  Under the most common experimental conditions, the adsorption of proteins to
solid surfaces is an spontaneous process that leads to a rather compact layer
of randomly oriented molecules. Due to the importance of this process for the
development of catalytic surfaces, a number of existing computational and
experimental approaches try to predict and control the orientation of such
molecules. However, and despite their own advantages, these tend to be either
too expensive computationally, or oversimplified, undermining their ability to
predict the most appropriate experimental conditions to maximize the catalytic
activity of adsorbed proteins. To address this current need, we present an
efficient computational approach to model the behavior of proteins near
surfaces in the presence of an external electric field, based on continuum
electrostatics. Our model can not only estimate the overall affinity of the
protein with the surface, but also their most likely orientation as a function
of the potential applied. In this way, a rational selection of the potential
can be performed to maximize the accessibility of the protein's active site to
the solvent. The model relies on the Poisson-Boltzmann equation and was
implemented in an extension of the code PyGBe that includes an external
electric field, and renders the electrostatic component of the solvation free
energy. To demonstrate the feasibility of this technique, we investigate the
adsorption of trypsin onto a carbon electrode under potentiostatic conditions
both numerically and experimentally. We found that even though the adsorption
process is largely dominated by hydrophobic effects, the orientation of trypsin
can be controlled through an external potential, influencing the position of
the active sites, and resulting in an important change in the catalytic
activity of the surface.
