The explicit polarization theory as a quantum mechanical force field and the development of coarse-grained models for simulating crowded systems of many proteins

University of Minnesota Ph.D. dissertation. January 2014. Major: Scientific Computation. Advisor: Prof. Jiali Gao. 1 computer file (PDF); xxviii, 274 pages, appendices A-B.This dissertation consists of two parts. The first part concerns the use of explicit polarization theory (X-Pol), the semiempirical polarized molecular orbital (PMO) method, and the dipole preserving, polarization consistent (DPPC) charge model as a quantum mechanical force field (QMFF). A detailed discussion of Hartree-Fock theory and X-Pol is provided, along with expressions for the energy and the analytical first derivative of this QMFF. Test cases for this QMFF with extensive comparisons to experimental data and other models are provided for water (XP3P) and hydrogen fluoride (XPHF), showing that the PMO/X-Pol/DPPC approach discussed in this dissertation is competitive with the most accurate models for those two chemical species over a wide range of chemical and physical properties.The second part of this dissertation concerns the development and application of coarse-grained models for protein dynamics. First, a coarse-grained force field (CGFF) for macromolecules in crowded environments is introduced and described along with a visualization environment for the cartoon-like rendering of biomolecules in vivo. This CGFF is tested against experimental diffusion coefficients for myoglobin (Mb) at a wide range of concentrations, including volume fractions as high as 40%, finding it to be surprisingly accurate for its simplicity and level of coarseness. Second, an analytical coarse-grained (ACG) model for mapping the internal dynamics of proteins into a spherical harmonic expansion is described

Lipophilicity in drug design: an overview of lipophilicity descriptors in 3D-QSAR studies

The pharmacophore concept is a fundamental cornerstone in drug discovery, playing a critical role in determining the success of in silico techniques, such as virtual screening and 3D-QSAR studies. The reliability of these approaches is influenced by the quality of the physicochemical descriptors used to characterize the chemical entities. In this context, a pivotal role is exerted by lipophilicity, which is a major contribution to host-guest interaction and ligand binding affinity. Several approaches have been undertaken to account for the descriptive and predictive capabilities of lipophilicity in 3D-QSAR modeling. Recent efforts encode the use of quantum mechanical-based descriptors derived from continuum solvation models, which open novel avenues for gaining insight into structure-activity relationships studies