A volumetric method for representing and comparing regions of electrostatic focusing in molecular structure

Publication Type:

Conference Paper

Source:

Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine, ACM, p.242–249 (2012)

Abstract:

Algorithms for protein structure comparison employ diverse and effective representations of molecular shape. However, they do not generally represent the shape of the electrostatic potential field, except at the molecular surface. This approach neglects the geometry of the field on the outside of the molecular surface, where electrostatic focusing can play an important role in molecular recognition: Narrow clefts and grooves can partially shield charged atoms from the high dielectric solvent, enhancing potentials inside the cavity and projecting the lines of the electric field outwards from the cavity. This interplay between molecular shape and electrostatic potential is an essential means of recognition in many biomolecular systems. To leverage this phenomenon for more accurate protein structure comparison algorithms, this paper presents the first comparative representation of the region where focusing occurs. We first verified our representation in a case study of superoxide dismutase, where electrostatic focusing was first observed. Our method accurately identified the site where electrostatic focusing was established in the past. We then applied our representation to compare regions of electrostatic focusing with the positions of charged amino acids, to determine where they coincide. Over 866 protein-DNA complexes, our representations correctly detected the enrichment of arginines that contact regions of electrostatic focusing in the minor grooves of DNA. These results indicate that our novel methods precisely represent and accurately compare regions where electrostatic focusing occurs. They also describe a novel and elegant technique for seamlessly integrating molecular shape and electrostatic focusing into the same structure comparison framework.

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