Narrowing Signal Distribution by Adamantane Derivatization for Amino Acid Identification Using an α-Hemolysin Nanopore

Abstract

The rapid progress in nanopore sensing has sparked interest in protein sequencing. Despite recent notable advancements in amino acid recognition using nanopores, chemical modifications usually employed in this process still need further refinements. One of the challenges is to enhance the chemical specificity to avoid downstream misidentification of amino acids. By employing adamantane to label proteinogenic amino acids, we developed an approach to fingerprint individual amino acids using the wild-type α-hemolysin nanopore. The unique structure of adamantane-labeled amino acids (ALAAs) improved the spatial resolution, resulting in distinctive current signals. Various nanopore parameters were explored using a machine-learning algorithm and achieved a validation accuracy of 81.3% for distinguishing nine selected amino acids. Our results not only advance the effort in single-molecule protein characterization using nanopores but also offer a potential platform for studying intrinsic and variant structures of individual molecules.