Isolating Influential Regions of Electrostatic Focusing in Protein and DNA Structure

Abstract

Electrostatic focusing is a general phenomenon that occurs in cavities and grooves on the molecular surface of biomolecules. Narrow surface features can partially shield charged atoms from the high-dielectric solvent, enhancing electrostatic potentials inside the cavity and projecting electric field lines outward into the solvent. This effect has been observed in many instances and is widely considered in the human examination of molecular structure, but it is rarely integrated into the digital representations used in protein structure comparison software. To create a computational representation of electrostatic focusing, that is compatible with structure comparison algorithms, this paper presents an approach that generates three-dimensional solids that approximate regions where focusing occurs. We verify the accuracy of this representation against instances of focusing in proteins and DNA. Noting that this representation also identifies thin focusing regions on the molecular surface that are unlikely to affect binding, we describe a second algorithm that conservatively isolates larger focusing regions. The resulting 3D solids can be compared with Boolean set operations, permitting a new range of analyses on the regions where electrostatic focusing occurs. They also represent a novel integration of molecular shape and electrostatic focusing into the same structure comparison framework.

Influential Mutations in the SMAD4 Trimer Complex Can Be Detected from Disruptions of Electrostatic Complementarity

“…Replacing a charged sidechain with an uncharged one alters the electrostatic field and it can also alter the pattern of sidechain packing and solvent exposure nearby. Changes in the solvent exposure of a charged group can locally decrease the dielectric of the polar solvent, enhancing nearby electrostatic potentials through electrostatic focusing Klapper et al (1986); Polticelli et al (1999); Rohs et al (2009); Blumenthal et al (2013). While the computationally intensive minimization of mutant structures could better approximate realistic packing, we hypothesized that computationally inexpensive modifications may be sufficient to approximate the effect of most mutations and thus rapidly screen for electrostatically influential amino acids.…”