Exopeptidase-assisted nanopore peptide sequence identification

Abstract

Despite substantial progress in nanopore sensing, residue-by-residue peptide sequencing remains a major challenge. Herein, we present EANPSeq, an exopeptidase-assisted nanopore peptide identification strategy based on peptide libraries to decode the peptide sequence. By continuously recognizing the resulting fragments from digesting peptides stepwise through a nanopore, this approach could achieve the identification of peptide sequence based on the comparison of fragment data with libraries of shortened and mutated peptides, with the assistance of machine learning. Notably, compared with previously reported nanopore peptide sensing strategies, EANPSeq shows sufficient resolution to recognize the continuous sequence of peptides containing adjacent identical residues and to precisely localize post-translational modification (PTM) sites within consecutive residues. These proof-of-concept results highlight our nanopore-based strategy as a new avenue for single-molecule protein sequencing. There is significant interest in developing nanopore-based methods for peptide sensing and sequencing. Here the authors report, EANPSeq, a method which enables residue-by-residue peptide sequencing using an exopeptidase-assisted approach, decoding stepwise digestion fragments with machine-learning to achieve single-molecule sequence identification and PTM localization.