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Updated: May 21, 2026
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A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
Published on: May 22, 2018
Site-specific post-translational modification detection by polar charged engineered MspA nanopores
Shijun Lin1, Yakun Yi2, Yiheng Liu1
1State Key Laboratory of Geomicrobiology and Environmental Changes, Faculty of Materials Science and Chemistry, China University of Geosciences Wuhan 430074 China louxiaoding@cug.edu.cn xiafan@cug.edu.cn.
Chemical Science
|May 20, 2026
Summary
Researchers developed engineered nanopores for sensitive and specific detection of post-translational modifications (PTMs). This breakthrough advances proteomics by enabling label-free identification of diverse PTMs and isomers at the single-molecule level.
Area of Science:
- Proteomics and Chemical Biology
- Nanotechnology and Single-Molecule Analysis
Background:
- Post-translational modifications (PTMs) are crucial for protein function, but their study is hindered by low abundance and difficulty distinguishing isomers.
- Current methods like mass spectrometry and affinity assays often lack the necessary sensitivity and specificity for comprehensive PTM analysis.
Purpose of the Study:
- To develop a generalizable strategy for site-specific recognition and discrimination of various post-translational modifications (PTMs).
- To overcome limitations of existing techniques in PTM detection and isomer resolution.
Main Methods:
- Engineering of MspA nanopores with specific polar charged residues at the constriction site (N91) to create tailored recognition interfaces.
- Utilizing the engineered nanopores for label-free, single-molecule sensing of peptides with different PTMs.
- Applying a machine learning algorithm for high-accuracy classification of single-molecule events detected by the nanopore.
Main Results:
- Demonstrated label-free discrimination of 10 distinct PTM types across 26 peptides, including phosphorylation, glycosylation, lysine crotonylation, and succinylation.
- Achieved differentiation of subtle positional isomers of PTMs.
- The machine learning classifier achieved >98% accuracy in distinguishing single-molecule events.
Conclusions:
- Established a generalizable principle of electrostatic gating in nanopores for effective PTM profiling.
- The engineered nanopore system offers a versatile and sensitive chemical tool for next-generation single-molecule proteomics.
- This approach significantly enhances the capability to decipher the complex chemical code of PTMs.
