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Updated: Jun 17, 2026

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High-resolution nanopore peptide sensing, profiling and sequence assembly.

Kefan Wang1,2, Xingwang An1,2, Xinmeng Gao1,2

  • 1State Key Laboratory of Analytical Chemistry for Life Sciences, School of Chemistry, Nanjing Drum Tower Hospital, Nanjing University, Nanjing, China.

Nature Nanotechnology
|June 15, 2026
PubMed
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This summary is machine-generated.

This study introduces a novel nickel-immobilized Mycobacterium smegmatis porin A (MspA-NTA-Ni) nanopore for protein analysis. This platform successfully identifies amino acids and peptides, achieving high accuracy with machine learning, paving the way for advanced proteomics.

Area of Science:

  • Biophysics
  • Analytical Chemistry
  • Proteomics

Background:

  • Nanopore technology has shown success in nucleic acid sequencing.
  • Protein analysis using nanopores remains a significant challenge for proteomics applications.

Purpose of the Study:

  • To establish a nanopore-based platform for identifying and characterizing proteomic analytes.
  • To demonstrate the utility of the MspA-NTA-Ni nanopore for direct peptide identification and sequence reconstruction.

Main Methods:

  • Utilized a nickel-immobilized Mycobacterium smegmatis porin A (MspA-NTA-Ni) nanopore.
  • Employed machine-learning algorithms for analyte classification.
  • Performed enzymatic hydrolysis for peptide fragment generation and analysis.

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Main Results:

  • Identified 20 proteinogenic amino acids, 4 modified amino acids, 32 peptides, 6 modified peptides, 11 bioactive peptides, and 2 neoantigen peptides.
  • Achieved up to 97.4% validation accuracy in analyte classification using machine learning.
  • Successfully reconstructed peptide sequences from enzymatic fragments, demonstrating sensitivity to sequence alterations.

Conclusions:

  • The MspA-NTA-Ni nanopore is a viable platform for identifying a wide range of proteomic analytes.
  • The combination of nanopore measurements and machine learning enables accurate peptide identification and characterization.
  • This approach holds potential for targeted peptide analysis, including the detection of mutations and post-translational modifications.