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Sequencing of mRNA from Whole Blood using Nanopore Sequencing
Published on: June 3, 2019
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
Summary
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.
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.
