Ultrafast colorimetric determination of predominant protein structure evolution with gold nanoplasmonic particles

The intracellular and extracellular accumulation of disordered proteins and aggregated proteins occurs in many protein conformational diseases, such as aging-related neurodegeneration and alcoholic liver diseases. However, the conventional methods to study protein structural changes are limited for the rapid detection and monitoring of protein aggregation because of long incubation times (i.e., usually several days), complicated sample pretreatment steps, and expensive instrumentation. Here, we describe an ultrafast colorimetric method for the real-time monitoring of protein structure evolution and the determination of predominant structures via nanoparticle-assisted protein aggregation. During the aggregation process, nanoparticles act as nucleation cores, which form networks depending on the structures of the protein aggregates, and accelerate the kinetics of the protein aggregation. Simultaneously, these nanoparticles exhibit colorimetric responses according to their embedded shapes (e.g., fibrillar and amorphous) on the protein aggregates. We observed distinct spectral shifts and concomitant colorimetric responses of concentration- and type-dependent protein aggregation with the naked eye within a few minutes (<2 min) under acidic conditions. Moreover, the morphological transitions from small aggregates to larger aggregates of nanoparticle-assisted protein aggregates were visualized with dark-field microscope imaging, which show a similar trend with that of protein aggregates formed without the aid of nanoparticles. Finally we show that our proposed method can be utilized to screen the protein aggregation propensity under a variety of conditions such as different pH levels, high temperature, and chemicals. These findings suggest that the proposed method is an easy way to study the molecular biophysics of protein aggregation and to rapidly screen anti-aggregation drugs for protein conformational diseases.