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GBr6NL:一种用于精确再现非线性泊松-玻尔兹曼方程溶剂化能的广义玻恩方法。

GBr6NL: a generalized Born method for accurately reproducing solvation energy of the nonlinear Poisson-Boltzmann equation.

作者信息

Tjong Harianto, Zhou Huan-Xiang

机构信息

Department of Physics and Institute of Molecular Biophysics, and School of Computational Science, Florida State University, Tallahassee, Florida 32306, USA.

出版信息

J Chem Phys. 2007 May 21;126(19):195102. doi: 10.1063/1.2735322.

Abstract

The nonlinear Poisson-Boltzmann (NLPB) equation can provide accurate modeling of electrostatic effects for nucleic acids and highly charged proteins. Generalized Born methods have been developed to mimic the linearized Poisson-Boltzmann (LPB) equation at substantially reduced cost. The computer time for solving the NLPB equation is approximately fivefold longer than for the LPB equation, thus presenting an even greater obstacle. Here we present the first generalized Born method, GBr(6)NL, for mimicking the NLPB equation. GBr(6)NL is adapted from GBr(6), a generalized Born method recently developed to reproduce the solvation energy of the LPB equation [Tjong and Zhou, J. Phys. Chem. B 111, 3055 (2007)]. Salt effects predicted by GBr(6)NL on 55 proteins overall deviate from NLPB counterparts by 0.5 kcal/mol from ionic strengths from 10 to 1000 mM, which is approximately 10% of the average magnitudes of the salt effects. GBr(6)NL predictions for the salts effects on the electrostatic interaction energies of two protein:RNA complexes are very promising.

摘要

非线性泊松-玻尔兹曼(NLPB)方程能够为核酸和高电荷蛋白质的静电效应提供精确建模。广义玻恩方法已被开发出来,以大幅降低的成本来模拟线性化泊松-玻尔兹曼(LPB)方程。求解NLPB方程所需的计算机时间大约比求解LPB方程长五倍,因此构成了更大的障碍。在此,我们提出了第一种用于模拟NLPB方程的广义玻恩方法GBr(6)NL。GBr(6)NL改编自GBr(6),GBr(6)是最近开发的一种广义玻恩方法,用于重现LPB方程的溶剂化能[Tjong和Zhou,《物理化学杂志B》111, 3055 (2007)]。GBr(6)NL预测的55种蛋白质的盐效应在离子强度为10至1000 mM时,总体上与NLPB对应的结果相差0.5千卡/摩尔,这大约是盐效应平均幅度的10%。GBr(6)NL对两种蛋白质:RNA复合物静电相互作用能的盐效应预测非常有前景。

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