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Unambiguous discrimination of all 20 proteinogenic amino acids and their modifications by nanopore

Nature Methods volume 21, pages 92–101 (2024)Cite this article

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Abstract

Natural proteins are composed of 20 proteinogenic amino acids and their post-translational modifications (PTMs). However, due to the lack of a suitable nanopore sensor that can simultaneously discriminate between all 20 amino acids and their PTMs, direct sequencing of protein with nanopores has not yet been realized. Here, we present an engineered hetero-octameric Mycobacterium smegmatis porin A (MspA) nanopore containing a sole Ni2+ modification. It enables full discrimination of all 20 proteinogenic amino acids and 4 representative modified amino acids, Nω,N’ω-dimethyl-arginine (Me-R), O-acetyl-threonine (Ac-T), N4-(β-N-acetyl-d-glucosaminyl)-asparagine (GlcNAc-N) and O-phosphoserine (P-S). Assisted by machine learning, an accuracy of 98.6% was achieved. Amino acid supplement tablets and peptidase-digested amino acids from peptides were also analyzed using this strategy. This capacity for simultaneous discrimination of all 20 proteinogenic amino acids and their PTMs suggests the potential to achieve protein sequencing using this nanopore-based strategy.

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Fig. 1: Construction of a Ni-NTA-modified nanopore for amino acid sensing.
Fig. 2: Discrimination of 20 amino acids using MspA-NTA-Ni.
Fig. 3: Identification of 20 amino acids by machine learning.
Fig. 4: Identification of amino acids with PTMs.
Fig. 5: Rapid analysis of amino acid tablets using MspA-NTA-Ni.
Fig. 6: Identification of proteolytically cleaved amino acids.

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Data availability

Data supporting the findings of this study are given in the main text and the Supplementary Information. All source data are provided with this paper. All data used to train, evaluate and test the machine learning model are available on figshare. Please follow the link: https://figshare.com/articles/software/Amino_acid-classifier/23995890 for download. Source data are provided with this paper.

Code availability

The custom machine learning code is available on figshare as ‘Amino acid-classifier’. Please follow the link: https://figshare.com/articles/software/Amino_acid-classifier/23995890 for download.

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Acknowledgements

This project was funded by the National Key R&D Program of China (grant no. 2022YFA1304602, to S.H.), National Natural Science Foundation of China (grant no. 22225405 and no. 31972917, to S.H.), the Fundamental Research Funds for the Central Universities (grant no. 020514380257 to S.H.), Programs for high-level entrepreneurial and innovative talents introduction of Jiangsu Province (individual and group program, to S.H.), Natural Science Foundation of Jiangsu Province (grant no. BK20200009, to S.H.), State Key Laboratory of Analytical Chemistry for Life Science (grant no. 5431ZZXM2204, to S.H.) and the China Postdoctoral Science Foundation (grant no. 2021M691508 and grant no. 2022T150308, to Y.W.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Kefan Wang, Shanyu Zhang, Xiao Zhou.

Authors and Affiliations

  1. State Key Laboratory of Analytical Chemistry for Life Sciences, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, China

    Kefan Wang, Shanyu Zhang, Xian Yang, Xinyue Li, Yuqin Wang, Pingping Fan, Yunqi Xiao, Wen Sun, Panke Zhang & Shuo Huang

  2. Chemistry and Biomedicine Innovation Center (ChemBIC), Nanjing University, Nanjing, China

    Kefan Wang, Shanyu Zhang, Xian Yang, Xinyue Li, Yuqin Wang, Pingping Fan, Yunqi Xiao, Wen Sun & Shuo Huang

  3. Collaborative Innovation Center of Advanced Microstructures, National Laboratory of Solid State Microstructure, Department of Physics, Nanjing University, Nanjing, China

    Xiao Zhou & Wenfei Li

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Contributions

S.H., K.W. and S.Z. conceived the project. S.Z. and K.W. performed the pore engineering. K.W., X.Y., X.L. and W.S. performed the measurements. X.Z. and W.L. conducted the molecular dynamics simulations. Y.W., P.F. and Y.X. designed the machine learning algorithms. K.W. and Y.W. prepared the supplementary videos. P.Z. set up the instruments. S.H. and K.W. wrote the paper. S.H. supervised the project.

Corresponding author

Correspondence to Shuo Huang.

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Competing interests

S.H., S.Z., K.W. and Y.W. have filed patents describing the preparation of heterogeneous MspA and its applications thereof. All other authors have no competing interests.

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Peer review information

Nature Methods thanks Jeff Nivala, Sukanya Punthambaker and Meni Wanunu for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Arunima Singh, in collaboration with the Nature Methods team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Simultaneous sensing of leucine and isoleucine.

The measurements were carried out as described in Methods. A 1.5 M KCl buffer (1.5 M KCl, 10 mM CHES, pH 9.0) was used. A transmembrane voltage of +100 mV was continually applied. Nickel sulfate was added to trans with a final concentration of 50 μM. (a) The chemical structures of leucine (Leu, L) and isoleucine (Ile, I). Leucine and isoleucine are isomers with identical mass. (b) Top: A representative trace acquired during simultaneous sensing of leucine and isoleucine. Each amino acid was added to cis with a final concentration of 1 mM. Bottom: Representative events of leucine and isoleucine. The events are taken from the continuous trace (top) marked with red arrows. I0 represents the open pore current of MspA-NTA-Ni. Events caused by leucine and isoleucine are easily identifiable. (c) The event scatter plot of ∆I versus S. D. generated from results of (b). 274 successive events were used to generate the statistics. Though leucine and isoleucine have indistinguishable MW, they are fully discriminated by nanopore.

Source data

Extended Data Fig. 2 Machine-learning assisted identification of twenty-four amino acids.

(a) The machine-learning workflow. Sensing events acquired with twenty proteinogenic amino acids and four modified amino acids were collected to form a database. Three-hundred events were randomly selected from each amino acid class to form a labeled dataset. Five event features including ΔI, S.D., skew, kurt and toff were extracted from the events to form a feature matrix. After evaluation with ten-fold cross-validation, the quadratic SVM model was found to be the optimum model by demonstrating a validation accuracy of 98.6% (Supplementary Table 9). (b) The confusion matrix result of twenty-four amino acids classification performed with the trained quadratic SVM model. The row of the matrix represents the true class and the column represents the predicted class. (c) The scatter plot of ∆I versus S. D. generated by results of nanopore measurements of 20 proteinogenic amino acids (gray dots) as well as four amino acids containing PTMs (colorful dots). One hundred successive events of each amino acid were used to generate the statistics. The distribution of the four modified amino acids can be fully discriminated from that of the twenty proteinogenic amino acids.

Source data

Supplementary information

Supplementary Information

Materials, Supplementary Tables 1–9, Supplementary Figs. 1–30, References

Reporting Summary

Peer Review File

Supplementary Video 1

Single-channel recording of glycine. The measurements were performed with MspA-NTA-Ni in a 1.5 M KCl buffer (1.5 M KCl, 10 mM CHES, pH 9.0). A voltage of +100 mV was continually applied. Nickel sulfate was added to trans with a final concentration of 50 μM. Glycine was added to cis with a final concentration of 2 mM. All glycine events are marked with ‘G’ above the trace. The trace is played back at twofold the speed of data acquisition. This demonstrates the consistency of events when the same type of amino acid is tested.

Supplementary Video 2

Single-channel recording of histidine. The measurements were performed with MspA-NTA-Ni in a 1.5 M KCl buffer (1.5 M KCl, 10 mM CHES, pH 9.0). A voltage of +100 mV was continually applied. Nickel sulfate was added to trans with a final concentration of 50 μM. Histidine was added to cis with a final concentration of 2 mM, and two characteristic types of events were immediately observed, marked with ‘H1’ and ‘H2’ above the trace. The trace is played back at twofold the speed of data acquisition. This demonstration shows amino acids that produce two types of events.

Supplementary Video 3

Sensing of amino acid mixture. The measurements were performed with MspA-NTA-Ni in a 1.5 M KCl buffer (1.5 M KCl, 10 mM CHES, pH 9.0). A voltage of +100 mV was continually applied. Nickel sulfate was added to trans with a final concentration of 50 μM. For demonstration purpose, five amino acids (glycine, asparagine, isoleucine, arginine, glutamic acid) were used as the representative analytes to perform simultaneous sensing. Each analyte was added to cis with a final concentration of 1 mM. The five amino acids can be clearly distinguished and were automatically recognized by machine learning. The trace is played back at twofold the speed of data acquisition. This demonstration shows simultaneous sensing of amino acids that produce visually different event features.

Supplementary Video 4

Simultaneous sensing of asparagine (N) and N4-(β-N-acetyl-d-glucosaminyl)-asparagine (GlcNAc-N). The measurements were performed with MspA-NTA-Ni in a 1.5 M KCl buffer (1.5 M KCl, 10 mM CHES, pH 9.0). A voltage of +100 mV was continually applied. Nickel sulfate was added to trans with a final concentration of 50 μM. N and GlcNAc-N were simultaneously added to cis, with a final concentration of 2 mM for each component. Two types of events corresponding to N and GlcNAc-N could be easily identified during the recording, and the identity of each event was labeled on the trace. The trace is played back at twofold the speed of data acquisition. This demonstration shows discrimination of modified and unmodified amino acids.

Supplementary Video 5

A cartoon demonstration of the sensing strategy. This demonstration provides a schematic overview of the sensing strategy.

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Wang, K., Zhang, S., Zhou, X. et al. Unambiguous discrimination of all 20 proteinogenic amino acids and their modifications by nanopore. Nat Methods 21, 92–101 (2024). https://doi.org/10.1038/s41592-023-02021-8

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