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Under the most common experimental conditions, the adsorption of proteins tosolid surfaces is an spontaneous process that leads to a rather compact layerof randomly oriented molecules. Due to the importance of this process for thedevelopment of catalytic surfaces, a number of existing computational andexperimental approaches try to predict and control the orientation of suchmolecules. However, and despite their own advantages, these tend to be eithertoo expensive computationally, or oversimplified, undermining their ability topredict the most appropriate experimental conditions to maximize the catalyticactivity of adsorbed proteins. To address this current need, we present anefficient computational approach to model the behavior of proteins nearsurfaces in the presence of an external electric field, based on continuumelectrostatics. Our model can not only estimate the overall affinity of theprotein with the surface, but also their most likely orientation as a functionof the potential applied. In this way, a rational selection of the potentialcan be performed to maximize the accessibility of the protein's active site tothe solvent. The model relies on the Poisson-Boltzmann equation and wasimplemented in an extension of the code PyGBe that includes an externalelectric field, and renders the electrostatic component of the solvation freeenergy. To demonstrate the feasibility of this technique, we investigate theadsorption of trypsin onto a carbon electrode under potentiostatic conditionsboth numerically and experimentally. We found that even though the adsorptionprocess is largely dominated by hydrophobic effects, the orientation of trypsincan be controlled through an external potential, influencing the position ofthe active sites, and resulting in an important change in the catalyticactivity of the surface.

Predicting the orientation of adsorbed proteins steered with electric fields using a simple electrostatic model