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Optimising CNT-FET biosensor design through modelling of biomolecular electrostatic gating and its application to β-lactamase detection

Author

Listed:
  • Rebecca E. A. Gwyther

    (School of Biosciences Cardiff University)

  • Sébastien Côté

    (Université de Montréal
    Cégep de Saint-Jérôme)

  • Chang-Seuk Lee

    (Queen Mary University of London
    Seoul Women’s University)

  • Haosen Miao

    (Queen Mary University of London)

  • Krithika Ramakrishnan

    (School of Biosciences Cardiff University)

  • Matteo Palma

    (Queen Mary University of London)

  • D. Dafydd Jones

    (School of Biosciences Cardiff University)

Abstract

Carbon nanotube field effect transistors (CNT-FET) hold great promise as next generation miniaturised biosensors. One bottleneck is modelling how proteins, with their distinctive electrostatic surfaces, interact with the CNT-FET to modulate conductance. Using advanced sampling molecular dynamics combined with non-canonical amino acid chemistry, we model protein electrostatic potential imparted on single walled CNTs (SWCNTs). We focus on using β-lactamase binding protein (BLIP2) as the receptor as it binds the antibiotic degrading enzymes, β-lactamases (BLs). BLIP2 is attached via the single selected residue to SWCNTs using genetically encoded phenyl azide photochemistry. Our devices detect two different BLs, TEM-1 and KPC-2, with each BL generating distinct conductance profiles due to their differing surface electrostatic profiles. Changes in conductance match the model electrostatic profile sampled by the SWCNTs on BL binding. Thus, our modelling approach combined with residue-specific receptor attachment could provide a general approach for systematic CNT-FET biosensor construction.

Suggested Citation

  • Rebecca E. A. Gwyther & Sébastien Côté & Chang-Seuk Lee & Haosen Miao & Krithika Ramakrishnan & Matteo Palma & D. Dafydd Jones, 2024. "Optimising CNT-FET biosensor design through modelling of biomolecular electrostatic gating and its application to β-lactamase detection," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51325-6
    DOI: 10.1038/s41467-024-51325-6
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    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. Sander J. Tans & Alwin R. M. Verschueren & Cees Dekker, 1998. "Room-temperature transistor based on a single carbon nanotube," Nature, Nature, vol. 393(6680), pages 49-52, May.
    3. 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.
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