COMPUTER SIMULATIONS OF PROTEIN STRUCTURE AND DYNAMICS

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The goal of this research is the development of more accurate methods for molecular dynamics simulations of solvated proteins, and their application to problems in biophysical chemistry involving the modeling of protein electrostatic effects in solution, the thermodynamics of ligand binding to proteins, and the long time dynamics of proteins. There are four specific aims: 1. Characterize protein dielectric properties using detailed atomic simulations: Qualitative and quantitative improvements are needed in the models currently used to predict electrostatic properties that govern protein activity and stability. We will develop a consistent framework for modeling protein dielectric properties that can bridge the current gap between continuum and explicit solvent viewpoints. 2. Determine the energy and entropy components of protein-ligand binding free energies Free energy simulations have been applied to determine the chemical potentials of solutes in water and to determine the binding free energies of ligands to proteins, but the corresponding enthalpy and entropy changes have not been analyzed because of stringent computer requirements and theoretical hurdles. Binding enthalpies and entrophies provide important information to help understand binding reactions. We will extend methods we recently developed to extract enthalpies and entrophies from free energy simulations of organic solutes in water to protein-ligand binding reactions. We will explore the role of active site and ligand flexibility in the binding thermodynamics. 3. Develop implicit solvent model of molecular dynamics simulations of solvated proteins The computational speed of implicit solvent models makes them attractive for use in molecular dynamics simulations of solvated proteins when either extensive conformational sampling or long simulation times are required. We will develop an implicit solvent model based on the Generalized Born framework which can be used in molecular dynamics simulations of proteins. 4. Study protein dynamics using computer simulations and the modeling of NMR phenomena. We will continue our collaboration with NMR groups on the study of protein dynamics. This includes the further development of more sophisticated statistical methods to extract dynamical parameters from NMR relaxation experiments and the development of efficient methods for simulating the long time dynamics of proteins using the Generalized Born implicit solvent model and Langevin equations of motion.