Abstract
Nanopores are single-molecule sensors used in nucleic acid analysis, whereas their applicability towards full protein identification has yet to be demonstrated. Here, we show that an engineered Fragaceatoxin C nanopore is capable of identifying individual proteins by measuring peptide spectra that are produced from hydrolyzed proteins. Using model proteins, we show that the spectra resulting from nanopore experiments and mass spectrometry share similar profiles, hence allowing protein fingerprinting. The intensity of individual peaks provides information on the concentration of individual peptides, indicating that this approach is quantitative. Our work shows the potential of a low-cost, portable nanopore-based analyzer for protein identification.
Highlights
Nanopores are single-molecule sensors used in nucleic acid analysis, whereas their applicability towards full protein identification has yet to be demonstrated
We showed that the introduction of an aromatic residue in the sensing region of Fragaceatoxin C (FraC)
We show that G13F-FraC-T1 can be used to directly sample proteins that are digested by a protease
Summary
Nanopores are single-molecule sensors used in nucleic acid analysis, whereas their applicability towards full protein identification has yet to be demonstrated. We show that an engineered Fragaceatoxin C nanopore is capable of identifying individual proteins by measuring peptide spectra that are produced from hydrolyzed proteins. We show that the spectra resulting from nanopore experiments and mass spectrometry share similar profiles, allowing protein fingerprinting. Our work shows the potential of a low-cost, portable nanopore-based analyzer for protein identification. Most mass analyzers are large, have a high cost of investment, are expensive to maintain, and require specialized operators to function[2,3,6]. In contrast to mass spectrometry devices, nanopore-based analyzers provide a low-cost and high-throughput platform with the adaptability towards native environments[7,8,9,10]. Peptides are of special interest for protein characterization, as they allow identification analogous to bottom-up MS-based proteomics
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