Text

theses

1 Introduction Chapter 1 contains a basic introduction to solvation models. Special attention is given to the Ornstein-Zernike and RISM statistical mechanical solvation models used throughout this work. 2 Correction of 3D-RISM Solvation Thermodynamics for Small Molecules Implicit solvent models offer a fast way to estimate the effects of solvation on solute without the complications of explicit simulations. One common test of model accuracy is to compute the transfer energy from gas to liquid for a variety of small molecules, since many of these values have been experimentally measured. Studies of the temperature dependence of these values can provide additional insights into the performance of implicit solvent models. In this work the temperature derivatives of solvation energies for the 3D-RISM integral equation approach are computed. Results for 1123 small drug-like molecules (both neutral and charged) in water are compared to results from molecular dynamics simulations and experiment. The uncorrected results are rather poor, but it is known that errors are strongly correlated with the partial molar volumes of the solutes. Several linear solvation energy corrections are examined and extended to deal with solvation enthalpies and entropies. A new temperature-dependent linear correction is introduced. 3 Crystal Structure Refinement with Periodic 3D-RISM X-ray scattering measurements from macromolecular crystals can provide valuable information about the solvent environment around biomolecules, but conventional refinement techniques use only very simplified solvation models. In this work solvent distributions for six protein structures are computed using molecular dynamics or integral equation (3D-RISM) solvation models. Bragg intensities for both models are in better agreement with experiment at all resolution ranges than those computed using the default “flat” solvent model in the refmac5 refinement program, with the greatest improvement in the 1.5 to 2.5 Å range. Results from MD simulation are generally closer to experiment than those from 3D-RISM, but the differences are small and should be balanced against the much larger computational resources required for MD simulations. The 3D-RISM solvent distributions can be derived in seconds (for unit cells with 50 Å sides), and could be updated regularly during the course of crystallographic refinement.

ETD_7144

Ph.D.

Includes bibliographical references

by Jesse A. Johnson

doi:10.7282/T3RR21D2

ETD doctoral

The author owns the copyright to this work.