Department of Biochemistry and Molecular Biophysics , University of Chicago , Chicago , Illinois 60637 , United States.
Department of Pharmaceutical Sciences, School of Pharmacy , University of Maryland , Baltimore , Maryland 21201 , United States.
J Chem Theory Comput. 2018 Jun 12;14(6):3121-3131. doi: 10.1021/acs.jctc.8b00172. Epub 2018 May 7.
Meaningful efforts in computer-aided drug design (CADD) require accurate molecular mechanical force fields to quantitatively characterize protein-ligand interactions, ligand hydration free energies, and other ligand physical properties. Atomic models of new compounds are commonly generated by analogy from the predefined tabulated parameters of a given force field. Two widely used approaches following this strategy are the General Amber Force Field (GAFF) and the CHARMM General Force Field (CGenFF). An important limitation of using pretabulated parameter values is that they may be inadequate in the context of a specific molecule. To resolve this issue, we previously introduced the General Automated Atomic Model Parameterization (GAAMP) for automatically generating the parameters of atomic models of small molecules, using the results from ab initio quantum mechanical (QM) calculations as target data. The GAAMP protocol uses QM data to optimize the bond, valence angle, and dihedral angle internal parameters, and atomic partial charges. However, since the treatment of van der Waals interactions based on QM is challenging and may often be unreliable, the Lennard-Jones 6-12 parameters are kept unchanged from the initial atom types assignments (GAFF or CGenFF), which limits the accuracy that can be achieved by these models. To address this issue, a new set of Lennard-Jones 6-12 parameters was systematically optimized to reproduce experimental neat liquid densities and enthalpies of vaporization for a large set of 430 compounds, covering a wide range of chemical functionalities. Calculations of the hydration free energy indicate that optimal accuracy for these models is achieved when the molecule-water van der Waals dispersion is rescaled by a factor of 1.115. The final optimized model yields an average unsigned error of 0.79 kcal/mol in the hydration free energies.
在计算机辅助药物设计（CADD）中，需要准确的分子力学力场来定量描述蛋白质-配体相互作用、配体水合自由能和其他配体物理性质。新化合物的原子模型通常通过类比给定力场的预定义表格参数来生成。遵循这一策略的两种广泛使用的方法是通用安伯力场（GAFF）和 CHARMM 通用力场（CGenFF）。使用预定义参数值的一个重要限制是，在特定分子的情况下，它们可能不够充分。为了解决这个问题，我们之前引入了通用自动原子模型参数化（GAAMP），用于使用从头算量子力学（QM）计算的结果作为目标数据，自动生成小分子原子模型的参数。GAAMP 协议使用 QM 数据来优化键、价角度和二面角内部参数以及原子部分电荷。然而，由于基于 QM 的范德华相互作用的处理具有挑战性，并且可能经常不可靠，因此 Lennard-Jones 6-12 参数保持不变，来自初始原子类型分配（GAFF 或 CGenFF），这限制了这些模型可以达到的准确性。为了解决这个问题，一组新的 Lennard-Jones 6-12 参数被系统地优化，以重现 430 多种化合物的大集合的实验纯液体密度和蒸发热，涵盖了广泛的化学官能团。水合自由能的计算表明，当分子-水范德华色散被缩放因子 1.115 时，这些模型达到最佳精度。最终优化的模型在水合自由能中产生 0.79 kcal/mol 的平均无符号误差。
