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A generalizable nanopore sensor for highly specific protein detection at single-molecule precision

Author

Listed:
  • Mohammad Ahmad

    (Syracuse University)

  • Jeung-Hoi Ha

    (State University of New York-Upstate Medical University)

  • Lauren A. Mayse

    (Syracuse University
    Syracuse University)

  • Maria F. Presti

    (State University of New York-Upstate Medical University)

  • Aaron J. Wolfe

    (Syracuse University
    Ichor Life Sciences, Inc.
    Clarkson University
    State University of New York)

  • Kelsey J. Moody

    (Syracuse University
    Ichor Life Sciences, Inc.
    Clarkson University
    State University of New York)

  • Stewart N. Loh

    (State University of New York-Upstate Medical University)

  • Liviu Movileanu

    (Syracuse University
    Syracuse University
    Syracuse University)

Abstract

Protein detection has wide-ranging implications in molecular diagnostics. Substantial progress has been made in protein analytics using nanopores and the resistive-pulse technique. Yet, a long-standing challenge is implementing specific interfaces for detecting proteins without the steric hindrance of the pore interior. Here, we formulate a class of sensing elements made of a programmable antibody-mimetic binder fused to a monomeric protein nanopore. This way, such a modular design significantly expands the utility of nanopore sensors to numerous proteins while preserving their architecture, specificity, and sensitivity. We prove the power of this approach by developing and validating nanopore sensors for protein analytes that drastically vary in size, charge, and structural complexity. These analytes produce unique electrical signatures that depend on their identity and quantity and the binder-analyte assembly at the nanopore tip. The outcomes of this work could impact biomedical diagnostics by providing a fundamental basis for biomarker detection in biofluids.

Suggested Citation

  • Mohammad Ahmad & Jeung-Hoi Ha & Lauren A. Mayse & Maria F. Presti & Aaron J. Wolfe & Kelsey J. Moody & Stewart N. Loh & Liviu Movileanu, 2023. "A generalizable nanopore sensor for highly specific protein detection at single-molecule precision," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36944-9
    DOI: 10.1038/s41467-023-36944-9
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    References listed on IDEAS

    as
    1. Kathryn Tunyasuvunakool & Jonas Adler & Zachary Wu & Tim Green & Michal Zielinski & Augustin Žídek & Alex Bridgland & Andrew Cowie & Clemens Meyer & Agata Laydon & Sameer Velankar & Gerard J. Kleywegt, 2021. "Highly accurate protein structure prediction for the human proteome," Nature, Nature, vol. 596(7873), pages 590-596, August.
    2. Lauren Ashley Mayse & Ali Imran & Motahareh Ghahari Larimi & Michael S. Cosgrove & Aaron James Wolfe & Liviu Movileanu, 2022. "Disentangling the recognition complexity of a protein hub using a nanopore," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    3. Florian Leonardus Rudolfus Lucas & Roderick Corstiaan Abraham Versloot & Liubov Yakovlieva & Marthe T. C. Walvoort & Giovanni Maglia, 2021. "Protein identification by nanopore peptide profiling," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    4. John Jumper & Richard Evans & Alexander Pritzel & Tim Green & Michael Figurnov & Olaf Ronneberger & Kathryn Tunyasuvunakool & Russ Bates & Augustin Žídek & Anna Potapenko & Alex Bridgland & Clemens Me, 2021. "Highly accurate protein structure prediction with AlphaFold," Nature, Nature, vol. 596(7873), pages 583-589, August.
    5. Fabien Piguet & Hadjer Ouldali & Manuela Pastoriza-Gallego & Philippe Manivet & Juan Pelta & Abdelghani Oukhaled, 2018. "Identification of single amino acid differences in uniformly charged homopolymeric peptides with aerolysin nanopore," Nature Communications, Nature, vol. 9(1), pages 1-13, December.
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