Improved Potential Energy Functions for Molecular Simulation

该项目旨在提高和扩展肽和蛋白质经验势能函数的准确性和应用。 这些功能形成了广泛的建模技术的基础，广泛用于合理化和预测有机分子和生物聚合物的结构和构象能量学。 基于经典化学物理学的简单原理，这种类型的计算机建模软件被广泛用于从本科化学和生物化学到结构生物学前沿研究的各种设置。 虽然经验势能函数在模拟非极性有机物时往往表现出非常精确的精度，但它们在典型生物系统中的应用存在一些额外的困难。 该项目的主要目标是找到最限制该方法在蛋白质建模中当前应用的问题的解决方案：（1）实现由基础物理学控制的蛋白质结构和能量学的化学准确性，以及（2）描述广泛变化的化学环境，例如蛋白质的溶剂暴露表面及其疏水内部。 由于静电相互作用的处理是这些计算中最大的误差来源，因此将特别强调发展一种以原子为中心的方法来研究静电学，其中包括偶极极化效应。 一类混合方法提出了进一步的发展，使用显式水壳耦合的反应场处理长程散装溶剂的影响。

The project intends to improve and extend the accuracy and application of empirical potential energy functions for peptides and proteins. These functions form the basis of an extensive set of modeling techniques widely used to rationalize and predict the structure and conformational energetics of organic molecules and biopolymers. Based on simple principles from classical chemical physics, this type computer modeling software is widely used in a variety of settings ranging from undergraduate chemistry and biochemistry to research at the forefront of structural biology. While empirical potential energy functions often exhibit exquisite accuracy in modeling nonpolar organics, their application to typical biological systems presents several additional difficulties. The major goal of this project is to find solutions to the problems that most limit current application of this methodology in protein modeling: (1) achieving chemical accuracy for protein structure and energetics as governed by basic physics, and (2) describing widely varied chemical environments such as the solvent exposed surface of a protein and its hydrophobic interior. Since the treatment of electrostatic interactions is the largest source of error in these calculations, particular emphasis will be placed upon developing an atom-centered approach to electrostatics with inclusion of dipole polarization effects. A class of hybrid methods is proposed for further development, using a shell of explicit water coupled with a reaction field treatment of long range bulk solvent effects.