COMPUTER SIMULATIONS OF PROTEIN STRUCTURE AND DYNAMICS

本研究的目标是为溶剂化蛋白质的分子动力学模拟开发更精确的方法，并将其应用于生物物理化学中的问题，包括溶液中蛋白质静电效应的建模、配体与蛋白质结合的热力学以及蛋白质的长时间动力学。有四个具体目标：1。使用详细的原子模拟表征蛋白质介电特性：目前用于预测控制蛋白质活性和稳定性的静电特性的模型需要定性和定量的改进。我们将开发一个一致的框架来建模蛋白质介电性质，可以弥合目前连续介质和明确溶剂观点之间的差距。2. 自由能模拟已被用于确定水中溶质的化学势和确定配体与蛋白质的结合自由能，但由于严格的计算机要求和理论障碍，尚未对相应的焓和熵变化进行分析。结合焓和熵提供了帮助理解结合反应的重要信息。我们将扩展我们最近开发的方法，从水中有机溶质的自由能模拟中提取焓和熵到蛋白质-配体结合反应。我们将探讨活性位点和配体柔韧性在结合热力学中的作用。3. 当需要大量的构象采样或长时间的模拟时，隐式溶剂模型的计算速度使其在溶剂化蛋白质的分子动力学模拟中具有吸引力。我们将建立一个基于广义玻恩框架的隐式溶剂模型，该模型可用于蛋白质的分子动力学模拟。4. 使用计算机模拟和核磁共振现象建模来研究蛋白质动力学。我们将继续与核磁共振组合作研究蛋白质动力学。这包括进一步发展更复杂的统计方法，从核磁共振弛豫实验中提取动力学参数，以及利用广义玻恩隐式溶剂模型和朗之万运动方程模拟蛋白质长时间动力学的有效方法。

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.