Probing enzyme-nanoparticle interactions using combinatorial gold nanoparticle libraries

Abstract

The interaction of nanoparticles with proteins is extremely complex, important for understanding the biological properties of nanomaterials, but is very poorly understood. We have employed a combinatorial library of surface modified gold nanoparticles to interrogate the relationships between the nanoparticle surface chemistry and the specific and nonspecific binding to a common, important, and representative enzyme, acetylcholinesterase (AChE). We also used Bayesian neural networks to generate robust quantitative structure-property relationship (QSPR) models relating the nanoparticle surface to the AChE binding that also provided significant understanding into the molecular basis for these interactions. The results illustrate the insights that result from a synergistic blending of experimental combinatorial synthesis and biological testing of nanoparticles with quantitative computational methods and molecular modeling.