface-grafted ligands.
In this work, we use classical atomistic molecular simulations to model SAM-protected GNPs and calculate molecular descriptors capable of characterizing GNP properties while accounting for variations in ligand chemistry, backbone structure, size, and ligand-ligand interactions. We first develop a generalized system preparation workflow that can model a range of SAM-protected GNPs. We then model GNPs in different solvation environments and calculate molecular descriptors that encode information about the GNP surface chemical (e.g. hydrophobicity, charge, hydrogen-bonding capabilities, etc.) and structural (e.g., eccentricity, ligand fluctuations, solvent-accessible surface area, etc.) properties. In particular, we show that GNP hydrophobicity â previously quantified by logP â also depends on SAM, which itself depends on NP core size. We expand the molecular descriptor set to capture GNP-lipid membrane interactions by modeling GNPs in the presence of a lipid bilayer and show that descriptors can predict biological outcomes. The molecular descriptors developed in this work will help predict the effects of GNP functionalization on GNP-biomolecule interactions, opening avenues towards efficient screening of GNPs for selective interactions in the biological environment.