Novel generalized Born methods
Novel generalized Born methods
复制标题
DOI：
10.1063/1.1480013
复制
发表时间：
2002-06-22
影响因子：
4.4
通讯作者：
Brooks, CL
中科院分区：
文献类型：
--
作者：
Lee, MS;Salsbury, FR;Brooks, CL
广义Born(GB)模型是计算分子静电溶剂化能的一个简单的连续介质模型。它是连续介质静电溶剂化泊松方程解的两两近似。GB方法的关键是计算系统中每个原子的出生半径。我们介绍了两种确定出生半径的新方法。第一种是基于网格的双参数方法，它使用的分子体积与传统泊松计算中使用的分子体积几乎相同。第二种是五参数分析方法，它利用由原子功能叠加而成的分子体积。与基于网格的算法不同，该分析方法适用于基于力的计算，例如能量最小化和分子动力学。与其他玻恩半径方法不同，这两种算法都使用了一种新的经验确定的修正项，其中包括库仑场近似之外的能量效应。有了这个修正项，基于网格的算法通常产生大于0.99相关性的Born半径，而不是收敛的数值推导的Poisson Born半径。与泊松导出的出生半径相比，该分析方法再现的出生半径具有大约0.95的相关性。在绝对溶剂化能方面，对于从布鲁克海文蛋白质数据库获得的一组3029个单链蛋白质，基于网格的方法相对于收敛的泊松解的总体误差为1.3%。另一方面，对于相同的数据集，与泊松解相比，分析方法提供了2%-4%的适度误差。还给出了两组蛋白质构象中RNA的绝对溶剂化能和相对溶剂化能的结果。(C)2002年美国物理研究所。
The generalized Born (GB) model is a simple continuum dielectric model for the calculation of molecular electrostatic solvation energies. It is a pairwise approximation to the solution of the Poisson equation for continuum electrostatic solvation. Key to the GB method is the calculation of Born radii for every atom in the system. We introduce two new methods for determining Born radii. The first is a two-parameter grid-based method that uses nearly the same molecular volume that is used in conventional Poisson calculations. The second is a five-parameter analytical method that utilizes a molecular volume built from a superposition of atomic functions. The analytical method, distinct from the grid-based algorithm, is amenable to force-based calculations, e.g., energy minimization and molecular dynamics. Unlike other Born radii methods, both algorithms employ a new empirically determined correction term that includes energetic effects beyond the Coulomb field approximation. With this correction term, the grid-based algorithm generally yields Born radii with greater than 0.99 correlation versus converged numerically derived Poisson Born radii. The analytical method reproduces Born radii with approximately 0.95 correlation versus Poisson-derived Born radii. With respect to absolute solvation energies, the grid-based method achieves an overall 1.3% error versus converged Poisson solutions for a set of 3029 single-chain proteins obtained from the Brookhaven Protein Data Bank. On the other hand, the analytic method delivers modest 2-4 % errors versus the Poisson solutions for the same data set. Results concerning absolute solvation energies of RNA and relative solvation energies in two sets of protein conformations are also presented. (C) 2002 American Institute of Physics.