Charge-based fingerprinting of unlabeled full-length proteins using an aerolysin nanopore

Abstract

Proteins play essential roles in cellular processes and are involved in numerous diseases, driving the need for efficient proteoform identification. While nanopore technology was highly successful for DNA sequencing, this approach has not yet delivered its full potential in protein identification. Here, we demonstrate the capabilities of an aerolysin nanopore for direct identification of a range of unlabeled full-length proteins. Combining low pH and guanidinium chloride, we generate a strong electroosmotic flow that enables an efficient capture and translocation, resulting in distinct fingerprints from single-protein translocations. Using machine learning classifier, we achieve 80% accuracy in distinguishing seven related proteins with 38%-70% pairwise sequence identities. Differences in fingerprints largely reflect the distribution of positive charges in the protein sequences, providing a rational basis for the observed sensitivity. With further development, fingerprint-predictions could allow to infer de novo protein sequence information from single-molecule data, offering a powerful tool for proteomics.