Optimising CNT-FET biosensor design through modelling of biomolecular electrostatic gating and its application to β-lactamase detection.

Abstract

Carbon nanotube field effect transistors (CNT-FET) hold great promise as next generation miniaturised biosensors. One bottleneck is modelling how proteins, with their distinctive electrostatic surfaces, interact with the CNT-FET to modulate conductance. Using advanced sampling molecular dynamics combined with non-canonical amino acid chemistry, we model protein electrostatic potential imparted on single walled CNTs (SWCNTs). We focus on using β-lactamase binding protein (BLIP2) as the receptor as it binds the antibiotic degrading enzymes, β-lactamases (BLs). BLIP2 is attached via the single selected residue to SWCNTs using genetically encoded phenyl azide photochemistry. Our devices detect two different BLs, TEM-1 and KPC-2, with each BL generating distinct conductance profiles due to their differing surface electrostatic profiles. Changes in conductance match the model electrostatic profile sampled by the SWCNTs on BL binding. Thus, our modelling approach combined with residue-specific receptor attachment could provide a general approach for systematic CNT-FET biosensor construction.