Transport effects on the kinetics of protein-surface binding

Understanding how proteins bind to surfaces is essential for biosensor design and surface chemistry. Prior research has shown that protein binding is influenced by transport phenomena and intrinsic reaction rates. However, distinguishing between these effects remains challenging. No prior work had resolved how to separate intrinsic chemical rates from transport limitations in surface binding experiments. This gap motivated the development of a model that integrates reaction-diffusion analysis with transport theory. Existing methods often overlook the role of hydration forces and site sparsity in surface binding. The need for a predictive framework that accounts for both kinetics and transport is well established. Experimental data alone cannot clarify the relative contributions of these factors. A detailed model is needed to separate intrinsic and extrinsic influences on binding rates.

This study aimed to develop a model that distinguishes intrinsic chemical rates from transport effects in protein-surface binding. The researchers focused on the binding of avidin to biotin-functionalized surfaces. They sought to quantify the intrinsic rate coefficient k for the biotin-avidin interaction. The model also aimed to predict the effective rate coefficient keff for surface binding. The goal was to test whether transport limitations influence binding in solution or on surfaces. The study aimed to validate the model against experimental data from Zhao and Reichert. The researchers wanted to determine if diffusion or kinetics dominate the binding process. The ultimate purpose was to clarify how surface properties affect binding behavior.

The researchers used coagulation theory to separate intrinsic rate coefficients from transport effects. They applied reaction-diffusion analysis to model binding on localized surface sites. The model incorporated the intrinsic rate coefficient k derived from solution data. The analysis considered the spatial distribution of biotin molecules on the sensor surface. The study accounted for hydration forces that may repel avidin molecules. The model predicted the effective rate coefficient keff for surface binding. The team solved the transport problem to estimate the flux of avidin molecules. Predictions were compared with experimental data from Zhao and Reichert.

The intrinsic rate coefficient for biotin-avidin binding was determined to be k = 0.00045 m/s. Translational diffusion limitations were found to be negligible in solution binding. The effective surface rate coefficient keff was calculated to be ~10(-7) m/s. This value is much smaller than the intrinsic k due to sparse biotin distribution. Hydration forces were identified as a repulsive barrier to avidin binding. The model predicted avidin flux that matched experimental data well. Binding on the sensor surface occurred in an intermediate regime. Both kinetic and diffusive effects contributed to the observed binding rate.

The study concludes that intrinsic chemical rates and transport effects must be separated in surface binding models. The intrinsic rate coefficient k is not sufficient to describe surface binding behavior. The effective rate coefficient keff is significantly lower than k due to surface properties. The model successfully predicted avidin flux based on transport and reaction principles. The findings suggest that surface binding is influenced by hydration forces and site sparsity. The intermediate regime indicates that both kinetics and diffusion play roles. The agreement with experimental data validates the model's assumptions. These conclusions support the use of reaction-diffusion analysis for surface binding studies.