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Deep learning-based classification of peptide analytes from single-channel nanopore translocation events
1Department of Microbial Pathogenesis, School of Dentistry, University of Maryland, Baltimore, Maryland, United States of America.
Plos One
|September 11, 2025
Summary
We developed a deep learning pipeline for classifying peptides using nanopore biosensor data. This method accurately identifies peptide types from translocation events, improving disease biomarker detection.
Area of Science:
- Biotechnology
- Bioinformatics
- Nanotechnology
Background:
- Accurate peptide detection via nanopore biosensors is vital for disease diagnosis.
- Processing complex single-channel translocation data for peptide classification is challenging.
Purpose of the Study:
- To present a supervised deep learning pipeline for peptide classification from nanopore translocation data.
- To enhance the accuracy and efficiency of peptide biomarker detection.
Main Methods:
- A convolutional and recurrent neural network classifies raw current recordings into conductance states.
- A branched input network processes conductance state sequences and kinetic features for peptide classification.
Main Results:
- High classification accuracy (0.9998) achieved for pure peptide samples using combined features.
- Modest accuracy (0.70) for classifying peptide mixtures using event-level features.
- Perfect 100% accuracy for pure peptide samples via vote aggregation from translocation event streams.
Conclusions:
- The deep learning framework demonstrates robust nanopore peptide classification using simulated data.
- This work provides a foundation for classifying peptides in complex mixtures from real experimental data.
