A dynamical anthrax toxin nanopore biosensor for high-fidelity single-peptide classification.

Area of Science:

• Biophysics
• Analytical Chemistry
• Biochemistry

Background:

• Current nanopore sensing for proteomics relies on ensemble aggregation, limiting analysis of complex samples.
• Real-time identification of individual peptides is crucial for deciphering heterogeneous biological mixtures.

Purpose of the Study:

• To develop a method for high-fidelity classification of peptides from single translocation events.
• To eliminate the need for ensemble averaging in nanopore-based proteomics.
• To enable true single-molecule proteomics for complex mixture analysis.

Main Methods:

• Utilized the anthrax toxin protective antigen (PA) nanopore, featuring dynamic active-site clamps.
• Employed high-affinity capture for analysis at low nanomolar concentrations.
• Developed a machine learning framework to interpret multi-state signals from single translocation events.

Main Results:

• Achieved high-fidelity classification of peptides from individual translocation events.
• Demonstrated ~91% accuracy in classifying single events using the machine learning framework.
• Successfully resolved complex mixtures that are intractable with bulk aggregation methods.

Conclusions:

• Established a framework for true single-molecule proteomics.
• Overcame limitations of ensemble averaging in nanopore sensing.
• Opened new possibilities for analyzing complex biological samples at the single-molecule level.