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Real-time detection of 20 amino acids and discrimination of pathologically relevant peptides with functionalized nanopore

Medicine and Health

Real-time detection of 20 amino acids and discrimination of pathologically relevant peptides with functionalized nanopore

M. Zhang, C. Tang, et al.

This groundbreaking research by Ming Zhang and colleagues presents a copper(II)-functionalized *Mycobacterium smegmatis* porin A (MspA) nanopore that accurately identifies all 20 proteinogenic amino acids. With a robust machine-learning algorithm achieving 99.1% accuracy, this innovative system not only quantifies amino acids at nanomolar levels but also analyzes various peptides, including those relevant to Alzheimer's and cancer, paving the way for advanced peptide sequence inference.

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~3 min • Beginner • English
Abstract
Precise identification and quantification of amino acids is crucial for many biological applications. Here we report a copper(II)-functionalized Mycobacterium smegmatis porin A (MspA) nanopore with the N91H substitution, which enables direct identification of all 20 proteinogenic amino acids when combined with a machine-learning algorithm. The validation accuracy reaches 99.1%, with 30.9% signal recovery. The feasibility of ultrasensitive quantification of amino acids was also demonstrated at the nanomolar range. Furthermore, the capability of this system for real-time analyses of two representative post-translational modifications (PTMs), one unnatural amino acid and ten synthetic peptides using exopeptidases, including clinically relevant peptides associated with Alzheimer’s disease and cancer neoantigens, was demonstrated. Notably, our strategy successfully distinguishes peptides with only one amino acid difference from the hydrolysate and provides the possibility to infer the peptide sequence.
Publisher
Nature Methods
Published On
Apr 01, 2024
Authors
Ming Zhang, Chao Tang, Zichun Wang, Shanchuan Chen, Dan Zhang, Kaiju Li, Ke Sun, Changjian Zhao, Yu Wang, Mengying Xu, Lunzhi Dai, Guangwen Lu, Hubing Shi, Haiyan Ren, Lu Chen, Jia Geng
Tags
nanopore
amino acids
machine learning
peptide analysis
post-translational modifications
Alzheimer's disease
cancer
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