Description: Nanopore-based single-molecule sensing technology shows promising advantages in protein identification. Its real-world applications, however, are significantly constrained by the lack of streamlined sample preparation methods and low throughput, limited detection capabilities for variable samples. We herein present a nanopore sequencing platform that can accurately differentiate peptide fragments derived from enzymatically digested proteins by integrating a universal peptide library preparation strategy and AI-assisted algorithms, facilitating practical protein identification. Benefiting from massively parallel sensing data output, the given peptides exhibit unique statistical signal characteristics. This enables a reliable density-matrix (DM) based filtering approach that discriminates both positive and negative samples. Additionally, combining DMs from different peptides also reveals specific fingerprints among various proteins, enabling effective protein differentiation. The accurate discrimination of real samples has validated the feasibility and reliability of this workflow for nanopore array-based protein detection applications.

Data type: Peptide Sensing

Sample scope: Synthetic

Relevance: Other

Release date: 2024-12-31

Last updated: 2024-12-31

Data size: 528GB