College of Science, Zhejiang University of Technology, Hangzhou, Zhejiang, 310023, China.
School of Information Science and Engineering, Shandong Agricultural University, Taian, 271018, China.
Sci Rep. 2018 Apr 3;8(1):5475. doi: 10.1038/s41598-018-23783-8.
An efficient computational approach for modeling protein electrostatic is developed according to static point-charge model distributions based on the linear-scaling EE-GMFCC (electrostatically embedded generalized molecular fractionation with conjugate caps) quantum mechanical (QM) method. In this approach, the Electrostatic-Potential atomic charges are obtained from ab initio calculation of protein, both polarization and charge transfer effect are taken into consideration. This approach shows a significant improvement in the description of electrostatic potential and solvation energy of proteins comparing with current popular molecular mechanics (MM) force fields. Therefore, it has gorgeous prospect in many applications, including accurate calculations of electric field or vibrational Stark spectroscopy in proteins and predicting protein-ligand binding affinity. It can also be applied in QM/MM calculations or electronic embedding method of ONIOM to provide a better electrostatic environment.
根据基于线性标度 EE-GMFCC（静电嵌入广义分子分馏与共轭帽）量子力学（QM）方法的静电点电荷模型分布，开发了一种用于蛋白质静电建模的高效计算方法。在这种方法中，通过蛋白质的从头算计算获得静电势原子电荷，同时考虑了极化和电荷转移效应。与当前流行的分子力学（MM）力场相比，该方法在描述蛋白质的静电势和溶剂化能方面有了显著的改进。因此，它在许多应用中具有广阔的前景，包括蛋白质中电场或振动斯塔克光谱的精确计算以及预测蛋白质-配体结合亲和力。它还可以应用于 QM/MM 计算或 ONIOM 的电子嵌入方法，以提供更好的静电环境。
