Fig. 2: The binding avidity of macromolecules on crowded surfaces is a direct reporter of the energetic penalty posed by the crowded surface.
From: Antibody binding reports spatial heterogeneities in cell membrane organization
A Coarse-grained molecular dynamics (MD) simulations of the IgG free energy versus height above the surface. The observed IgG avidity (Unet, green) is a superposition of an attractive enthalpic binding energy (U0, blue) and a crowding-induced penalty due to crowding polymers (ΔU, black). Therefore, the normalized dissociation constants from Fig. 1C report the local energy penalty of the crowded surface. B (Top left) MD simulations of crowding penalty are re-plotted from Fig. 2A, with solid line indicating classical polymer brush theory28. (Top right) Simulation snapshot of antibody (red) on polymer (gray) coated surface. Weighting the crowding penalty by the FITC distribution yields a mean crowding penalty \(\left\langle {{\Delta }}U\right\rangle\) for a sensor of given \(\left\langle h\right\rangle\). (Bottom left) FITC position probability distributions for different polymer contour lengths based on continuous Gaussian chain model34 (curve) and MD simulations (crosses). (Bottom right) MD snapshots of sensors of different linker lengths and illustration of average height \(\left\langle h\right\rangle\) of a fluctuating FITC antigen. C Antibody avidity data from Fig. 1C are re-plotted to report steric crowding energy as a function of the mean FITC antigen height from Fig. 1B (black circles). Energies are normalized by the smallest antigen sensor size, ΔU0.5k = 0.9kBT. Theoretical prediction based on polymer brush theory (curve) agrees with experimental data and MD simulations (crosses). Horizontal error bars are generated based on CSOP data presented in Fig. 1, using populations of n = 65, 81, 79, and 54 beads. Experimental data points are derived from binding isotherm curve fits, and vertical error bars are derived by fitting curves to data points 1 standard error of the mean above and below each data point, propagated with the 95% CI error of the fits. Curve fits for KD,0 are generated from populations of n = 912, 905, 1062, and 635 beads, respectively. Curve fits for KD are generated from populations of n = 2115, 1281, 1237, and 578 beads, respectively.
