Computational modeling of solvent in structural biology
- Gaurav Chopra.
- Dec. 2009, c2010.
- Physical description
- online resource (xx, 233 pages) : color illustrations
- Chopra, Gaurav.
- Altman, Russ thesis advisor.
- Guibas, Leonidas J. thesis advisor.
- Levitt, Michael, 1947- thesis advisor (primary).
- Pande, Vijay. thesis advisor.
- Stanford University. Committee on Graduate Studies. degree grantor.
- Stanford University. Institute for Computational and Mathematical Engineering. degree grantor.
- Includes bibliographical references (p. 212-233).
- Computational structural biology is a field that involves modeling of physical interactions between complex biological macromolecules in the aqueous environment in the cell. We model the solvent (water) environment around biological macromolecules, to better understand the physical interactions needed to improve methods of protein structure prediction and, more generally, for the protein folding problem. In this thesis, we model the effect of solvent environment on protein structure refinement using implicit and explicit water models. Specifically we used the Generalized Born Surface Area (GBSA) implicit water model and the SPC and TIP4P explicit water models with the all-atom OPLS force field. We also used the knowledge-based (KB) statistical potential functions, derived from high-resolution X-ray crystals of protein structures. The KB potentials include the affect of solvent implicitly, in that the distribution of distances between atoms in protein crystals is effected by the water in the unit cell. These potentials and water models were tested for refinement of an extensive set of protein structures, using energy minimization and molecular dynamics. Energy minimization with GBSA outperformed KB potential energy minimization, in that large magnitude of refinement was observed. Energy minimization with KB potential was more consistent, in that it refined more protein structures than GBSA. We also tested our computationally inexpensive KB energy minimization in the refinement category at the eight world-wide experiments on Critical Assessment of techniques for protein Structure Prediction (CASP) that performed well. We performed a consistency test on the all the predicted protein structure models by all groups at CASP that improved streorechemistry and refined models for the best performing groups. This warrants the use of this simple and computationally inexpensive, but consistent refinement protocol to act as a natural "end" step for all participating groups at CASP. Accurate description of the water structure around the solute of interest could improve our understanding of various biological processes such as protein folding. We study the hydration of hydrophobic solutes of varying sizes (methane, benzene, cyclohexane and Buckminsterfullerene) with Molecular Dynamics (MD) simulations using a recently introduced state-of-the-art quantum general purpose quantum mechanical polarizable force field (QMPFF3) fitted solely to high-level quantum mechanical data at MP2/cc-pVTZ level with a simple model correction using CCSD(T) data for higher accuracy of aromatic carbon atom type. We ask how well the hydrophobic affect is represented in classical force fields when compared to a more rigorous quantum mechanical force field. Polarization increases ordered water structure, in that the imprint of the hydrophobic surface extends to long range effect (up to 10Å for Buckminsterfullerene). Similar surface water affects, with less ordering are also observed for classical force fields. Most of the water molecules point their dipole moment away from the hydrophobic solutes but often one OH bond points towards the hydrophobic solute surface. The major conclusion from this study is that a quantum mechanical force field increases the strength of the hydrophobic effect; this could have a profound affect on protein folding.
- Publication date
- Copyright date
- Submitted to the Institute for Computational and Mathematical Engineering and the Committee on Graduate Studies of Stanford University.
- Thesis (Ph.D.)--Stanford University, 2009.