Under the most common experimental conditions, the adsorption
of
proteins to solid surfaces is a spontaneous process that leads to
a rather compact layer of randomly oriented molecules. However, controlling
such orientation is critically important for the development of catalytic
surfaces. In this regard, the use of electric fields is one of the
most promising alternatives. Our work is motivated by experimental
observations that show important differences in catalytic activity
of a trypsin-covered surface, which depended on the applied potential
during the adsorption. Even though adsorption results from the combination
of several processes, we were able to determine that (under the selected
conditions) mean-field electrostatics play a dominant role, determining
the orientation and yielding a difference in catalytic activity. We
simulated the electrostatic potential numerically, using an implicit-solvent
model based on the linearized Poisson–Boltzmann equation. This
was implemented in an extension of the code PyGBe that included an
external electric field, and rendered the electrostatic component
of the solvation free energy. Our model (extensions available at the
Github repository) allowed estimating the overall affinity of the
protein with the surface, and their most likely orientation as a function
of the potential applied. Our results show that the active sites of
trypsin are, on average, more exposed when the electric field is negative,
which agrees with the experimental results of catalytic activity,
and confirm the premise that electrostatic interactions can be used
to control the orientation of adsorbed proteins.