Construction of Programmable Nanopore using de novo Designed β-Sheet Peptide

Abstract

Nanopore structures have been employed in biosensing technologies such as single molecule detection and DNA sequencing. However, it is difficult to adjust the size and shape of nanopores for each target molecule using natural proteins. To construct programmable nanopore, de novo design method, which is able to construct artificial proteins/peptides structure accurately, will be promising. Recently, the de novo design technics have been developed, but there are few clues to design pore-forming proteins in the bilayer lipid membrane (BLM). Therefore, we attempted to design pore-forming peptides, which assemble to construct nanopore, because the sequence space of amino acids is less than proteins. Previously, we designed pore-forming α-helical peptides, but the stability of these nanopores was not enough to use nanopore sensing. This paper describes de novo design of β-sheet peptides to construct a nanopore in BLM. The β-sheet peptides are possible to construct more stable nanopore because of the hydrogen bonds between the β-sheet structures. We designed the peptide using four strategies. First, we adopted a β-hairpin structure as a minimum motif of β-sheet structure. Second, the amphiphilic regions coincided with the transmembrane regions. Third, π-π stacking would stabilize the nanopore structure. Last, charged residues would decide the peptide orientations by applying voltage. To prevent from aggregating, a precursor was synthesized using isoacyl dipeptide. As results, the circular dichroism spectrum and solid-state NMR confirmed the peptide has the β-hairpin structure in BLM. The channel current measurements revealed the pore formations of the peptide using the analysis of the shape of channel current signals. Furthermore, the peptide mostly displayed the pentamer pore structures, and we confirmed the pore stability using molecular dynamics simulation. For the next, we will control the pore size by optimization of amino acid sequences.