Antibody Subclass Detection Using Graphene Nanopores.

Abstract

Solid-state nanopores are promising for label-free protein detection. The large thickness, ranging from several tens of nanometers to micrometers and larger, of solid-state nanopores prohibits atomic-scale scanning or interrogation of proteins. Here, a single-atom thick graphene nanopore is shown to be highly capable of sensing and discriminating between different subclasses of IgG antibodies despite their minor and subtle variation in atomic structure. Extensive molecular dynamics (MD) simulations, rigorous statistical analysis with a total aggregate simulation time of 2.7 μs, supervised machine learning (ML), and classification techniques are employed to distinguish IgG2 from IgG3. The water flux and ionic current during IgG translocation reveal distinct clusters for IgG subclasses facilitating an additional recognition mechanism. In addition, the histogram of ionic current for each segment of IgG can provide high-resolution spatial detection. Our results show that nanoporous graphene can be used to detect and distinguish antibody subclasses with good accuracy.