GlycoGrip: Cell Surface-Inspired Universal Sensor for Betacoronaviruses

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  • Kim, S.H.
    • Affiliation: College of Arts and Sciences, Department of Applied Physical Sciences
  • Kearns, F.L.
    • Other Affiliation: University of California-San Diego
  • Rosenfeld, M.A.
    • Other Affiliation: University of California-San Diego
  • Casalino, L.
    • Other Affiliation: University of California-San Diego
  • Papanikolas, M.J.
    • Affiliation: College of Arts and Sciences, Department of Applied Physical Sciences
  • Simmerling, C.
    • Other Affiliation: SUNY Stony Brook
  • Amaro, R.E.
    • Other Affiliation: University of California-San Diego
  • Freeman, R.
    • Affiliation: College of Arts and Sciences, Department of Applied Physical Sciences
Abstract
  • Inspired by the role of cell-surface glycoproteins as coreceptors for pathogens, we report the development of GlycoGrip: a glycopolymer-based lateral flow assay for detecting SARS-CoV-2 and its variants. GlycoGrip utilizes glycopolymers for primary capture and antispike antibodies labeled with gold nanoparticles for signal-generating detection. A lock-step integration between experiment and computation has enabled efficient optimization of GlycoGrip test strips which can selectively, sensitively, and rapidly detect SARS-CoV-2 and its variants in biofluids. Employing the power of the glycocalyx in a diagnostic assay has distinct advantages over conventional immunoassays as glycopolymers can bind to antigens in a multivalent capacity and are highly adaptable for mutated strains. As new variants of SARS-CoV-2 are identified, GlycoGrip will serve as a highly reconfigurable biosensor for their detection. Additionally, via extensive ensemble-based docking simulations which incorporate protein and glycan motion, we have elucidated important clues as to how heparan sulfate and other glycocalyx components may bind the spike glycoprotein during SARS-CoV-2 host-cell infection. GlycoGrip is a promising and generalizable alternative to costly, labor-intensive RT-PCR, and we envision it will be broadly useful, including for rural or low-income populations that are historically undertested and under-reported in infection statistics.
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  • Article
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  • In Copyright
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  • Attribution-NonCommercial-NoDerivs 3.0 United States
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  • ACS Central Science
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  • English
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  • 2374-7943

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