CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2013 vol.10 Issue No.05 - Sept.-Oct.
Issue No.05 - Sept.-Oct. (2013 vol.10)
Brian Y. Chen , Dept. of Comput. Sci. & Eng., Lehigh Univ., Bethlehem, PA, USA
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2013.124
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.
Solid geometry, Electrostatics, Proteins, DNA, Lattices, Electric potential, Splines (mathematics),structural bioinformatics, Electrostatic focusing, protein structure comparison, constructive solid geometry
Seth Blumenthal, Yisheng Tang, Wenjie Yang, Brian Y. Chen, "Isolating Influential Regions of Electrostatic Focusing in Protein and DNA Structure", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.10, no. 5, pp. 1188-1198, Sept.-Oct. 2013, doi:10.1109/TCBB.2013.124