A random sequential adsorption model for the prediction of SARS-CoV-2 spike protein spatial and orientational distribution on a sensing surface

The interaction between antibodies and viral proteins is crucial for immune response, and understanding these interactions is therefore of great importance. The sensitivity of immunosensors, where antigens are immobilized, largely depends on their orientation. However, experimental methods to determine the orientation are often time-consuming and require specialized equipment. In this paper, we present a random sequential adsorption (RSA) model for the spatial and orientational distribution of the SARS-CoV-2 spike protein immobilized on an 11-mercaptoundecanoic acid self-assembling monolayer. We compare the RSA model prediction with experimentally obtained results for the surface mass density, $581\pm 28$ fmol/${\mathrm{cm}}^2$, and the average number of available RBD per spike protein, 1.19. Experimental results were in good agreement for both immobilized SCoV2-S monolayer (560 fmol/cm²) and SCoV2-S/mAb (634 fmol/cm²). This validation of our simulation results allows us to draw conclusions about binding site density and the observed high sensitivity of the immunosensors. Our findings provide important insights into epitope density and immunosensor sensitivity, offering substantial utility for advancing biosensor research methodologies.