Roy J Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa 52242, United States.
Department of Biochemistry, University of Iowa, Iowa City, Iowa 52242, United States.
J Chem Theory Comput. 2021 Apr 13;17(4):2323-2341. doi: 10.1021/acs.jctc.0c01286. Epub 2021 Mar 26.
Computational protein design, ab initio protein/RNA folding, and protein-ligand screening can be too computationally demanding for explicit treatment of solvent. For these applications, implicit solvent offers a compelling alternative, which we describe here for the polarizable atomic multipole AMOEBA force field based on three treatments of continuum electrostatics: numerical solutions to the nonlinear and linearized versions of the Poisson-Boltzmann equation (PBE), the domain-decomposition conductor-like screening model (ddCOSMO) approximation to the PBE, and the analytic generalized Kirkwood (GK) approximation. The continuum electrostatics models are combined with a nonpolar estimator based on novel cavitation and dispersion terms. Electrostatic model parameters are numerically optimized using a least-squares style target function based on a library of 103 small-molecule solvation free energy differences. Mean signed errors for the adaptive Poisson-Boltzmann solver (APBS), ddCOSMO, and GK models are 0.05, 0.00, and 0.00 kcal/mol, respectively, while the mean unsigned errors are 0.70, 0.63, and 0.58 kcal/mol, respectively. Validation of the electrostatic response of the resulting implicit solvents, which are available in the Tinker (or Tinker-HP), OpenMM, and Force Field X software packages, is based on comparisons to explicit solvent simulations for a series of proteins and nucleic acids. Overall, the emergence of performative implicit solvent models for polarizable force fields opens the door to their use for folding and design applications.
计算蛋白质设计、从头蛋白质/RNA 折叠和蛋白质配体筛选对于显式溶剂处理可能计算要求过高。对于这些应用,隐式溶剂提供了一个引人注目的替代方案,我们在这里描述了基于三种连续体静电处理的可极化原子多极 AMOEBA 力场:非线性和线性泊松-玻尔兹曼方程 (PBE) 的数值解、PBE 的域分解导体相似性屏蔽模型 (ddCOSMO) 逼近和解析广义 Kirkwood (GK) 逼近。连续体静电模型与基于新型空化和弥散项的非极性估算器相结合。静电模型参数使用基于 103 个小分子溶剂化自由能差异库的最小二乘样式目标函数进行数值优化。自适应泊松-玻尔兹曼求解器 (APBS)、ddCOSMO 和 GK 模型的平均符号误差分别为 0.05、0.00 和 0.00 kcal/mol,而平均无符号误差分别为 0.70、0.63 和 0.58 kcal/mol。所得隐式溶剂静电响应的验证,这些溶剂可在 Tinker(或 Tinker-HP)、OpenMM 和 Force Field X 软件包中使用,是基于与一系列蛋白质和核酸的显式溶剂模拟的比较。总的来说,可极化力场的表现性隐式溶剂模型的出现为它们在折叠和设计应用中的使用开辟了道路。