Liu Y, Ichiye T
Department of Biochemistry/Biophysics, Washington State University, Pullman, WA 99163-4660, USA.
Biophys Chem. 1999 Apr 5;78(1-2):97-111. doi: 10.1016/s0301-4622(99)00008-3.
Water plays a crucial role in the structure and function of proteins and other biological macromolecules; thus, theories of aqueous solvation for these molecules are of great importance. However, water is a complex solvent whose properties are still not completely understood. Statistical mechanical integral equation theories predict the density distribution of water molecules around a solute so that all particles are fully represented and thus potentially both molecular and macroscopic properties are included. Here we discuss how several theoretical tools we have developed have been integrated into an integral equation theory designed for globular macromolecular solutes such as proteins. Our approach predicts the three-dimensional spatial and orientational distribution of water molecules around a solute. Beginning with a three-dimensional Ornstein-Zernike equation, a separation is made between a reference part dependent only on the spatial distribution of solvent and a perturbation part dependent also on the orientational distribution of solvent. The spatial part is treated at a molecular level by a modified hypernetted chain closure whereas the orientational part is treated as a Boltzmann prefactor using a quasi-continuum theory we developed for solvation of simple ions. A potential energy function for water molecules is also needed and the sticky dipole models of water, such as our recently developed soft-sticky dipole (SSD) model, are ideal for the proposed separation. Moreover, SSD water is as good as or better than three point models typically used for simulations of biological macromolecules in structural, dielectric and dynamics properties and yet is seven times faster in Monte Carlo and four times faster in molecular dynamics simulations. Since our integral equation theory accurately predicts results from Monte Carlo simulations for solvation of a variety of test cases from a single water or ion to ice-like clusters and ion pairs, the application of this theory to biological macromolecules is promising.
水在蛋白质和其他生物大分子的结构与功能中起着至关重要的作用;因此,针对这些分子的水溶剂化理论具有重要意义。然而,水是一种复杂的溶剂,其性质仍未被完全理解。统计力学积分方程理论可预测溶质周围水分子的密度分布,从而使所有粒子都能得到充分体现,进而可能涵盖分子和宏观性质。在此,我们讨论我们所开发的几种理论工具是如何被整合到一种为球状大分子溶质(如蛋白质)设计的积分方程理论中的。我们的方法可预测溶质周围水分子的三维空间和取向分布。从三维奥恩斯坦 - 泽尼克方程出发,将仅依赖于溶剂空间分布的参考部分与也依赖于溶剂取向分布的微扰部分区分开来。空间部分通过修正的超网链闭合在分子层面进行处理,而取向部分则使用我们为简单离子溶剂化开发的准连续介质理论作为玻尔兹曼前置因子来处理。还需要一个水分子的势能函数,水的粘性偶极子模型,如我们最近开发的软粘性偶极子(SSD)模型,非常适合所提出的区分。此外,SSD水在结构、介电和动力学性质方面与通常用于生物大分子模拟的三点模型一样好甚至更好,但在蒙特卡罗模拟中快七倍,在分子动力学模拟中快四倍。由于我们的积分方程理论能准确预测从单个水或离子到类冰簇和离子对等各种测试案例的溶剂化蒙特卡罗模拟结果,将该理论应用于生物大分子具有广阔前景。