Versatile and High-Throughput Strategy for the Quantification of Proteins Bound to Nanoparticles

Abstract

Biofunctionalized nanomaterials have been extensively studied as a tool for a wide range of applications in biomedical fields. Despite many existing strategies to conjugate proteins to colloidal particles, determining the grafting efficiency that is, the amount of protein conjugated to the surface of a nanoparticle (NP)remains challenging. Formulations for biomedical applications are subjected to strict constraints, and a lack of precise characterization can prevent otherwise promising formulations to be explored further. Here, we propose a simple approach to precisely measure the grafting efficiency of biological molecules on the surface of three types of widely used NPs: polymeric NPs, inorganic NPs, and metallic NPs. This approach relies on the simultaneous hydrolysis of the grafted protein and the NP degradation in acidic conditions, followed by a spectrophotometric quantification of primary amines in solution. This strategy can be applied to any type of protein and does not require any labeling agent. It can be performed in a high-throughput manner as a routine experiment and only requires a conventional oven and a microplate reader.

Gold Nanoparticles Are an Immobilization Platform for Active and Stable Acetylcholinesterase: Demonstration of a General Surface Protein Functionalization Strategy

“…A modified AAA protocol reported by Oliverio et al was used to determine the number of AChE per AuNP. 37 A calibration curve was created using varying concentrations of pure AChE in solution. To a 50 μL solution of 0.056 μM AuNP− AChE was added 100 μL of 6 M HCl.…”

“…Quantifying the Number of AChE Bound per AuNP. The ratio of protein to AuNP in the AuNP−AChE was then determined using a modified amino acid analysis (AAA) procedure reported by Oliverio et al 37 Accurately quantifying the number of bound enzymes per AuNP, whether or not they remained functional, was crucial for comparing the activity of AChE when bound to AuNP versus the native aqueous enzyme. It was also necessary to assess the structure of the AuNPbound protein using circular dichroism(CD) spectroscopy (discussed below).…”

Supervised learning model predicts protein adsorption to carbon nanotubes

“…Yet, knowledge of the proteins adsorbed in this corona phase would enable better prediction of the biological identity, and thus fate, of the applied nanotechnologies ( 35 , 36 ). Experimental testing to fully characterize the protein corona on all synthesized nanoparticle constructs within all intended biological environments is laborious and costly: While recent work has made headway toward high-throughput experimental methods ( 37 , 38 ), the most strategies rely on labor-intensive mass spectrometry (MS)–based proteomics ( 33 , 39 , 40 ). The ability to predict the protein corona that will form on nanoparticles in vivo remains a challenge that, if overcome, would improve applied nanotechnology performance.…”

Supervised Learning Model Predicts Protein Adsorption to Carbon Nanotubes

“…28,34,38,39 Yet, knowledge of the proteins adsorbed in this corona phase would enable better prediction of the biological identity, and thus fate, of the applied nanotechnologies. 40,41 Experimental testing to fully characterize the protein corona on all synthesized nanoparticle constructs within all intended biological environments is laborious and costly: while recent work has made headway toward high-throughput experimental methods, 42,43 the most common strategies rely on labor-intensive mass spectrometry-based proteomics. 38,44,45 The ability to predict the protein corona that will form on nanoparticles in vivo remains a challenge that, if overcome, would improve applied nanotechnology performance.…”

“…(28, 41) Experimental testing to fully characterize the protein corona on all synthesized nanoparticle constructs within all intended biological environments is laborious and costly. While recent work has made headway toward high-throughput experimental methods,(47, 48) the most common strategies still rely on labor-intensive mass spectrometry-based proteomics. (41, 49) The ability to predict the protein corona that will form on nanoparticles in vivo remains a challenge that, if overcome, would move the field toward better clinical translatability.…”