Nanopore-based massively parallel sensing for peptide profiling and protein identification

The source code is released on GitHub under the BSD-2-Clause License in this link [https://github.com/BGINPS/npspy].

1. Ying, Y.-L. et al. Nanopore-based technologies beyond DNA sequencing. Nat. Nanotechnol. 17, 1136–1146 (2022).

   Google Scholar 

2. Dorey, A. & Howorka, S. Nanopore DNA sequencing technologies and their applications towards single-molecule proteomics. Nat. Chem. 16, 314–334 (2024).

   Google Scholar 

3. Asandei, A. et al. Nanopore-based protein sequencing using biopores: current achievements and open challenges. Small Methods 4, 1900595 (2020).

   Google Scholar 

4. Cressiot, B., Bacri, L. & Pelta, J. The promise of nanopore technology: advances in the discrimination of protein sequences and chemical modifications. Small Methods 4, 2000090 (2020).

   Google Scholar 

5. Hu, Z.-L., Huo, M.-Z., Ying, Y.-L. & Long, Y.-T. Biological nanopore approach for single-molecule protein sequencing. Angew. Chem. 133, 14862–14873 (2021).

   Google Scholar 

6. Alfaro, J. A. et al. The emerging landscape of single-molecule protein sequencing technologies. Nat. Methods 18, 604–617 (2021).

   Google Scholar 

7. Wei, X. et al. Engineering biological nanopore approaches toward protein sequencing. ACS Nano 17, 16369–16395 (2023).

   Google Scholar 

8. Yan, S. et al. Single molecule ratcheting motion of peptides in a Mycobacterium smegmatis porin A (MspA) nanopore. Nano Lett. 21, 6703–6710 (2021).

   Google Scholar 

9. Brinkerhoff, H., Kang, A. S. W., Liu, J., Aksimentiev, A. & Dekker, C. Multiple rereads of single proteins at single–amino acid resolution using nanopores. Science 374, 1509–1513 (2021).

   Google Scholar 

10. Chen, Z. et al. Controlled movement of ssDNA conjugated peptide through Mycobacterium smegmatis porin A (MspA) nanopore by a helicase motor for peptide sequencing application. Chem. Sci. 12, 15750–15756 (2021).

    Google Scholar 

11. Wanunu, M. Back and forth with nanopore peptide sequencing. Nat. Biotechnol. 40, 172–173 (2022).

    Google Scholar 

12. Nivala, J., Marks, D. B. & Akeson, M. Unfoldase-mediated protein translocation through an α-hemolysin nanopore. Nat. Biotechnol. 31, 247–250 (2013).

    Google Scholar 

13. Nivala, J., Mulroney, L., Li, G., Schreiber, J. & Akeson, M. Discrimination among protein variants using an unfoldase-coupled nanopore. ACS Nano 8, 12365–12375 (2014).

    Google Scholar 

14. Motone, K. et al. Multi-pass, single-molecule nanopore reading of long protein strands. Nature 633, 662–669 (2024).

    Google Scholar 

15. Dutt, S. et al. High accuracy protein identification: fusion of solid-state nanopore sensing and machine learning. Small Methods 7, 2300676 (2023).

    Google Scholar 

16. Soni, N. et al. Full-length protein classification via cysteine fingerprinting in solid-state nanopores. Nat. Nanotechnol. 20, 1482–1490 (2025).

    Google Scholar 

17. Edfors, F. et al. Enhanced validation of antibodies for research applications. Nat Commun 9, 4130 (2018).

    Google Scholar 

18. Kahn, R. A., Virk, H. S. & McPherson, P. S. Heed a decade of calls for antibody validation. Nature 620, 492–492 (2023).

    Google Scholar 

19. Lund-Johansen, F. A strong case for third-party testing. eLife 12, e93329 (2023).

    Google Scholar 

20. Nova, I. C. et al. Detection of phosphorylation post-translational modifications along single peptides with nanopores. Nat. Biotechnol. 42, 710–714 (2024).

    Google Scholar 

21. Meng, G. et al. Modular click chemistry libraries for functional screens using a diazotizing reagent. Nature 574, 86–89 (2019).

    Google Scholar 

22. Venkatesan, N. & Kim, B. H. Peptide conjugates of oligonucleotides: synthesis and applications. Chem. Rev. 106, 3712–3761 (2006).

    Google Scholar 

23. Rosen, C. B., Rodriguez-Larrea, D. & Bayley, H. Single-molecule site-specific detection of protein phosphorylation with a nanopore. Nat. Biotechnol. 32, 179–181 (2014).

    Google Scholar 

24. Motone, K., Cardozo, N. & Nivala, J. Herding cats: Label-based approaches in protein translocation through nanopore sensors for single-molecule protein sequence analysis. iScience 24, 103032 (2021).

    Google Scholar 

25. Cardozo, N. et al. Multiplexed direct detection of barcoded protein reporters on a nanopore array. Nat. Biotechnol. 40, 42–46 (2022).

    Google Scholar 

26. Bakshloo, M. A. et al. Polypeptide analysis for nanopore-based protein identification. Nano Res. 15, 9831–9842 (2022).

    Google Scholar 

27. Zhang, Y. et al. Peptide sequencing based on host–guest interaction-assisted nanopore sensing. Nat. Methods 21, 102–109 (2024).

    Google Scholar 

28. Zhang, M. et al. Real-time detection of 20 amino acids and discrimination of pathologically relevant peptides with functionalized nanopore. Nat. Methods 21, 609–618 (2024).

    Google Scholar 

29. Wang, K. et al. Unambiguous discrimination of all 20 proteinogenic amino acids and their modifications by nanopore. Nat. Methods 21, 92–101 (2024).

    Google Scholar 

30. Papakyriacou, I. et al. Loss of NEDD8 in cancer cells causes vulnerability to immune checkpoint blockade in triple-negative breast cancer. Nat. Commun. 15, 3581 (2024).

    Google Scholar 

31. Yang, M. et al. Prognosis and modulation mechanisms of COMMD6 in human tumours based on expression profiling and comprehensive bioinformatics analysis. Br. J. Cancer 121, 699–709 (2019).

    Google Scholar 

32. Zhang, Z. et al. GABARAPL2 is critical for growth restriction of Toxoplasma gondii in HeLa cells treated with gamma interferon. Infect. Immun. 88, e00054–20 (2020).

    Google Scholar 

33. Sauciuc, A. et al. Blobs form during the single-file transport of proteins across nanopores. Proc. Natl. Acad. Sci. USA 121, e2405018121 (2024).

    Google Scholar 

34. Pommié, C., Levadoux, S., Sabatier, R., Lefranc, G. & Lefranc, M.-P. IMGT standardized criteria for statistical analysis of immunoglobulin V-REGION amino acid properties. J. Mol. Recognit. 17, 17–32 (2004).

    Google Scholar 

35. Lan, W.-H., He, H., Bayley, H. & Qing, Y. Location of phosphorylation sites within long polypeptide chains by binder-assisted nanopore detection. J. Am. Chem. Soc. 146, 24265–24270 (2024).

    Google Scholar 

36. Restrepo-Pérez, L., Wong, C. H., Maglia, G., Dekker, C. & Joo, C. Label-free detection of post-translational modifications with a nanopore. Nano Lett. 19, 7957–7964 (2019).

    Google Scholar 

37. Yu, C. et al. Gln40 deamidation blocks structural reconfiguration and activation of SCF ubiquitin ligase complex by Nedd8. Nat. Commun. 6, 10053 (2015).

    Google Scholar 

38. Herhaus, L. et al. TBK1-mediated phosphorylation of LC3C and GABARAP-L2 controls autophagosome shedding by ATG4 protease. EMBO Rep. 21, e48317 (2020).

    Google Scholar 

39. Layton, C. J., McMahon, P. L. & Greenleaf, W. J. Large-scale, quantitative protein assays on a high-throughput DNA sequencing chip. Mol. Cell 73, 1075–1082.e4 (2019).

    Google Scholar 

40. Wegner, G. J., Lee, H. J. & Corn, R. M. Characterization and optimization of peptide arrays for the study of epitope−antibody interactions using surface plasmon resonance imaging. Anal. Chem. 74, 5161–5168 (2002).

    Google Scholar 

41. Zhang, J.-Y. et al. A single-molecule nanopore sequencing platform. https://doi.org/10.1101/2024.08.19.608720 (2024).

42. Ip, C. L. C. et al. MinION analysis and reference consortium: phase 1 data release and analysis. F1000Res 4, 1075 (2015).

    Google Scholar 

43. Rosen, C. B. & Francis, M. B. Targeting the N terminus for site-selective protein modification. Nat. Chem. Biol. 13, 697–705 (2017).

    Google Scholar 

44. Vaitheeswaran, S. & Thirumalai, D. Interactions between amino acid side chains in cylindrical hydrophobic nanopores with applications to peptide stability. Proc. Natl Acad. Sci. USA 105, 17636–17641 (2008).

    Google Scholar 

45. Wang, W. et al. The China National GeneBank Sequence Archive (CNSA) 2024 update. Hortic. Res. 12, uhaf036 (2025).

    Google Scholar 

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Author notes

1. These authors contributed equally: Ji Wang, Junyi Chen, Hailin Pan, Fengqin Luo, Wenbing Qin.

Authors and Affiliations

1. State Key Laboratory of Genome and Multi-Omics Technologies, BGI Research, Shenzhen, China

   Ji Wang, Junyi Chen, Hailin Pan, Fengqin Luo, Wenbing Qin, Huixian Zeng, Xilong Yuan, Yuchen Qiao, Yunfeng Zhang, Yishuo Zhang, Dapeng Wang, Liang Shen, Zhiwei Zhai, Qianhua Zhu, Yuqing Deng, Xiaojing Sheng, Yuning Zhang, Xu Yan, Tao Zeng, Mengzhe Shen, Bo Teng, Yuxiang LI, Chuanyu Liu, Ou Wang, Yuliang Dong & Xun Xu

2. BGI Research, Wuhan, China

   Zhiwei Zhai & Yuxiang LI

3. State Key Laboratory of Genome and Multi-Omics Technologies, BGI Research, Hangzhou, China

   Qingqing Xie, Tao Zeng, Yinqi Bai & Yuliang Dong

4. BGI-Shenzhen, Shenzhen, China

   Siqi Liu & Xun Xu

Contributions

Ji Wang and Junyi Chen designed the experiment, managed the project, and wrote the paper; Wenbing Qin, Yuqing Deng, and Liang Shen examined the azide modification conditions; Huixian zeng, Yishuo Zhang, Yunfeng Zhang optimized the DTC experiment; Xiaojing Sheng, Qingqing Xie, and Dapeng Wang, performed the pore protein preparation and instrument setup; Fengqin Luo, Yuchen Qiao, and Xilong Yuan managed the sensing experiment and data acquisition; Hailin Pan, Zhiwei Zhai, and Qianhua Zhu conducted the machine learning algorithms; Xu Yan provided AI arithmetic support, Tao Zeng, Yuning Zhang, Yinqi Bai, Mengzhe Shen, Bo Teng, Ou Wang, Yuxiang Li, and Chuanyu Liu provided valuable input during the conceptual development of the project, Yuliang Dong, Siqi Liu and Xun Xu co-supervised the project.

Corresponding authors

Correspondence to Yuliang Dong, Siqi Liu or Xun Xu.

Competing interests

J. Wang, F. Luo, Y. Qiao, Y. Deng, T. Zeng, O. Wang, and Y. Dong are listed as inventors on three related patent applications (WO/2024/182947, WO/2025/129587, and WO/2025/260292), which disclose methods for OPO library construction and purification. J. Wang, F. Luo, Y. Qiao, X. Yan, T. Zeng, Y. Li, and Y. Dong are listed as inventors on patent applications (WO/2025/123211 and WO/2025/123212), which disclose methods for peptide signal extraction and classification. J. Wang, F. Luo, W. Qin, Y. Deng, O. Wang, and Y. Dong are listed as inventors on the patent application (WO/2025/138302), which discloses a method for protein detection. All applications were filed by BGI Research, Shenzhen. The other authors declare no competing interests.

Peer review information

Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

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