Monolayer-protected gold nanoparticles (GNPs) have attracted significant interest in 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 ligands with a sulfur head group, alkane backbone, and terminal end groups. SAM-protected GNPs are advantageous because of their ease of fabrication and tunable parameters, such as the gold core size, the ligand chain length, and ligand end group chemistry. However, the use of GNPs is hindered by the missing connection between the selection of GNP parameters and its influence on GNP-biomolecule interactions. With approximately 3,700 different types of proteins in human blood, experimentally exploring factors affecting the interactions between each GNP-biomolecule combination is time-consuming and cost-prohibitive, motivating the use of computational techniques to narrow down the GNP design space. In this work, we use classical atomistic molecular simulations to model SAM-protected GNPs and understand how tuning GNP size and ligand properties can affect GNP surface properties relevant to interactions with biomolecules. We develop a generalized system preparation workflow for SAM-protected GNPs and simulate more than 100 GNPs from experimentally curated libraries with sub-10-nm sizes and different ligand coatings. We then develop a library of simulation-derived molecular descriptors that inform on GNP properties, such as hydrophobicity, electrostatics, and structural characteristics. Finally, we use data-centric techniques to develop structure-property relationships that could predict GNP behavior more broadly. The computational tools developed here will help predict the effects of GNP functionalization on its behavior in the biological environment, opening avenues towards efficient screening of GNPs for selective biomolecular interactions.

Predicting the behavior of monolayer-protected gold nanoparticles using molecular simulation-derived descriptors

ACS Spring Meeting 2021

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