Due to surface rugosity, the physical and chemical microenvironment at the surface is heterogeneous. Therefore, proteins that have a single class of binding sites in solution can show a dispersion in the binding properties once chemically crosslinked to the surface. As a tool to study this ensemble of surface sites, we have previously introduced a computational approach to determine distributions of affinity and kinetic binding site parameters from experimental surface binding data. This allowed us, for the first time, to account simultaneously for the two most commonly encountered experimental problems of surface binding when using biosensors for characterizing protein interactions, site heterogeneity and mass transport limitation, and thereby model experimental data consistently to within the level of noise of data acquisition. This fully exploits the high sensitivity of surface plasmon resonance biosensors for the study of protein interactions, and provides information of the surface binding sites with unprecendented level of detail.? ? Recently, we have refined the computational approach with Bayesian regularization that enables us to introduce different prior expectations or hypotheses of the shape of the distribution. Using this strategy, we were able to experimentally demonstrate the presence of micro-heterogeneity of the ensemble of surface sites of immobilized proteins. We were also able to demonstrate that, for some systems, the functional distribution of surface sites can depend on the total surface density of sites. This has important implications for the understanding of the process of protein immobilization into polymeric matrices. ? ? We have continued the study of how, in detail, different surface properties affect the distribution of functional properties of immobilized proteins. The goal is to establish surfaces, polymeric matrics, and immobilization conditions that affect minimally the binding properties of the immobilized proteins and result in uniformly active surface proteins.
