Predicting Protein Corona Formation on Carbon Nanotubes

Abstract

Engineered nanoparticles are poised to transform sensing, imaging, and delivery in biological systems. In particular, single-walled carbon nanotubes (SWCNTs) are uniquely suited for biological sensing and imaging due to their tissue-transparent and photostable near-infrared fluorescence. SWCNTs have been functionalized with various biomolecules, including synthetic peptides and proteins, to construct fluorescence-based nanosensors. Optimizing these -nanoparticle interactions is key in enhancing nanotechnology function. Yet, functionalized SWCNTs and other nanotechnologies more broadly suffer from as-of-yet unpredictable interactions with the biological environments in which they are applied: when nanoparticles are introduced into biological systems, endogenous proteins rapidly bind and often lead to decreased ability of the nanoparticles to perform their intended functions. As such, testing compatibility and function of nanotechnologies in biological systems requires a heuristic approach, where this unpredictable protein corona formation often prevents effective implementation.In this work, we develop a classifier that identifies proteins binding to nanotubes based solely on the protein’s amino acid sequence. We build and validate our classifier using mass spectrometry-based quantitative proteomic data that characterizes proteins bound to SWCNT-based nanosensors. Distribution changes among protein properties allow us to determine protein features associated with increased likelihood of SWCNT binding, including high content of solvent-exposed glycines and non-secondary structure-associated amino acids. Understanding these key protein features that drive protein-nanotube binding informs the design of physics-based modeling approaches of such interfacial nano-bio interactions. Lastly, we apply the classifier to rapidly identify candidate proteins with high binding affinity to SWCNTs and experimentally validate adsorption of these proteins with a kinetic exchange assay to evaluate the predictive power of our model. In sum, this classifier provides a step toward undertaking the otherwise intractable problem of predicting protein-nanoparticle interactions.