Gallicchio Emilio, Levy Ronald M
Department of Chemistry and Chemical Biology and BIOMAPS Institute of Quantitative Biology, Rutgers University, Piscataway New Jersey 08854, USA.
J Comput Chem. 2004 Mar;25(4):479-99. doi: 10.1002/jcc.10400.
We have developed an implicit solvent effective potential (AGBNP) that is suitable for molecular dynamics simulations and high-resolution modeling. It is based on a novel implementation of the pairwise descreening Generalized Born model for the electrostatic component and a new nonpolar hydration free energy estimator. The nonpolar term consists of an estimator for the solute-solvent van der Waals dispersion energy designed to mimic the continuum solvent solute-solvent van der Waals interaction energy, in addition to a surface area term corresponding to the work of cavity formation. AGBNP makes use of a new parameter-free algorithm to calculate the scaling coefficients used in the pairwise descreening scheme to take into account atomic overlaps. The same algorithm is also used to calculate atomic surface areas. We show that excellent agreement is achieved for the GB self-energies and surface areas in comparison to accurate, but much more expensive, numerical evaluations. The parameter-free approach used in AGBNP and the sensitivity of the AGBNP model with respect to large and small conformational changes makes the model suitable for high-resolution modeling of protein loops and receptor sites as well as high-resolution prediction of the structure and thermodynamics of protein-ligand complexes. We present illustrative results for these kinds of benchmarks. The model is fully analytical with first derivatives and is computationally efficient. It has been incorporated into the IMPACT molecular simulation program.
我们开发了一种适用于分子动力学模拟和高分辨率建模的隐式溶剂有效势(AGBNP)。它基于静电成分的成对去屏蔽广义玻恩模型的一种新颖实现方式以及一种新的非极性水化自由能估计器。非极性项由一个用于溶质 - 溶剂范德华色散能的估计器组成,该估计器旨在模拟连续介质溶剂中溶质 - 溶剂范德华相互作用能,此外还有一个对应于空穴形成功的表面积项。AGBNP利用一种新的无参数算法来计算成对去屏蔽方案中用于考虑原子重叠的缩放系数。相同的算法也用于计算原子表面积。我们表明,与精确但成本高得多的数值评估相比,GB自能和表面积方面取得了极好的一致性。AGBNP中使用的无参数方法以及AGBNP模型对大的和小的构象变化的敏感性使得该模型适用于蛋白质环和受体位点的高分辨率建模以及蛋白质 - 配体复合物结构和热力学的高分辨率预测。我们给出了这类基准测试的说明性结果。该模型具有一阶导数的完全解析性且计算效率高。它已被纳入IMPACT分子模拟程序。