Exopeptidase-assisted nanopore peptide sequence identification

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. The EANPSeq method leverages the enzymatic activity of exopeptidases to generate specific fragmentation patterns in peptides, which are then analyzed by machine learning algorithms to reconstruct the original peptide sequence. This approach offers a promising solution for the sequencing of peptides, particularly those with complex structures or post-translational modifications (PTMs). The EANPSeq method has been validated using synthetic peptides and has shown high accuracy in identifying peptide sequences. However, further studies are needed to explore its applicability to more complex biological samples. The use of exopeptidases in nanopore-based sequencing methods has also raised interest in the development of novel enzymatic tools for peptide analysis. Overall, the EANPSeq method represents a significant advancement in the field of peptide sequencing and offers new avenues for research and applications. However, it is essential to note that this method is intended for research use only (RUO) and should not be used for clinical or diagnostic purposes without further validation.