GlycoGrip: Cell Surface-Inspired Universal Sensor for Betacoronaviruses

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.

Evidence for multiple binding modes in the initial contact between SARS-CoV-2 spike S1 protein and cell surface glycans

“…Interestingly, the two modes involved opposite saccharide orientation and revealed no detectable changes in the conformation of L-IdoA2S residues, or of the glycosidic linkage geometries that might enable tighter binding to the protein surface. More precisely, experimental data supports the previously identified sequence of amino acids, i. e., R346, N354, R355, K356, R357, R466, and K444 (representing the core of site I) in accord to Kim et al [19] and Paiardi et al [20] , as the principal site for interaction, leaving the flexible loops of S1-RBD free to engage ACE2 (Figure 7). The simulated intra-residue and inter-glycosidic trNOEs of (1) in interaction with S1-RBD agreed better with experiment when at least two (A and B) binding modes were considered.…”

“…[14] Since the first demonstration of binding and conformational change induced in S1-RBD by heparin, [16] a considerable effort has gone into defining the molecular 'keys' underpinning this interaction. Heparin, a proxy for HS was observed to promote the 'down' to 'up' conformational change in the S1-RBD and cell infectivity by SARS-CoV-2, [12,17] while, progress has been made in identifying potential HS binding sites on S1-RBD [17,18] and potential saccharide structures in HS that engage the S protein [18][19][20][21][22][23][24] . These results, while hinting at apparent high specificity and selectivity of S1 RBD for HS, more likely represent a correspondence between high charge and binding, a property that is commonly observed among HS-protein interactions, when heparin is used as an experimental proxy.…”

Spike-heparan sulfate interactions in SARS-CoV-2 infection

“…Similarly, a decrease in SARS-CoV-2 binding to the cell surface occurred when ACE2 was genetically depleted, and the residual ACE2-independent binding was shown to depend on HS. Other studies have confirmed that the spike protein can engage cells independently of ACE2 expression, although the efficiency of engagement was diminished [ 3 , 27 , 28∗∗ , 28 ]. Infection may occur independently of ACE2, possibly through micropinocytosis mediated by HSPG internalization [ 29 ].…”

Modeling insights into SARS-CoV-2 respiratory tract infections prior to immune protection

“…Our within-host model of RT exposure, clearance, and infection from inhaled viruses is unique by explicitly incorporating MCC, and thus is different from and complementary to other successful within-host models of viral infection of the RT (cf. (10)(11)(12)(13)(14)(15)(16)(17)(18)(19) and (20)(21)(22)). The computational modeling platform incorporates the following (see Tables S1 and S2):…”